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Traditional physical methods for measuring solubility were often affected by viscosity, density, and surface tension. In this study, we selected appropriate electrolytes to determine the solubility of impurities in the lead-bismuth circuit at reactor operating temperatures using electrochemical methods. Taking the measurement of the solubility of impurity Ce in liquid Bi as an example, we demonstrated that electrochemical methods can accurately determine its solubility in the LiCl-KCl molten salt. Furthermore, the successful measurement of other impurities such as La, Ce, U, and Mn in lead-bismuth eutectic (LBE) using the designed system has confirmed the universality of this method.

Piezocatalysis has emerged as a promising environmental remediation technique, and the exploration of environmentally friendly and high-performance piezocatalysts is crucial for their practical applications. In this work, the bismuth sodium titanate (Na[Formula: see text]Bi[Formula: see text]TiO3 (NBT)) exhibited efficient piezocatalytic activity toward typical organic pollutants degradation, including acid orange 7, methylene blue, rhodamine B and methyl orange. Notably, rhodamine B was degraded by 98.1% within 30[Formula: see text]min with a reaction rate constant of 0.130[Formula: see text]min[Formula: see text]. Furthermore, the NBT achieved a hydrogen peroxide production efficiency of 538[Formula: see text][Formula: see text]mol/g⋅h without the sacrificial agent, indicating that the NBT is a superior piezocatalyst for dye degradation and hydrogen peroxide generation. This work demonstrated that by using mechanical energy, the NBT can be used for degrading organic pollutants in wastewater and hydrogen peroxide generation.

The roofs of heritage buildings are subject to long-term degradation, resulting in poor heat insulation, heat regulation, and water leakage prevention. Researchers have predominantly employed feature-based traditional machine learning methods or individual deep learning techniques for the detection of natural deterioration and human-made damage on the surfaces of heritage building roofs for preservation. Despite their success, balancing accuracy, efficiency, timeliness, and cost remains a challenge, hindering practical application. The paper proposes an integrated method that employs a convolutional autoencoder, thresholding techniques, and a residual network to automatically detect anomalies on heritage roof surfaces. Firstly, unmanned aerial vehicles (UAVs) were employed to collect the image data of the heritage building roofs. Subsequently, an artificial intelligence (AI)-based system was developed to detect, extract, and classify anomalies on heritage roof surfaces by integrating a convolutional autoencoder, threshold techniques, and residual networks (ResNets). A heritage building project was selected as a case study. The experiments demonstrate that the proposed approach improved the detection accuracy and efficiency when compared with a single detection method. The proposed method addresses certain limitations of existing approaches, especially the reliance on extensive data labeling. It is anticipated that this approach will provide a basis for the formulation of repair schemes and timely maintenance for preventive conservation, enhancing the actual benefits of heritage building restoration.

This study examines the overall needs of the green construction scheme with ‘carbon neutrality’ as the centre in the Zhejiang provincial green development target area. By aggregating and organising the construction and development data of Zhejiang Province, the entropy weight TOPSIS model is formed according to the statistical modelling for quantitative examination of the data, and the scientific assessment scheme of ‘carbon neutrality’ in the regional construction industry of Zhejiang Province is developed. This study aids in completely exhibiting and dynamically understanding the advancement of the ‘carbon neutral’ capacity of the urban construction industry. The objective is to discover the weak link in the advancement of carbon neutrality in several regional construction industries, which is of great relevance for further examining and forecasting the strategic outlook of carbon neutrality and modifying the planning of carbon neutrality strategy in special regional construction industries.

Dual-mode imaging combining photoacoustic and ultrasound modalities holds great promise for comprehensive tissue characterization. In traditional dual-mode imaging systems, however, the opaque ultrasound transducers are integrated in complex optical and ultrasound paths, leading to compromises in imaging efficiency and sensitivity. To address these challenges, we design and produce highly transparent Pb(Mg,Nb)O3PbTiO3 ceramics with exceptional transparency of 68%, alongside a remarkable ultrahigh piezoelectric d33 of 1500 pC/N. Leveraging this material, we fabricate a high-sensitivity transparent piezoelectric ultrasound transducer (TPUT) with a center frequency of 16 MHz and a bandwidth of 30%, which seamlessly integrating ultrasound and photoacoustic capabilities into one system to achieve simultaneous photoacoustic/ultrasound dual-mode imaging, showcasing a remarkable sensitivity for deep-tissue detection (e.g., 7.5 mm thick chicken breast meat). Additionally, in vivo photoacoustic imaging of subcutaneous microvasculature in a mouse ear was successfully achieved via 28 MHz TPUT. Our innovation not only advances imaging performance but also offers a cost-effective solution, paving the way for transformative biomedical imaging applications.

Walnut shell breaking is an important aspect of post-harvest processing. However, shell-breaking results differ significantly from ideal uniaxial compression results. In this paper, the effect of multipoint loading on the walnut shell-breaking mechanism by combining FDEM with cohesive elements was investigated. The model for walnuts was created using micro-X-ray CT scans and experimental data. Simulation parameters describing mechanical properties were calibrated through modeling the uniaxial compression experiment. The loading point locations were quantitatively described in terms of top and azimuthal angles. The results show that the crack ex-tension rate increases with the number of loading points and the singularity value, and the maximum value of the four-point loading speed is about 450 m/s. The number of cracks is only related to the number of loading points and is not related to the location of the loading point. In addition, three loading methods are proposed, and the fastest crack propagation rate is achieved when the azimuthal angle of four-point loading is 0, 150, 180, and 330, respectively. These findings may serve as a theoretical foundation for studying shell crushing.

By adopting a systematic literature and science mapping review, this paper aims to explore the impact of artificial intelligence (AI) on organizational justice and project performance. A total of 47 bibliographic records from the Scopus database were analyzed. The results revealed the annual publication trends of research articles and relevant peer-reviewed journals in the studied domain. It was found that while AI technology has made significant progress in several fields, its application areas in project management and organizational justice are still relatively low. Moreover, it objectively discussed the co-occurrence analysis of keywords, co-authors, countries/regions, and documents in the fields, revealing the current research topics. The main research topics include the (1) AI’s influence on organizational justice, decision analysis, and digital transformation, (2) fostering organizational justice and AI’s role in enhancing project performance, and (3) improving organizational performance approaches. Furthermore, this paper proposed research gaps and future research directions, including (1) advancing business intelligence strategies, (2) unlocking AI technology potential on organizational justice and project performance, (3) the adaption of cultural, diversity, environmental, and social factors, (4) the impact of AI on complex and challenging leadership styles, and (5) developing a comprehensive understanding of the agile framework. The findings of this paper could contribute to a better understanding of how AI shapes project/construction management and organizational justice, providing practical solutions for innovative development for researchers and policymakers.

To understand the moisture transfer mechanism of walnuts during the combination of hot air (HA) and microwave–vacuum (MV) drying (HA-MVD) process, the drying characteristics and moisture diffusion characteristics of walnut during HA-MVD were investigated. The results indicated that the HA-MVD of walnuts occurred mainly in the falling-rate stage. The value of effective moisture diffusivity (Deff) dropped continuously with the decrease in moisture content (MC) during the HA drying, while switching to MV drying could truncate the decrease in Deff and still maintain a high value until the end of drying. The HA temperature, MC of the transition point, microwave power, and MV thermostatic temperature have significant effects on the moisture diffusion characteristics of walnuts. The values of Deff for walnuts ranged from 2.33 × 10−9 m2/s to 6.89 × 10−8 m2/s. The third-order polynomial prediction model of Deff related to the sample MC and drying conditions was established to describe the dynamic change in the Deff of walnuts during the HA-MVD process. The application of MVD in the final stage of drying could rapidly increase the internal vapor pressure of the walnuts, accelerate the diffusion speed of the internal moisture, and re-enhance the drying rate. The findings have practical value for the development of efficient and energy-saving drying methods in the walnut industry.

This study was conducted to investigate the drying kinetics and internal and external mass transfer characteristics of walnuts for an understanding of the drying mechanism. The drying characteristics, mass transfer characteristics, and color of walnut during hot air drying (HAD) were investigated under different initial moisture content (IMC) (0.35, 0.39, and 0.43 g water/g wet mass) and drying temperatures (50, 60, 70, and 80 °C). The results indicated that the IMC and drying temperature both have significant effects on the drying process of walnut, showing the higher the IMC, the longer the preheating time, the smaller the effective moisture diffusivity (Deff) and mass transfer coefficient (hm), and the longer the drying time, but reverse results for drying temperature. The values of Deff and hm for walnut ranged from 4.94 × 10−10 to 1.44 × 10−9 m2/s and 1.24 × 10−7 to 3.90 × 10−7 m/s, respectively. The values of activation energy for moisture diffusion and mass transfer ranged from 21.56 to 23.35 kJ/mol and 28.92 to 33.43 kJ/mol, respectively. Multivariate linear prediction models were also established for estimating the Deff and hm as a function of the HAD process parameters. The drying temperature has a greater effect on the walnut kernel lightness than the IMC. The Verma et al model could be used to describe the HAD process of the walnut. The findings contribute to the understanding of moisture transfer mechanisms in walnuts and have practical value for the evaluation and improvement of drying systems.

The detection method for technological parameter is outdates as the traditional test cycle is long as well as the measurement error and the test amount are huge. Moreover, it is difficult to disclose the operation mechanism of devices as the operation is time-consuming and laborious. Therefore, numerical simulation was used in this study to reveal the mechanism of the walnut shell-kernel winnowing device. Moreover, the influence of baffle opening combinations, inlet wind velocity and inlet angle on cleaning rate and loss rate was predicted by the neural network model. The results demonstrated that inlet wind velocity was the primary influencing factor of cleaning rate, followed by baffle opening and inlet angle. Besides, inlet wind velocity was the primary influencing factor of loss rate, followed by inlet angle and baffle opening. The winnowing device performed best (79.91% cleaning rate, 14.37% loss rate) when the baffle opening, inlet wind velocity and inlet angle were 7.01 cm, 24.36 m/s, and 9.47°. In addition, 1/8 walnut shells and 1/4 walnut kernels were incorrectly classified due to the increase in inlet wind velocity. The inlet wind velocity was considered the major cause behind the deteriorating winnowing performance of the device. Finally, the bench test and simulation optimization results were compared. The cleaning rate and loss rate relative error during the simulation test was lower than 1.06%, which ascertained the feasibility and validity of the neural network as well as the combined numerical simulation method. This study could be useful for future research and development of shell-kernel winnowing devices for hard nuts.

The conditioning of moisture content is an effective way to improve walnut-crushing performance. In this study, firstly, walnuts with different moisture contents were used to conduct the crushing experiments. The distributions of fragment sizes of shells and kernels with different moisture contents were analyzed by an image processing and sieving method, respectively. The results show that moisture content significantly affects the fragmentation degree of the shell and kernel, as shown by the differences in the fractal dimensions of shell fragments and the average fragment size of kernel fragments. The critical moisture contents of the shells and kernels, corresponding to the specific states of broken shells and whole kernels, were determined. Then, taking the critical moisture content as the target moisture content, the combination of hot air and microwave vacuum drying applied to dry walnuts was presented and the process parameters were optimized. Optimized process parameters include a hot air temperature of 63.23 °C, a moisture content of the transition point of 24.88%, microwave power of 588.24 W and a microwave vacuum thermostatic temperature of 49.01 °C. At this point, the unit time drying rate, unit mass drying energy consumption, mean square error of moisture content and kernel color parameters were 0.215%/min, 3.03 kW·h/kg, 2.93% and 6.42, respectively. It was confirmed that drying the walnuts to a critical moisture content using an optimized process could both maximally improve the crushing performance and significantly reduce the drying time. The findings provide important information for conditioning the crushing state of walnuts and improving crushing performance and have practical value for the improvement of drying systems.

Clarifying the dissipated energy required for crack expansion is an effective way to control material crushing. Therefore, based on the material fracture probability model and fractal theory, the energy range required for crack extension was determined, and the morphology of the cracks was quantified. This study investigates the influence of walnut size on crack propagation characteristics; this includes its effects on the crack initiation threshold energy, representing resistance to crack initiation, and the crack bifurcation threshold energy, representing resistance to crack bifurcation. The results show that crack extension has a well-defined threshold energy below which cracks do not initiate or bifurcate. The size of walnuts significantly impacts crack propagation characteristics, showing that both crack initiation threshold energy and crack bifurcation threshold energy decrease with increasing walnut sizes. In addition, there is a positive correlation function between the crack initiation threshold energy and the crack bifurcation threshold energy. The experimental results can offer fresh insights into material fracture prediction and serve as a reference for numerical simulations.

Walnut shell–kernel separation is an essential step in the deep processing of walnut. It is a crucial factor that prevents the increase in the added value and industrial development of walnuts. This study proposes a walnut shell–kernel detection method based on YOLOX deep learning using machine vision and deep-learning technology to address common issues, such as incomplete shell–kernel separation in the current airflow screening, high costs and the low efficiency of manually assisted screening. A dataset was produced using Labelme by acquiring walnut shell and kernel images following shellshock. This dataset was transformed into the COCO dataset format. Next, 110 epochs of training were performed on the network. When the intersection over the union threshold was 0.5, the average precision (AP), the average recall rate (AR), the model size, and floating point operations per second were 96.3%, 84.7%, 99 MB, and 351.9, respectively. Compared with YOLOv3, Faster Region-based Convolutional Neural Network (Faster R-CNN), and Single Shot MultiBox Detector algorithms (SSD), the AP value of the proposed algorithm was increased by 2.1%, 1.3%, and 3.4%, respectively. Similarly, the AR was increased by 10%, 2.3%, and 9%, respectively. Meanwhile, walnut shell–kernel detection was performed under different situations, such as distinct species, supplementary lighting, or shielding conditions. This model exhibits high recognition and positioning precision under different walnut species, supplementary lighting, and shielding conditions. It has high robustness. Moreover, the small size of this model is beneficial for migration applications. This study’s results can provide some technological references to develop faster walnut shell–kernel separation methods.

This study designed two levels of quantitative fertilizer distribution to investigate precision fertilization applications in orchards in South Xinjiang, China, which have vast rows and narrow plant spaces. The machine comprised a base frame, a ditching device, a fertilizing apparatus and an earth-covering device. The design parameters of the flow stabilization screw, conveyor screw and single-ring fertilizer quantity were summarised using theoretical analysis. The single-ring fertilizer quantity of the conveyor screw was verified via an experiment by combining EDEM software. Three-factor and three-level Box–Behnken tests were conducted using the spiral rotation speed of the conveyor, advancing of the speed and the opening degree of fertilizer outlet as the test factors and using the coefficient of variation (CV) of uniformity as the test index—thus obtaining the optimal working parameters. The simulation test results revealed that the single-ring fertilizer quantity of the fertilizing apparatus was 145.6 g, fulfilling the design requirements. The prototype testing results showed that the CV of uniformity was 6.521% when the spiral rotation speed of the conveyor, the opening degree of the fertilizer outlet and the advancing speed were 66 RPM, 42% and 2.7 km/h—thus meeting the needs of precision fertilization operations. The two designed levels of the quantitative fertilizer distributors were applied to fertilization processes in orchards with wide-row spaces and narrow plant spaces in South Xinjiang, China and were able to effectively carry out the precision fertilization applications. These data could also provide references for the optimization of spiral quantitative fertilizer distributors.

With the increasing application of lithium-ion batteries, accurate estimation of the state of health (SOH) of lithium-ion batteries is of great significance for the safe operation of lithium-ion battery systems and the reduction of operation and maintenance costs. The complex physical and chemical reactions inside the lithium battery and the complex external working conditions make it challenging to achieve an accurate health-state estimation and life prediction. Therefore, the accurate estimation of the SOH of lithium-ion batteries is an important issue. At present, electrochemical impedance spectroscopy (EIS) is widely used in the study of battery-power impedance characteristics and battery-state estimation due to its advantage of nondestructive measurement. For this reason, this paper summarizes the research progress of lithium-ion SOH estimation based on EIS in recent years and details it layer by layer, mainly from two aspects: first, the quantitative relationship model between the characteristic parameters and SOH is established by constructing a frequency domain-equivalent circuit model. Secondly, we construct a quantitative relationship model between EIS data and SOH using the data-driven method. Finally, the advantages and disadvantages of different methods and estimation accuracy are analyzed and compared, and the future estimation of SOH based on EIS is prospected.

The treatment of complex cerebrovascular diseases (CCVDs) at the skull base, such as complex intracranial aneurysms, carotid-cavernous sinus fistulas, and intracranial artery traumatic injuries, is a difficult clinical problem despite advances in endovascular and surgical therapies. Covered stents or stent graft insertion is a new concept for endovascular treatment that focuses on arterial wall defect reconstruction, differing from endovascular lesion embolization or flow diverter therapies. In recent years, covered stents specifically designed for cerebrovascular treatment have been applied in the clinical setting, allowing thousands of patients with CCVDs to undergo intraluminal reconstruction treatment and achieving positive results, even in the era of flow diverters. Since there is no unified reference standard for the application of covered stents for treating CCVDs, it is necessary to further standardize and guide the clinical application of this technique. Thus, we organized authoritative experts in the field of neurointervention in China to write an expert consensus, which aims to summarize the results of covered stent insertion in the treatment of CCVDs and propose suitable standards for its application in the clinical setting. Based on the contents of this consensus, clinicians can use individualized intraluminal reconstruction treatment techniques for patients with CCVDs.

The pursuit of electrochemical energy storage has led to a pressing need on materials with high capacities and energy densities; however, further progress is plagued by the restrictive capacity (372 mAh g−1) of conventional graphite materials. Tungsten trioxide (WO3)-based anodes feature high theoretical capacity (693 mAh g−1), suitable potential, and affordable cost, arousing ever-increasing attention and intense efforts. Nonetheless, developing high-performance WO3 electrodes that accommodate lithium ions remains a daunting challenge on account of sluggish kinetics characteristics and large volume strain. Herein, the well-designed hierarchical WO3 agglomerates assembled with straight and parallel aligned nanoribbons are fabricated and evaluated as an anode of lithium-ion batteries (LIBs), which exhibits an ultra-high capacity and excellent rate capability. At a current density of 1,000 mA g−1, a reversible capacity as high as 522.7 mAh g−1 can be maintained after 800 cycles, corresponding to a high capacity retention of ∼80%, demonstrating an exceptional long-durability cyclic performance. Furthermore, the mechanistic studies on the lithium storage processes of WO3 are probed, providing a foundation for further optimizations and rational designs. These results indicate that the well-designed hierarchical WO3 agglomerates display great potential for applications in the field of high-performance LIBs.

The fourth basic circuit component, the memristor, has been proposed for a long time, but it is not mentioned in the experiment teaching system of Electrical and Electronic Technology. In this paper, an RC bridge oscillation chaotic circuit based on memristor is designed to solve this problem. The dynamical behavior of the circuit system is analyzed using Lyapunov exponents spectrum, bifurcation diagram, phase portrait and Poincaré map. A series of complex dynamical behaviors such as symmetric single-scroll coexistence, asymmetrical single-scroll coexistence, symmetric double-scroll coexistence and asymmetrical limit–cycle coexistence exist in the circuit system. This research plays a critical role in enriching students’ knowledge and improving the experiment teaching system of Electrical and Electronic Technology.

We have experimentally studied the fabrication of ridge waveguides in zinc sulfide (ZnS) crystal by femtosecond laser ablation combined with Kr8+ ion irradiation. At the wavelength of 4 μm, the waveguide at TE mode shows better guiding properties than TM mode. The transmission performance of the waveguide is improved by using thermal annealing technology to reduce the color centers and point defects in the waveguide. The waveguide propagation loss at TE mode at 4 μm wavelength is reduced to as low as 0.6 dB/cm after annealing. Raman spectroscopy shows that Kr8+ ion irradiation does not cause large lattice damage to ZnS crystal.

Safety risk identification throughout deep excavation construction is an information-intensive task, involving construction information scattered in project planning documentation and dynamic information obtained from different field sensors. However, inefficient information integration and exchange have been an important obstacle to the development of automatic safety risk identification in actual applications. This research aims to achieve the requirements for information integration and exchange by developing a semantic industry foundation classes (IFC) data model based on a central database of Building Information Modeling (BIM) in dynamic deep excavation process. Construction information required for risk identification in dynamic deep excavation is analyzed. The relationships among construction information are identified based on the semantic IFC data model, involved relationships (i.e., logical relationships and constraints among risk events, risk factors, construction parameters, and construction phases), and BIM elements. Furthermore, an automatic safety risk identification approach is presented based on the semantic data model, and it is tested through a construction risk identification prototype established under the BIM environment. Results illustrate the effectiveness of the BIM-based central database in accelerating automatic safety risk identification by linking BIM elements and required construction information corresponding to the dynamic construction process.

Generalized-regression-neural-network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) models were utilized to develop mathematical models for assessing the effects of harvest maturity and storage time on the storage quality of Korla fragrant pear. The results showed that fruit firmness had high correlations with harvest maturity and storage time. At different harvest maturities, the firmness of fragrant pear decreased with increasing storage time; while under the same storage time, a lower harvest maturity resulted in a faster decrease in the fruit firmness of fragrant pear. The GRNN and ANFIS models were validated to be reliable in predicting the variation tendency of fruit firmness of fragrant pear. In addition, the ANFIS model with generalized bell (gbellmf) (RMSE = 0.161; R2 = 0.98) was proved to be the optimum model. The findings of this study can provide reference for the research and practice of the storage of fragrant pear. [ABSTRACT FROM AUTHOR]

Strong converse magnetoelectric coupling was observed in a multiferroic heterostructure of Fe59.3Co28.0Hf12.7 film on (011) cut lead zinc niobate-lead titanate (PZN-PT) slab, which exhibited a large electric field (E-field) tunability of microwave magnetic properties. With the increase of E-field from 0 to 6 kV/cm on PZN-PT, the ferromagnetic resonance (FMR) field Hr shifts downwards by 430.7 Oe along [100] direction and upwards by 492.9 Oe along [100] direction of the PZN-PT. Accordingly, the strong magnetoelectric coupling led to a significantly enhanced self-biased FMR frequency from 4.2 to 7.9 GHz under zero bias magnetic field, and the magnetic damping constant α was decreased from 0.0260 to 0.0185 at the same time. These features demonstrate that this multiferroic laminate is promising in fabrication of E-field tunable microwave components. [ABSTRACT FROM AUTHOR]

A Fe0.7Co0.3-B multilayer was laminated by three Fe0.7Co0.3-B ferromagnetic sublayers prepared by composition gradient sputtering. Three Fe0.7Co0.3-B ferromagnetic sublayers have their individual directions of uniaxial magnetic anisotropy, and the easy magnetic axis of neighboring sublayer successively rotates 60° in the film plane. It is exciting that a quasi magnetic isotropy was achieved in the designed multilayer with a quasi-isotropic hysteresis loop and quasi-isotorpic ferromagnetic resonance around 3.7 GHz. This omnidirectional multilayer is promising for the application in inductors since the 100% hard-axis excitation is achieved for any shaped inductors. [ABSTRACT FROM AUTHOR]

Recently, ferroelectric tunnel junctions (FTJs) have attracted great attention due to promising applications in non-volatile memories. In this study, we report high-temperature tunneling electroresistance (TER) of metal/ferroelectric/semiconductor FTJs. Hysteretic resistance-voltage loops are observed in the Pt/BaTiO3/Nb:SrTiO3 tunnel junction from 300 to 513K due to the modulation of interfacial Schottky barrier by polarization switching in the 4 u.c.-thick BaTiO3 barrier via a ferroelectric field effect. The Pt/BaTiO3/Nb:SrTiO3 device exhibits a giant ROFF/RON resistance ratio of ~3 x 105 at 383K and maintains bipolar resistance switching up to 513K, suggesting excellent thermal endurance of the FTJs. The temperature-dependent TER behaviors are discussed in terms of the decrease of polarization in the BaTiO3 barrier, and the associated junction barrier profiles are deduced by transport and capacitance analyses. In addition, by extrapolating the retention time at elevated temperature in an Arrhenius-type relation, activation energy of ~0.93 eV and room-temperature retention time of ~70 years can be extracted. [ABSTRACT FROM AUTHOR]

Planning of construction technical specifications (CTS) for deep foundations is critical for ensuring works performed safely. k-nearest neighbors (kNN) is regarded as a practical algorithm for case retrieval in a case-based reasoning (CBR) cycle to search for past similar plans for new plan making. The parameter k and neighbors' weights affect the performance of the CBR cycle deeply but kNN neglects the weights' effect on case retrieval. The massive and multisource data of CTS of deep foundations presents a challenge for retaining case data in a database and for decision making due to an inefficient data process of the traditional tool. This paper presents a new framework to integrate weighted k-nearest neighbors (kkNN) to improve the performance of a CBR system for technical planning of deep foundations. It contains two parts: (1) a process to deal with a large amount of data derived from CTS; and (2) kkNN to obtain similar cases considering k and the weights of neighbors'. The feasibility of the proposed approach is validated through a case study and the evaluation result shows that the approach enhances the performance of the CBR cycle in creating construction technical specifications in deep foundation projects. [ABSTRACT FROM AUTHOR]

In this study, we report on enhanced resistive memory in BaTiO3-based ferroelectric diodes due to the doping of donors. A large ON/OFF current ratio of ~2000, about two orders of magnitude higher than that of Au/BaTiO3/SrRuO3, is achieved in a Au/Nb:BaTiO3/SrRuO3 diode at room temperature. This can be ascribed to the enhanced ferroelectric-modulation on the potential barrier at the Nb:BaTiO3/SrRuO3 interface associated with the (NbTi4+5+). donors, which gives rise to an efficient control of device transport between a bulk-limited current in the ON state and an interface-limited Schottky emission in the OFF state. In contrast, the resistance switching is suppressed in a Au/Fe:BaTiO3/SrRuO3 device since the (FeTi4+3+)' acceptors suppress semiconducting character of the BaTiO3 thin film and make the polarization-modulation of the band diagram negligible. The present work facilitates the design of high-performance resistive memory devices based on ferroelectric diodes with controllable charged defects. [ABSTRACT FROM AUTHOR]

Elliptical scatterers possess the properties of anisotropy and multiple degrees of freedom. With the plane wave expansion method, the slow light effect with high ng and low dispersion can be achieved by optimizing the structure parameters of photonic crystal waveguide with line defect, including changing the length of major axis or minor axis, and rotating scatterers relative to the direction of line defect. With operating frequency f = 1 THz (wavelength λ = 300um), the simulation shows: 1) selecting the group index ng from 38.5 to 107.4, slow light with low-dispersion bandwidth (ng varies within a 10% range) from 0.385 to 1.272 ìm, ultralow- -dispersion bandwidth (ng varies within a 1% range) from 0.188 to 0.548 ìm, and scalar product ngÄë /ë from 0.138 to 0.180 were obtained by changing the length of major axis or minor axis; 2) selecting the group index ng from 38.6 to 107.7, slow light with low-dispersion bandwidth from 0.436 to 1.443 ìm, ultralow-dispersion bandwidth from 0.201 to 0.907 ìm, and scalar product ngδ&λ/λ from 0.157 to 0.186 were achieved by rotating scatterers θ = 30° relative to the direction of line defect. Moreover, slow light with near-zero dispersion (dispersion parameter D less than 0.01 ps/mm·nm) can also be obtained by these methods, which implies that choosing suitable scatterers and adjusting their parameters we can efficiently achieve slow light with high ng with wideband and low dispersion. [ABSTRACT FROM AUTHOR]

A series of clay pillaring agents with amino, olefin, and epoxy groups were synthesized. These pillaring agents were used to modify montmorillonite clay by ion-exchange reactions. TGA studies showed that organoclays with imidazolium pillaring agents have a higher thermal stability than those with ammonium groups. The d-spacings of organoclays were not affected by the pillaring agent functional groups and were sensitive to the size of pillaring agents. The dynamic-mechanical properties of the resulting clay/epoxy composites determined by DMA were similar. The addition of these clays to this epoxy resin enhanced the T value of the resulting composites and greatly enhanced the storage moduli versus the pure epoxy resin by 1.6-1.8 times at 135°C. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011 [ABSTRACT FROM AUTHOR]

This article focuses on a study of air bubbling and dietary sulfur reduction to mitigate hydrogen sulfide and odor from swine waste. Modern swine operations generate large volumes of slurry. The slurry is usually stored in earthen or concrete structures in an anaerobic condition that can produce toxic gases particularly during agitation and removal. When the slurry is disturbed during the agitation process, reduced sulfurs, volatile organic compounds, phenols, and others are suddenly released through off-gasing. The primary objective of this study was to evaluate, at the bench scale, the two potential methods for reducing hydrogen sulfide emissions from swine manure slurry. Bench-scale methods offer much greater control over experimental and environmental variables than do production-scale studies, in which potential treatment effects of diet on manure emissions are often masked or confounded. The objective was addressed through two experiments: in the first, air was bubbled at low rates through stored manure slurry and hydrogen sulfide emission rates were measured during a 4-week period; in the second, dietary sulfide was adjusted and hydrogen sulfide and odor emission rates were measured from the stored slurry.