Rapid Rise Research Articles

Preparation of Geopolymers with Nanosilica and Water-in-Air Pickering Emulsion: Mechanisms Underlying Its Rheology, Polymerization, and Strength.

Geopolymers are alkaline-activated aluminosilicate binders recognized as a promising alternative to traditional Portland cement due to their significantly lower greenhouse emissions, energy consumption, and carbon footprint. However, the challenge is meeting or exceeding the strength of Portland cement concrete while being prepared within a desired setting time and possessing workable rheology. A "water-in-air" Pickering emulsion, also called dry water, was prepared by stabilizing water droplets with hydrophobic nano silica and using them to control the geopolymer's strength, setting time, and workability. The mechanisms that underlie the effects of dry water on the rheology, setting, and strength were studied in detail through a combination of rheological, thermal, morphological, chemical, and microstructural assessments. A reduction in the viscosity and yield shear stress manifests in a higher flow diameter, principally due to the particle size coarsening in the precursor and the flowability of hydrophobic nano silica. There was a rapid rise in temperature during the setting process as the dry water temporarily increased the local alkalinity in the mixture, which boosted the dissolution of the precursor and, hence, the reaction. Outcomes from X-ray diffraction, thermogravimetric analysis, and Fourier-transform infrared confirm the highest degree of polycondensation for the principal N-A-S-H framework in mixtures containing dry water. These eventually correspond to a denser microstructure under scanning electron microscopy and, in turn, a superior mechanical strength. Depending on the unique combination of characteristics, including size coarsening, temporary water encapsulation, microfilling effect, and supplementary silica source, dry water resolves the "trade-off" between geopolymer's fresh and hardened properties when introducing nanoparticles.

Pressure Changes Across a Membrane Formed by Coacervation of Oppositely Charged Polymer-Surfactant Systems.

We investigate the mass transfer and membrane growth processes during capsule formation by the interaction of the biopolymer xanthan gum with CnTAB surfactants. When a drop of xanthan gum polymer solution is added to the surfactant solution, a membrane is formed by coacervation. It encapsulates the polymer drop in the surfactant solution. The underlying mechanisms and dynamic processes during capsule formation are not yet understood in detail. Therefore, we characterized the polymer-surfactant complex formation during coacervation by measuring the surface tension and surface elasticity at the solution-air interface for different surfactant chain lengths and concentrations. The adsorption behavior of the mixed polymer-surfactant system at the solution-air interface supports the understanding of observed trends during the capsule formation. We further measured the change in capsule pressure over time and simultaneously imaged the membrane growth via confocal microscopy. The cross-linking and shrinkage during the membrane formation by coacervation leads to an increasing tensile stress in the elastic membrane, resulting in a rapid pressure rise. Afterward, the pressure gradually decreases and the capsule shrinks as water diffuses out. This is not only due to the initial capsule overpressure but also due to osmosis caused by the higher ionic strength of the surfactant solution outside the capsule compared to the polymer solution inside the capsule. The influence of polymer concentration and surfactant type and concentration on the pressure changes and the membrane structure are studied in this work, providing detailed insights into the dynamic membrane formation process by coacervation. This knowledge can be used to produce capsules with tailored membrane properties and to develop a suitable encapsulation protocol in technological applications. The obtained insights into the mass transfer of water across the capsule membrane are important for future usage in separation techniques and the food industry and allow us to better predict the capsule time stability.

Long range transport of South and East Asian anthropogenic aerosols counteracting Arctic warming

The large-scale convection during the Asian summer monsoon plays an important role in the rapid transport of boundary layer aerosols into the Asian summer monsoon anticyclone. Here, using the state-of-the-art ECHAM6–HAMMOZ aerosol-chemistry-climate model, we show that these aerosols are further transported to the Arctic along isentropic surfaces by the Brewer-Dobson-Circulation (BDC) during the monsoon season. Our model simulations show that East and South Asian anthropogenic emissions contribute significantly to the aerosol transported to the Arctic, which causes a higher negative net aerosol radiative forcing at the surface (dimming) of −0.09 ± 0.02 Wm−2 and −0.07 ± 0.02 Wm−2, respectively. Over the Arctic, the East Asian anthropogenic aerosols that include large amounts of sulfate cause a seasonal mean net radiative forcing at the top of the atmosphere (TOA) of −0.003 ± 0.001Wm−2 and a surface cooling of −0.56 K while the black carbon dominated aerosol from South Asia shows a positive TOA forcing of +0.004 ± 0.001Wm−2 with an only minor surface cooling of −0.043 K. Overall, the long-range transport of South Asian aerosols results in a notably warming throughout the atmospheric column but minimal temperature response at the Arctic surface. Conversely, East Asian aerosols cool the troposphere and heat the lower stratosphere in the Arctic. The Asian aerosol thus plays an ambivalent role, with the East Asian sources in particular having the potential to counteract the rapid rise in Arctic temperatures and the associated melting of snow and ice.

Asymmetric relationship between competitive industrial performance, renewable energy, industrialization, and carbon footprint: Does artificial intelligence matter for environmental sustainability?

Developing nations have emerged as integral contributors to the global economy since the beginning of the 21st century. Their swift economic expansion and associated activities lead to a rise in carbon footprint. Keeping this in view, unlike previous studies, this study unfolds the asymmetric repercussions of competitive industrial performance and renewable energy within the context of the industrialization era in seven Asian developing countries from 1990 to 2020. The panel NARDL and asymmetric panel causality methodologies have been applied for the purpose of nonlinear analysis. According to the results, the advantageous influence of competitive industrial performance is considerably more pronounced than that of industrialization. The coefficient magnitude associated with the negative shock demonstrates a significantly substantial value in comparison to the positive shock concerning the influence of competitive industrial performance on carbon footprint. Both positive and negative changes in competitive industrial performance mitigate carbon footprint. Renewable energy generates favorable consequences by promoting environmental quality via a reduction in carbon footprint. Across various emerging economies in Asia, the rapid rise of industrialization is linked to the deterioration of environmental quality. Finally, artificial intelligence and natural resource rents decrease carbon footprints while information and communication technology intensifies environmental concerns. The research emphasizes the critical nature of integrating artificial intelligence, robotics, and technological industrial competitiveness in order to adequately tackle the carbon footprint in Asian developing countries.

Unravelling male advertisement call variability in the brown tree frog (Litoria ewingii) complex by using citizen science

In the face of global biodiversity declines, conservation measures are urgently needed. However, our lack of knowledge on species’ diversity, distributions and population trends presents a major obstacle, particularly for morphologically cryptic species. Field surveys to gather this information present a logistical challenge, but the rapid rise of citizen science presents a new opportunity, generating huge volumes of data rapidly across a species’ range. We use data from a national citizen-science project to document the distribution and advertisement call variability in a group of five morphologically conserved treefrogs. Using 542 male advertisement calls across the entire range of five species, we found considerable levels of acoustic variation both among and within species. Note shape, notes per call and positions of the longest and shortest notes were most useful to delineate species. Our research will assist in species delineation and identification in the field and form the basis for a more accurate understanding of species diversity and distributions in the brown tree frog complex. We also demonstrated the utility of citizen science in documenting species distributions and diversity.

Patient influencer: the impact of homophily on value co-creation behaviour in online health communities

ABSTRACT The world has witnessed a rapid rise of online health communities (OHCs) where patients can create a powerful network of support and resources to make a meaningful difference in people’s health lives. Despite this rapid rise, healthcare authorities and organisations are under enormous pressure to create a sustainable environment for OHCs. Prior studies revealed that encouraging patient value co-creation behaviour (VCB) is substantially beneficial for successful OHCs; however, what factors influence these patient behaviours to co-create value is still unknown. To fill this gap, this study uses the service-dominant logic perspective of value co-creation theory and discloses how homophily between patient influencers and patients in OHCs effect VCB. The data was collected through a self-administered survey from 422 patients who are users of OHC in China and was analysed via partial least squares structural equation modelling and fuzzy set qualitative comparative analysis (fsQCA). The results revealed that homophily positively influences VCBs, i.e. participation behaviour (PB) and citizenship behaviour (CB). A parasocial relationship only moderates the relations between homophily and (PB), whereas the influencer ethical interaction capability significantly moderates the relationship of both homophily with patient PB and CB. Furthermore, the fsQCA findings signify the robustness of the study model.

Diet-omics in the Study of Urban and Rural Crohn disease Evolution (SOURCE) cohort

Crohn disease (CD) burden has increased with globalization/urbanization, and the rapid rise is attributed to environmental changes rather than genetic drift. The Study Of Urban and Rural CD Evolution (SOURCE, n = 380) has considered diet-omics domains simultaneously to detect complex interactions and identify potential beneficial and pathogenic factors linked with rural-urban transition and CD. We characterize exposures, diet, ileal transcriptomics, metabolomics, and microbiome in newly diagnosed CD patients and controls in rural and urban China and Israel. We show that time spent by rural residents in urban environments is linked with changes in gut microbial composition and metabolomics, which mirror those seen in CD. Ileal transcriptomics highlights personal metabolic and immune gene expression modules, that are directly linked to potential protective dietary exposures (coffee, manganese, vitamin D), fecal metabolites, and the microbiome. Bacteria-associated metabolites are primarily linked with host immune modules, whereas diet-linked metabolites are associated with host epithelial metabolic functions.

Roadmap on Perovskite light-emitting diodes

Abstract In recent years, the field of metal-halide perovskite emitters has rapidly emerged as a new community in solid-state lighting. Their exceptional optoelectronic properties have contributed to the rapid rise in external quantum efficiencies (EQEs) in perovskite light-emitting diodes (PeLEDs) from <1% (in 2014) to approaching 30% (in 2023) across a wide range of wavelengths. However, several challenges still hinder their commercialization, including the relatively low EQEs of blue/white devices, limited EQEs in large-area devices, poor device stability, as well as the toxicity of the easily accessible lead components and the solvents used in the synthesis and processing of PeLEDs. This roadmap addresses the current and future challenges in PeLEDs across fundamental and applied research areas, by sharing the community's perspectives. This work will provide the field with practical guidelines to advance PeLED development and facilitate more rapid commercialization.

Non-farm income and environmental efficiency of the farmers: Evidence from India

In the face of various agro-climatic shocks when agricultural income becomes highly volatile, farmers often undertake multiple jobholding and engage in non-farm activities for income smoothing. The earnings from these activities are often used to purchase productivity enhancing agricultural inputs. In this context, the impact of non-farm income on intensification of agricultural inputs and the consequent impact on over-all farm efficiency is well documented in the literature. However, with a rapid rise in usage of agricultural inputs with environmentally detrimental impact, very little is known about whether non-farm income has any impact on farmers’ environmental efficiency-ability to reduce the amount of polluting inputs to the largest extent possible without reducing the amount of agricultural production. Our study fills the gap in the literature by analysing the impact of non-farm income on environmental efficiency of the farmers. We first estimate the environmental efficiency scores adopting the non-parametric data envelopment analysis (DEA) method and using a household level panel data from Village Dynamics of South Asia project on Indian states for a span of five years (2010–2014). We then estimate the impact of non-farm income on environmental efficiency using Instrumental Variable Tobit Model. Our results show that average environmental efficiency of the Indian farmers was 46 % during the study period indicating the fact that a reduction in polluting agricultural inputs by 54 % was possible without compromising the level of farm production. We also find that for 1 % increase in non-farm income, environmental efficiency of farmers rises by around 4 %. This reflects the environment friendly behaviour of farmers as a channel through which non-farm activities affect usage of environmentally linked inputs. These results provide vital policy insights in terms of how non-farm activities could be integrated with policies related to farming, in order to ensure sustainable agricultural practices.

Cannabis Legalization and Resource Use for Ingestions by Young Children.

In conjunction with widening legalization, there has been a rapid rise in unintentional cannabis ingestions in young children. We sought to determine if the legal status of recreational cannabis was associated with resource use in young children with cannabis poisoning. This retrospective cross-sectional study of the Pediatric Health Information System included emergency department encounters between January 2016 and April 2023 for children <6 years of age with a diagnosis indicating cannabis ingestion. The primary exposure was recreational cannabis legalization status in the state in which the encounter occurred. We used logistic regression models to determine the association of recreational cannabis legality with resource utilization outcomes, adjusting for demographic covariates. We included 3649 children from 47 hospitals; 29% of encounters occurred in places in which recreational cannabis was legal. Compared with environments in which recreational cannabis was illegal, cannabis-legal locations had lower uses of advanced neuroimaging (24% vs 35%; adjusted odds ratio [aOR], 0.65; 95% confidence interval [CI]: 0.55-0.77), lumbar puncture (1.6% vs 3.8%; aOR, 0.42; 95% CI: 0.24-0.70), ICU admission (7.9% vs 11%; aOR, 0.71; 95% CI: 0.54-0.93), and mechanical ventilation (0.8% vs 2.9%; aOR, 0.30; 95% CI: 0.14-0.58). Urine testing was more common in places in which recreational cannabis was legal (71% vs 58%; aOR, 1.87; 95% CI: 1.59-2.20). State-level legalization of recreational cannabis was associated with a significant decrease in the utilization of advanced medical resources in cases of cannabis intoxication in children. These findings suggest the need for a focus on policies and procedures to minimize invasive testing in cases of cannabis intoxication in children.

An exhaustive exploration of Zn0.15Sn0.85(Se0.95S0.05)2 crystal-based photodetector and its potential application as a photocatalytic material

The research pioneers the exploration of synthesized quaternary Zn0.15Sn0.85(Se0.95S0.05)2 (ZSSS) crystals, demonstrating remarkable efficacy as optoelectronic switching devices and potent photocatalysts. The ZSSS photodetector recorded a stable photoresponse with remarkable rapid rise and decay times. The Langmuir-Hinshelwood kinetic model was used to look at the photocatalytic activity at different methylene blue concentrations. The researchers grew highly pure ZSSS crystals using the direct vapour transport (DVT) method. Field effect scanning electron microscopy (FESEM) and high-resolution transmission electron microscopy (HRTEM) images reveal the layer growth mechanism in the grown crystals. The structural analyses of the grown crystals were carried out using an X-ray diffraction (XRD) pattern and temperature-dependent Raman spectroscopy. The Lorentzian function was fitted to experimental data to carry out the temperature-dependent Raman analysis. The grown crystals exhibit a direct optical bandgap of 1.69 eV. The electrical analyses explain the semiconducting nature of the crystals.

Full kinetic modeling analysis of [18F]fluorocholine PET at initial diagnosis of high-grade glioma

PurposeThe main objective was to characterize the tracer uptake kinetics of [18F]fluoromethylcholine ([18F]F-CHO) in high-grade gliomas (HGG) through a full modeling approach. Secondarily, we aimed to explore the relationship between the PET uptake measures and the HGG molecular features. Materials and methodsTwenty-four patients with a suspected diagnosis of HGG were prospectively included. They underwent a dynamic brain [18F]F-CHO-PET/CT, from which a tumoral time-activity curve was extracted. The plasma input function was obtained through arterial blood sampling with metabolite correction. These data were fitted to 1- and 2-tissue-compartment models, the best of which was selected through the Akaike information criterion. We assessed the correlation between the kinetic parameters and the conventional static PET metrics (SUVmax, SUVmean and tumor-to-background ratio TBR). We explored the association between the [18F]F-CHO-PET quantitative parameters and relevant molecular biomarkers in HGG. ResultsTumoral time-activity curves in all patients showed a rapid rise of [18F]F-CHO uptake followed by a plateau-like shape. Best fits were obtained with near-irreversible 2-tissue-compartment models. The perfusion-transport constant K1 and the net influx rate Ki showed strong correlation with SUVmax (r = 0.808–0.861), SUVmean (r = 0.794–0.851) and TBR (r = 0.643–0.784), p < 0.002. HGG was confirmed in 21 patients, of which those with methylation of the O-6-methylguanine-DNA methyltransferase (MGMT) gene promoter showed higher mean Ki (p = 0.020), K1 (p = 0.025) and TBR (p = 0.001) than the unmethylated ones. Conclusion[18F]F-CHO uptake kinetics in HGG is best explained by a 2-tissue-compartment model. The conventional static [18F]F-CHO-PET measures have been validated against the perfusion-transport constant (K1) and the net influx rate (Ki) derived from kinetic modeling. A relationship between [18F]F-CHO uptake rate and MGMT methylation is suggested but needs further confirmation.

Application of convolutional neural networks in medical images: a bibliometric analysis.

In the field of medical imaging, the rapid rise of convolutional neural networks (CNNs) has presented significant opportunities for conserving healthcare resources. However, with the wide spread application of CNNs, several challenges have emerged, such as enormous data annotation costs, difficulties in ensuring user privacy and security, weak model interpretability, and the consumption of substantial computational resources. The fundamental challenge lies in optimizing and seamlessly integrating CNN technology to enhance the precision and efficiency of medical diagnosis. This study sought to provide a comprehensive bibliometric overview of current research on the application of CNNs in medical imaging. Initially, bibliometric methods were used to calculate the frequency statistics, and perform the cluster analysis and the co-citation analysis of countries, institutions, authors, keywords, and references. Subsequently, the latent Dirichlet allocation (LDA) method was employed for the topic modeling of the literature. Next, an in-depth analysis of the topics was conducted, and the topics in the medical field, technical aspects, and trends in topic evolution were summarized. Finally, by integrating the bibliometrics and LDA results, the developmental trajectory, milestones, and future directions in this field were outlined. A data set containing 6,310 articles in this field published from January 2013 to December 2023 was complied. With a total of 55,538 articles, the United States led in terms of the citation count, while in terms of the publication volume, China led with 2,385 articles. Harvard University emerged as the most influential institution, boasting an average of 69.92 citations per article. Within the realm of CNNs, residual neural network (ResNet) and U-Net stood out, receiving 1,602 and 1,419 citations, respectively, which highlights the significant attention these models have received. The impact of coronavirus disease 2019 (COVID-19) was unmistakable, as reflected by the publication of 597 articles, making it a focal point of research. Additionally, among various disease topics, with 290 articles, brain-related research was the most prevalent. Computed tomography (CT) imaging dominated the research landscape, representing 73% of the 30 different topics. Over the past 11 years, CNN-related research in medical imaging has grown exponentially. The findings of the present study provide insights into the field's status and research hotspots. In addition, this article meticulously chronicled the development of CNNs and highlighted key milestones, starting with LeNet in 1989, followed by a challenging 20-year exploration period, and culminating in the breakthrough moment with AlexNet in 2012. Finally, this article explored recent advancements in CNN technology, including semi-supervised learning, efficient learning, trustworthy artificial intelligence (AI), and federated learning methods, and also addressed challenges related to data annotation costs, diagnostic efficiency, model performance, and data privacy.

Telemedicine Is Reshaping the Healthcare Landscape

The rapid rise of telemedicine in healthcare has revolutionized the way patients and healthcare professionals interact, offering a convenient and accessible alternative to traditional healthcare visits. Telemedicine proved to be indispensable during the COVID-19 pandemic, providing a safe way for individuals to seek medical guidance from the comfort of their homes while minimizing the risk of virus transmission. This article explores the significance of telemedicine in modern healthcare, highlighting its role in bridging geographical divides and offering consultations, diagnoses, and treatment monitoring to patients in remote locations. Telemedicine encompasses various services, including real-time telemedicine, store-and-forward telemedicine, and remote patient monitoring, providing patients with a range of options for accessing medical care. As the healthcare industry continues to evolve, telemedicine will likely remain a cornerstone of transformative technologies, with future trends and innovations poised to redefine the landscape of telemedicine and ensure its role as a transformative force in healthcare.

Evaluation of the number of events’ influence on model performance and uncertainty in urban data-scarce areas based on behavioral parameter ranking method

Rainfall-runoff data in drainage systems in urban areas is the essential input variable for urban hydrological modeling. Obtaining high quality and sufficient size of urban flood events is always difficult due to the inconvenient underground observation and the untimely capture of the rapid rising and recession stages of urban runoff. It largely constrains the efficiency of model calibration and brings large uncertainty in urban rainfall-runoff simulation. Therefore, the evaluation of the number of events’ influence on model performance and uncertainty is of great significance, which can provide useful information to help the decision makers select sufficient useful observation data for model calibration with relatively fewer events. In this study, we constructed a comprehensive method of behavioral parameter ranking of multi-events (BPROME) based on coupling the Generalized Likelihood Uncertainty Estimation (GLUE) algorithm and the Storm Water Management Model (SWMM). The results in the two small demonstration areas (named Case #A and Case #B) in Shenzhen city of China proved the good performance of the BPROME in uncertainty assessment in urban areas. We found the model uncertainty (average bandwidth (B) and average deviation amplitude (D)) and model performance (containing Ratio (CR) and the maximum value of behavioral samples’ NSE (NSEmax)) became stable at a certain number of events in both two case areas. The B and D decrease and the CR and NSEmax increase as the number of event increases. In particular, the model performance and uncertainty reach their appropriate state concerning the limited observations at a certain range of numbers of events (both three to five in our two case areas). Our results demonstrate the potential influence of the numbers of events for the urban rainfall-runoff modeling calibration which can balance the model efficiency and data collection cost (the number of input rainfall-runoff data). The findings could help decision-makers seek a trade-off between data investment and acceptable model performance.

Aluminium Scandium Nitride Thin Film BAW Resonators (FBAR) for 5G Applications

With the growing impact of 5G communication system, Radio-Frequency Micro-Electro-Mechanical-Systems (RF MEMS) technologies are becoming increasingly important in our daily lives. Development of filters in the 5G frequency range is being carried out tremendously. The rapid rise in smartphone usage, heightened data demands, congestion in the frequency spectrum, and advancements in technology have posed numerous challenges for the development of RF filters. The ultimate goal is to simplify the RF Front End Module used in smart phones, thereby achieving better selectivity. In this paper, a high frequency - high Q thin film bulk acoustic wave resonator of piezoelectric material Aluminium scandium nitride (AlScN)based FBAR is designed for 5G Application. Finite element analysis conducted using the COMSOL Multiphysics software assess the characteristics of the constructed FBAR. The outcomes indicate that the device resonates effectively at 5 GHz, exhibiting commendable return loss and a Q-factor of 1490. A high-resonance-frequency FBAR device offers advantages for 5G mobile technology.

Money Supply, Inflation and Dollarization: An Analysis on Türkiye Using Fourier Models

This study examines the impact of inflation and money supply on dollarization. Especially in recent years, the rapid rise of the exchange rate and inflation in Türkiye has put the phenomenon of dollarization back on the agenda. The study covers the period 2012Q4-2023Q4. The study first applies the Fourier ADL cointegration test, which allows for soft breaks. As a result of the test, it was found that the variables have a long run relationship. In the short run, only budget revenue has a positive effect on dollarization. In the long run, the exchange rate, exports, money supply, and deposit rates have a positive effect, while inflation has a negative effect. The study also applied the Fourier-Toda-Yamamoto causality test. As a result of the test, bidirectional causality was found between exchange rate, inflation and money supply variables and dollarization. The striking result of the study is that inflation has a negative impact on dollarization. This result, which is consistent with some studies in the literature, is attributed to the money supply. Although the demand for foreign exchange increases because the money supply is higher than inflation, the dollarization index decreases. This explanation can explain the negative relationship.

Assessment of stereotactic high-resolution detectors for stereotactic body radiotherapy: comparative analysis between myQA® SRS and Gafchromic EBT-XD films.

The study aimed to evaluate dosimetry systems used for stereotactic body radiotherapy (SBRT), specifically 2D array dosimetry and film dosimetry systems, for exploring their characteristics and clinical suitability. For this, high-resolution myQA SRS detectors and Gafchromic EBT-XD films were employed. Film analysis included net optical density (OD) values depending on energy, dose rate, scanner orientation, scanning side, and post-exposure growth. For myQA SRS, signal values were evaluated in terms of dose rate (400-1400 MU/min) and angular dependence (0-180° at 30° intervals) along with couch angles of 0°, 45°, and 90°. Pre-treatment verification included 32 SBRT patients for whom myQA SRS results were compared with those obtained with Gafchromic EBT-XD films. Analysis revealed less than 1% deviation in net OD for energy and dose rate dependence. Scanner orientation caused 2.5% net OD variation, with minimal differences between film front and back scan orientations (variance < 1.0%). A rapid OD rise occurred within six hours post-exposure, followed by gradual increase. The myQA SRS detector showed -3.7% dose rate dependence (400 MU/min), while the angular dependence at 90° was -26.7%. A correction factor effectively reduced these differences to < 1%. For myQA SRS, gamma passing rates were-93.6% (2%/1mm), while those for EBT-XD films were-92.8%. Improved rates were observed with 3%/1mm: for myQA SRS-97.9%, and for EBT-XD film-98.16%. In contrast, for 2%/2mm with 10% threshold, for myQA SRS-97.7% and for EBT-XD film-98.97% were obtained. It is concluded that both myQA SRS detectors and EBT-XD films are suitable for SBRT pre-treatment verification, ensuring accuracy and reliability. However, myQA SRS detectors are preferred over EBT-XD film due to the fact that they offer real-time measurements and user-friendly features.

Microstructure, mechanical properties, and strengthening mechanisms of ultra-high strength Al-Zn-Mg-Cu alloy prepared by continuous extrusion forming process

In recent years, the strategy of obtaining ultra-high strength bulk Al-Zn-Mg-Cu alloys materials through implementing severe plastic deformation (SPD) has emerged as the prominent trend in structural lightweight development in aerospace and transportation fields. However, the inherent poor formability makes the SPD process of high-alloying Al-Zn-Mg-Cu alloys a great challenge. In the present work, a continuous SPD process named the Conform, utilizing a rotational speed of 4 rpm and a mold preheating temperature of 300 °C, was performed on the high-alloying 7055 Al alloy and prepared rod materials with excellent mechanical properties. The results indicated that violent shear deformation accompanied by rapid temperature rise contributes to the specific grain refinement mechanism of continuous dynamic recrystallization (CDRX) and discontinuous dynamic recrystallization (DDRX). The grain configuration formed by this cooperative mechanism is advantageous for the alloy's compatible deformation capability. As a result, a synergistic improvement in the strength and ductility of the Conformed 7055 Al alloy was obtained. Additionally, the strengthening mechanisms induced by the Conform process were quantitatively calculated, revealing that solid solution strengthening and dislocation strengthening were the primary reasons for the enhanced strength of processed alloys.

Experimental investigation on the effect of diesel-ethanol blends for a constant-speed diesel engine

ABSTRACT Keeping the need for energy security and the world’s transition to a thriving low-carbon economy in mind, the research, on the feasibility of diesel ethanol blends, has gained momentum. The present work is related to comparing the performance and emission characteristics of pure diesel [D100] with 4 diesel-ethanol blends [D90E10, D85E15, D80E20 and D75E25]. The single-cylinder 4-stroke un-modified diesel engine running at constant 1500 rpm was used for experimentation which ran at different loads. Experimental results showed that with the addition of ethanol in blends, the brake thermal efficiency (BTE) and exhaust gas temperature (EGT) reduced while brake-specific fuel consumption (BSFC) increased. The addition of ethanol shortened the combustion duration. Hence, D75E25 showed a rapid rise in combustion pressure and heat release rate (HRR). As the concentration of ethanol increases in blends, the oxide of nitrogen (NOx) is reduced and carbon monoxide and hydrocarbon (HC) are increased. Thus, D75E5 was good blend producing less NOx with the penalty of fuel consumption on an unmodified diesel engine. In the long run, the effect of blends on the engine should be checked to make ethanol a feasible fuel for diesel engines. Abbreviations: CA: Crank Angle; ATDC: After Top Dead Centre; BMEP: Brake Mean Effective Pressure; BSFC: Brake Specific Fuel Consumption; BTE: Brake Thermal Efficiency; BTDC: Before Top Dead Centre; CO: Carbon Monoxide; COV: Coefficient of Variation; D100: 100% Diesel + 0% Ethanol, Pure petroleum diesel; D90E10: 90% Diesel + 10% Ethanol; D85E15: 85% Diesel + 15% Ethanol; D80E20: 80% Diesel + 20% Ethanol; D75E25: 75% Diesel + 25% Ethanol; HC: Hydrocarbon; IMEP: Indicated Mean Effective Pressure; LHV: Lower Heating Value; NOx: Nitric Oxide.