bluuebunny/crossref_metadata_2025 huggingface dataset:
Measuring Climate Risks with Indirect Emissions
Authors: Szendrey Orsolya , Dombi Mihály
Published: March 2023

Climate change poses completely new challenges for the financial markets, and thus the consideration of green aspects is becoming explicitly required by regulators and investors. Most of the reports which influence the market in this way evaluate the climate or environmental impact of a product or process based on their alignment with a regulatory standard. However, the methods applied for measurement do not always provide a proper description of the relationship between the investments and the natural resources. Most analyses evaluate investments related to economic sectors based exclusively on direct emissions, while indirect impacts, which represent a substantial part of total emissions, are not taken into account. In the study, the methods and results which are currently applied are compared to calculations including indirect impacts as well.
On the Semantics of Hybrid ASP Systems Based on Clingo
Authors: Cabalar Pedro , Fandinno Jorge , Schaub Torsten , Wanko Philipp
Published: March 2023

Over the last decades, the development of Answer Set Programming (ASP) has brought about an expressive modeling language powered by highly performant systems. At the same time, it gets more and more difficult to provide semantic underpinnings capturing the resulting constructs and inferences. This is even more severe when it comes to hybrid ASP languages and systems that are often needed to handle real-world applications. We address this challenge and introduce the concept of abstract and structured theories that allow us to formally elaborate upon their integration with ASP. We then use this concept to make the semantic characterization of clingo’s theory-reasoning framework precise. This provides us with a formal framework in which we can elaborate upon the formal properties of existing hybridizations of clingo, such as clingcon, clingo[dl], and clingo[lp].
Insecticidal Potential of Matricaria chamomilla’s Essential Oil and Its Components (E)-β-Farnesene, Germacrene D, and α-Bisabolol Oxide A against Agricultural Pests, Malaria, and Zika Virus Vectors
Authors: Al-Ghanim Khalid A. , Krishnappa Kaliyamoorthy , Pandiyan Jeganathan , Nicoletti Marcello , Gurunathan Baskar , Govindarajan Marimuthu
Published: March 2023

Agricultural and medical insect pests are damaging edible crops, spreading diseases, and harming non-target fauna and flora. Prominent polyphagous insect pests harass farmers in the agronomy sector, causing uncountable revenue corrosion. Ecofriendly phytopesticides can avoid the consequences of the bulk usage of synthetic chemicals. In this study, the toxic effect on third-instar larvae of four different insect species (Spodoptera litura, Helicoverpa armigera, Aedes vittatus, and Anopheles subpictus) and the bio-toxicity on non-target fauna (NTF) (Stegodyphus sarasinorum and Gambusia affinis) were evaluated using Matricaria chamomilla (Mc) essential oil (EO) and its major phytoconstituents (Mc-MPCs). GC–MS analysis of the studied M. chamomilla EO gathered 39 constituents, with (E)-β-Farnesene (24.3%), Germacrene D (9.4%), and α-Bisabolol oxide A (10.2%) accounting for the major constituents. Remarkable larval death was seen in H. armigera and Ae. vittatus. In addition, (E)-β-Farnesene, Germacrene D, and α-Bisabolol oxide A exhibited a relevant maximum toxic effect on the target pest’s third-instar larvae. The bio-toxicity of M. chamomilla EO and Mc-MPCs was tested on terrestrial and aquatic NTF. The LC50 values for S. sarasinorum and G. affinis ranged from 922.65 to 1750.49 µg/mL. M. chamomilla EO and its MPCs evidenced prospective phytopesticidal efficiency on selected agricultural and medical insect pests.
Transgene-Free Genome Editing for Biotic and Abiotic Stress Resistance in Sugarcane: Prospects and Challenges
Authors: Surya Krishna Sakthivel , Harish Chandar S R , Ravi Maruthachalam , Valarmathi Ramanathan , Lakshmi Kasirajan , Prathima Perumal Thirugnanasambandam , Manimekalai Ramaswamy , Viswanathan Rasappa , Hemaprabha Govindkurup , Appunu Chinnaswamy
Published: March 2023

Sugarcane (Saccharum spp.) is one of the most valuable food and industrial crops. Its production is constrained due to major biotic (fungi, bacteria, viruses and insect pests) and abiotic (drought, salt, cold/heat, water logging and heavy metals) stresses. The ever-increasing demand for sugar and biofuel and the rise of new pest and disease variants call for the use of innovative technologies to speed up the sugarcane genetic improvement process. Developing new cultivars through conventional breeding techniques requires much time and resources. The advent of CRISPR/Cas genome editing technology enables the creation of new cultivars with improved resistance/tolerance to various biotic and abiotic stresses. The presence of genome editing cassette inside the genome of genome-edited plants hinders commercial exploitation due to regulatory issues. However, this limitation can be overcome by using transgene-free genome editing techniques. Transgene-free genome editing approaches, such as delivery of the RNPs through biolistics or protoplast fusion, virus-induced genome editing (VIGE), transient expression of CRISPR/Cas reagents through Agrobacterium-mediated transformation and other approaches, are discussed. A well-established PCR-based assay and advanced screening systems such as visual marker system and Transgene killer CRISPR system (TKC) rapidly identify transgene-free genome edits. These advancements in CRISPR/Cas technology speed up the creation of genome-edited climate-smart cultivars that combat various biotic and abiotic stresses and produce good yields under ever-changing conditions.
Effects of Planting Pre-Germinated Buds on Stand Establishment in Sugarcane
Authors: Madala Hima Varsha , Lesmes-Vesga Ricardo A. , Odero Calvin D. , Sharma Lakesh K. , Sandhu Hardev S.
Published: March 2023

Sugarcane (a complex hybrid of Saccharum spp.) is propagated vegetatively by using stem pieces of mature cane with healthy buds. Abiotic and biotic stress may cause pre-germination of these buds, which may have an impact on both emergence and plant cane stand establishment. There is very limited information available in the literature. A greenhouse study was conducted with single-budded seed pieces of three levels of bud germination (ungerminated buds, Pop-eyes, and Lalas) from three different cultivars (CP 96-1252, CPCL 05-1201, and CPCL 02-0926) planted in pots and repeated over time. Data on growth parameters (tiller count, primary shoot height, SPAD, and dry biomass of shoots and roots) at early growth showed that Lalas produced more tillers and higher shoot dry biomass than Pop-eyes and ungerminated buds. Both Lalas and Pop-eyes produced higher root dry biomass than ungerminated buds in one of the two experiments. The cultivar had a significant effect on primary shoot height and SPAD. A small plot field experiment was conducted with cultivar CP 96-1252 to validate the results of greenhouse experiments, and similar results were reported for tiller count. The results indicate that pre-germinated buds may have a neutral or positive effect on early sugarcane growth and establishment. Further on-farm research needs to be conducted to confirm these results before using pre-germinated buds as a potential seed source for the late season planting of sugarcane.
Habituation to Livestock Trailer and Its Influence on Stress Responses during Transportation in Goats
Authors: Kannan Govind , Batchu Phaneendra , Naldurtiker Aditya , Dykes Gregory S. , Gurrapu Priyanka , Kouakou Brou , Terrill Thomas H. , McCommon George W.
Published: March 2023

This experiment was conducted to determine the effects of habituation to livestock trailers on stress responses in goats transported for long periods. Intact male Spanish goats (12-month old; BW = 31.6 ± 0.34 kg; N = 168) were separated into two treatment (TRT) groups and maintained on two different paddocks. Concentrate supplement was fed to one group inside two livestock trailers (5.0 × 2.3 m each; habituated group, H), while the other group received the concentrate supplement, but not inside the trailers (non-habituated, NH). After 4 weeks of habituation period, goats were subjected to a 10-h transportation stress in four replicates (n = 21 goats/replicate/TRT). Blood samples were collected by a trained individual by jugular venipuncture into vacutainer tubes before loading (Preload), 20 min after loading (0 h), and at 2-h intervals thereafter (Time) for analysis of stress responses. There was a tendency for a TRT effect (p < 0.1) on tyramine and metanephrine concentrations. Phenylethylamine and 5-methoxytryptamine concentrations were significantly greater (p < 0.05) in the H group compared to the NH group. Both dopamine and 5-methoxytryptamine concentrations decreased (p < 0.05) with transportation time; however, TRT × Time interaction effects were not significant. Habituation to trailers may be beneficial in mood and energy stabilization in goats during long-distance transportation.
Clinical Aspects of Bacterial Distribution and Antibiotic Resistance in the Reproductive System of Equids
Authors: Tyrnenopoulou Panagiota , Fthenakis George C.
Published: March 2023

Antibiotic administration is a standard therapeutic practice for the treatment of reproductive disorders of equids. This might lead to undesirable microbial imbalance and could favour the acquisition of antibiotic resistance. Therefore, it is imperative for clinicians to understand patterns of antibiotic resistance when considering and developing treatment regimes. Continued engagement of clinicians with novel alternative approaches to treat reproductive infections would be essential in order to address this rising threat within the One Health perspective. The objectives of the present review were to present the bacterial infections in the reproductive system of equids (horses, donkeys), to upraise the literature related to the issue of antibiotic resistance of bacteria causing these infections and to discuss the topic from a clinical perspective. Initially, the review summarised the various infections of the reproductive system of equids (genital system of females, genital system of males, mammary glands) and the causal bacteria, providing relevant information about horses and donkeys. Subsequently, the clinical therapeutics of these infections were presented, taking into account the significance of antibiotic resistance of bacteria as a limiting factor in treating the infections. Finally, approaches to circumvent antibiotic resistance in clinical settings were summarized. It was concluded that awareness regarding antibiotic resistance in equine reproductive medicine would increase, as we would recognise the multifaceted problem of resistance. Actions and initiatives within the One Health approach, minimizing the potential dissemination of resistant strains to humans and to the environment, with specific applications in medicine of equids should be appropriately instituted internationally.
How the Disruption of Mitochondrial Redox Signalling Contributes to Ageing
Authors: Castejon-Vega Beatriz , Cordero Mario D. , Sanz Alberto
Published: March 2023

In the past, mitochondrial reactive oxygen species (mtROS) were considered a byproduct of cellular metabolism. Due to the capacity of mtROS to cause oxidative damage, they were proposed as the main drivers of ageing and age-related diseases. Today, we know that mtROS are cellular messengers instrumental in maintaining cellular homeostasis. As cellular messengers, they are produced in specific places at specific times, and the intensity and duration of the ROS signal determine the downstream effects of mitochondrial redox signalling. We do not know yet all the processes for which mtROS are important, but we have learnt that they are essential in decisions that affect cellular differentiation, proliferation and survival. On top of causing damage due to their capacity to oxidize cellular components, mtROS contribute to the onset of degenerative diseases when redox signalling becomes dysregulated. Here, we review the best-characterized signalling pathways in which mtROS participate and those pathological processes in which they are involved. We focus on how mtROS signalling is altered during ageing and discuss whether the accumulation of damaged mitochondria without signalling capacity is a cause or a consequence of ageing.
Applicability of Sun’s Empirical Relations for Non-Uniform Sediment in Jiaojiang Estuaries, Zhejiang, China
Authors: Ni Wuming , Sun Zhilin , Guo Cong , Li Zongyu , Zheng Rong
Published: March 2023

The numerical simulation of non-uniform sediment transport under tidal flow in estuaries is a complicated, yet important, issue in Zhejiang estuaries. In this paper, a depth-averaged two-dimensional (2D) mathematical model for non-uniform sediment transport in estuaries is established and applied in Zhejiang tidal estuaries based on several newly derived formulas by Zhlin Sun et al. The model is validated using data from several experiments, including an aggradation test and an erosion test. Good performance in the tests indicates that the present model can simulate aggradation and erosion processes of non-uniform sediment. The model is also verified by observational data from the Jiaojiang estuary, and calculations agree well with measurements. The model is thus adaptable to simulating flow and non-uniform sediment transport in tidal estuaries.
Assessment of the Critical Defect in Additive Manufacturing Components through Machine Learning Algorithms
Authors: Tridello Andrea , Ciampaglia Alberto , Berto Filippo , Paolino Davide Salvatore
Published: March 2023

The design against fatigue failures of Additively Manufactured (AM) components is a fundamental research topic for industries and universities. The fatigue response of AM parts is driven by manufacturing defects, which contribute to the experimental scatter and are strongly dependent on the process parameters, making the design process rather complex. The most effective design procedure would involve the assessment of the defect population and the defect size distribution directly from the process parameters. However, the number of process parameters is wide and the assessment of a direct relationship between them and the defect population would require an unfeasible number of expensive experimental tests. These multivariate problems can be effectively managed by Machine Learning (ML) algorithms. In this paper, two ML algorithms for assessing the most critical defect in parts produced by means of the Selective Laser Melting (SLM) process are developed. The probability of a defect with a specific size and the location and scale parameters of the statistical distribution of the defect size, assumed to follow a Largest Extreme Value Distribution, are estimated directly from the SLM process parameters. Both approaches have been validated using literature data obtained by testing the AlSi10Mg and the Ti6Al4V alloy, proving their effectiveness and predicting capability.
Experimental Study on Fatigue Performance of M24 Twisted-Shear High-Strength Bolt for Assembled Steel Structure
Authors: Xing Rong , Qiu Bin , Lei Honggang
Published: March 2023

High-strength bolt connection is a kind of main connection mode of prefabricated steel structures. Due to the insufficient fatigue performance of high-strength bolts, the degree of damage in the steel structure is very serious, so the fatigue performance research of high strength bolts cannot be ignored. The research object of the paper is M24 twisted-shear high-strength bolts in a steel structure buildings. Some special tests and results analysis on the normal fatigue performance were carried out, establishing the fatigue S-N curve of M24 twisted-shear high-strength bolts, revealing the fatigue failure mechanism of M24 torsion-shear high-strength bolts; obtaining the fatigue S-N curve equation; and estimating the fatigue life of high-strength bolts by using the Paris formula. In addition, by comparing the test data in this paper with the constant fatigue test data of high-strength bolts in the existing research literature, it can be seen how the strength grade of the bolts and the pretension force have an impact on the fatigue strength. It is further revealed that the M24 torsion-shear high-strength bolt with full pretension force has twice as long fatigue life than the other two types of bolts. By comparing the test results of M24 and M20 bolts under full pretension, it is known the relation between the fatigue strength of the bolts and diameter decreases. The research data and useful conclusions can provide scientific basis and theoretical reference for the anti-fatigue design of M24 torsion-shear high-strength bolt connection.
Improving Chinese Named Entity Recognition by Interactive Fusion of Contextual Representation and Glyph Representation
Authors: Gu Ruiming , Wang Tao , Deng Jianfeng , Cheng Lianglun
Published: March 2023

Named entity recognition (NER) is a fundamental task in natural language processing. In Chinese NER, additional resources such as lexicons, syntactic features and knowledge graphs are usually introduced to improve the recognition performance of the model. However, Chinese characters evolved from pictographs, and their glyphs contain rich semantic information, which is often ignored. Therefore, in order to make full use of the semantic information contained in Chinese character glyphs, we propose a Chinese NER model that combines character contextual representation and glyph representation, named CGR-NER (Character–Glyph Representation for NER). First, CGR-NER uses the large-scale pre-trained language model to dynamically generate contextual semantic representations of characters. Secondly, a hybrid neural network combining a three-dimensional convolutional neural network (3DCNN) and bi-directional long short-term memory network (BiLSTM) is designed to extract the semantic information contained in a Chinese character glyph, the potential word formation knowledge between adjacent glyphs and the contextual semantic and global dependency features of the glyph sequence. Thirdly, an interactive fusion method with a crossmodal attention and gate mechanism is proposed to fuse the contextual representation and glyph representation from different models dynamically. The experimental results show that our proposed model achieves 82.97% and 70.70% F1 scores on the OntoNotes 4 and Weibo datasets. Multiple ablation studies also verify the advantages and effectiveness of our proposed model.
Modified Equation of Shear Strength with Respect to Saturation
Authors: Tian Wenjing , Peiffer Herman , Malengier Benny , Liu Gang , Cheng Liangliang
Published: March 2023

Unsaturated soil shear strength is a significant topic in geotechnical engineering. The measurement of unsaturated soil properties such as shear strength and matric suction could be costly, hard, time-consuming, and often impractical to obtain. The purpose of this research is to propose a new shear strength model for unsaturated soil and to predict the shear strength by using the degree of saturation directly because the water saturation is easy to obtain. More specifically, this study focused on Bishop’s shear strength theory and van Genuchten’s soil–water characteristic curve to describe the effect of saturation on shear strength. The new shear strength model was expressed as a function of saturation degree, residual degree of saturation, effective shear strength indices, net normal stress, and five parameters. The performance of the presented model in this paper was verified by fitting it to data obtained by laboratory tests on silty sand including the triaxial shear test and soil–water characteristic curve test. From these two laboratory tests, we obtained the variation in the matric suction and shear strength with the degree of saturation under various dry densities. This proposed model was also validated against the shear strength results of the clayed-silty sand and Ankara clay recorded in the literature. Comparing the result of the shear strength under the variation of one parameter, this model was much more sensitive regarding parameter m, which was related to the material characteristic. The comparison between the predicted values and experimental points was particularly fine and showed the ability of this model to be applied to a wide range of soils.
Design Method of Core-Separated Assembled Buckling Restrained Braces Confined by Two Lightweight Concrete-Infilled Tubes
Authors: Zhu Boli , Zhao Junyuan , Yang Yuqing
Published: March 2023

This paper introduces a novel type of buckling restrained braces (BRBs) called core-separated assembled BRBs (CSA-BRBs). These braces are comprised of two single BRBs that are confined by lightweight concrete-infilled tubes, which are longitudinally connected by two continuous webs. The CSA-BRBs utilize materials more efficiently by increasing the height of the webs to create a large inner cavity, leading to an economical design. This paper predicts the threshold of the restraint ratio of CSA-BRBs approximately. This is achieved by assuming that the maximum moment resulting from applied loads at mid-height is less than the moment-bearing resistance that is conducted according to the outermost fiber of the external restraining section reading yielding. Elastic-plastic numerical analysis is conducted using FEM with beam elements for CSA-BRBs that are subjected to both monotonic and cyclic axial loads. The load resistance, hysteretic performance, and failure mechanism of CSA-BRBs are investigated by varying their restraining ratios. It is recommended that the restraint ratio threshold of CSA-BRBs under monotonic axial compression is used as a bearing type and the restraint ratio threshold of CSA-BRBs under axially compressive-tensile cyclic loads as an energy-dissipation type. This method provides a complete design for CSA-BRBs.
PESO: A Seq2Seq-Based Vessel Trajectory Prediction Method with Parallel Encoders and Ship-Oriented Decoder
Authors: Zhang Yuanben , Han Zhonghe , Zhou Xue , Zhang Lili , Wang Lei , Zhen Enqiang , Wang Sijun , Zhao Zhihao , Guo Zhi
Published: March 2023

Vessel trajectory prediction supports navigation services and collision detection. To maintain safety and efficiency in maritime transportation, vessel trajectory prediction is always an important topic. By using automatic identification system (AIS) data and deep learning methods, the task of vessel trajectory prediction has made significant progress. However, this task is still full of challenges due to the complexity of historical information dependencies and the strong influence of spatial correlations. In this paper, we introduce a novel deep learning model, PESO, based on the structure of Seq2Seq, consisting of Parallel Encoders and a Ship-Oriented Decoder. The Parallel Encoders, including the Location Encoder and the Sailing Status Encoder are designed to integrate more information into feature representation. The Ship-Oriented Decoder is targeted to utilize the Semantic Location Vector (SLV) to guide the prediction, which better represents the spatial correlation of historical track points. In order to verify the efficiency and efficacy of PESO, we conducted comparative experiments with several baseline models. The experimental results demonstrate that PESO is superior to them both quantitatively and qualitatively.
UFODMV: Unsupervised Feature Selection for Online Dynamic Multi-Views
Authors: Alarfaj Fawaz , Almusallam Naif , Alabdulatif Abdulatif , Alomair Mohammed Ahmed , Alsharidi Abdulaziz Khalid , Moulahi Tarek
Published: March 2023

In most machine learning (ML) applications, data that arrive from heterogeneous views (i.e., multiple heterogeneous sources of data) are more likely to provide complementary information than does a single view. Hence, these are known as multi-view data. In real-world applications, such as web clustering, data arrive from diverse groups (i.e., sets of features) and therefore have heterogeneous properties. Each feature group is referred to as a particular view. Although multi-view learning provides complementary information for machine learning algorithms, it results in high dimensionality. However, to reduce the dimensionality, feature selection is an efficient method that can be used to select only the representative features of the views so to reduce the dimensionality. In this paper, an unsupervised feature selection for online dynamic multi-views (UFODMV) is developed, which is a novel and efficient mechanism for the dynamic selection of features from multi-views in an unsupervised stream. UFODMV consists of a clustering-based feature selection mechanism enabling the dynamic selection of representative features and a merging process whereby both features and views are received incrementally in a streamed fashion over time. The experimental evaluation demonstrates that the UFODMV model has the best classification accuracy with values of 20% and 50% compared with well-known single-view and multi-view unsupervised feature selection methods, namely OMVFS, USSSF, and SPEC.
Performance Predictions of Sci-Fi Films via Machine Learning
Authors: Al Fahoum Amjed , Ghobon Tahani A.
Published: March 2023

The films teenagers watch have a significant influence on their behavior. After witnessing a film starring an actor with a particular social habit or personality trait, viewers, particularly youngsters, may attempt to adopt the actor’s behavior. This study proposes an algorithm-based technique for predicting the market potential of upcoming science fiction films. Numerous science fiction films are released annually, and working in the film industry is both profitable and delightful. Before the film’s release, it is necessary to conduct research and make informed predictions about its success. In this investigation, different machine learning methods written in MATLAB are examined to identify and forecast the future performance of movies. Using 14 methods for machine learning, it was feasible to predict how individuals would vote on science fiction films. Due to their superior performance, the fine, medium, and weighted KNN algorithms were given more consideration. In comparison to earlier studies, the KNN-adopted methods displayed greater precision (0.89–0.93), recall (0.88–0.92), and accuracy (90.1–93.0%), as well as a rapid execution rate, more robust estimations, and a shorter execution time. These tabulated statistics illustrate that the weighted KNN method is effective and trustworthy. If several KNN algorithms targeting specific viewer behavior are logically coupled, the film business and its global expansion can benefit from precise and consistent forecast outcomes. This study illustrates how prospective data analytics could improve the film industry. It is possible to develop a model that predicts a film’s success, effect, and social behavior by assessing features that contribute to its success based on historical data.
Marketing Decision Support System Based on Data Mining Technology
Authors: Hou Rong , Ye Xu , Zaki Hafizah Binti Omar , Omar Nor Asiah Binti
Published: March 2023

With the continuous development of business intelligence technology, the application research of decision support systems (DSSs) is deepening. In China, the work in this area started relatively late, and there are few DSS research cases to assist in marketing decision-making. Currently, marketing decision support systems have shortcomings in data integration, historical data, query functions, and data analysis. This article analyzes the characteristics of marketing decision-making, discusses the application of data warehouse, OLAP, and data mining technology in marketing decision support systems, and designs a marketing decision support system based on data mining technology. The system uses a BP neural network to conduct data mining marketing forecasting. A three-layer network model for marketing prediction is established, with sales time, product price, and customer purchasing power as network inputs and output as the sales volume of a certain type of product in different locations. The test results show that the average absolute percentage error of this method is 15.13%, and the prediction accuracy is high. Research shows that with the continuous development of data mining technology, the system cannot only help users conduct scientific and reasonable marketing decision-making analyses, making the marketing decision-making process more scientific and reasonable, but also can bring new ideas to enterprise decision-makers, promoting the continuous improvement and progress of the system.
Effect of the Dynamic Froude–Krylov Force on Energy Extraction from a Point Absorber Wave Energy Converter with an Hourglass-Shaped Buoy
Authors: Yassin Houssein , Demonte Gonzalez Tania , Parker Gordon , Wilson David
Published: March 2023

Point absorber wave energy converter (WEC) control strategies often require accurate models for maximum energy extraction. While linear models are suitable for small motions, the focus is on the nonlinear model of an hour-glass shaped buoy undergoing large vertical displacements. Closed-form expressions for the static and dynamic Froude–Krylov forces are developed. It is shown that, in general, the dynamic and static forces are of similar magnitude, which is not the case for a spherical buoy. While the dynamic force reduces the amplitude of the net buoy force, its shape predicts a larger buoy response than if neglected, causing the nonlinear terms to have an even more significant effect. An input-state feedback linearizing controller is developed to show how the nonlinear model can be used in a control law. A 2.5 m buoy example is simulated to illustrate the approach of tracking an arbitrary displacement reference. For the case considered, the extracted power is 30% larger when the nonlinear dynamic FK force is used in the control law. The hourglass buoy is also compared to a spherical buoy to illustrate differences in their response to regular waves and energy extraction when using the same control laws. A spherical buoy diameter of 7.5 m was required to obtain the same power output as a 5 m tall hourglass buoy. A power-force-amplitude (PFA) metric is introduced to compare energy extraction performance and power take-off requirements. The hourglass buoy’s PFA was 13% larger than the spherical buoy implying that it can produce similar power but with less control effort.
Strategic Development of Dielectric Strength Prediction Protocol for Perfluorocarbon and Nonperfluorocarbon Compounds
Authors: Choi Min Kyu , Kim Ki Chul
Published: March 2023

Predicting the dielectric strengths of organic compounds is critical for identifying potential insulating gases. However, experimental evaluation techniques are time-consuming, and current computational protocols are limited in scope. In this study, to develop a reliable prediction protocol for the dielectric strengths of a broad array of perfluorocarbon (PFC) and non-PFC compounds, systematic linear regression is combined with computational calculations of relevant core factors. The designed equation-based protocol is demonstrated to have four core factors, including two high-correlation factors (polarizability and molecular weight) and two critical factors (ionization energy and highest occupied molecular orbital (HOMO)–lowest unoccupied molecular orbital (LUMO) gap). The two critical factors are crucial for determining a suitable protocol, as reliable predictions of dielectric strength are only possible if the ionization energy and HOMO–LUMO gap are maintained within specified ranges for all the compounds. These findings can act as design guidelines for future computational protocols to predict the insulating properties of PFC and non-PFC compounds.
Artificial Intelligence in Symptomatic Carotid Plaque Detection: A Narrative Review
Authors: Miceli Giuseppe , Rizzo Giuliana , Basso Maria Grazia , Cocciola Elena , Pennacchio Andrea Roberta , Pintus Chiara , Tuttolomondo Antonino
Published: March 2023

Identifying atherosclerotic disease is the mainstay for the correct diagnosis of the large artery atherosclerosis ischemic stroke subtype and for choosing the right therapeutic strategy in acute ischemic stroke. Classification into symptomatic and asymptomatic plaque and estimation of the cardiovascular risk are essential to select patients eligible for pharmacological and/or surgical therapy in order to prevent future cerebral ischemic events. The difficulties in a “vulnerability” definition and the methodical issues concerning its detectability and quantification are still subjects of debate. Non-invasive imaging studies commonly used to detect arterial plaque are computed tomographic angiography, magnetic resonance imaging, and ultrasound. Characterization of a carotid plaque type using the abovementioned imaging modalities represents the basis for carotid atherosclerosis management. Classification into symptomatic and asymptomatic plaque and estimation of the cardiovascular risk are essential to select patients eligible for pharmacological and/or surgical therapy in order to prevent future cerebral ischemic events. In this setting, artificial intelligence (AI) can offer suggestive solutions for tissue characterization and classification concerning carotid artery plaque imaging by analyzing complex data and using automated algorithms to obtain a final output. The aim of this review is to provide overall knowledge about the role of AI models applied to non-invasive imaging studies for the detection of symptomatic and vulnerable carotid plaques.
Response of Extratropical Transitioning Tropical Cyclone Size to Ocean Warming: A Case Study for Typhoon Songda in 2016
Authors: Miao Ziwei , Tang Xiaodong
Published: March 2023

This study attempts to investigate how future sea surface temperature increases will affect the size (radius of gale-force [17 m s−1] wind at 10 m height; i.e., R17) evolution of tropical cyclones that undergo extratropical transition (ET) through sensitivity experiments of sea surface temperature (SST) for Typhoon Songda (2016) in the northwestern Pacific. Two numerical experiments were carried out, including a control simulation (control) and a sensitivity experiment (SST4.5) with SST increased by 4.5 degrees in the entire domain. The results showed that Songda tended to be stronger and larger with projected higher SSTs. Moreover, the momentum equation for tangential wind was utilized to study the mechanism of R17 evolution in different SST scenarios, in which the radial absolute vorticity flux term played a dominant role in generating a positive tendency of tangential wind. The results indicate that before ET, higher SSTs in the entire domain led to more active rainbands in both inner-core and outer-core regions. As a result, stronger secondary circulation and low-level inflow extended outward, and the absolute angular momentum (AAM) importing from the outer region increased, which led to a larger R17 in SST4.5. During the ET, the peripheral baroclinically driven frontal convection induced extensive boundary layer inflow, which accelerated the tangential flow in the outer frontal region through strong inward AAM transport. However, due to the lower latitude of the cyclone and the strong frontolysis at the outer side of the cold pool in SST4.5, the peripheral frontal convection reached the location of R17 later; thus, the increase in the cyclone size lagged behind that in the control.
ECG Heartbeat Classification Using Machine Learning and Metaheuristic Optimization for Smart Healthcare Systems
Authors: Hassaballah Mahmoud , Wazery Yaser M. , Ibrahim Ibrahim E. , Farag Aly
Published: March 2023

Early diagnosis and classification of arrhythmia from an electrocardiogram (ECG) plays a significant role in smart healthcare systems for the health monitoring of individuals with cardiovascular diseases. Unfortunately, the nonlinearity and low amplitude of ECG recordings make the classification process difficult. Thus, the performance of most traditional machine learning (ML) classifiers is questionable, as the interrelationship between the learning parameters is not well modeled, especially for data features with high dimensions. To address the limitations of ML classifiers, this paper introduces an automatic arrhythmia classification approach based on the integration of a recent metaheuristic optimization (MHO) algorithm and ML classifiers. The role of the MHO is to optimize the search parameters of the classifiers. The approach consists of three steps: the preprocessing of the ECG signal, the extraction of the features, and the classification. The learning parameters of four supervised ML classifiers were utilized for the classification task; support vector machine (SVM), k-nearest neighbors (kNNs), gradient boosting decision tree (GBDT), and random forest (RF) were optimized using the MHO algorithm. To validate the advantage of the proposed approach, several experiments were conducted on three common databases, including the Massachusetts Institute of Technology (MIT-BIH), the European Society of Cardiology ST-T (EDB), and the St. Petersburg Institute of Cardiological Techniques 12-lead Arrhythmia (INCART). The obtained results showed that the performance of all the tested classifiers were significantly improved after integrating the MHO algorithm, with the average ECG arrhythmia classification accuracy reaching 99.92% and a sensitivity of 99.81%, outperforming the state-of the-art methods.
Leucine Repeat Rich Kinase 1 Controls Osteoclast Activity by Managing Lysosomal Trafficking and Secretion
Authors: Shen Sandi , Si Mingjue , Zeng Canjun , Liu Elaine K. , Chen Yian , Vacher Jean , Zhao Haibo , Mohan Subburaman , Xing Weirong
Published: March 2023

We previously demonstrated that mice with targeted deletion of the leucine repeat rich kinase 1 (Lrrk1) gene were osteopetrotic due to the failure of osteoclasts to resorb bone. To determine how LRRK1 regulates osteoclast activity, we examined the intracellular and extracellular acidification with an acidotropic probe, acridine orange, in live osteoclasts on bone slices. We examined lysosome distribution in osteoclasts by localization of LAMP-2, cathepsin K, and v-ATPase by immunofluorescent staining with specific antibodies. We found that both vertical and horizontal cross-sectional images of the wild-type (WT) osteoclasts showed orange-staining of the intracellular acidic vacuoles/lysosomes dispersed to the ruffled border. By contrast, the LRRK1 deficient osteoclasts exhibited fluorescent orange staining in the cytoplasm away from the extracellular lacunae because of an altered distribution of the acidic vacuoles/lysosomes. In addition, WT osteoclasts displayed a peripheral distribution of LAMP-2 positive lysosomes with a typical actin ring. The clustered F-actin constitutes a peripheral sealing zone and a ruffled border which was stretched out into a resorption pit. The LAMP-2 positive lysosomes were also distributed to the sealing zone, and the cell was associated with a resorption pit. By contrast, LRRK1-deficient osteoclasts showed diffused F-actin throughout the cytoplasm. The sealing zone was weak and not associated with a resorption pit. LAMP-2 positive lysosomes were also diffuse in the cytoplasm and were not distributed to the ruffled border. Although the LRRK1-deficient osteoclast expressed normal levels of cathepsin K and v-ATPase, the lysosomal-associated cathepsin K and v-ATPase were not accumulated at the ruffled border in Lrrk1 KO osteoclasts. Our data indicate that LRRK1 controls osteoclast activity by regulating lysosomal distribution, acid secretion, and protease exocytosis.
Novel Biomarkers for Early Detection of Acute Kidney Injury and Prediction of Long-Term Kidney Function Decline after Partial Nephrectomy
Authors: Allinovi Marco , Sessa Francesco , Villa Gianluca , Cocci Andrea , Innocenti Samantha , Zanazzi Maria , Tofani Lorenzo , Paparella Laura , Curi Dritan , Cirami Calogero Lino , Campi Riccardo , Mari Andrea , Ognibene Agostino , Lorubbio Maria , Fanelli Alessandra , Romagnoli Stefano , Romagnani Paola , Minervini Andrea
Published: March 2023

Background: Identifying acute kidney injury (AKI) within few hours of onset is certainly helpful. However, early prediction of a long-term eGFR decline may be an even more important goal. Our aim was to identify and compare serum [creatinine, kineticGFR, cystatin C, neutrophil gelatinase–associated lipocalin (NGAL)] and urinary (NephroCheck, NGAL, proteinuria, albuminuria, acantocytes at urinary sediment) predictors of AKI that might efficiently predict long-term GFR decline after robotic Nephron-Spearing Surgery (rNSS). Methods: Monocentric prospective observational study. Patients scheduled for rNSS for suspected localized Renal Cell Carcinoma from May 2017 to October 2017 were enrolled. Samples were collected preoperatively and postoperatively (timepoints: 4 h, 10 h, 24 h, 48 h), while kidney function was re-assessed up to 24 months. Results: 38 patients were included; 16 (42%) developed clinical AKI. The eGFR decline at 24 months was more pronounced after postoperative AKI (−20.75 vs. −7.20, p < 0.0001). KineticGFR at 4 h (p = 0.008) and NephroCheck at 10 h (p = 0.001) were, at multivariable linear regression analysis, efficient predictors of post-operative AKI and long-term eGFR decline if compared to creatinine (R2 0.33 vs. 0.04). Conclusions: NephroCheck and kineticGFR have emerged as promising noninvasive, accurate, and early biomarkers of postoperative AKI and long-term GFR decline after rNSS. Combining NephroCheck and kineticGFR in clinical practice would allow to identify high risk of postoperative AKI and long-term GFR decline as early as 10 h after surgery.
Urinary Biomarkers and Point-of-Care Urinalysis Devices for Early Diagnosis and Management of Disease: A Review
Authors: Sequeira-Antunes Beatriz , Ferreira Hugo Alexandre
Published: March 2023

Biosensing and microfluidics technologies are transforming diagnostic medicine by accurately detecting biomolecules in biological samples. Urine is a promising biological fluid for diagnostics due to its noninvasive collection and wide range of diagnostic biomarkers. Point-of-care urinalysis, which integrates biosensing and microfluidics, has the potential to bring affordable and rapid diagnostics into the home to continuing monitoring, but challenges still remain. As such, this review aims to provide an overview of biomarkers that are or could be used to diagnose and monitor diseases, including cancer, cardiovascular diseases, kidney diseases, and neurodegenerative disorders, such as Alzheimer’s disease. Additionally, the different materials and techniques for the fabrication of microfluidic structures along with the biosensing technologies often used to detect and quantify biological molecules and organisms are reviewed. Ultimately, this review discusses the current state of point-of-care urinalysis devices and highlights the potential of these technologies to improve patient outcomes. Traditional point-of-care urinalysis devices require the manual collection of urine, which may be unpleasant, cumbersome, or prone to errors. To overcome this issue, the toilet itself can be used as an alternative specimen collection and urinalysis device. This review then presents several smart toilet systems and incorporated sanitary devices for this purpose.
A Systematic Review of Responsible Sourcing in the Architecture, Engineering, and Construction Sectors of the UK
Authors: Ball Sophie , Booth Colin A. , Prabhakaran Abhinesh , Mahamadu Abdul-Majeed , Glass Jacqueline
Published: March 2023

In recent years, responsible sourcing and procurement (including green supply chain management) across the architecture, engineering, and construction (AEC) sectors have become notable contributors to sustainable construction strategies. This study aims to provide a systematic review of responsible sourcing literature across the AEC sectors of the UK to identify a suite of opportunities and obstacles to adopting ethical and environmentally supportive practices and to highlight avenues for further investigation. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) review process was adopted to identify, screen, and select (n = 80) articles (from 2000–2020) for review. Analysis of these articles identified the most frequently reported opportunities: (i) stakeholder value (40%); (ii) improved supply chain performance (39%); and (iii) competitive advantage (30%). The most frequently reported obstacles relating to the uptake of responsible sourcing are the following: (i) cost (44%); (ii) industry constraints (28%); and (iii) lack of frameworks/information and lack of awareness (25%). Given the industry importance of these themes, it is proposed that there is a necessity to explore and unravel the perceptions of industry experts about these issues so as to clarify the progress and possibilities for more widely adopting responsible sourcing across the AEC sector and, moreover, for clarifying their contribution towards achieving national and international sustainability targets (e.g., UN Sustainable Development Goals).
The Challenge of Multiple Thermal Comfort Prediction Models: Is TSV Enough?
Authors: Lala Betty , Biju Amogh , Vanshita  , Rastogi Anmol , Dahiya Kunal , Kala Srikant Manas , Hagishima Aya
Published: March 2023

Classroom thermal comfort has a direct effect on student health and educational outcomes. However, measuring thermal comfort (TC) is a non-trivial task. It is represented by several subjective metrics e.g., Thermal Sensation Vote, Thermal Comfort Vote, Thermal Preference Vote, etc. Since machine learning (ML) is being increasingly used to predict occupant comfort, multiple TC metrics for the same indoor space may yield contradictory results. This poses the challenge of selecting the most suitable single TC metric or the minimal TC metric combination for a given indoor space. Ideally, it will be a metric that can be used to predict all other TC metrics and occupant behavior with high accuracy. This work addresses this problem by using a primary student thermal comfort dataset gathered from 11 schools and over 500 unique students. A comprehensive evaluation is carried out through hundreds of TC prediction models using several ML algorithms. It evaluates the ability of TC metrics to predict (a) other TC metrics, and (b) the adaptive behavior of primary students. An algorithm is proposed to select the most suitable single TC metric or the minimal TC metric input combination. Results show that ML models can accurately predict all TC metrics and occupant-adaptive behavior using a small subset of TC metrics with an average accuracy as high as 79%. This work also found Thermal Sensation Vote to be the most significant single TC predictor, followed by Thermal Satisfaction Level. Interestingly, satisfaction with clothing was found to be as equally relevant as thermal preference. Furthermore, the impact of seasons and choice of ML algorithms on TC metric and occupant behavior prediction is shown.