Browse Items (7684 total)
Sort by:
-
Linear and weakly nonlinear stability analyses of Darcy Bard convection with feedback control
In this paper, the effect of feedback control on the criterion for the onset of DarcyBard convection in a horizontal Boussinesq Newtonian fluid is studied theoretically. The bounding isothermal lower and upper surfaces are considered to be rigid. The single term Galerkin method and the Maclaurin series expansion are combined with the Newton-Raphson method of three variables to perform a linear stability analysis in order to determine eigen value. To make a weakly nonlinear stability analysis of the system, a Vadasz Lorenz model is constructed. The models various properties are discovered to be identical to those of the standard Lorenz model. The exhibits both dissipative and conservative characteristics and the bounded nature of its solution is demonstrated by the trapping region, which takes the form of an ellipsoid. The Hopf-Rayleigh number determined from the autonomous dynamical system predicts the onset of chaos. The influence of the controller gain parameter and the Biot number on the onset of convection has been analyzed. Results from the study reveal that the controller gain parameter stabilizes the system and further delays the onset of chaos. Overall, the study establishes that an increase in the Biot number promotes long-term periodic motion over chaotic behavior, while an increase in the controller gain parameter enlarges the trapping region, thereby contributing to improved system stability. The Author(s) 2025. -
An enhancement of machine learning model performance in disease prediction with synthetic data generation
The challenges of handling imbalanced datasets in machine learning significantly affect the model performance and predictive accuracy. Classifiers tend to favor the majority class, leading to biased training and poor generalization of minority classes. Initially, the model incorrectly treats the target variable as an independent feature during data generation, resulting in suboptimal outcomes. To address this limitation, the model was adjusted to more effectively manage target variable generation and mitigate the issue. This study employed advanced techniques for synthetic data generation, such as synthetic minority oversampling (SMOTE) and Adaptive Synthetic Sampling (ADASYN), to enhance the representation of minority classes by generating synthetic samples. In addition, data augmentation strategies using Deep Conditional Tabular Generative Adversarial Networks (Deep-CTGANs) integrated with ResNet have been utilized to improve model robustness and overall generalizability. For classification, TabNet, a model tailored specifically for tabular data, proved highly effective with its sequential attention mechanism that dynamically processes features, making it well suited for handling complex and imbalanced datasets. Model performance was evaluated using a novel approach of training synthetic data and testing on real data (TSTR). The framework was validated on the COVID-19, Kidney, and Dengue datasets, achieving impressive testing accuracies of 99.2%, 99.4%, and 99.5%, respectively. Furthermore, similarity scores of 84.25%, 87.35%, and 86.73% between the real and synthetic data for the COVID-19, Kidney, and Dengue datasets, respectively, confirmed the reliability of the synthetic data. TabNet consistently showed substantial improvements in F1-scores compared to other models, such as Random Forest, XGBoost, and KNN, emphasizing the importance of selecting the right synthetic data augmentation techniques and classifiers. Additionally, SHapley Additive exPlanations (SHAP)-based explainable AI tools were used to interpret model performance, providing insights into feature importance and its impact on predictions. These findings confirm that the proposed approach enhances the accuracy, robustness, and interpretability, offering a valuable solution for addressing data imbalance in classification tasks. The Author(s) 2025. -
Design of a novel shunt active harmonic compensator with AUV-PQ-SRF reference current extraction, OSV-MPC and SMC techniques
Harmonic distortion makes it difficult to maintain good Electrical Power Quality (EPQ) in distribution networks with many nonlinear loads. Three significant advances are combined in this papers innovative Shunt Active Harmonic Compensator (SAHC) design: (i) a new technique for extracting reference currents, called AUV-PQ-SRF, which combines the Unit Vector, PQ, and SRF techniques in a unique way to improve harmonic detection; (ii) an OSV-MPC strategy that improves reference current tracking accuracy by doing away with traditional pulse width modulation; and (iii) a Sliding Mode Controller (SMC) for dynamic and reliable DC link voltage regulation under a range of load conditions. The accuracy, robustness, and response time issues with traditional methods are addressed by the suggested approach. Results from simulations conducted in accordance with IEEE-519-2022 standards show a considerable decrease in total harmonic distortion (THD), along with increased power factor and real and reactive power compensation. This study provides a thorough and useful solution for dynamic power quality issues, setting a new standard in active filtering. The Author(s) 2025. -
Emerging applications of sustainable modified CdO/Ag-CdO NPs for electrochemical sensitive and selective detection of mercury (Hg+) heavy metal
The sensitivity of developed electrode has gained significant attention for potential energy storage and electrochemical sensor activities. The modified nano-CdO/Ag-CdO-carbon paste electrodes were developed for electrochemical detection of mercury (Hg+) heavy metal. The synthesized samples were well characterized through PXRD (Powder X-ray diffraction), SEM-EDAX (Scanning Electron Microscopy-Energy Dispersive X-ray Analysis), XPS (X-ray photo-electron spectroscopy), FT-IR (Fourier transform Infra-Red), and UV-Visible spectroscopy. The Ag-CdO modified electrode endowed with higher sensing current and Csp (188F/g) than pure CdO NPs (94.6F/g) measured by Linear Sweep (LS), Cyclic Voltammetric (CV) and Electrochemical Impedance Spectral (EIS) techniques. The excellent electrochemical sensing action of developed Ag-CdO electrode was examined on heavy metal Hg+ ions at 15 mM scan rate in 0.1M KCl. The linear relationship of sensing measurements with smaller concentration (15 mM) was observed with its increased current (+ 1.64 104 A/cm2 at 1 mM) at 30 mV/s. LOD of CdO and Ag-CdO electrodes (Hg+Oxid) were found at 1.91 mM & 2.41 mM (Hg+Red) respectively with maximum sensitivity at -0.006V. LOQ of CdO and Ag-CdO electrodes (Hg+Oxid) were 5.78 mM & 6.98 mM respectively with maximum sensitivity at -0.006V. The antibacterial measurements of prepared samples were examined for their susceptibility to inhibit the growth of gram-negative (Escherichia coli) and gram-positive (Staphylococcus aureus) bacteria. Thus, the synthesized Ag-CdO electrode provides a new insight for determining the concentrations of critical pollutants and processing the various nanoparticles for sensing of cyanogenic heavy metals. The Author(s) 2025. -
One pot synthesis of a novel bioactive compound employing a deep eutectic solvent grafted MWCNT system in a solventless environment
Heterogeneous catalysis is considered as a suitable alternative to conventional organic synthesis for the selective production of industrially significant fine chemicals. The development of supported catalysts by dispersing minimal quantities of active component can reduce production costs and enhance energy efficiency. The current work reports the development of Deep eutectic solvent (DES) modified multiwalled carbon nanotube (MWCNT) system and its activity in the Knoevenagel condensation reaction. The catalytic system was developed by grinding a very low concentration (0.83mM) of DES with desired amount of MWCNT. Various interactions of the three component DES with MWCNT were analysed by X-ray photoelectron spectroscopy (XPS). The reaction favoured a novel compound selectively with yield around 92% in solvent free medium. Anti-cancerous studies of the synthesized compound demonstrated a strong IC50 value of 15.62g/ml and a statistically calculated IC50 value of 9.8g/ml. Acridine orange/ Ethidium Bromide (AO/EB) dual fluorescence staining studies revealed that the test ligand with lowest concentration of 7.8g/ml was capable to induce apoptosis in 100% of MCF-7 cells. It is evident from the studies that the synthesized compound is a strong anticancer agent with potential to be investigated further. The Author(s) 2025. -
An efficient intelligent transportation system for traffic flow prediction using meta-temporal hyperbolic quantum graph neural networks
Intelligent Transportation Systems (ITS) necessitate scalable, real-time, and adaptive traffic flow prediction models to enhance urban mobility and alleviate congestion. Conventional Graph Neural Network methodologies encounter difficulties in managing extensive road networks, long-range temporal relationships, and computing efficiency for real-time applications. An innovative deep learning framework named Meta Temporal Hyperbolic Quantum Graph Neural Networks that integrates hyperbolic embeddings, meta learning, quantum graph, Neural Ordinary Differential Equation (NODEs) to improve the ITS Performance. Across many cities, meta learning facilitates swift adaptation with minimum retraining whereas hyperbolic graph embeddings efficiently depict hierarchical route configurations The usage of Quantum Graph Neural Networks (QGNNs) enhances graph-based scheming, enabling real-time traffic flow to forecast for extensive networks. Also, NODEs summarize ongoing traffic progress, enhancing precision under dynamic sceneries. Datasets like Los-loop and SZ-taxi datasets are validated by experiments which highlights the impact of the proposed MTH-QGNN model, acquiringamean value RMSE of 4.5 and MAE of 3.5, ensuring minimal prediction error. MTH-QGNN model constantly sustained accuracy above 80% and R2 values exceeding 83%, representing robust predictive trustworthiness. MTH-QGNN effectively captures complex spatiotemporal traffic patterns with a variance score above threshold value. The Author(s) 2025. -
Optimized placement and sizing of solar photovoltaic distributed generation using jellyfish search algorithm for enhanced power system performance
The strategic integration of distributed generation (DG) units into distribution power networks (DPNs) is pivotal for augmenting system efficiency and stability. This study introduces an advanced metaheuristic optimization framework leveraging the Jellyfish Search Algorithm (JSA) for the optimal placement and sizing of solar photovoltaic (PV) DG units. The formulated multi-objective function incorporates real power loss (RPL) minimization, voltage deviation index (VDI) reduction, and voltage stability index (VSI) enhancement, employing a weighted sum approach (WSA) to ensure computational rigor. The efficacy of the proposed methodology is rigorously validated on the IEEE 33-bus radial DPN under single and multiple PV system deployment scenarios. For single PV system optimized inclusion, RPL of the DPN is cut down from 210.98kW to 102.89kW, total VDI is reduced from 1.8047 p.u to 0.5331 p.u, and minimum VSI is increased from 0.6671 to 0.7559. For two PV DG units inclusion, RPL is reduced to 82.99kW, total VDI is reduced to 0.6518 p.u with a least VSI improved to 0.8848. However, better result is obtained with three units of DG placement with RPL reduced to 69.59kW, total VDI decreased to 0.3293 p.u with a least VSI of the test system increased to 0.8916. Comparative analyses against state-of-the-art metaheuristic algorithms underscore the superior convergence efficiency and optimality of JSA in addressing nonlinearity and high-dimensionality constraints. Empirical results substantiate substantial RPL reduction, bus voltage enhancement, and system stability reinforcement, establishing JSA as an avant-garde paradigm in DG optimization. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2025. -
Changing climate and its impacts on the dynamics of future malaria transmission over certain endemic regions in India
As climate change plays a major role in evaluating the malaria disease over India, it is highly relevant to assess the spatio-temporal variability of malaria transmission dynamics over different climatic zones in India using modelling studies. In this study, VECTRI (vector-borne disease community model of the International Centre for Theoretical Physics, Trieste) model is simulated to predict the future malaria transmission dynamics over four major climatological zones of India, forced with the different climatic parameters such as temperature and rainfall and non-climatic parameter such as population density. The climate data is obtained from multi model mean of different CMIP6 global climate models under the SSP5-8.5 scenario. Results indicate that there is an overall decrease in EIR (Entomological Inoculation Rate) values of 10 to 30% are seen over most of the Indian regions with an increase in temperature about 4 to 5C and rainfall about 10 to 40%, by end of the century (2080s) when compared with the baseline period (19852014). However, few exceptions are seen over few parts of western and peninsular region where increase in EIR values are seen. This decrease (increase) in EIR values which describes the intensity of malaria transmission is predominantly controlled by temperature and rainfall during summer (winter) monsoon seasons. Such results from the VECTRI model may be useful for policymakers towards various malaria disease control programs in India and this may provide a basis for climate change impact assessments on malaria risk at a regional scale. The Author(s) 2025. -
Improving EEG based brain computer interface emotion detection with EKO ALSTM model
Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered compelling communication equipment enabling command, communication, and action without using neuromuscular or muscle channels. Various techniques for automatic emotion identification based on body language, speech, or facial expressions are nowadays in use. However, the monitoring of exterior emotions, which are easily manipulated, limits the applicability of these procedures. EEG-based emotion detection research might yield significant benefits for enhancing BCI application performance and user experience. To overcome these issues, this study proposed a novel EKO-ALSTM for emotion detection in EEG-based braincomputer interfaces. The proposed study comprises EEG-based signals that record the electrical activity of the brain connected to various emotional states, which are gathered as real-time acquired EEG signals for emotion detection. The data was pre-processed using a bandpass filter to remove unwanted frequency noise for the obtained data. Then, feature extraction is performed using DWT from pre-processed data. Specifically, the proposed approach is implemented using Python software. The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. The results demonstrated that the proposed method achieved better performance in EEG-based BCI emotion detection with an accuracy of 97.93%, a positive predictive value of 96.24%, a sensitivity of 97.81%, and a specificity of 97.75%. This study emphasizes that innovative approaches have significantly increased the accuracy of emotion identification when applied to EEG-based emotion recognition systems. Additionally, the findings suggest that integrating advanced machine learning techniques can further enhance the effectiveness and reliability of these systems in real-world applications, paving the way for more responsive and intuitive BCI technologies. The Author(s) 2025. -
An enhanced performance analysis of load based resource sharing framework for MIMO systems in 5G communication systems
Resource sharing serves as a cost-effective and dynamically adjustable method for alleviating traffic congestion in wireless networks. Advancements in multi-input multi-output (MIMO) technologies for 5G communication systems have led to the exploration of resource sharing across various cells or sectors. This approach aims to optimise network performance, focussing on coverage, capacity, and quality of service. This document presents a new load-based resource-sharing framework designed for multi-cell MIMO systems. The proposed framework utilises channel-loading data from local base stations and dynamically allocates available resources among adjacent base stations. The proposed framework facilitates dynamic resource sharing, effectively addressing traffic overload in 5G networks. The proposed LBRS achieved a delta-P value of 90.91%, a prevalence threshold value of 89.84%, a critical success index value of 91.01%, and a Mathews correlation coefficient value of 91.27% at the terminal access. At the resource transmission, the system recorded a delta-P value of 92.10%, a prevalence threshold value of 92.18%, a critical success index value of 91.65%, and a Mathews correlation coefficient value of 88.31%. The simulation results indicate that the proposed framework effectively enhances dynamic resource sharing, resulting in a notable improvement in network performance. The Author(s) 2025. -
Design and Implementation of universal converter using ANN controller
This paper details the hardware implementation of a Universal Converter controlled by an Artificial Neural Network (ANN), utilizing key components such as six Insulated Gate Bipolar Transistors (IGBTs), two inductors, and two capacitors for energy storage and voltage smoothing. A Digital Signal Processor (DSP) serves as the core controller, processing real-time input and feedback signals, including voltage and current measurements, to dynamically manage five operational modes: rectifier buck, inverter boost, DC-DC buck, DC-DC boost, and AC voltage control. The pre-trained ANN algorithm generates pulse-width modulation (PWM) signals to control the switching of the IGBTs, optimizing timing and duty cycles for efficient operation. The system effectively accommodates both AC and DC inputs, ensuring stable outputs with minimal ripple by dynamically selecting the appropriate mode based on load requirements. Experimental results demonstrated that the ANN controller maintained total harmonic distortion (THD) below 5% in rectifier and inverter modes while achieving an overall efficiency of 9496% in DC-DC modes. The controllers capability to adapt to real-time feedback significantly improved power conversion quality and reduced switching losses. This study confirms the efficacy of the ANN-controlled Universal Converter in meeting the demands of modern power systems through versatile and adaptive control. The Author(s) 2025. -
AI-enhanced approaches for personalized cardiac treatment: insights from ECG data
The analysis of drug-induced alterations in the electrocardiogram (ECG) is essential in measuring cardiac safety, but manual analysis is not always accurate enough to identify subtle but important effects. This paper examines how machine learning (ML) models can be used to categorize various pharmacological treatments according to their distinct ECG patterns to establish a platform of individualized therapeutic evaluation. Using the public ECG Effects of Dofetilide, Moxifloxacin, Dofetilide+Mexiletine, Dofetilide+Lidocaine and Moxifloxacin+Diltiazem (ECGDMMLD) database, key electrophysiological features were extractedincluding heart rate variability (HRV) and standard cardiac intervals (RR, PR, QT, QRS) to train and compare three different classifiers: XGBoost, Random Forest, and a Support Vector Machine (SVM). The analysis showed that tree-based ensemble techniques were very useful in this task. The XGBoost model had a better classification accuracy of 98.1%, which was closely followed by the random forest at 97.3%. Conversely, the SVM had much lower accuracy, implying that it was not as well adapted to the complexity of the high-dimensional ECG data. These results establish that ML models, particularly XGBoost, can accurately decode complex drug-induced cardiac signatures from ECG data. This work is a powerful demonstration of the proof-of-concept of automated and data-driven analytics integration into clinical processes to enhance drug safety and promote personalized medicine. The Author(s) 2026. -
The Application of Expressive Art Therapies for Survivors of Child Sexual Abuse
Objective: The purpose of this study is to explore and summarize existing literature on the application of expressive art therapies like dance movement therapy (DMT), art therapy and music therapy among survivors of child sexual abuse (CSA). Method: A narrative review was conducted following a structured search strategy. The search strategy involved identifying relevant studies from electronic databases, such as PubMed, APA PsycInfo, and Google Scholar, focusing on peer-reviewed articles and dissertations published between 2003 and 2024. The literature, studies, and empirical data pertaining to the application of expressive art therapies in meeting the requirements of CSA survivors are reviewed as part of the research process. Results: Research suggests that for survivors of CSA, DMT, music therapy, and art therapy offer a safe space for recovery that encourages hope, self-determination, and healthy relationships. These treatments encourage cognitive processing, emotional expression, and physical connectedness. However, there are shortcomings in the research including the lack of rigorous research methods and inclusion of long-term studies. Conclusions: Adult survivors of CSA have a lot of promise for therapeutic interventions from expressive art therapies, which include DMT, art therapy, and music therapy. They provide survivors with invaluable support as they work toward empowerment and healing. 2025 American Psychological Association -
Considering Cultural Responsiveness in the Creation of the International Competences for Undergraduate Psychology (ICUP) Model: What Can Psychology Learn?
This article aims to describe the development of foundational competencies relevant to cultural responsiveness (CR), within the context of the International Competences for Undergraduate Psychology (ICUP) model (Nolan et al., 2025). The underlying premise of the ICUP model is that the acquisition of undergraduate-level foundational psychology competences can and should have high value in personal, work, and community contextsregardless of graduate career destination. A targeted background on CR is given, followed by brief descriptions of the International Collaboration on Undergraduate Psychology Outcomes (ICUPO) project (which created the ICUP model; International Collaboration on Undergraduate Psychology Outcomes, n.d.) and of the CR competences themselves. Then, procedural aspects of the ICUPO project relevant to CR are described, followed by quantitative and qualitative approaches to exploring the CR of the diverse ICUPO Committee members. The findings are discussed in terms of implications for (a) psychology educatorsin particular, they need to possess the capacity to be culturally responsive in order to be able to support students in acquiring or improving their own CR; (b) psychology education leaders undertaking undergraduate curricular renewal; and (c) the sustainable future of the discipline of psychology. 2025 American Psychological Association -
A worldwide test of the predictive validity of ideal partner preference matching.
Ideal partner preferences (i.e., ratings of the desirability of attributes like attractiveness or intelligence) are the source of numerous foundational findings in the interdisciplinary literature on human mating. Recently, research on the predictive validity of ideal partner preference matching (i.e., Do people positively evaluate partners who match vs. mismatch their ideals?) has become mired in several problems. First, articles exhibit discrepant analytic and reporting practices. Second, different findings emerge across laboratories worldwide, perhaps because they sample different relationship contexts and/or populations. This registered reportpartnered with the Psychological Science Acceleratoruses a highly powered design (N = 10,358) across 43 countries and 22 languages to estimate preference-matching effect sizes. The most rigorous tests revealed significant preference-matching effects in the whole sample and for partnered and single participants separately. The corrected pattern metric that collapses across 35 traits revealed a zero-order effect of ? =.19 and an effect of ? =.11 when included alongside a normative preference-matching metric. Specific traits in the level metric (interaction) tests revealed very small (average ? =.04) effects. Effect sizes were similar for partnered participants who reported ideals before entering a relationship, and there was no consistent evidence that individual differences moderated any effects. Comparisons between stated and revealed preferences shed light on gender differences and similarities: For attractiveness, men's and (especially) women's stated preferences underestimated revealed preferences (i.e., they thought attractiveness was less important than it actually was). For earning potential, men's stated preferences underestimatedand women's stated preferences overestimatedrevealed preferences. Implications for the literature on human mating are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved) 2024 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Unveiling mental health nuances of male Indian classical dancers.
This study explores the lives of male Indian classical dancers, highlighting the duality of dance as a sanctuary and a stressor. As male Indian classical dancers negotiate and redefine norms of masculinity, the study calls for recognition of diverse masculine identities within traditionally feminized spaces. (PsycInfo Database Record (c) 2026 APA, all rights reserved) 2025 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved.; This research explores the mental health nuances of male Indian classical dancers (MICDs), through a lens of redefining masculinity, focusing on their perceived quality of life, psychosocial challenges, and coping strategies. This study follows an interpretive phenomenological approach to follow the lived experiences of MICDs. The participants are male, fluent in English, and pursuing Indian classical dance styles professionally, like Kathak, Bharatanatyam, Chhau, etc. Six participants were recruited for personal, semistructured, in-depth interviews, whereas, a focus group discussion with four participants was conducted to explore the stigma. The data were analyzed using interpretive phenomenological analysis, revealing themes of (a) identity fragmentation and negotiation in gendered social contexts, (b) gendered experiences, (c) emotional distress and psychological challenges, (d) coping mechanisms and resilience, and (e) stigmatization and social integration dynamics. MICDs grapple with identity formation, navigating a paradox of self-perception, artistic identity, and societal expectation. They reported experiencing emasculation, compromising artistic expression, and struggling with gender norms and gendered training constraints. They have faced name-calling, bullying, taunting, slandering, and discrimination leading to psychological challenges and distress. However, the paradox continues as male dancers use adaptive coping strategies despite the adversities that intertwine self-perception, societal pressures, and their passion for dance. These findings provide a strong foundation for making changes in the dance community for acceptance of male dancers, policy making for better job opportunities for male dancers, and mental health services to be provided to help them deal with distress. (PsycInfo Database Record (c) 2026 APA, all rights reserved) 2025 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
International Competences for Undergraduate Psychology: Relevance to the United Nations Sustainable Development Goals
The 17 global goals of the United Nations Sustainable Development Goals (SDGs) are a call to action for governments and organizations around the world to work towards a sustainable future for all people and the planet. Human behaviour is directly or indirectly tied to all of the SDGs; therefore, psychology as a discipline is critical to their achievement. In this article, wea team of 12 psychology educators from eight countries (three from the Global South) representing six continentsoutline connections between psychology and the SDGs. We argue that psychology education at the foundational undergraduate level should integrate the SDGs into curricula. We describe the framework of psychological literacy that we believe is central to a strong undergraduate education in psychology and outline its conceptual relationship to the SDGs. We then describe the International Competences for Undergraduate Psychology, which explicitly mention the SDGs, but are also closely linked to them across all seven International Competences for Undergraduate Psychology competence categories (psychological knowledge, psychological research methodologies and methods, and the five psychology-relevant areas: values and ethics; cultural responsiveness and diversity; critical thinking and problem-solving; communication and interpersonal skills; and personal and professional development). Finally, psychology educators from six countries (Aotearoa New Zealand, Australia, Brazil, Cameroon, India, and the United States) describe teaching and assessment strategies that harness both the International Competences for Undergraduate Psychology and the SDGs. These strategies offer examples to spur psychology educators to consider how they might make these connections in their own classes and curricula and in their own culture and context. 2025 Canadian Psychological Association -
Time of Emergence and Future Projections of Extremes of Malaria Infections in Africa
The spread of malaria is a major health burden, which affects many people in Africa, depends on climate but also socio-economic conditions. Thus, it is important to gauge the impact of anthropogenic global warming on malaria and attribute anthropogenic causes. Here we compute the Time Of Emergence of vector density and of the entomological inoculation rate (EIR) in the SSP3-7.0 scenario using 50 bias-corrected members of Community Earth System Model version 2 Large Ensemble simulations. This reveals that vector density, which depends on climate conditions, and EIR, which depends on both climate and population density, will rise significantly and permanently above the pre-industrial background variability due to anthropogenic causes in Africa. Both the vector density and EIR have areas, mainly in central Africa, where anthropogenic causes have already significantly changed, and many more areas will experience anthropogenic caused changes in the period 20302050 and toward the end of this century. Our simulations also show clear evidence that extremes of vector density and EIR increase in the future by almost 100%, suggesting that major malaria epidemic outbreaks will become much more likely. We also perform simulations with constant population and with no global warming which partly reveal underlying malaria dynamics. Our results highlight the need to prepare for an expansion and intensification of the malaria burden if no health interventions are being taken. 2025 The Author(s). GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union. -
Cross Correlation Between Plasmaspheric Hiss Waves and Enhanced Radiation Levels at Aviation Altitudes
Enhanced radiation in the Earth's atmosphere can pose serious hazards to pilots, aircraft passengers, and commercial space travelers. Recent results have shown, statistically, that there is a strong correlation between dose rates observed by Automated Radiation Measurements for Aerospace Safety (ARMAS) instruments at aviation altitudes (>9km) and plasmaspheric hiss wave power measured by NASA's Van Allen Probes within the inner magnetosphere. Plasmaspheric hiss waves play a very important role in removing energetic electrons from the Earth's radiation belts by precipitating them into the upper atmosphere. These relativistic electrons generally drift eastwards along closed magnetic drift shells. In this study, we use magnetic conjunction events between ARMAS and the Van Allen Probes to analyze the causality between plasmaspheric hiss waves and enhanced radiation observed at aviation altitude. We specifically study how the size of the conjunction window and a shift in L and MLT of the conjunction window affect the correlation between dose rates and plasmaspheric hiss wave power. This is to determine if the observed enhanced radiation at aviation altitude is indeed caused by the plasmaspheric hiss waves in the inner magnetosphere. The results show that the enhanced radiation levels are only correlated with plasmaspheric hiss waves within conjunction windows of ?1 (Formula presented.) L (Formula presented.) 1 and 0 (Formula presented.) MLT (Formula presented.) 2. The correlation between dose rate and hiss wave power increases slightly if ARMAS is shifted approximately 1hr in MLT to the east of the Van Allen Probes, consistent with the drift trajectory of the electrons precipitating into the atmosphere. 2025. The Author(s). -
Electrosynthesized Metal/Polymer Hybrid: Unlocking Selective Formate Production via CO2Electroreduction
Carbon dioxide reduction via electrochemical means offers a sustainable pathway to mitigate CO2emissions and synthesize value-added chemicals. Here, we report the synthesis and performance of a metal/polymer-carbon paper (CuxOy/PoPD/CFP) electrode prepared via a simple two-step in situ electrodeposition method for the electrochemical CO2reduction reaction (CO2ER). Unlike most reported catalysts that yield multiple liquid products and complicate downstream separation processes, CuxOy/PoPD/CFP selectively produces formate as the sole liquid product across all of the test potentials. The amine-rich and porous PoPD matrix synergistically enhanced CO2capture, provided a conductive scaffold for efficient electron transfer, and facilitated intimate interfacial contact with copper oxides, enabling improved catalytic performance. The catalyst demonstrated an onset potential of ??0.27 V (vs RHE) and achieved a faradaic efficiency of 72.6% for formate with a current density of 6.70 mA/cm2at ?0.80 V (vs RHE). Studies showcased an electrochemically active surface area (ECSA) of 16.625 cm2and a roughness factor of 8.31. The long-duration electrolysis experiment demonstrated stable performance for an extended period, maintaining continuous electrolysis for up to 9.5 h without significant fluctuations or degradation in activity. 2025 American Chemical Society