Original Ones Research Articles

Influence of Propeller Parameters on the Aerodynamic Performance of Shrouded Coaxial Dual Rotors in Hover

A numerical method is used to evaluate the influence of propeller parameters on the aerodynamic performance of shrouded coaxial dual rotors in hover. Compared with the open-rotor configuration, the shrouded rotors reduce the tip vortex, resulting in a higher thrust and figure of merit (FoM). The varying inflow distribution over the inlet lip of the shroud introduces a different working condition for the propellers, thereby providing an opportunity for propeller parameter optimization. The pitch length, chord length, and tip clearance are chosen as the design parameters to improve the aerodynamic performance of the rotors. When the pitch lengths of the upper and lower propellers increase, the shrouded rotors outperform the original ones in both the coefficient of thrust (Ct) and in terms of the (FoM). The results show that increasing the chord length improves the Ct but that the (FoM) is almost unaffected. Moreover, smaller tip clearance can significantly improve the aerodynamic performance of the shrouded rotors. Results presented in this work provide insights into the parametric design and optimization of shrouded rotors.

Data correction and verification of thermal simulation experiments under the influence of bulging belly

During the thermal simulation compression test, the formation of an obvious bulge in the specimen leads to a certain deviation between the calculated and actual values of the true stress. The finite element method was used to simulate the single-pass compression of specimens of 34CrNi3MoV steel and obtain the actual nonuniform deformation of the bulging belly during the compression process, and the results were applied to correct experimental flow curves. The results showed that the deformation conditions had a significant influence on the nonuniformity of the specimen deformation during the compression process, and all the modified flow curves were lower than the original ones. The size of the bulge and the metal flow line in the finite element simulation were consistent with the test results. The load value obtained by using the modified flow curve was similar to the load value measured in the test, which indicated that the modified flow curve was very close to the real flow force curve of the material. The method used to modify the flow force curve is simple and practical.

Preparation of bio-based heat storage microcapsules of cellulose acetate butyrate/ polymethylmethacrylate composite by micro-suspension polymerization

ABSTRACT Because of environmental concerns, the utilization of bio-based, renewable resource materials has attracted great attention for a wide range of applications. In this research, green cellulose-based microcapsules containing capric acid (CA) as a bio-heat storage material were prepared. Cellulose acetate butyrate (CAB)/methacrylate polymer composite particles were produced by microsuspension iodine transfer polymerization to encapsulate CA. The influences of methacrylate polymer type and amount on microcapsule morphology, encapsulation efficiency (EE) and thermal properties were investigated. Using methyl methacrylate (MMA) monomer, the colloidal stable spherical microcapsules with smooth surfaces and core-shell morphology were obtained. Although high percentages of EE, ≥70%, were obtained at all CAB:MMA weight ratios, the latent heats of the encapsulated CA were lower than those of the original ones which may be due to the incomplete phase separation of the CAB/PMMA shell and CA core. The increase of CA content up to 60% provided stable spherical microcapsules with a maximum loading of 59% and 97% EE. After thermal cycling for 100 cycles, stable microcapsules still remained with stable thermal properties. The obtained bio-based microcapsules would be well applied for heat storage and temperature control applications.

The BE-UJI hand function activity set: a reduced set of activities for the evaluation of the healthy and pathological hand

BackgroundHand kinematics during hand function tests based on the performance of activities of daily living (ADLs) can provide objective data to determine patients’ functional loss. However, they are rarely used during clinical assessments because of their long duration. Starting with the 20 Sollerman Hand Function Test (SHFT) tasks, we propose identifying a reduced set of ADLs that provides similar kinematic information to the original full set in terms of synergies, ranges of motion and velocities.MethodsWe followed an iterative method with the kinematics of 16 hand joints while performing the 20 ADLs of the SHFT. For each subject, ADLs were ordered according to their influence on the synergies obtained by means of a principal component analysis, the minimum number of ADLs that represented the original kinematic synergies (maximum angle of 30° between synergies), and the maintained ranges of joint movements (85% of the original ones) were selected for each subject. The set of the most frequently selected ADLs was verified to be representative of the SHFT ADLs in terms of motion strategies, ranges of motion and joint velocities when considering healthy subjects and Hand Osteoarthritis patients.ResultsA set of 10 tasks, the BE-UJI activity set, was identified by ensuring a certain (minimum) similarity in synergy (maximum mean angle between synergies of 25.5°), functional joint ranges (maximum differences of 10°) and joint velocities (maximum differences of 15°/s). The obtained tasks were: pick up coins from purses, lift wooden cubes, pick up nuts and turn them, write with a pen, cut with a knife, lift a telephone, unscrew jar lids and pour water from a cup, a jar and a Pure-Pak. These activities guarantee using the seven commonest handgrips in ADLs.ConclusionThe BE-UJI activity set for the hand function assessment can be used to obtain quantitative data in clinics as an alternative to the SHFT. It reduces the test time and allows clinicians to obtain objective kinematic data of the motor strategies, ranges of motion and joint velocities used by patients.

Developing a Semantically Based Query Recommendation for an Electronic Medical Record Search Engine: Query Log Analysis and Design Implications.

An effective and scalable information retrieval (IR) system plays a crucial role in enabling clinicians and researchers to harness the valuable information present in electronic health records. In a previous study, we developed a prototype medical IR system, which incorporated a semantically based query recommendation (SBQR) feature. The system was evaluated empirically and demonstrated high perceived performance by end users. To delve deeper into the factors contributing to this perceived performance, we conducted a follow-up study using query log analysis. One of the primary challenges faced in IR is that users often have limited knowledge regarding their specific information needs. Consequently, an IR system, particularly its user interface, needs to be thoughtfully designed to assist users through the iterative process of refining their queries as they encounter relevant documents during their search. To address these challenges, we incorporated "query recommendation" into our Electronic Medical Record Search Engine (EMERSE), drawing inspiration from the success of similar features in modern IR systems for general purposes. The query log data analyzed in this study were collected during our previous experimental study, where we developed EMERSE with the SBQR feature. We implemented a logging mechanism to capture user query behaviors and the output of the IR system (retrieved documents). In this analysis, we compared the initial query entered by users with the query formulated with the assistance of the SBQR. By examining the results of this comparison, we could examine whether the use of SBQR helped in constructing improved queries that differed from the original ones. Our findings revealed that the first query entered without SBQR and the final query with SBQR assistance were highly similar (Jaccard similarity coefficient=0.77). This suggests that the perceived positive performance of the system was primarily attributed to the automatic query expansion facilitated by the SBQR rather than users manually manipulating their queries. In addition, through entropy analysis, we observed that search results converged in scenarios of moderate difficulty, and the degree of convergence correlated strongly with the perceived system performance. The study demonstrated the potential contribution of the SBQR in shaping participants' positive perceptions of system performance, contingent upon the difficulty of the search scenario. Medical IR systems should therefore consider incorporating an SBQR as a user-controlled option or a semiautomated feature. Future work entails redesigning the experiment in a more controlled manner and conducting multisite studies to demonstrate the effectiveness of EMERSE with SBQR for patient cohort identification. By further exploring and validating these findings, we can enhance the usability and functionality of medical IR systems in real-world settings.

A High-Accuracy Star Centroid Extraction Method Based on Kalman Filter for Multi-Exposure Imaging Star Sensors.

A multi-exposure imaging approach proposed in earlier studies is used to increase star sensors' attitude update rate by N times. Unfortunately, serious noises are also introduced in the star image due to multiple exposures. Therefore, a star centroid extraction method based on Kalman Filter is proposed in this paper. Firstly, star point prediction windows are generated based on centroids' kinematic model. Secondly, the classic centroid method is used to calculate the coarse centroids of the star points within the prediction windows. Lastly, the coarse centroids are, respectively, processed by each Kalman Filter to filter image noises, and thus fine centroids are obtained. Simulations are conducted to verify the Kalman-Filter-based estimation model. Under noises with zero mean and ±0.4, ±1.0, and ±2.5 pixel maximum deviations, the coordinate errors after filtering are reduced to about 37.5%, 26.3%, and 20.7% of the original ones, respectively. In addition, experiments are conducted to verify the star point prediction windows. Among 100 star images, the average proportion of the number of effective star point objects obtained by the star point prediction windows in the total object number of each star image is calculated as only 0.95%. Both the simulated and experimental results demonstrate the feasibility and effectiveness of the proposed method.

Single-crystallization of electrolytic copper foils

Depending on the production process, copper (Cu) foils can be classified into two types, i.e., rolled copper (r-Cu) foils and electrolytic copper (e-Cu) foils. Owing to their high electrical conductivity and ductility at low cost, e-Cu foils are employed extensively in modern industries and account for more than 98% of the Cu foil market share. However, industrial e-Cu foils have never been single-crystallized due to their high density of grain boundaries, various grain orientations and vast impurities originating from the electrochemical deposition process. Here, we report a methodology of transforming industrial e-Cu foils into single crystals by facet copy from a single-crystal template. Different facets of both low and high indices are successfully produced, and the thickness of the single crystal can reach 500 µm. Crystallographic characterizations directly recognized the single-crystal copy process, confirming the complete assimilation impact from the template. The obtained single-crystal e-Cu foils exhibit remarkably improved ductility (elongation-to-fracture of 105% vs. 25%), fatigue performance (the average numbers of cycles to failure of 1600 vs. 200) and electrical property (electrical conductivity of 102.6% of the international annealed copper standard (IACS) vs. 98.5%) than original ones. This work opens up a new avenue for the preparation of single-crystal e-Cu foils and may expand their applications in high-speed, flexible, and wearable devices.

Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects.

Background: Inferring drug-related side effects is beneficial for reducing drug development cost and time. Current computational prediction methods have concentrated on graph reasoning over heterogeneous graphs comprising the drug and side effect nodes. However, the various topologies and node attributes within multiple drug-side effect heterogeneous graphs have not been completely exploited. Methods: We proposed a new drug-side effect association prediction method, GGSC, to deeply integrate the diverse topologies and attributes from multiple heterogeneous graphs and the self-calibration attributes of each drug-side effect node pair. First, we created two heterogeneous graphs comprising the drug and side effect nodes and their related similarity and association connections. Since each heterogeneous graph has its specific topology and node attributes, a node feature learning strategy was designed and the learning for each graph was enhanced from a graph generative and adversarial perspective. We constructed a generator based on a graph convolutional autoencoder to encode the topological structure and node attributes from the whole heterogeneous graph and then generate the node features embedding the graph topology. A discriminator based on multilayer perceptron was designed to distinguish the generated topological features from the original ones. We also designed representation-level attention to discriminate the contributions of topological representations from multiple heterogeneous graphs and adaptively fused them. Finally, we constructed a self-calibration module based on convolutional neural networks to guide pairwise attribute learning through the features of the small latent space. Results: The comparison experiment results showed that GGSC had higher prediction performance than several state-of-the-art prediction methods. The ablation experiments demonstrated the effectiveness of topological enhancement learning, representation-level attention, and self-calibrated pairwise attribute learning. In addition, case studies over five drugs demonstrated GGSC's ability in discovering the potential drug-related side effect candidates. Conclusion: We proposed a drug-side effect association prediction method, and the method is beneficial for screening the reliable association candidates for the biologists to discover the actual associations.

Silent Cerebral Infarcts in Iraqi Patients with Sickle Cell Disease

Silent ischemic infarcts have been reported to be the most frequent neurological abnormalities in sickle cell disease (SCD) in several studies worldwide. However, no previous studies investigated this neurological disorder in Iraqi SCD patients. To address this issue, a total of 52 patients with a median age of 20 years (range 10–46) and including 46.2% males were enrolled. Patients were clinically evaluated and their records were reviewed. They had full blood and reticulocyte counts, hemoglobin F estimation, serum lactic dehydrogenase and bilirubin assayed, as well as brain magnetic resonance imaging (MRI) to screen for silent cerebral infarcts. Six out of the 52 patients (11.5%) had silent cerebral infarcts, all of which were in the deep white matter, ranging from 6 to 10 mm in their largest diameters. There were no significant differences in age, sex, or sickle cell genotype between those with silent cerebral infarcts and those without it. Those with silent cerebral infarcts had lower median hemoglobin, higher reticulocytes and lower pain frequencies than those without it, yet again this was not significant. Follow up MRI in four out of the six silent infarct patients showed no additional lesions and no increase in size of the original ones after six to eight months. In conclusion, it appears that the frequency of silent cerebral infarcts in Iraqi SCD patients is lower than the bulk of the literature from other populations. Further studies to screen for genetic polymorphisms that may explain this lower rate may be informative.

An integrated design method for used product remanufacturing scheme considering carbon emission

Remanufacturing scheme design (RSD) is a necessary process to improve the utilization of waste resources and reduce the remanufacturing carbon emissions. However, the lack of a mapping between the RSD parameters and carbon emissions makes it challenging to generate remanufacturing schemes that satisfy the low-carbon target. This would lead to large-scale remanufacturing of carbon emissions resulting from unreasonable RSD solutions, depriving remanufacturing of its energy saving and emission reduction benefits. To this end, this paper proposes an integrated design method for remanufacturing scheme considering the carbon emission. For establishing the relationship between the RSD parameters and the carbon emissions, the axiomatic design (AD) matrix and sensitivity analysis method are applied to select the mapping model between RSD parameters and remanufacturing carbon emissions, which analyzed the correlation between the three levels of RSD-remanufacturing process-carbon emission. Then, a multi-constrained objective optimization model with the depreciation of remanufacturing carbon emissions as the optimization objective is established, which is set the RSD parameters as the optimization variables. Meanwhile, an improved bald eagle search optimization algorithm (IBES) is used to solve the optimization model, contributing to the rapid solution of an optimal remanufacturing scheme. Finally, the feasibility of the method is verified by the RSD of the used injection mold as an example. The results show that the optimized RSD parameters reduce carbon emissions by 35.48 % compared to the original ones, which also demonstrates that the RSD method can effectively reduce carbon emissions from the remanufacturing of used products, as well as greatly reduce carbon emissions from large-scale remanufacturing industries, and promote sustainable development of the global industry.

Testing the suitability of Marginal Distribution Sampling as a gap‐filling method using some meteorological data from seven sites in West Africa

AbstractMeteorological data are useful in many fields related to climate change studies and their use often requires them to be continuous. To date, marginal distribution sampling (MDS), which consists of filling a missing value with an average of the data that are found in similar meteorological conditions over a flexible time window, is widely adopted in the FLUXNET community. In this work, we evaluate the performance of MDS at diurnal and monthly scales for the incoming shortwave radiation (Swin), relative humidity (RH), vapour pressure deficit (VPD), air and soil temperatures (Tair, Tsoil) acquired across seven sites in West Africa. The criteria tested are the MDS's ability to (i) fill gaps while reducing the error rate, (ii) represent proper variability within data and finally (iii) ensure homogeneity between its output and original data. We found during the daytime that MDS is adequate for filling gaps in Swin when both reducing error rate and a good representation of variability are targeted. If the goal is to have a small error rate, then this approach is recommended for all investigated variables except VPD. During nighttime, MDS is satisfactory to minimize the error when filling gaps in Tair, Tsoil and RH while to represent their variabilities it becomes more sensitive to the rate of missing data. At a monthly scale, the gap‐filled data are consistent with the original ones for all variables attributable to data size and a wider sliding window that allows more data under similar conditions to be considered.

Secure Beamforming in Multi-User Multi-IRS Millimeter Wave Systems

We study the secrecy rate maximization problem in a millimeter wave (mmWave) network, consisting of a base station (BS), multiple intelligent reflecting surfaces (IRSs) (or reconfigurable intelligent surfaces (RISs)), multiple users, and a single eavesdropper. To ensure a fair secrecy rate among all the users, we adopt a max-min fairness criterion which results in a mixed integer problem. We first relax discrete IRSs phase shifts to the continuous ones. To cope with the non-convexity of the relaxed optimization problem, we leverage the penalty method and block coordinate descent approach to divide it into two sub-problems, which are solved by successive convex approximation (SCA). Then, we propose a low-complexity mapping algorithm where feasible IRSs phase shifts are obtained. Mathematical evaluation shows the convergence of sub-problems to a Karush-Kuhn-Tucker (KKT) point of the original ones. Furthermore, the convergence guarantee of the overall proposed algorithm and computational complexity are investigated. Finally, simulation results show our proposed algorithm outweighs the conventional solutions based on the semi-definite programming (SDP) in terms of convergence and secrecy rate, especially in a larger number of IRSs and phase shifts where SDP suffers from rank-one approximation. Maximum ratio transmission (MRT) and IRS-free systems are also considered as other benchmarks.

Vanshlochan Substitution and Adulteration: Discussions on the Controversy over its Original Source

In India, about 80% of the rural population relies on herbal remedies for their basic medical needs. The increasing demand and trading of raw materials for herbal medicines often involve adulterants and substitutes. The reasons behind intentional adulteration are typically commercial and include degradation, admixture, sophistication, inferiority, spoiling, and other unidentified causes. Substitution is the use of comparable medications instead of the original ones. Similar Rasa, Guna, Virya, Vipaka, and most importantly Karma are the guiding principles for choosing alternative medicaments. The adulteration and substitution of herbal medications are currently a major issue in both the herbal business and Ayurvedic therapies. Vanshlochan or Tabasheer is one of the example, which has long been used as an anti-tussive in traditional medicine to cure a variety of illnesses. When this compound was first derived, the major constituent was silica with traces of other elements. Vanshlochan is prescribed as a bioavailability booster and offers a synergistic component to some well-known medications, including Sitopaladi, Talisadi, and Dadimashtaka in Ayurveda and other Unani formulations. To be utilized as a medication in conventional medicine, Vanshlochan needs stringent pharmaco-analytical approaches with evidence-based studies regarding its drug safety for human consumption. Therefore, it is essential to provide trustworthy procedures for the accurate identification, standardization, and quality control of Vanshlochan, Tabasheer, or Tugaksheeri.

Timoshenko & Lekhnitskii's puzzle ‒ Rule of swapping for the torsional rigidity of a rectangular bar

Timoshenko presented the torsional rigidity of an isotropic rectangular bar, and Lekhnitskii presented that of an orthotropic rectangular bar. The solutions of Timoshenko and Lekhnitskii (T&L) are functions of the bar's length, width, thickness and shear modulus or moduli. However, the functions of T&L solutions become different from their original ones when the width and thickness are swapped. Swapping the width and thickness definitions does not alter the bar's physical properties, named the “rule of swapping” by the authors. In the last century, no research has shown the T&L solutions to satisfy the rule of swapping, an observation hereinafter referred to as the “Timoshenko & Lekhnitskii Puzzle”. Roughly 90 years later, Tsai et al. re-solved T&L cases using the TSAI technique. The derived solutions are nearly if not completely identical to T&L's numerically and satisfy the rule of swapping automatically. The rule of swapping is a novel issue and has never been mentioned before. Based on the Weierstrass factorization theorem, this study mathematically proves that they are identical for isotropic and orthotropic bars and satisfy the rule of swapping. The result of a torsional pendulum test is analyzed to support the rule.

Compressed Sensing Techniques Applied to Medical Images Obtained with Magnetic Resonance

The fast and reliable processing of medical images is of paramount importance to adequately generate data to feed machine learning algorithms that can prevent and diagnose health issues. Here, different compressed sensing techniques applied to magnetic resonance imaging are benchmarked as a means to reduce the acquisition time spent in the collection of data and signals that form the image. It is shown that by using these techniques, it is possible to reduce the number of signals needed and, therefore, substantially decrease the time to acquire the measurements. To this end, different algorithms are considered and compared: the iterative re-weighted least squares, the iterative soft thresholding algorithm, the iterative hard thresholding algorithm, the primal dual algorithm and the log barrier algorithm. Such algorithms have been implemented in different analysis programs that have been used to perform the reconstruction of the images, and it was found that the iterative soft thresholding algorithm gives the optimal results. It is found that the images obtained with this algorithm have lower quality than the original ones, but in any case, the quality should be good enough to distinguish each body structure and detect any health problems under an expert evaluation and/or statistical analysis.

The Contextual Model for Poem Translations and Criticisms: An Enlightenment from Wang Guowei’s Realm of Poetic World

ABSTRACT Combining the poet’s cultivated character, tones and rhythms, and spirit in Wang Guowei’s poetic realm theories with the viewpoints of poem compositions in Shi Sou, the article proposes the four essential elements, cultivated character, genre, tones and rhythms, and style, in poem translations, and argues that the translator should find the poet who has similar cultivated characters with him to translate, and meters and rhythms of translated poems should show the emotions and meanings of the original, and the styles of the translated poems should agree with those of the original ones. The article, therefore, constructs a contextual model integrating the context of situation consisting of the genre (field), tones (meters) and rhythms (mode), and style (tenor) into the context of culture containing the cultivated character and puts forth the principles for poem translations and criticisms.

Disease-specific resting-state EEG network variations in schizophrenia revealed by the contrastive machine learning

Given a multitude of genetic and environmental factors, when investigating the variability in schizophrenia (SCZ) and the first-degree relatives (R-SCZ), latent disease-specific variation is usually hidden. To reliably investigate the mechanism underlying the brain deficits from the aspect of functional networks, we newly iterated a framework of contrastive variational autoencoders (cVAEs) applied in the contrasts among three groups, to disentangle the latent resting-state network patterns specified for the SCZ and R-SCZ. We demonstrated that the comparison in reconstructed resting-state networks among SCZ, R-SCZ, and healthy controls (HC) revealed network distortions of the inner-frontal hypoconnectivity and frontal-occipital hyperconnectivity, while the original ones illustrated no differences. And only the classification by adopting the reconstructed network metrics achieved satisfying performances, as the highest accuracy of 96.80% ± 2.87%, along with the precision of 95.05% ± 4.28%, recall of 98.18% ± 3.83%, and F1-score of 96.51% ± 2.83%, was obtained. These findings consistently verified the validity of the newly proposed framework for the contrasts among the three groups and provided related resting-state network evidence for illustrating the pathological mechanism underlying the brain deficits in SCZ, as well as facilitating the diagnosis of SCZ.

Chatgpt: A New Era In Research Writing Assistance.

The integration of artificial intelligence in everyday tasks is becoming prevalent, and its application has extended to academic and scientific writing. The utilization of large language models, such as ChatGPT, has gained significant traction among researchers and scientific writers in recent times. According to a recent article published in Nature, ChatGPT is capable of generating text that sounds both realistic and intelligent in response to user prompts (1). A recent study published in December 2022 evaluated the ability of ChatGPT, to execute complex cognitive tasks and generate text that is indistinguishable from text produced by humans. (2), suggesting that it can be difficult for researchers to distinguish between abstracts created by AI and original ones (1). Another study published in December 2022 showed that ChatGPT could generate precise and believable scientific abstracts (3).Likewise An additional study published in 2022 postulated that based on the analysis of ChatGPT's applications, capabilities, and limitations, it can significantly enhance academic research across a broad range of fields. (4). A recently published literature review using ChatGPT has been cited as an example of ChatGPT application in the field of scientific writing and research (5). Moreover, ChatGPT has also been used to write and publish editorials and research papers (1, 6, 7). Despite the benefits, some researchers are still evaluating the implications and effects of using these models in the long term. (1). This letter aims to present the advantages of utilizing ChatGPT, a large language model developed by OpenAI, in the field of scientific writing. ChatGPT can help researchers in a various ways, including generating text, summarizing text, completing the text, editing text, and creating references, bibliography and citations. The most significant application of ChatGPT in scientific writing is text generation, as the model can be used to generate outlines, abstracts, and introductions to research papers. However, it is imperative to point out that ChatGPT cannot provide text with proper citations to research papers and sources. ChatGPT can also support researchers in the text summarization process, enabling them to extract meaningful information from vast amounts of text data like research papers or articles. Additionally, some researchers use ChatGPT to analyze large volumes of text data to gain deeper insights and knowledge, which is particularly useful in qualitative research and in writing literature reviews. ---Continue

Robust Image Inpainting Forensics by Using an Attention-Based Feature Pyramid Network

Deep learning has injected a new sense of vitality into the field of image inpainting, allowing for the creation of more realistic inpainted images that are difficult to distinguish from the original ones. However, this also means that the malicious use of image inpainting technology to tamper with images could lead to more serious consequences. In this paper, we use an attention-based feature pyramid network (AFPN) to locate the inpainting traces left by deep learning. AFPN employs a feature pyramid to extract low- and high-level features of inpainted images. It further utilizes a multi-scale convolution attention (MSCA) module to optimize the high-level feature maps. The optimized high-level feature map is then fused with the low-level feature map to detect inpainted regions. Additionally, we introduce a fusion loss function to improve the training effectiveness. The experimental results show that AFPN exhibits remarkable precision in deep inpainting forensics and effectively resists JPEG compression and additive noise attacks.

Smart Asphalt Mixtures: A Bibliometric Analysis of the Research Trends

A smart asphalt mixture holds new capabilities different from the original ones or can react to a stimulus. These capabilities can be categorized based on smartness or function: smartness, mechanical, electrical, optical, energy harvesting, electromagnetic wave/radiation shielding/absorbing, and water related. The most important capabilities applied to asphalt mixtures are the photocatalytic, self-cleaning, self-healing, superhydrophobic, thermochromic, deicing/anti-icing, and latent heat thermal energy storage abilities. This research deals with a bibliometric review of the peer-reviewed journal articles published on the Scopus database, with the strings of terms related to these capabilities and asphalt or bitum in their titles, abstracts, and keywords. The review analysis highlighted the increasing number of accumulated publications, confirming the relevance of this research topic in recent years. The capability most often referred to was self-healing. The study showed that China was the most productive country. Research articles were mostly published in the journal Construction and Building Materials. Several techniques and methods are being developed regarding smart asphalt mixtures; for that reason, this research work aims to evaluate the literature under a bibliometric analysis.