Search Results (531)

UNS S41003 is a low-cost, low-carbon ferritic stainless steel that exhibits moderate corrosion resistance but limited mechanical performance. This study evaluates the electrochemical behavior of untreated and thermomechanically treated UNS S41003 samples. Corrosion tests were conducted in acidic electrolytes with varying pH to simulate aggressive environments relevant to industrial and structural applications where exposure to acidic media and corrosive pollutants occurs. Potentiodynamic polarization curves for all samples displayed passive regions typically associated with protective oxide film formation; however, localized pitting corrosion was detected post-test. Electrochemical impedance spectroscopy indicated a marked decrease in corrosion resistance as pH decreased. The corrosion resistance of the treated alloy remained comparable to that of the untreated condition, indicating that thermomechanical processing did not detrimentally affect passivity or corrosion performance under the tested conditions. The literature suggests that the applied treatment enhances mechanical properties, supporting the potential use of this alloy in structural components subjected to acidic environments requiring a balance of mechanical strength and corrosion resistance. Full article

Smartphones have revolutionized our daily lives, becoming portable pocket computers with easy internet access. India, the second-highest smartphone and internet user, experienced a significant rise in smartphone usage between 2013 and 2024. Prolonged smartphone use, exceeding 20 min at a time, can lead to physical and mental health issues, including psychophysiological disorders. Digital devices and their extended exposure to blue light cause digital eyestrain, sleep disorders and visual-related problems. This research examines the impact of 1 h smartphone usage on visual fatigue among young Indian adults. A portable, low-cost system has been developed to measure visual activity to address this. The developed visual activity measurement system measures blink rate, inter-blink interval, and pupil diameter. Measured eye activity was recorded during 1 h smartphone usage of e-book reading, video watching, and social-media reels (short videos). Social media reels show increased screen variations, affecting pupil dilation and reducing blink rate due to continuous screen brightness and intensity changes. This reduction in blink rate and increase in inter-blink interval or pupil dilation could lead to visual fatigue. Full article

Physics concepts are considered an essential component of STEM (science, technology, engineering, and mathematics) education and fundamental for economic and technological development in the world. However, there can be student academic underperformance, such as the school environment, learning media and infrastructure, student interest and emotions, as well as social and economic development factors in communities. These problems are even more acute in rural areas of developing countries, where poverty is high and teachers often lack the necessary technological skills. The aim of this study was to evaluate the impact of a low-cost STEM tool focused on motivation in learning, in terms of five variables of interest in physics in rural areas, as well as the durability of the tools used to learn 12 physics concepts. A quasi-experimental study was conducted with the participation of 78 high school students, with an average age of 15.82 years, in a rural area of Guerrero, Mexico. The results showed that using the STEM tool significantly increased students’ interest in learning methodology, active participation, and attitude towards physics, facilitating the teacher’s work. In addition, the 3D construction kit used in the experimentation, besides being low-cost, proved to be affordable and durable, making it ideal for use in rural areas. Full article

The removal of antibiotics from aqueous media along with their recovery is still an open research topic, due to their practical and economical importance. Adsorption allows these two objectives to be achieved, provided that the adsorbent used is chemically and mechanically stable and has a low preparation cost. In this study, PET (polyethylene terephthalate) fibers, obtained by mechanically processing PET waste, were used for the adsorption of rifampicin (RIF) and rifaximin (RIX) antibiotics from aqueous media. The experimental adsorption capacity of PET fibers for the two antibiotics (RIF and RIX) was determined at different pH values (2.0–6.5), adsorbent dose (0.4–20.0 g/L), contact time (5–1440 min), initial antibiotic concentration (4.0–67.0 mg/L), and temperature (10, 22, and 50 °C); the experimental values of these parameters were analyzed using a neuro-evolutive technique (ANE) combining sequential deep learning (DL) models with a differential evolution algorithm. The obtained optimal ANN-DL algorithm was then used to obtain the optimal models for the adsorption of RIF and RIX on PET fibers, which should adequately describe the adsorption dynamics for both antibiotics. The adsorption processes are spontaneous and endothermic (ΔG < 0, ΔH > 0) and are described by the Langmuir model (R² > 0.97) and the pseudo-second order kinetic model (R² > 0.99). The retention of RIF and RIX on the surface of PET fibers occurs through physicochemical interactions, and the FTIR spectra and microscopic images support this hypothesis. The presence of inorganic anions in the aqueous solution leads to an increase in the adsorption capacities of RIF (max. 7.6 mg/g) and RIX (max. 3.6 mg/g) on PET fibers, which is mainly due to the ordering of water molecules in the solution. The experimental results presented in this study allowed for the development of the adsorption mechanism of RIF and RIX on PET fibers, highlighting the potential practical applications of these adsorption processes. Full article

The increasing integration of 3D technologies and machine learning is fundamentally reshaping mineral sciences and cultural heritage, establishing the foundation for an emerging “Mineralogy 4.0” framework. However, public engagement with digital 3D collections is often limited by complex or costly interfaces, such as VR/AR systems and traditional touchscreen kiosks, creating a clear need for more intuitive, accessible, and more engaging and inclusive solutions. This paper presents PETRA, an open-source, gesture-controlled system for exploring 3D rocks and minerals. Developed in the TouchDesigner environment, PETRA utilizes a standard webcam and the MediaPipe framework to translate natural hand movements into real-time manipulation of digital specimens, requiring no specialized hardware. The system provides a customizable, node-based framework for creating touchless, interactive exhibits. Successfully evaluated during a “Long Night of Museums” public event with 550 visitors, direct qualitative observations confirmed high user engagement, rapid instruction-free learnability across diverse age groups, and robust system stability in a continuous-use setting. As a practical case study, PETRA demonstrates that low-cost, webcam-based gesture control is a viable solution for creating accessible and immersive learning experiences. This work offers a significant contribution to the fields of digital mineralogy, human–machine interaction, and cultural heritage by providing a hygienic, scalable, and socially engaging method for interacting with geological collections. This research confirms that as digital archives grow, the development of human-centered interfaces is paramount in unlocking their full scientific and educational potential. Full article

Although carbon emissions trading systems are universally acknowledged as one of the most potent policy instruments for counteracting hazardous climate trends, and digitalization is seen as a favorable technological means to promote corporate green and low-carbon transformation, few studies have investigated the dark side of both. Using data on Chinese listed companies from 2011 to 2020 and adopting a multi-period DID methodology, this research reveals that, in response to the carbon emissions trading system, firms often adopt low-cost, strategic environmental governance behaviors—namely, carbon washing—to reduce compliance costs and maintain their reputation and image. Furthermore, the study reveals that the information advantages of digital transformation create conditions for the opportunistic manipulation of carbon disclosure. Digitalization amplifies the positive influence of the carbon trading system on corporate carbon washing behavior. Mechanism analysis confirms that the carbon emissions trading system increases the production costs of regulated firms, thereby increasing their carbon washing behavior. Economic consequence analysis confirms that firms engage in carbon washing to gain legitimacy and maintain their reputation and image, which may allow them to obtain opportunistic benefits in the capital market. Finally, this study suggests that the government should adopt supplementary policy tools, such as environmental subsidies, enhanced use of digital technologies to strengthen regulatory capacity, and increased media oversight, to mitigate the unintended consequences of the carbon trading system on corporate behavior. Full article

Moroccan oil shale ash (MOSA) represents an underutilized industrial by-product, particularly in the Rif region, where its high mineral content has often led to its neglect in value-added applications. This study highlights the successful conversion of MOSA into amorphous mesoporous silica (AS-Si) using a sol–gel process assisted by polyethylene glycol (PEG-6000) as a soft template. The resulting AS-Si material was extensively characterized to confirm its potential for environmental remediation. FTIR analysis revealed characteristic vibrational bands corresponding to Si–OH and Si–O–Si bonds, while XRD confirmed its amorphous nature with a broad diffraction peak at 2θ ≈ 22.5°. SEM imaging revealed a highly porous, sponge-like morphology composed of aggregated nanoscale particles, consistent with the nitrogen adsorption–desorption isotherm. The material exhibited a specific surface area of 68 m²/g, a maximum in the pore size distribution at a pore diameter of 2.4 nm, and a cumulative pore volume of 0.11 cm³/g for pores up to 78 nm. DLS analysis indicated an average hydrodynamic diameter of 779 nm with moderate polydispersity (PDI = 0.48), while a zeta potential of –34.10 mV confirmed good colloidal stability. Furthermore, thermogravimetric analysis (TGA) and DSC suggested the thermal stability of our amorphous silica. The adsorption performance of AS-Si was evaluated using methylene blue (MB) and ciprofloxacin (Cipro) as model pollutants. Kinetic data were best fitted by the pseudo-second-order model, while isotherm studies favored the Langmuir model, suggesting monolayer adsorption. AS-Si could be used four times for the removal of MB and Cipro. These results collectively demonstrate that AS-Si is a promising, low-cost, and sustainable adsorbent derived from Moroccan oil shale ash for the effective removal of organic contaminants from aqueous media. Full article

The vast volume of media content, combined with the costs of manual annotation, challenges scalable codebook analysis and risks reducing decision-making accuracy. This study evaluates the effectiveness of large language models (LLMs) and multi-agent teams in structured media content analysis based on codebook-driven annotation. We construct a dataset of 200 news articles on U.S. tariff policies, manually annotated using a 26-question codebook encompassing 122 distinct codes, to establish a rigorous ground truth. Seven state-of-the-art LLMs, spanning low- to high-capacity tiers, are assessed under a unified zero-shot prompting framework incorporating role-based instructions and schema-constrained outputs. Experimental results show weighted global F1-scores between 0.636 and 0.822, with Claude-3-7-Sonnet achieving the highest direct-prompt performance. To examine the potential of agentic orchestration, we propose and develop a multi-agent system using Meta’s Llama 4 Maverick, incorporating expert role profiling, shared memory, and coordinated planning. This architecture improves the overall F1-score over the direct prompting baseline from 0.757 to 0.805 and demonstrates consistent gains across binary, categorical, and multi-label tasks, approaching commercial-level accuracy while maintaining a favorable cost–performance profile. These findings highlight the viability of LLMs, both in direct and agentic configurations, for automating structured content analysis. Full article

Improving photovoltaic (PV) panel performance under extreme climatic conditions is critical for advancing sustainable energy systems. In hyper-arid regions, elevated operating temperatures significantly reduce panel efficiency. This study investigates and compares three cooling techniques—air cooling, water cooling, and porous media cooling—using thermal and electrical modeling based on CFD simulations in ANSYS. The numerical model replicates a PV system operating under peak solar irradiance (900 W/m²) and realistic ambient conditions in Adrar, Algeria. Simulation results show that air cooling leads to a modest temperature reduction of 6 °C and a marginal efficiency gain of 0.25%. Water cooling, employing a top-down laminar flow, reduces cell temperature by over 35 °C and improves net electrical output by 30.9%, despite pump energy consumption. Porous media cooling, leveraging passive evaporation through gravel, decreases panel temperature by around 30 °C and achieves a net output gain of 26.3%. Mesh sensitivity and validation against experimental data support the accuracy of the model. These findings highlight the significant potential of water and porous material cooling strategies to enhance PV performance in hyper-arid environments. The study also demonstrates that porous media can deliver high thermal effectiveness with minimal energy input, making it a suitable low-cost option for off-grid applications. Future work will integrate long-term climate data, real diffuser geometries, and experimental validation to further refine these models. Full article

Medical phantoms are essential to imaging calibration, clinician training, and the validation of therapeutic procedures. However, most ultrasound phantoms prioritize acoustic realism while neglecting the viscoelastic and anisotropic properties of fibrous soft tissues. This gap limits their effectiveness in modeling realistic biomechanical behavior, especially in wave-based diagnostics and therapeutic ultrasound. Current materials like gelatine and agarose fall short in reproducing the complex interplay between the solid and fluid components found in biological tissues. To address this, we developed a soft, anisotropic composite whose dynamic mechanical properties resemble fibrous biological tissues such as skin and skeletal muscle. This material enables wave propagation and vibration studies in controllably anisotropic media, which are rare and highly valuable. We demonstrate the tunability of damping and stiffness aligned with fiber orientation, providing a versatile platform for modeling soft-tissue dynamics and validating biomechanical simulations. The phantoms achieved Young’s moduli of 7.16–11.04 MPa for skin and 0.494–1.743 MPa for muscles, shear wave speeds of 1.51–5.93 m/s, longitudinal wave speeds of 1086–1127 m/s, and sound absorption coefficients of 0.13–0.76 dB/cm/MHz, with storage, loss, and complex moduli reaching 1.035–6.652 kPa, 0.1831–0.8546 kPa, and 2.138–10.82 kPa. These values reveal anisotropic response patterns analogous to native tissues. This novel natural fibrous composite system affords sustainable, low-cost ultrasound phantoms that support both mechanical fidelity and acoustic realism. Our approach offers a route to next-gen tissue-mimicking phantoms for elastography, wave propagation studies, and dynamic calibration across diverse clinical and research applications. Full article

The development of efficient and sustainable water recycling systems is essential for long-term human missions and the establishment of space habitats on the Moon, Mars, and beyond. Humidity condensate (HC) is a low-strength wastewater that is currently recycled on the International Space Station (ISS). The main contaminants in HC are primarily low-molecular-weight organics and ammonia. This has caused operational issues due to microbial growth in the Water Process Assembly (WPA) storage tank as well as failure of downstream systems. In addition, treatment of this wastewater primarily uses adsorptive and exchange media, which must be continually resupplied and represent a significant life-cycle cost. This study demonstrates the integration of a membrane-aerated biological reactor (MABR) for pretreatment and storage of HC, followed by brackish water reverse osmosis (BWRO). Two system configurations were tested: (1) periodic MABR fluid was sent to batch RO operating at 90% water recovery with the RO concentrate sent to a separate waste tank; and (2) periodic MABR fluid was sent to batch RO operating at 90% recovery with the RO concentrate returned to the MABR (accumulating salinity in the MABR). With an external recycle tank (configuration 2), the system produced 2160 L (i.e., 1080 crew-days) of near potable water (dissolved organic carbon (DOC) < 10 mg/L, total nitrogen (TN) < 12 mg/L, total dissolved solids (TDS) < 30 mg/L) with a single membrane (weight of 260 g). When the MABR was used as the RO recycle tank (configuration 1), 1100 L of permeate could be produced on a single membrane; RO permeate quality was slightly better but generally similar to the first configuration even though no brine was wasted during the run. The results suggest that this hybrid system has the potential to significantly enhance the self-sufficiency of space habitats, supporting sustainable extraterrestrial human habitation, as well as reducing current operational problems on the ISS. These systems may also apply to extreme locations such as remote/isolated terrestrial locations, especially in arid and semi-arid regions. Full article

The COVID-19 pandemic and recent viral outbreaks have highlighted the need for viral diagnostics that balance accuracy with accessibility. While traditional laboratory methods remain essential, point-of-care solutions are critical for decentralized testing at the population level. However, a gap persists between academic proof-of-concept studies and clinically viable tools, with novel technologies remaining inaccessible to clinics due to cost, complexity, training, and logistical constraints. Recent advances in surface functionalization, assay simplification, multiplexing, and performance in complex media have improved the feasibility of both optical and non-optical sensing techniques. These innovations, coupled with scalable manufacturing methods such as 3D printing and streamlined hardware production, pave the way for practical deployment in real-world settings. Additionally, software-assisted data interpretation, through simplified readouts, smartphone integration, and machine learning, enables the broader use of diagnostics once limited to experts. This review explores improvements in viral diagnostic approaches, including colorimetric, optical, and electrochemical assays, showcasing their potential for democratization efforts targeting the clinic. We also examine trends such as open-source hardware, modular assay design, and standardized reporting, which collectively reduce barriers to clinical adoption and the public dissemination of information. By analyzing these interdisciplinary advances, we demonstrate how emerging technologies can mature into accessible, low-cost diagnostic tools for widespread testing. Full article

In this study, kaolinite-based clay (Ka-Clay) was used as an adsorbent for the efficient removal of Pb(II), Cd(II) and Hg(II) ions from aqueous media. The selection of Pb(II), Cd(II) and Hg(II) ions for experimental studies took into account their high toxicity, while the choice of Ka-Clay, the ease of preparation and high availability of this material were the most important arguments. Ka-Clay exhibits high adsorption performance, with removal percents over 98% for Pb(II) and 93% for Cd(II), even at high concentrations of metal ions (over 150 mg/L, pH = 6.5, 4 g adsorbent/L, 21 ± 1 °C). For Hg(II) ions, the adsorption percent does not exceed 55%, and this moderate value is mainly due to the significant change in pH. The adsorption behavior was in accordance with the Langmuir model (R² > 0.95) and the pseudo-second order kinetic model (R² > 0.99), indicating an adsorption process that occurs mainly through chemical interactions at the adsorbent surface between the metal ions and the functional groups. Adsorption processes are spontaneous (ΔG = −8.66 ÷ −15.76 kJ/mol) and endothermic (ΔH = 7.09 ÷ 21.81 kJ/mol), and the adsorption mechanism is the results of elementary processes of electrostatic attraction, ion exchange and superficial complexation. The insignificant effect of other ions (Ca(II), Mg(II), Na(I), K(I)) present in real wastewater samples as well as the desorption behavior of exhausted adsorbent highlight the practical utility of this adsorbent on a large scale. The experimental results included in this study suggest that Ka-Clay can be used as a promising adsorbent for the removal of high concentrations of toxic heavy metals with low cost and high efficiency, and this can contribute to the design of a sustainable wastewater treatment method. Full article

Proton exchange membrane water electrolyzers (PEMWEs) are a promising technology for green hydrogen production. However, the adoption of PEMWE-based hydrogen production systems remains limited due to several challenges, including high material costs, limited performance and durability, and difficulties in scaling the technology. Computational modeling serves as a powerful tool to address these challenges by optimizing system design, improving material performance, and reducing overall costs, thereby accelerating the commercial rollout of PEMWE technology. Despite this, conventional models often oversimplify key components, such as porous transport and catalyst layers, by assuming constant porosity and neglecting the spatial heterogeneity found in real electrodes. This simplification can significantly impact the accuracy of performance predictions and the overall efficiency of electrolyzers. This study develops a mathematical framework for modeling variable porosity distributions—including constant, linearly graded, and stepwise profiles—and derives analytical expressions for permeability, effective diffusivity, and electrical conductivity. These functions are integrated into a three-dimensional multi-domain COMSOL simulation to assess their impact on electrochemical performance and transport behavior. The results reveal that although porosity variations have minimal effect on polarization at low voltages, they significantly influence internal pressure, species distribution, and gas evacuation at higher loads. A notable finding is that reversing stepwise porosity—placing high porosity near the membrane rather than the channel—can alleviate oxygen accumulation and improve current density. A multi-factor comparison highlights this reversed configuration as the most favorable among the tested strategies. The proposed modeling approach effectively connects porous media theory and system-level electrochemical analysis, offering a flexible platform for the future design of porous electrodes in PEMWE and other energy conversion systems. Full article

Ensuring that older adults perform chair-based exercises (CBEs) correctly is essential for improving physical outcomes and reducing the risk of injury, particularly in home and community rehabilitation settings. However, evaluating the correctness of movements accurately and objectively outside clinical environments remains challenging. In this study, camera-based methods have been used to evaluate practical exercise quality. A single-camera system utilizing MediaPipe pose estimation was used to capture joint angle data as twenty older adults performed eight CBEs. Simultaneously, surface electromyography (sEMG) recorded muscle activity. Participants were guided to perform both proper and commonly observed incorrect forms of each movement. Statistical analyses compared joint angles and sEMG signals, and a support vector machine (SVM) was trained to classify movement correctness. The analysis showed that correct executions consistently produced distinct joint angle patterns and significantly higher sEMG activity than incorrect ones (p < 0.001). After modifying the selection of joint angle features for Movement 5 (M5), the classification accuracy improved to 96.26%. Including M5, the average classification accuracy across all eight exercises reached 97.77%, demonstrating the overall robustness and consistency of the proposed approach. In contrast, high variability across individuals made sEMG less reliable as a standalone indicator of correctness. The strong classification performance based on joint angles highlights the potential of this approach for real-world applications. While sEMG signals confirmed the physiological differences between correct and incorrect executions, their individual variability limits their generalizability as a sole criterion. Joint angle data derived from a simple single-camera setup can effectively distinguish movement quality in older adults, offering a low-cost, user-friendly solution for real-time feedback in home and community settings. This approach may help support independent exercise and reduce reliance on professional supervision. Full article