Results Of Model Research Articles

Comparison of the CASA and InVEST models’ effects for estimating spatiotemporal differences in carbon storage of green spaces in megacities

Urban green space is a direct way to improve the carbon sink capacity of urban ecosystems. The carbon storage assessment of megacity green spaces is of great significance to the service function of urban ecosystems and the management of urban carbon zoning in the future. Based on multi-period remote sensing image data, this paper used the CASA model and the InVEST model to analyze the spatio-temporal variation and driving mechanism of carbon storage in Shenzhen green space and discussed the applicability of the two models to the estimation of carbon storage in urban green space. The research results showed that, from 2008 to 2022, in addition to the rapid expansion of construction land, the area of green space and other land types in Shenzhen showed a significant decrease trend. The estimation results of the carbon storage model showed that the carbon storage of green space shows a significant trend of reduction from 2008 to 2022, and the reduction amounts are 0.8 × 106 t (CASA model) and 0.64 × 106 t (InVEST model), respectively. The evaluation results of the model show that, in megacities, the spatial applicability of InVEST model is lower than that of CASA model, and the CASA model is more accurate in estimating the carbon storage of urban green space. The research results can provide a scientific basis for the assessment of the carbon sink capacity of megacity ecosystems with the goal of "dual carbon".

An intelligent prediction model of epidemic characters based on multi‐feature

AbstractThe epidemic characters of Omicron (e.g. large‐scale transmission) are significantly different from the initial variants of COVID‐19. The data generated by large‐scale transmission is important to predict the trend of epidemic characters. However, the results of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission. In consequence, these inaccurate results have negative impacts on the process of the manufacturing and the service industry, for example, the production of masks and the recovery of the tourism industry. The authors have studied the epidemic characters in two ways, that is, investigation and prediction. First, a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters. Second, the β‐SEIDR model is established, where the population is classified as Susceptible, Exposed, Infected, Dead and β‐Recovered persons, to intelligently predict the epidemic characters of COVID‐19. Note that β‐Recovered persons denote that the Recovered persons may become Susceptible persons with probability β. The simulation results show that the model can accurately predict the epidemic characters.

An efficient parameter-retrieval-based surrogate-assisted optimization of on-platform honeycomb absorbing structures

An electromagnetic parameter-retrieval-based surrogate-assisted optimization (PSAO) algorithm is presented to reduce radar cross section (RCS) by optimizing the on-platform honeycomb absorbing structures. To facilitate the optimization process, the honeycomb structure is transformed to an anisotropic homogeneous slab, and the effective parameters of the slab are extracted by the retrieval algorithm. A multi-fidelity model is employed to reduce the computing-time consumption, in which a Gaussian process (GP) regression model is used as the substitute for the coarse model. The GP model establishes a relationship between the geometry of the honeycomb structure and the RCS response of the target coated with the equivalent slab. Finally, the optimization result of the fine model is achieved through a space mapping strategy. The accuracy of the parameter extraction algorithm is verified by analyzing the honeycomb absorbing structure. Subsequently, the proposed optimization method is applied to a metal plate and a metal cylinder, resulting in a 10 dB reduction of RCS in broadband and wide-angle scenarios. This demonstrates the applicability of the proposed PSAO algorithm to both planar and conformal on-platform honeycomb absorbing structures. Furthermore, an NACA0015 foil is analyzed, showing an average RCS reduction of 10 dB and a minimum RCS reduction of 5 dB in the X-band. These results indicate that the PSAO approach can effectively apply to complicated targets. Additionally, the proposed method exhibits significant advantages in terms of computational accuracy and efficiency compared to the traditional genetic algorithm.

Development and Validation of a Natural Language Processing Algorithm to Pseudonymize Documents in the Context of a Clinical Data Warehouse.

The objective of this study is to address the critical issue of deidentification of clinical reports to allow access to data for research purposes, while ensuring patient privacy. The study highlights the difficulties faced in sharing tools and resources in this domain and presents the experience of the Greater Paris University Hospitals (AP-HP for Assistance Publique-Hôpitaux de Paris) in implementing a systematic pseudonymization of text documents from its Clinical Data Warehouse. We annotated a corpus of clinical documents according to 12 types of identifying entities and built a hybrid system, merging the results of a deep learning model as well as manual rules. Our results show an overall performance of 0.99 of F1-score. We discuss implementation choices and present experiments to better understand the effort involved in such a task, including dataset size, document types, language models, or rule addition. We share guidelines and code under a 3-Clause BSD license.

A Novel Approach for the Assessment of Logistics Performance Index of EU Countries

Abstract The impact of logistics performance in the era of sustainable mobility on the overall economic development of a country is inevitable. It can even be said to represent an extremely important component in identifying economic conditions and provides the possibility of defining adequate strategies. In this article, the evaluation of the member countries of the European Union was carried out on the basis of the logistics performance index (LPI) according to the latest report of the World Bank (WB). A unique and original Multiple-Criteria Decision Making (MCDM) approach has been created, and it involves the application of four methods: Criteria Importance Through Intercriteria Correlation, Method based on the Removal Effects of Criteria, and Entropy and Fuzzy ROV (Range of Value). The weighting coefficients of six factors were obtained with the first three methods in crisp form, so they were converted into Triangular Fuzzy Number. The Fuzzy ROV method has been created for the first time in the literature and represents a great contribution from the methodological aspect. The results of the developed model and the applied steps show that there are certain differences in the rankings compared to the World Bank report, with a note that the best-ranked countries have maintained their positions. In addition, verification tests of the originally obtained results were created, with an emphasis on the importance of evaluation parameter values and their impact on the LPI ranking.

Correcting the Cognitive Bias for Commuting Time to Relieve the Driving Stress Level in Snow Weather Condition: A Naturalistic Driving Study in Harbin, China

As a negative emotion, professional drivers’ stress levels significantly affected driving behavior and thus were related to driving safety issues. Nevertheless, current evidence fell considerably short of explaining whether and why private drivers’ stress levels might be influenced while commuting driving in a specific scenario and how to relieve their stress levels. This study aimed to identify and analyze the contributing factors of the drivers’ stress levels while commuting driving in various scenarios (clear or snow weather conditions). On weekdays between 1st October 2020 and 31st January 2022, the questionnaire data from a sample of 985 private drivers were collected from six different locations of business districts in Harbin, China. Based on the naturalistic driving study (NDS) database, a 7-item questionnaire was designed for participants to self-report their driving stress levels in various scenarios, which was generated from the shortened and adapted version of the Perceived Stress Scale (PSS). The results showed that participants’ stress levels had significantly increased in snow weather conditions, especially nervous and stressed feeling, and unable to control the arrival time, which indicated that participants’ highly increased cognitive bias for commuting time could be the critical reason. The results of hierarchical linear regression models indicated that overall stress scores could be predicted through participants’ sociodemographic characteristics, driving experience, commuting driving, and cognitive bias for commuting time. Such an association was significantly strongest with commuting time gaps, especially in snow weather conditions. In addition, a recommendation was derived from these results that correcting the cognitive bias for commuting time could relieve participants’ stress levels. The implication of the reminder message supported this recommendation. The participants’ stress levels were reduced significantly after providing a reminder message every 10 mins while commuting driving in clear weather conditions and every 5 mins in snow weather conditions.

Healthcare Workers’ General Health and Its Relation with Anxiety, Anger, and Posttraumatic Stress Disorder during COVID-19 Outbreak in Southeast Iran

Background. Healthcare workers during the COVID-19 pandemic played an effective role in providing preventive and curative measures. Scientific evidence confirmed that the outbreak of this disease has caused numerous psychological problems such as pre- and posttraumatic stress disorder (PTSD), anxiety, depression, and anger at a global level. This study is aimed at investigating the general health of healthcare workers and its relationship with anxiety, anger, and posttraumatic stress disorder during the outbreak of COVID-19. Methods. This research was a cross-sectional study conducted on 455 healthcare workers of four teaching hospitals in Kerman, southeast of Iran. The convenience sampling method was used. The research tools included the 12-Item General Health Questionnaire (GHQ-12), the Impact of Event Scale-Revised (IES-R), the trait anxiety section of State-Trait Anxiety Inventory (STAI), and the trait anger section of Trait-State Anger Expression Inventory 2 (STAXI-2). Data analysis was done in IBM SPSS Statistics version 25 and using Pearson’s correlation tests, independent t-test, ANOVA, and multivariate linear regression. Results. 28.1% (n=128) of the participants had mental health disorders. The mean score of anxiety was 43.99±9.24 which was at moderate to high level of anxiety. The mean score of anger was 18.65±5.72 which was at the moderate level. The mean score of PTSD was 34.77±15.24 which was less than the midpoint of the questionnaire, i.e., the score of 44. The results of the multiple linear regression model that showed anxiety, anger, gender, and hospital were predictors of mental health (P<0.05). Conclusion. The results of the present study showed that about a quarter of the healthcare workers faced with the COVID-19 pandemic had mental health disorders and suffered from moderate to high anxiety, moderate anger, and PTSD. It was also observed in this study that there was a weak to moderate significant correlation between general health and anxiety, anger, and PTSD in healthcare workers. So, healthcare workers need a lot of social and psychological support.

Childhood maltreatment is longitudinally associated with cardiometabolic biomarkers through marital quality: Do health locus of control and eating habits matter?

Childhood maltreatment is longitudinally associated with cardiometabolic biomarkers through marital quality: Do health locus of control and eating habits matter?

Targeted knockdown of PGAM5 in synovial macrophages efficiently alleviates osteoarthritis

Osteoarthritis (OA) is a common degenerative disease worldwide and new therapeutics that target inflammation and the crosstalk between immunocytes and chondrocytes are being developed to prevent and treat OA. These attempts involve repolarizing pro-inflammatory M1 macrophages into the anti-inflammatory M2 phenotype in synovium. In this study, we found that phosphoglycerate mutase 5 (PGAM5) significantly increased in macrophages in OA synovium compared to controls based on histology of human samples and single-cell RNA sequencing results of mice models. To address the role of PGAM5 in macrophages in OA, we found conditional knockout of PGAM5 in macrophages greatly alleviated OA symptoms and promoted anabolic metabolism of chondrocytes in vitro and in vivo. Mechanistically, we found that PGAM5 enhanced M1 polarization via AKT-mTOR/p38/ERK pathways, whereas inhibited M2 polarization via STAT6-PPARγ pathway in murine bone marrow-derived macrophages. Furthermore, we found that PGAM5 directly dephosphorylated Dishevelled Segment Polarity Protein 2 (DVL2) which resulted in the inhibition of β-catenin and repolarization of M2 macrophages into M1 macrophages. Conditional knockout of both PGAM5 and β-catenin in macrophages significantly exacerbated osteoarthritis compared to PGAM5-deficient mice. Motivated by these findings, we successfully designed mannose modified fluoropolymers combined with siPGAM5 to inhibit PGAM5 specifically in synovial macrophages via intra-articular injection, which possessed desired targeting abilities of synovial macrophages and greatly attenuated murine osteoarthritis. Collectively, these findings defined a key role for PGAM5 in orchestrating macrophage polarization and provides insights into novel macrophage-targeted strategy for treating OA.

Linking direct rainfall hydrodynamic and fuzzy loss models for generating flood damage map

ABSTRACT This research work proposes a combined method for mapping flood loss in catchment scale in which direct rainfall modelling and fuzzy approach are linked. The direct rainfall modelling was carried out using HEC-RAS 2D in which rainfall event hyetograph was defined as the boundary condition, and infiltration layer and roughness layer were other main inputs of the model. The fuzzy loss model was developed to assess direct-tangible damages of the flood in which expert opinions were applied to generate verbal fuzzy rules of flood loss. In this model, depth and velocity are inputs and normalized flood loss (between 0 and 1) is output. The results of the direct rainfall model and the fuzzy loss model were combined to generate loss map using python scripting in geographical information system. The output of direct rainfall model was verified based on recorded depths at downstream hydrometric station in which the Nash–Sutcliffe efficiency (NSE) and root mean square error (RMSE) were applied as the evaluation indices. Due to acceptability of indices (NSE = 0.75, RMSE = 0.83 m), the direct rainfall model was reliable. Maximum flood loss was 0.91 in the case study. Using the proposed approach is recommendable for to improve flood damage assessment in the catchments.

Optimal intestinal graft selection for reconstruction of extended ureteral stricture: an animal model study

Introduction. The prevalence of patients with extended ureteral strictures has been steadily increasing over the past decades. When it is not possible to perform reconstruction with the tissues of the urinary system, the best solution is intestinal replacement plasty. However, despite the great world experience, the search for the optimal method of intestinal reconstruction does not stop, which determines the relevance of conducting an experimental prospective study.Objective. To evaluate morphological changes in renal parenchyma after ureteral replacement reconstruction by segments of small and large intestine.Materials & methods. Fifteen white giant rabbits (6-months-old, weighing 5 kg) were divided into three groups. Group A (n = 5) underwent small intestine [ilealplasty] replacement plasty. Group B (n = 5) double-flap ileoplasty using the Yang-Monti technique. Group C (n = 5) – large intestine [coloplasy] ureteral reconstruction. Before the operation and before withdrawal from the experiment, the level of creatinine and electrolytes was assessed, kidney ultrasound was performed on days 2, 5, 10. Excretory urography was performed to assess the patency of the anastomoses. Animals were withdrawn from the experiment from day 10 to 30. The material used for morphological study included kidneys from the operated and intact sides, proximal and distal anastomoses.Results. No increase in creatinine level, metabolic disorders were detected in animals. According to ultrasound data, hydronephrosis developed in all animals on day 2 and remained unchanged during the entire follow-up. Group A: no complications were detected. Histologically, the renal parenchyma showed moderate signs of chronic inflammation, single foci of lymphoid infiltration, but there were no irreversible processes in the form of necrosis and sclerosis. Group B: complications — stricture in the anastomosis area of the detubularised fragments and necrosis of the small intestine due to compression of the mesentery by the graft vascular stem. Histologically there were marked dilatation of the tubules at all levels, enlargement of the Bowman-Shumlansky capsule, as well as signs of moderate inflammatory process, there were hydropic and hyaline-droplet dystrophy, tubular necrosis foci. Group C: complications — accumulation of a significant amount of grit, mucus, and fibrin in the colocystoanastomosis area. Histologically, the renal parenchyma showed a marked inflammatory process, in particular, purulent inflammation with demarcation zones, infiltration with polymorphonuclear leukocytes and bacterial cells, foci of necrosis and sclerosis of both stroma and tubules, dilatation of tubules and Bowman-Schumlansky capsules.Conclusion. The use of an unchanged ileal-graft reconstruction of an extended ureteral defect showed acceptable histological results in an animal model, which confirms the feasibility of its use in clinical practice. The absence of foci of necrosis and fibrosis in the renal parenchyma indicates the preserved functional potential, which suggests the stabilization of renal function in the long term.

Analysis of following vehicle driver injury severity in rear-end collision on straight road based on LightGBM and SHAP

Rear-end accidents, as one of the common accident types, may cause serious injuries to the driver and passengers. There are mutual coupling effects among causal features of rear-end accidents, and traditional analytical methods may lead to modeling distortion due to assumption constraints. In this study, the Light Gradient Boosting Machine (LightGBM), a machine learning algorithm, is used to model the injury severity of the following vehicle driver in a rear-end crash accident on a straight road, based on the data from the China In-depth Accident Study data. The Shapley Additive Explanations (SHAP) method was used to interpret the results of the LightGBM model and to analyze the relationship between factors and driver injury severity. The results show that location familiarity, willingness to take risks, and driving time have a significant impact on the injury severity for following vehicle drivers involved in rear-end crashes. Cloudy conditions increase a driver’s risk of being involved in a fatal or injured rear-end collision. In rain, snow, hail, and foggy conditions, have a higher propensity to cause driver fatalities in the crash event. The rear-end crash of passenger cars resulted in a higher death probability of the following vehicle driver compared to sedan and truck, and female drivers are more likely to be involved in uninjured accidents compared to males. These results are informative for preventing rear-end accidents and reducing the extent of accidental injuries.

Biodirected Screening and Preparation of Larimichthys crocea Angiotensin-I-Converting Enzyme-Inhibitory Peptides by a Combined In Vitro and In Silico Approach.

Food-derived angiotensin-I-converting enzyme (ACE)-inhibitory peptides have gained attention for their potent and safe treatment of hypertensive disorders. However, there are some limitations of conventional methods for preparing ACE-inhibitory peptides. In this study, in silico hydrolysis, the quantitative structure-activity relationship (QSAR) model, LC-MS/MS, inhibition kinetics, and molecular docking were used to investigate the stability, hydrolyzability, in vitro activity, and inhibition mechanism of bioactive peptides during the actual hydrolysis process. Six novel ACE-inhibitory peptides were screened from the Larimichthys crocea protein (LCP) and had low IC50 values (from 0.63 ± 0.09 µM to 10.26 ± 0.21 µM), which were close to the results of the QSAR model. After in vitro gastrointestinal simulated digestion activity of IPYADFK, FYEPFM and NWPWMK were found to remain almost unchanged, whereas LYDHLGK, INEMLDTK, and IHFGTTGK were affected by gastrointestinal digestion. Meanwhile, the inhibition kinetics and molecular docking results were consistent in that ACE-inhibitory peptides of different inhibition forms could effectively bind to the active or non-central active centers of ACE through hydrogen bonding. Our proposed method has better reproducibility, accuracy, and higher directivity than previous methods. This study can provide new approaches for the deep processing, identification, and preparation of Larimichthys crocea.

Experimental and numerical study of the gated and ungated ogee spillway

This study was carried out by combining numerical modeling and experimental measurements to investigate the hydraulic characteristics of ungated and gated ogee spillways with high head ratios. The primary objective was to validate the use of a numerical model as a complementary approach to the experimental model for simulating the hydraulic behavior of these spillways, providing a more comprehensive understanding of their hydraulic properties under varying conditions of head ratios and relative gate openings. An Acoustic Doppler Velocimeter (ADV) was used to measure the vertical flow velocity distributions, and ultrasonic sensor wave gauges were used to obtain the time history of the water level. The results of the measurements were compared with the simulation results using a model fitted with three different turbulence models (realizable k-ε, RNG k-ε, k-ω SST). The numerical model was developed using OpenFOAM. With respect to the ungated spillway, three different head ratios ranging from 1.4 to 4.6, which correspond to high head ratios, were investigated. Similarly, three different relative gate openings ranging from 0.5 to 2 were investigated for the gated spillway. The results of water surface profiles and velocity profiles suggest that the numerical and experimental models achieve a good agreement for sections located further away from the spillway. For the ungated spillway, the simulation results for the near-spillway sections are enhanced when the head ratio increases. Considering the velocity profiles and error analysis, the realizable k–ε model was found to best predict the results of the experimental model. A discussion about the discharge equation, velocity fields, pressure fields, and the corner separation zone is also included in this study.

Research on Electric Oil-Pneumatic Active Suspension Based on Fractional-Order PID Position Control.

In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. An FOPID algorithm is used to control the motor's rotational angle to realize the damping adjustment of the suspension system. In this process, the road roughness is collected by the sensors as the criterion of damping adjustment, and the particle swarm algorithm is utilized to find the optimal objective function under different road surface slopes, to obtain the optimal cornering value. According to the mathematical and physical model of the suspension system, the simulation model and the corresponding test platform of this type of suspension system are built. The simulation and experimental results show that the simulation results of the fractional-order nonlinear suspension model are closer to the actual experimental values than those of the traditional linear suspension model, and the accuracy of each performance index is improved by more than 18.5%. The designed active suspension system optimizes the body acceleration, suspension dynamic deflection, and tire dynamic load to 89.8%, 56.7%, and 73.4% of the passive suspension, respectively. It is worth noting that, compared to traditional PID control circuits, the FOPID control circuit designed for motors has an improved control performance. This study provides an effective theoretical and empirical basis for the control and optimization of fractional-order nonlinear suspension systems.

An Intelligent Health Surveillance System: Predictive Modeling of Cardiovascular Parameters through Machine Learning Algorithms Using LoRa Communication and Internet of Medical Things (IoMT)

In several nations, the majority of heart attacks lead to fatality prior to patients receiving any kind of medical intervention. The traditional healthcare system is mostly passive, requiring patients to initiate contact with healthcare services independently. People often do not request the treatment if they are unconscious during a heart disease episode. The use of Internet of Medical Things (IoMT) methods offers significant advantages in addressing the issue of caring for patients with cardiac problems. These techniques may transform service delivery into ubiquitous and activate healthcare services. Low-cost remote monitoring systems are essential to implementing a widespread healthcare service. In this article, we proposed a cost-effective Personal Health Care Device(PHCD) based on the Internet of Things (IoT). The PHCD transmits user somatic signals to data acquisition devices using a LoRa (Long-range and low-power) wireless communication network. The received data is uploaded to the cloud using IoT platforms like Adafruit IO. Further, various Machine learning (ML) algorithms, Naïve Bayes, ANN, CNN, and LSTM, were applied to collected data to predict heart rate and SpO2 behavior. The performance results of different forecast models were compared to identify precise modeling and reliable forecasts to prevent emergency cardiovascular conditions.

Cities and Urbanization: Balancing the Environmental and Socioeconomic Dimensions of Sustainability

AbstractWith an increasingly urbanized world, there is an urgent need to examine how cities may evolve and achieve sustainability. This paper systematically looks at the Greater Bay Area (GBA) and the Poyang Lake Region (PLR) in China to examine the spatial processes for insights into cities and urbanization, balancing the environmental and socio‐economic dimensions. A total of 226 805 cells are analyzed to unveil the relationship between sustainability changes in 2015–2019 period and urban form indicators, considering sociodemographic variables, geographical features, and city size as control variables. Two tree‐based machine learning models (Random Forest and XGBoost) are developed. This study provides evidence that a monocentric urban form and a high share of small activity clusters are not good for sustainability. For each urban form indicator, there is a non‐linear relationship with sustainability. The results of the machine learning models reconfirm the sustainability benefits of having a strong second activity cluster comparable to the largest one. When planning cities, some forms of land use buffering are desirable. There is also support for developing relatively large activity nodes and promoting compactness in urban form. Beyond urban form characteristics, the levels of urbanization, economic development, and population are still highly relevant.

Increasing abdominal aortic aneurysm curvature visibility using 3D dual probe bistatic ultrasound imaging combined with probe translation

High frame rate ultrasound (US) imaging techniques in 3D are promising tools for capturing abdominal aortic aneurysms (AAAs) over time, however, with the limited number of channel-to-element connections current footprints are small, which limits the field of view. Moreover, the maximal steering angle of the ultrasound beams in transmit and the maximal receptance angle in receive are insufficient for capturing the curvy shape of the AAA. Therefore, an approach is needed towards large arrays. In this study, high frame rate bistatic 3D US data (17 Hz) were acquired with two synchronized matrix arrays positioned at different locations (multi-aperture imaging) using a translation stage to simulate what a larger array with limited channel-to-element connections can potentially achieve. Acquisitions were performed along an AAA shaped phantom with different probe tilting angles (0 up to ± 30°). The performance of different multi-aperture configurations was quantified using the generalized contrast-to-noise ratio of the wall and lumen (gCNR). Furthermore, a parametric model of the multi-aperture system was used to estimate in which AAA wall regions the contrast is expected to be high. This was evaluated for AAAs with increasing diameters and curvature. With an eight-aperture 0° probe angle configuration a 69 % increase in field of view was measured in the longitudinal direction compared to the field of view of a single aperture configuration. When increasing the number of apertures from two to eight, the gCNR improved for the upper wall and lower wall by 35 % and 13 % (monostatic) and by 36 % and 13 % (bistatic). Contrast improvements up to 22 % (upper wall) and 12 % (lower wall) are achieved with tilted probe configurations compared to non-tilted configurations. Moreover, with bistatic imaging with tilted probe configurations gCNR improvements up to 4 % (upper wall) and 7 % (lower wall) are achieved compared to monostatic imaging. Furthermore, imaging with a larger inter-probe distance improved the gCNR for a ± 15° probe angle configuration. The gCNR has an expected pattern over time, where the contrast is lower when there is more wall motion (systole) and higher when motion is reduced (diastole). Furthermore, a higher frame rate (45 Hz) yields a lower gCNR, because fewer compound angles are used. The results of the parametric model suggest that a flat array is suitable for imaging AAA shapes with limited curvature, but that it is not suitable for imaging larger AAA shapes with more curvature. According to the model, tilted multi-aperture configurations combined with bistatic imaging can achieve a larger region with high contrast compared to non-tilted configurations. The findings of the model are in agreement with experimental findings. To conclude, this study demonstrates the vast improvements in field of view and AAA wall visibility that a large, sparsely populated 3D array can potentially achieve when imaging AAAs compared to single or dual aperture imaging. In the future, larger arrays, less thermal noise, more steering, and more channel-to-element connections combined with carefully chosen orientations of (sub-) apertures will likely advance 3D imaging of AAAs.

Wind resistance performance analysis of metal roof system of the long-span integrated photovoltaic building

Wind resistance is an important factor in the operation of Building Integrated Photovoltaic (BIPV) systems, especially for long-span roofs, where lifting of the roof can result in significant economic losses. This paper proposes a periodic boundary numerical simulation method for long-span metal roof systems to address the problem of meshes and contact pairs in the numerical simulation of wind resistance. In order to calibrate the BIPV metal roof's material parameters, indoor tensile test was conducted, and the results of the global metal roof model were compared with those of the single metal roof model with periodic boundaries for verification of periodic boundary efficiency and accuracy. The wind-resistant capacity of laboratory and long-span sizes for the BIPV metal roof system are compared, and a conversion formula between them is proposed to enable the rapid calculation from the laboratory to long-span roof system. The results show that the periodic boundary is suitable for the analysis of wind resistance of the long-span BIPV metal roof. Wind resistance of the long-span BIPV metal roof system is 1.77 kPa, whereas that of the laboratory size is 4.50 kPa. According to the proposed formula, the wind-resistant capacity of the long-span metal roof can be converted from the laboratory test, which can provide technical support for the design of BIPV systems.

Are Chinese live hog futures useful hedging tools?

ABSTRACT China’s newly listed live hog futures provide a standardized risk management tool for the hog market. Our paper provides the first systematic and quantitative evaluation of live hog futures’ hedging ability based on various traditional static and dynamic hedging models and an innovative quantile hedging model. Consistent results confirm that live hog futures are useful hedging tools. Specifically, the in-sample results of multiple traditional models show that the hedging ratios are around 0.4 and hedging effectiveness is 0.13. Out-of-sample hedging effectiveness increases significantly to 0.22. Quantile hedging results show more significant risk-management ability in bearish and average futures markets. We further compare live hog futures’ hedging effectiveness and market performance to other commodities. The comparison shows that the live hog futures market has modest hedging effectiveness and underperforms in liquidity, volatility, and basis. Following the empirical results, it is recommended to encourage hedging transactions, simplify trading processes, and enable night trading to enhance liquidity, lower volatility, and eventually improve hedging ability.