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Abstract Child pornography—better known as child sexual abuse material (CSAM)—represents a severe form of exploitation and victimization of children, leaving the victims with emotional and physical trauma. In this study, we aim to analyze local patterns of CSAM consumption across 1341 French communes in 20 metropolitan regions of France between March 16 to May 31, 2019 using fine-grained mobile traffic data of Tor network-related web services. We estimate that approx. 0.08% of Tor mobile download traffic observed in France is linked to the consumption of CSAM by correlating it with local-level temporal porn consumption patterns. This compares to 0.19% of what we conservatively estimate to be the share of CSAM content in global Tor traffic. In line with existing literature on the link between sexual child abuse and the consumption of image-based content thereof, we observe a positive and statistically significant effect of our CSAM consumption estimates on the reported number of victims of sexual violence and vice versa, which validates our findings, after controlling for a set of geographically disaggregated features including socio-demographic characteristics, voting behavior, nearby points of interest and Google Trends queries. While this is a first, exploratory attempt to look at CSAM from a spatial epidemiological angle, we believe this research provides public health officials with valuable information to prioritize target areas for public awareness campaigns as another step to fulfill the global community’s pledge to target 16.2 of the sustainable development goals: “end abuse, exploitation, trafficking and all forms of violence and torture against children".

Abstract The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model’s exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms.

Abstract The integration of smart homes into smart grids presents numerous challenges, particularly in managing energy consumption efficiently. Non-intrusive load management (NILM) has emerged as a viable solution for optimizing energy usage. However, as smart grids incorporate more distributed energy resources, the complexity of demand-side management and energy optimization escalates. Various techniques have been proposed to address these challenges, but the evolving grid necessitates intelligent optimization strategies. This article explores the potential of data-driven NILM (DNILM) by leveraging multiple machine learning algorithms and neural network architectures for appliance state monitoring and predicting future energy consumption. It underscores the significance of intelligent optimization techniques in enhancing prediction accuracy. The article compares several data-driven mechanisms, including decision trees, sequence-to-point models, denoising autoencoders, recurrent neural networks, long short-term memory, and gated recurrent unit models. Furthermore, the article categorizes different forms of NILM and discusses the impact of calibration and load division. A detailed comparative analysis is conducted using evaluation metrics such as root-mean-square error, mean absolute error, and accuracy for each method. The proposed DNILM approach is implemented using Python 3.10.5 on the REDD dataset, demonstrating its effectiveness in addressing the complexities of energy optimization in smart grid environments.

High-temperature fuel cells (HT-FCs) indeed hold significant promise for enhancing energy systems' efficiency and environmental sustainability. Consequently, there is growing interest in exploring and developing innovative strategies to optimize the integration of heat recovery processes with HT-FC technology. The current research presents an innovative and environmentally friendly poly-generation unit that integrates advanced subprocesses to generate essential products. These final products include electricity, refrigeration, pure water, and hydrogen. This study signifies a significant step towards sustainable and efficient energy production while meeting diverse needs for different utilities. The design unit incorporates a novel modified dual ejector-based organic flash cycle, reverse osmosis water purification, and a water electrolyzer for hydrogen extraction, all integrated with a high-temperature solid oxide fuel cell. Energy, exergy, economic, and environmental (4E) analysis is conducted to thoroughly evaluate the proposed plan. Furthermore, a comprehensive parametric analysis and sensitivity study are performed to pinpoint the key design parameters of the poly-generation unit. To attain the optimal operational status of the poly-generation unit, a three-objective NSGA-II optimization technique is employed to fine-tune the system's performance within the exergy-cost-environmental framework. This approach aims to strike a balance between maximizing efficiency, minimizing costs, and reducing environmental impact for sustainable operations. In addition, a net present value analysis spanning a 20-year timeframe was conducted to assess the profitability of the devised unit. Based on the findings, it is evident that the sensitivity index of the fuel cell operating temperature carries substantial importance, registering a notable value of 0.60. Additionally, the optimization outcomes reveal the system's enhanced performance metrics: an exergetic efficiency of 41.11 %, a unit cost of 58.96 $/GJ, and a carbon dioxide emission reduction rate of 418 kg/MWh. Also, the unit's payback period is shortened from 11.33 years to 8.817 years. Moreover, it improved the unit's sustainability index and net present value, raising them from 1.544 to 4.98 M$ to 1.66 and 7.38 M$.

Spontaneous regression of testicular tumors known as burnt-out tumor of testis is a rare entity and is seen in about 5% of testicular germ cell tumors. It is described as a spontaneously, completely, or partially regressed testicular tumor with or without metastasis in the absence of any treatment. This entity is now included in the 2016 classification of testicular tumors. This is probably only the second case presented in India. High degree of awareness of this entity by the pathologist, clinician, and radiologist is necessary for proper management and survival of the patient.

In this study, a multi-generation energy system is modeled for the purpose of simultaneous producing distilled water as well as biofuels, electrical power, and thermal energy from wood chip biomass. The system is triggered by a gas turbine cycle with two different agents of steam and air. The system performance is evaluated by changing the gas turbine cycle working fluid and their performances are compared in details. The research findings indicate that the air-powered system demonstrated an efficiency of 67.38 %, surpassing the steam-powered system which exhibited a slightly lower efficiency rate of 67.17 %. The results indicated that the air-driven system displayed superior emission characteristics in comparison to the steam-driven system. The air-based system was found to release 510.3 g/kWh of carbon dioxide, whereas the steam-based system exhibited a marginally higher emission rate of 511.9 g/kWh. The study's results indicated that the air-powered system exhibited superior performance in comparison to the steam-powered system. The study illustrated that the integration of wood chip biomass into energy systems offers an environmentally sustainable and economically viable solution to address the increasing need for clean technologies.

Marine transportation is a significant contributor to overall energy consumption among transportation sectors and is responsible for producing a considerable amount of greenhouse gases. A potential solution for alternative applications involves implementing combined heat integration, thereby enabling the production of additional utilities and mitigating emissions. The current work introduces an innovative heat integration process for a marine engine, focusing on implementing an optimal thermal matching technique to minimize overall irreversibility in producing liquefied hydrogen and coolant. The process incorporates an organic flash-bi-evaporator cooling cycle, a humidification dehumidification desalination, a polymer electrolyte membrane water electrolysis process, and a Claude cycle. The generated freshwater is delivered to the electrolyzer to produce gaseous hydrogen. This product and the cooling for freezing are utilized in the Claude cycle for hydrogen liquefaction. The study utilizes an advanced thermo-environmental multi-criteria investigation and optimization, considering sensitivity analysis and optimization based on artificial intelligence method. The optimization process incorporates the training and testing of artificial neural networks, NSGA-II method, and TOPSIS decision-making. The primary objective functions include exergetic efficiency and carbon dioxide emission. The findings demonstrate that the specified objectives are computed to be 0.121 and 2.67 kg/MWh, correspondingly. Besides, this condition exhibits a liquefied hydrogen flow rate of 6.44 L/h and a cooling output of 43.61 kW, showing an energy efficiency of 0.1145. Also, the total exergy destruction rate associated with the arranged structure is 124.5 kW. Furthermore, the optimum state reveals an exergoenvironmental index of 0.840 and an exergetic stability factor of 0.869.