Search Results (27)

Generalized aquifers are widely used in various fields, such as groundwater use, mine water prevention and control, and geothermal energy. This paper presents a transformer-model-based automatic aquifer generalization method using borehole logs in scenarios with scarce experimental parameters. Relying only on basic borehole data, the method used an agent-assisted approach to extract and clean key lithological and coordinate information, which was then fused using a dual embedding mechanism. The model leveraged multi-head self-attention to calculate attention weights between the target stratum and its adjacent strata, capturing the potential contextual correlations in aquifer potential across strata. The resulting deep feature vectors from the transformer’s encoder were fed into a classification head to predict aquifer potential labels. Evaluation results demonstrated a model accuracy of 0.86, significantly outperforming the random classification baseline in precision, recall, the F1-score, and the kappa coefficient. Full article

The northern coast of Mauritania presents a strategic opportunity for clean energy investment due to its remarkable potential for green hydrogen production through wind energy. To determine the best location for wind-based green hydrogen production, this paper established a Multi-Criteria Decision-Making framework (MCDM) that combines the Analytic Hierarchy Process (AHP) and Evidential Reasoning (ER) to assess five coastal sites: Nouakchott, Nouamghar, Tasiast, Boulanoir, and Nouadhibou. Four main criteria (i.e., economic, technical, environmental, and social) and twelve sub-criteria were taken into account in the assessment. To ensure reliability and contextual accuracy, the data used in this study were obtained from geographic databases, peer-reviewed literature, and structured expert questionnaires. The results indicate that site 5 (Nouadhibou) is the most suitable location for green hydrogen generation using wind energy. Sensitivity analysis confirms the robustness of the ranking results, validating the reliability of the proposed hybrid framework. The findings of this study provide critical, data-driven decision-support insights for investors and policymakers, guiding the strategic development of sustainable wind-based green hydrogen projects along Mauritania’s coastline. Full article

To address the computational redundancy, inadequate multi-scale feature capture, and poor noise robustness of traditional deep networks used for bearing vibration and acoustic signal feature extraction, this paper proposes a fault diagnosis method based on Depthwise Separable Atrous Convolution (DSAC) and Acoustic Spatial Pyramid Pooling (ASPP). First, the Continuous Wavelet Transform (CWT) is applied to the vibration and acoustic signals to convert them into time–frequency representations. The vibration CWT is then fed into a multi-scale feature extraction module to obtain preliminary vibration features, whereas the acoustic CWT is processed by a Deep Residual Shrinkage Network (DRSN). The two feature streams are concatenated in a feature fusion module and subsequently fed into the DSAC and ASPP modules, which together expand the effective receptive field and aggregate multi-scale contextual information. Finally, global pooling followed by a classifier outputs the bearing fault category, enabling high-precision bearing fault identification. Experimental results show that, under both clean data and multiple low signal-to-noise ratio (SNR) noise conditions, the proposed DSAC-ASPP method achieves higher accuracy and lower variance than baselines such as ResNet, VGG, and MobileNet, while requiring fewer parameters and FLOPs and exhibiting superior robustness and deployability. Full article

Polymer Electrolyte Membrane and Direct Methanol Fuel Cells (PEMFCs/DMFCs) are vital clean energy technologies, yet their adoption is hindered by limitations in industry-standard PFSA membranes. PFSA degrades above 80 °C, suffers substantial methanol crossover, and contains environmentally persistent PFAS, which raises significant environmental and cost concerns due to its persistence and bioaccumulation, driving a global imperative for sustainable, fluorine-free alternatives. In response to these challenges, the PCAM Lab has dedicated extensive research efforts to developing advanced PEMs. A primary focus is non-fluorinated alternatives (NFPs), including sulfonated Polysulfone (sPSU) and Sulfonated polyether ether ketone (sPEEK), which have emerged as a compelling, cost-effective, and environmentally friendly alternative to the PFSA benchmark. Beyond NFPs’ intrinsic advantages, the lab’s implementation of nanocomposite strategies, involving the incorporation of various functional nanofillers, has proven transformative. This report provides a comprehensive, critical analysis of the state of the art in PEM research, contextualizing the specific contributions of the Physical Chemistry Applied Materials (PCAM) Lab within the broader global scientific dialog. While the PCAM Lab has made notable strides in utilizing Sulfonated Polysulfone (sPSU) and nanocomposite strategies, a true assessment of the field requires integrating these findings with the seminal works of leading international research groups. By synthesizing data on sulfonated polyphenylenes, advanced graphene architectures, and industrial manufacturing constraints, this analysis illuminates the divergent pathways currently being explored to overcome the “Nafion Dilemma”. Full article

This study evaluates the environmental sustainability of hydrogen production from high-calorific mixed waste gasification through a Gate-to-Gate (GtG) Life Cycle Assessment (LCA) based on operational data from a 2 TPD pilot plant. The Global Warming Potential (GWP) was calculated to be 9.80 kg CO₂-eq per kg of H₂ produced. A contribution analysis identified the primary environmental hotspots as external electricity consumption (37.0%), chelated iron production for syngas cleaning (19.5%), externally supplied oxygen 18.6%), and plant construction (12.3%). A comparative analysis, contextualized within South Korea’s energy structure, demonstrates this GWP is competitive with regionally contextualized Steam Methane Reforming (SMR) and lower than coal gasification. Furthermore, a scenario analysis based on national energy policies reveals a clear pathway for GWP reduction. Aligning with the 2030 renewable energy target (20% RE share) reduces the GWP to 9.14 kg CO₂-eq, while a full transition to 100% wind power lowers it to 6.27 kg CO₂-eq. These findings establish this Waste-to-Hydrogen (WtH) technology as a promising transitional solution that simultaneously valorizes problematic waste. This research provides a critical empirical benchmark for the technology’s commercialization and establishes an internationally transferable framework. It confirms that the technology’s ultimate environmental sustainability is intrinsically linked to the decarbonization of the local electricity grid. Full article

This study focuses on human awareness, a critical component in human–robot interaction, particularly within agricultural environments where interactions are enriched by complex contextual information. The main objective is identifying human activities occurring during collaborative harvesting tasks involving humans and robots. To achieve this, we propose a novel and lightweight deep learning model, named 1D-ResNeXt, designed explicitly for recognizing activities in agriculture-related human–robot collaboration. The model is built as an end-to-end architecture incorporating feature fusion and a multi-kernel convolutional block strategy. It utilizes residual connections and a split–transform–merge mechanism to mitigate performance degradation and reduce model complexity by limiting the number of trainable parameters. Sensor data were collected from twenty individuals with five wearable devices placed on different body parts. Each sensor was embedded with tri-axial accelerometers, gyroscopes, and magnetometers. Under real field conditions, the participants performed several sub-tasks commonly associated with agricultural labor, such as lifting and carrying loads. Before classification, the raw sensor signals were pre-processed to eliminate noise. The cleaned time-series data were then input into the proposed deep learning network for sequential pattern recognition. Experimental results showed that the chest-mounted sensor achieved the highest F1-score of 99.86%, outperforming other sensor placements and combinations. An analysis of temporal window sizes (0.5, 1.0, 1.5, and 2.0 s) demonstrated that the 0.5 s window provided the best recognition performance, indicating that key activity features in agriculture can be captured over short intervals. Moreover, a comprehensive evaluation of sensor modalities revealed that multimodal fusion of accelerometer, gyroscope, and magnetometer data yielded the best accuracy at 99.92%. The combination of accelerometer and gyroscope data offered an optimal compromise, achieving 99.49% accuracy while maintaining lower system complexity. These findings highlight the importance of strategic sensor placement and data fusion in enhancing activity recognition performance while reducing the need for extensive data and computational resources. This work contributes to developing intelligent, efficient, and adaptive collaborative systems, offering promising applications in agriculture and beyond, with improved safety, cost-efficiency, and real-time operational capability. Full article

Swarm Robotics (SR) is a relatively new field, inspired by the collective intelligence of social insects. It involves using local rules to control and coordinate large groups (swarms) of relatively simple physical robots. Important tasks that robot swarms can handle include demining, search, rescue, and cleaning up toxic spills. Over the past decade, the research effort in the field of Swarm Robotics has intensified significantly in terms of hardware, software, and systems integrated developments, yet significant challenges remain, particularly regarding standardization, scalability, and cost-effective deployment. To contextualize the state of Swarm Robotics technologies, this paper provides a systematic literature review (SLR) of Swarm Robotic technologies published from 2014 to 2024, with an emphasis on how hardware and software subsystems have co-evolved. This work provides an overview of 40 studies in peer-reviewed journals along with a well-defined and replicable systematic review protocol. The protocol describes criteria for including and excluding studies and outlines a data extraction approach. We explored trends in sensor hardware, actuation methods, communication devices, and energy systems, as well as an examination of software platforms to produce swarm behavior, covering meta-heuristic algorithms and generic middleware platforms such as ROS. Our results demonstrate how dependent hardware and software are to achieve Swarm Intelligence, the lack of uniform standards for their design, and the pragmatic limits which hinder scalability and deployment. We conclude by noting ongoing challenges and proposing future directions for developing interoperable, energy-efficient Swarm Robotics (SR) systems incorporating machine learning (ML). Full article

The increasing urgency of global sustainability challenges has elevated the role of the United Nations Sustainable Development Goals (SDGs) as benchmarks for both academic research and policy development. Within the European Union, measuring how national research systems contribute to SDG-related knowledge is critical for guiding evidence-based policymaking and evaluating progress toward the 2030 Agenda. Since the adoption of the UN 2030 Agenda, research related to the Sustainable Development Goals (SDGs) has expanded significantly, reflecting their central role in guiding both global and European science policy. Despite this growing attention, systematic comparative evidence on how EU27 countries contribute to SDG-related knowledge production remains limited. This study provides a bibliometric analysis of research related to the SDGs across EU27 countries between 2019 and 2023. Drawing on data from Elsevier’s Scopus and SciVal platforms, we examine publication volume, relative share (RS), citation impact (FWCI), growth dynamics (CAGR), and thematic distributions. The dataset includes all document types associated with SDG1–SDG16. Germany, Italy, and France lead in absolute publication output, while smaller member states such as Cyprus, Malta, and Luxembourg display disproportionately high RS values. Health-related research (SDG3) dominates, followed by SDG7 (Affordable and Clean Energy) and SDG12 (Responsible Consumption and Production), whereas socially oriented goals (SDG2 and SDG5) remain underrepresented. Hierarchical cluster analysis, validated through silhouette and agglomeration tests, identifies three groups of countries: (1) high-output, high-impact Northern and Western leaders; (2) diversified performers with balanced portfolios; and (3) emerging contributors from Eastern and Southern Europe. Explanatory analyses link bibliometric outcomes to contextual variables, showing strong correlations with Horizon Europe funding per capita and international collaboration, and moderate associations with GDP per capita and GERD. Institutional-level findings highlight the prominence of leading universities and research institutes, particularly in health sciences. The study introduces a robust cluster-based typology and a multidimensional framework that connects bibliometric performance with economic capacity, research investment, EU funding participation, and collaboration intensity. Policy recommendations are proposed to strengthen thematic balance, improve equitable participation in EU research programs, and foster international cooperation across the European Research Area. Full article

Offensive language and hate speech have a detrimental effect on victims and have become a significant problem on social media platforms. Recent research has developed automated techniques for detecting Arabic offensive language and hate speech but remains limited, and further research is required compared to the research on high-resource languages such as English due to limited resources, annotated corpora, and morphological analysis. Most social media users who use profanities attempt to modify their text while maintaining the same meaning, thereby deceiving detection methods that forbid offending phrases. Therefore, this study proposes an adversarially robust multitask learning framework for detection of Arabic offensive and hate speech. For this purpose, this study used the OSACT2020 dataset, augmented with additional posts collected from the X social media platform. To improve contextual understanding, classification models based on various configurations were constructed using four pre-trained Arabic language models integrated with various sequential layers that were trained and evaluated in three different settings: single-task learning with the original dataset, single-task learning with the augmented dataset, and multitask learning with the augmented dataset. The multitask MARBERTv2+BiGRU model achieved the best results, with an 88% macro-F1 for hate speech and 93% for offensive language on clean data. To improve the model’s robustness, adversarial samples were generated using attacks on both the character and sentence levels. These attacks subtly change the text to mislead the model while maintaining the overall appearance and meaning. The clean model’s performance dropped significantly under attack, especially for hate speech, to a 74% macro-F1; however, adversarial training, which re-trains the model using both clean and adversarial data, improved the results to a 78% macro-F1 for hate speech. Further improvements were achieved with input transformation techniques, boosting the macro-F1 to 81%. Notably, the adversarially trained model maintained high performance on clean data, demonstrating both robustness and generalization. Full article

The increasing recognition of historical emissions and uneven financial capacities among developed and developing nations has highlighted the need to look for equity and fairness in global climate action. This study aims to present a revised method that enables mapping the current state of fairness in the global energy transition, addressing both the contribution to the climate crisis and the burden that different countries face in coping with the climate disasters resulting from it. For this purpose, we revise various methods and indices used to measure the progress of energy transition efforts, as well as existing methodologies to appraise the responsibility for climate change and the resulting financial capacity. We propose changes to the existing methods to allow for a clearer analysis of the fairness of the global energy transition. An exemplary use of the proposed modified methodology is applied to six countries that represent developing and developed countries using publicly available data from renowned sources such as IRENA, EM-DAT, and the World Bank, showing the applicability of the method. The main trends in the results highlight the added value of the proposed method. The progress in the energy transition is evaluated in terms of fairness as a transition index by taking into account historical responsibility and financial capacity. Damage from climate-induced disasters and contribution towards climate financing are added as contextual considerations. The country’s historical emissions, GDP, NDC, financial costs of climate-induced disaster, and financing from the Green Climate Fund are used as the basis for the analysis. The findings underscore the differences in energy transition achievement, as well as the differences in pledged and deposited funds among various types of countries. The results demonstrate a disproportionate burden experienced by lower-income nations and depict the ongoing challenges in translating principles of “common but differentiated responsibilities” into concrete outcomes. This study provides an open-source and data-driven perspective that highlights the need for change in global policy discourse and also advocates for the creation of more nuanced, just, and effective approaches to accelerate the clean energy transition worldwide. Full article

Objective: This study aimed to explore the acute neurophysiological effects of a single oral dose of Astragaloside IV (AS-IV) on EEG-measured brain oscillations and cognitive-relevant spectral markers in healthy young adults. Methods: Twenty healthy adults (8 females, 12 males; mean age: $23.4\pm 2.1$) underwent eyes-closed resting-state EEG recordings before and approximately 90 min after oral intake of 150 mg AS-IV. EEG data were collected using a 21-channel 10–20 system and cleaned via Artifact Subspace Reconstruction and Independent Component Analysis. Data quality was confirmed using a signal-to-noise ratio and 1/f spectral slope. Absolute and relative power values, band ratios, and frontal alpha asymmetry were computed. Statistical comparisons were made using paired t-tests or Wilcoxon signed-rank tests. Results: Absolute power decreased in delta, theta, beta, and gamma bands (p < 0.05) but remained stable for alpha. Relative alpha power increased significantly (p = 0.002), with rises in relative beta, theta, and delta and a drop in relative gamma (p = 0.003). Alpha/beta and theta/beta ratios increased, while delta/alpha decreased. Frontal alpha asymmetry was unchanged. Sex differences were examined in all measures that showed significant changes; however, no sex-dependent effects were found. Conclusions: A single AS-IV dose may acutely modulate brain oscillations, supporting its potential neuroactive properties. Larger placebo-controlled trials, including concurrent psychometric assessments, are needed to verify and contextualize these findings. A single AS-IV dose may acutely modulate brain oscillations, supporting its potential neuroactive properties. Full article

The construction of marine ranching is a crucial component of China’s Blue Granary strategy, yet the fragmented knowledge system in marine ranching equipment impedes intelligent management and operational efficiency. This study proposes the first knowledge graph (KG) framework tailored for marine ranching equipment, integrating hybrid ontology design, joint entity–relation extraction, and graph-based knowledge storage: (1) The limitations in existing KG are obtained through targeted questionnaires for diverse users and employees; (2) A domain ontology was constructed through a combination of the top-down and the bottom-up approach, defining seven key concepts and eight semantic relationships; (3) Semi-structured data from enterprises and standards, combined with unstructured data from the literature were systematically collected, cleaned via Scrapy and regular expression, and standardized into JSON format, forming a domain-specific corpus of 1456 annotated sentences; (4) A novel BERT-BiGRU-CRF model was developed, leveraging contextual embeddings from BERT, parameter-efficient sequence modeling via BiGRU (Bidirectional Gated Recurrent Unit), and label dependency optimization using CRF (Conditional Random Field). The TE + SE + R_i + BMESO tagging strategy was introduced to address multi-relation extraction challenges by linking theme entities to secondary entities; (5) The Neo4j-based KG encapsulated 2153 nodes and 3872 edges, enabling scalable visualization and dynamic updates. Experimental results demonstrated superior performance over BiLSTM-CRF and BERT-BiLSTM-CRF, achieving 86.58% precision, 77.82% recall, and 81.97% F1 score. This study not only proposes the first structured KG framework for marine ranching equipment but also offers a transferable methodology for vertical domain knowledge extraction. Full article

Access to clean and sustainable energy technologies is critical for all nations, particularly developing countries in Africa. Ghana has committed to ambitious greenhouse gas emission reduction targets, aiming for 10% and 20% variable renewable energy integration by 2030 and 2070, respectively. This study explores potential pathways for Ghana to achieve its renewable energy production targets amidst a growing energy demand. An open-source energy modelling tool was used to assess four scenarios accounting for current policies and additional alternatives to pursue energy transition goals. The scenarios include Business as Usual (BAU), Government Target (GT), Renewable Energy (REW), and Net Zero (NZ). The results indicate that total power generation and installed capacity would increase across all scenarios, with natural gas accounting for approximately 60% of total generation under the BAU scenario in 2070. Total electricity generation is projected to grow between 10 and 20 times due to different electrification levels. Greenhouse gas emission reduction is achievable with nuclear energy being critical to support renewables. Alternative pathways based on clean energy production may provide cost savings of around USD 11–14 billion compared to a Business as Usual case. The findings underscore the necessity of robust policies and regulatory frameworks to support this transition, providing insights applicable to other developing countries with similar energy profiles. This study proposes a unique contextualized open-source modelling framework for a data-constrained, lower–middle-income country, offering a replicable approach for similar contexts in Sub-Saharan Africa. Its novelty also extended towards contributing to the knowledge of energy system modelling, with nuclear energy playing a crucial role in meeting future demand and achieving the country’s objectives under the Paris Agreement. Full article

Atmospheric correction is essential in remote sensing, as it reduces the effects of light absorption and scattering by suspended particles and gases, enabling accurate surface reflectance computation from the observed Top-of-Atmosphere (TOA) reflectance. Each satellite sensor requires a customized atmospheric correction processor due to its unique system characteristics. Currently, PeruSAT-1, the first Peruvian remote sensing satellite, does not include this capability in its image processing pipeline, which poses challenges for its effectiveness in defense, security, and natural disaster management applications. This research investigated pixel-based machine learning methods for atmospheric correction of PeruSAT-1, using Sentinel-2 harmonized Bottom-of-Atmosphere (BOA) surface reflectance images as a benchmark, alongside additional atmospheric, terrain, and acquisition parameters. A robust dataset was developed to align data across temporal, spatial, geometric, and contextual conditions. Experimental results showed R² values between 0.886 and 0.938, and RMSE values ranging from 0.009 to 0.025 compared to the benchmarks. Among the models tested, the Feedforward Neural Network (FFNN) using the Leaky ReLU activation function achieved the best overall performance. These findings confirm the robustness of this approach, offering a scalable methodology for satellites with similar characteristics and establishing a foundation for a customized atmospheric correction pipeline for PeruSAT-1. Future work will focus on diversifying the dataset across spectral and seasonal conditions and optimizing the modeling to address challenges in shorter wavelengths and high-reflectance surfaces. Full article

The mitigating effects of straw burning bans on air pollution are widely known; however, their effects on indoor air pollution are generally ignored. Cooking fuel use is an important factor that affects indoor air quality. However, the debate over the pros and cons of a province-wide ban on straw burning has been a major issue in environmental economics. By utilizing household survey data, this study investigates the role of straw burning bans on cooking fuel use in households. To infer causal relationships, difference-in-difference models that compare households in provinces with and without a complete ban on open straw burning (COSB) are employed. The results show that COSBs promote the use of clean cooking fuels and discourage the use of firewood for cooking by households. These results hold true after a series of robustness tests, such as parallel trends and placebo tests. However, the results show that the effect of COSBs on the household use of coal as a cooking fuel is not significant. Further analysis shows heterogeneity in the effects of COSBs on the use of household cooking fuels. Thus, COSBs promote the conversion to cleaner cooking fuels in rural households, but the implementation of these policies needs to be contextualized. Full article