Interest in the sustainable materials sector is growing and accelerated. These materials are designed to reduce the use of non-renewable resources, limit greenhouse gas emissions, and be recyclable or biodegradable, making them highly attractive to both academia and industry. Constantly updating on innovations in this field is essential to speed up the transition to a circular economy and significantly reduce environmental impact. The paper analyzes the current status and future trends of the scientific literature for seven sustainability-related materials categories, such as sustainable materials, green materials, biomaterials, eco-friendly materials, alternative materials, material recycling and material recovery from complex products, and sustainable applied materials. Next, it assesses the impacts, benefits, and challenges associated with sustainable materials from the scientific literature according to six research fields (impact on the environment, performance and durability, economic efficiency, health and safety, social sustainability, and implementation and use). Furthermore, the paper outlines recent advances in sustainable material design, including biomimicry, nanotechnology, additive manufacturing, 3D printing, and sustainable composite materials. Additionally, a bibliometric analysis of 545 studies on sustainable materials published between 1999 and 2023 was conducted based on eight criteria, namely trend, source, author, country, keywords, thematic, co-citation, and content. The findings show that the sustainability-related materials categories have a particular distribution among the domains. Also, the thematic map analysis outlines that biopolymers, nanocellulose, and biocomposites are critical research areas for developing sustainable materials.

The acceleration of digitalization has caused an increase in demand for autonomous devices. In this paper, the technologies of artificial intelligence (AI), and especially machine learning (ML), integrated into applications that use self-powered Internet of Things (IoT) sensors are analyzed. The study addresses the issue of the lack of a standardized classification of IoT domains and the uneven distribution of AI integration in these domains. The systematic bibliometric analysis of the scientific literature between 1 January 2020 and 30 April 2025, using the Web of Science database, outlines the seven main areas of IoT sensor usage: smart cities, wearable devices, industrial IoT, smart homes, environmental monitoring, healthcare IoT, and smart mobility. The thematic searches highlight the consistent number of articles in the health sector and the underrepresentation of other areas, such as agriculture. The study identifies that the most commonly used sensors are the accelerometer, electrocardiogram, humidity sensor, motion sensor, and temperature sensor, and analyzes the performance of AI models in self-powered systems, identifying accuracies that can reach up to 99.92% in medical and industrial applications. The conclusions drawn from these results underscore the need for an interdisciplinary approach and detailed exploration of ML algorithms to be adapted to the hardware infrastructures of autonomous sensors. The paper proposes future research directions to expand AI’s applicability in developing systems that integrate self-powered IoT sensors. The paper lays the groundwork for future projects in this field, serving as a reference for researchers who wish to explore these areas.

Wind energy represents a solution for reducing environmental impact. For this reason, this research studies the elements that propose optimizing wind energy production through intelligent solutions. Although there are studies that address the optimization of turbine performance or other indirectly related factors in wind energy production, the optimization of wind energy production remains a topic insufficiently explored and synthesized in the literature. This research studies how machine learning (ML) techniques can be applied to optimize wind energy production. This research aims to study the systematic applications of ML to identify and analyze the key stages of optimized wind energy production. Through this research, case studies are highlighted by which ML methods are proposed that directly target the issue of optimizing the wind power process through wind turbines. From the total of 1049 articles obtained from the Web of Science database, the most studied ML models in the context of wind energy are the artificial neural networks, with 478 papers identified. Additionally, the literature identifies 224 articles that have studied random forest and 114 that have incorporated gradient boosting about wind power. Among these, 60 articles have specifically addressed the issue of optimizing wind energy production. This aspect allows for the identification of gaps in the literature. The research notes that previous studies have focused on wind forecasting, fault detection, or turbine efficiency. The existing literature addresses the indirect optimization of component performance. Thus, this paper identifies gaps in the current research, discusses ML algorithms in the context of optimizing wind energy production processes, and identifies future directions for increasing the efficiency of wind turbines through integrated predictive methods.

The world is currently facing the issue of text authenticity in different areas. The implications of generated text can raise concerns about manipulation. When a photo of a celebrity is posted alongside an impactful message, it can generate outrage, hatred, or other manipulative beliefs. Numerous artificial intelligence tools use different techniques to determine whether a text is artificial intelligence-generated or authentic. However, these tools fail to accurately determine cases in which a text is written by a person who uses patterns specific to artificial intelligence tools. For these reasons, this article presents a new approach to the issue of deepfake texts. The authors propose methods to determine whether a text is associated with a specific person by using specific written patterns. Each person has their own written style, which can be identified in the average number of words, the average length of the words, the ratios of unique words, and the sentiments expressed in the sentences. These features are used to develop a custom-made written-style machine learning model named the custom deepfake text model. The model’s results show an accuracy of 99%, a precision of 97.83%, and a recall of 90%. A second model, the anomaly deepfake text model, determines whether the text is associated with a specific author. For this model, an attempt was made to determine anomalies at the level of textual characteristics that are assumed to be associated with particular patterns of a certain author. The results show an accuracy of 88.9%, a precision of 100%, and a recall of 89.9%. The findings outline the possibility of using the model to determine if a text is associated with a certain author. The paper positions itself as a starting point for identifying deepfakes at the text level.

Air quality (AQ) is one of the most important urban environment indicators for the quality of life. The paper proposes a software solution for predicting and forecasting the air quality index (AQI) in urban areas. The study integrates pollutant factors (CO, NO₂, SO₂, PM_2.5), meteorological parameters (temperature, humidity, wind speed), and traffic data to determine air quality. For this purpose, 19 predictive models were developed and compared: 12 machine learning algorithms, 7 deep learning, and 1 forecasting model based on structural component analysis. The Random Forest Regression model, customized within the study, achieved the best results, with an R² score of 99.59%, an MAE of 0.22%, an MAPE of 0.68%, and an OP (Overall Precision) score of 95.61%. It was subsequently validated on unseen data and recorded a mean deviation of 0.58%. For short-term AQI forecasting (5 days), the AQIF model achieved an R² of 71.62%, an MAE of 0.4%, and an MAPE of 0.9%. The proposed solution was integrated into a web application with IoT infrastructure and real-time alert mechanisms. Future directions include expanding the dataset and optimizing hyperparameters for the deep learning models to increase accuracy, as well as integrating PM₁₀ and O₃ factors, along with the degree of industrialization and demographic level.

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASP.NET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios.