(Nitrato-κO){N,N,N′,N′-tetra­kis­[(1H-benzimidazol-2-yl-κN 3)meth­yl]cyclo­hexane-1,2-diamine}­lead(II) hemiaqua­{N,N,N′,N′-tetra­kis­[(1H-benzimidazol-2-yl-κN 3)meth­yl]cyclo­hexane-1,2-diamine}­lead(II) trinitrate dihydrate

In the title compound, [Pb(NO3)(C38H38N10)][Pb(C38H38N10)(H2O)0.5](NO3)3·2H2O, both PbII ions are coordinated in a distorted trigonal–prismatic environment by a hexa­dentate N,N,N′,N′-tetra­kis­[(1H-benzimidazol-2-yl)meth­yl]cyclo­hex­ane-1,2-diamine ligand. A nitrate and a half-occupancy water ligand form long coordination bonds to the PbII ions capping the trigonal–prismatic environment. In the crystal, the components are linked by N—H⋯O and O—H⋯O hydrogen bonds, forming a three-dimensional network. C—H⋯O inter­actions also occur

Impact of User Satisfaction with Mandated RM Use on Employee Service Quality

An increasing number of organizations are now implementing customer relationship management (CRM) systems to support front-line employees’ service tasks. With the belief that CRM can enhance employees’ service quality, management often mandates employees to use the implemented CRM. However, challenges emerge if/when employees are dissatisfied with using the system. To understand the role of front-line employee users’ satisfaction with their mandated use of CRM in determining their service quality, we conducted a field study in one of the largest telecommunications service organizations in China and gathered time-lagged data from self-reported employee surveys, as well as from the firm’s archival data sources. Our results suggest that employees’ overall user satisfaction (UserSat) with their mandated use of CRM has a positive impact on employee service quality (ESQ) above and beyond the expected positive impacts that job dedication (JD) and embodied service knowledge (ESK) have on ESQ. Interestingly, the positive effect of UserSat on ESQ is comparable to the positive effects of JD and ESK, respectively, on ESQ. Importantly, UserSat and ESK have a substitutive effect on ESQ, suggesting that the impact of UserSat on ESQ is stronger/weaker for employees with lower/higher levels of ESK. Finally, ESQ predicts customer satisfaction with customer service employees (CSWCSE); ESQ also fully mediates the impacts of UserSat and ESK, and partially mediates the impact of JD, on CSWCSE. The results of this study emphasize the importance of user satisfaction in determining employees’ task outcomes when use of an information system is mandated

Multi-axis fatigue loading system of wind turbine blade and vibration coupling characteristics

This paper presents a new method which focuses on the multi-axis fatigue loading mode for wind turbine blade and aims to shorten the fatigue loading cycle. The whole test scheme is design for the measurement of fatigue loading system. The two leading sources of fatigue loading system are asymmetric arrangement in the space. In addition, its vibration mathematical model is derived according to the Lagrange equation. The numerical simulation model is developed by means of Matlab Simulink. The vibration coupling characteristics including motor revolution speed, phase and amplitude of wind turbine blade is obtained. Moreover, the trajectory of wind turbine blade is obtained. Finally, a multi-axis fatigue loading platform for small wind turbine blade is built for the proposed study. The on-site test showed that if the revolution speeds of the two loading sources is the same as the natural frequency of wind turbine blade, the revolution speed, the phase angle of motor and the blade trajectory were relative changed smoothly. Thus, the amplitude of blade is state and the largest. Otherwise when the revolution speed of motor is different with the natural frequency of blade, the revolution speeds and phase angle of the two loading sources fluctuated largely. The above conclusion provided the theoretical basis for the subsequent decoupling control algorithm of multi-axis fatigue loading test

Fractal analysis in particle dissolution: a review

Fractal is a geometric language to describe the objects, the systems, and the phenomenon spatially and temporally. This paper reviews the literature on fractal models developed to describe the dissolution of particles. Dissolution, the process by which a solid forms a homogeneous mixture with a solution, is the behavior of a population of particles rather than a single one in most of the cases. The fractal models developed for the particle population are reviewed on the basis of two key particle surface properties, namely, the surface fractal nature and the chemical reactivity of particle surfaces. In terms of the surface fractal nature, fractals have been used to describe the change in the superficial roughness of particles, surface area-particle size relation, and particle size distribution (PSD). In terms of the reactive fractal dimensions, the models that describe the dissolution process have been developed to obtain the empirical noninteger exponent, the reactive fractal dimension that can dictate the chemical reactivity of a solid surface. The comparison between the surface fractal dimension and the reactive fractal dimension provides the dissolution mechanisms in many aspects of surface morphology. Further research is necessary to modify the current models to coincide with the real industrial processes and production and to develop the specific models for a better understanding of many processes involving the dissolution of particles encountered in many areas, including pharmaceutical and chemical applications and hydrometallurgy

DUFormer: Solving Power Line Detection Task in Aerial Images using Semantic Segmentation

Unmanned aerial vehicles (UAVs) are frequently used for inspecting power lines and capturing high-resolution aerial images. However, detecting power lines in aerial images is difficult,as the foreground data(i.e, power lines) is small and the background information is abundant.To tackle this problem, we introduce DUFormer, a semantic segmentation algorithm explicitly designed to detect power lines in aerial images. We presuppose that it is advantageous to train an efficient Transformer model with sufficient feature extraction using a convolutional neural network(CNN) with a strong inductive bias.With this goal in mind, we introduce a heavy token encoder that performs overlapping feature remodeling and tokenization. The encoder comprises a pyramid CNN feature extraction module and a power line feature enhancement module.After successful local feature extraction for power lines, feature fusion is conducted.Then,the Transformer block is used for global modeling. The final segmentation result is achieved by amalgamating local and global features in the decode head.Moreover, we demonstrate the importance of the joint multi-weight loss function in power line segmentation. Our experimental results show that our proposed method outperforms all state-of-the-art methods in power line segmentation on the publicly accessible TTPLA dataset

An epithelial–mesenchymal transition-related mRNA signature associated with the prognosis, immune infiltration and therapeutic response of colon adenocarcinoma

Background: Epithelial-mesenchymal transition (EMT) is closely associated with cancer cell metastasis. Colon adenocarcinoma (COAD) is one of the most common malignancies in the world, and its metastasis leading to poor prognosis remains a challenge for clinicians. The purpose of this study was to explore the prognostic value of EMT-related genes (EMTRGs) by bioinformatics analysis and to develop a new EMTRGs prognostic signature for COAD.Methods: The TCGA-COAD dataset was downloaded from the TCGA portal as the training cohort, and the GSE17538 and GSE29621 datasets were obtained from the GEO database as the validation cohort. The best EMTRGs prognostic signature was constructed by differential expression analysis, Cox, and LASSO regression analysis. Gene set enrichment analysis (GSEA) is used to reveal pathways that are enriched in high-risk and low-risk groups. Differences in tumor immune cell levels were analyzed using microenvironmental cell population counter and single sample gene set enrichment analysis. Subclass mapping analysis and Genomics of Drug Sensitivity in Cancer were applied for prediction of immunotherapy response and chemotherapy response, respectively.Results: A total of 77 differentially expressed EMTRGs were identified in the TCGA-COAD cohort, and they were significantly associated with functions and pathways related to cancer cell metastasis, proliferation, and apoptosis. We constructed EMTRGs prognostic signature with COMP, MYL9, PCOLCE2, SCG2, and TIMP1 as new COAD prognostic biomarkers. The high-risk group had a poorer prognosis with enhanced immune cell infiltration. The GSEA demonstrated that the high-risk group was involved in “ECM Receptor Interaction,” “WNT Signaling Pathway” and “Colorectal Cancer.” Furthermore, patients with high risk scores may respond to anti-CTLA4 therapy and may be more resistant to targeted therapy agents BI 2536 and ABT-888.Conclusion: Together, we developed a new EMTRGs prognostic signature that can be an independent prognostic factor for COAD. This study has guiding implications for individualized counseling and treatment of COAD patients

Spatial and temporal EEG dynamics of dual-task driving performance

<p>Abstract</p> <p>Background</p> <p>Driver distraction is a significant cause of traffic accidents. The aim of this study is to investigate Electroencephalography (EEG) dynamics in relation to distraction during driving. To study human cognition under a specific driving task, simulated real driving using virtual reality (VR)-based simulation and designed dual-task events are built, which include unexpected car deviations and mathematics questions.</p> <p>Methods</p> <p>We designed five cases with different stimulus onset asynchrony (SOA) to investigate the distraction effects between the deviations and equations. The EEG channel signals are first converted into separated brain sources by independent component analysis (ICA). Then, event-related spectral perturbation (ERSP) changes of the EEG power spectrum are used to evaluate brain dynamics in time-frequency domains.</p> <p>Results</p> <p>Power increases in the theta and beta bands are observed in relation with distraction effects in the frontal cortex. In the motor area, alpha and beta power suppressions are also observed. All of the above results are consistently observed across 15 subjects. Additionally, further analysis demonstrates that response time and multiple cortical EEG power both changed significantly with different SOA.</p> <p>Conclusions</p> <p>This study suggests that theta power increases in the frontal area is related to driver distraction and represents the strength of distraction in real-life situations.</p

Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection

Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds on infrared images. Recently, deep learning based methods have achieved promising performance on SIRST detection, but at the cost of a large amount of training data with expensive pixel-level annotations. To reduce the annotation burden, we propose the first method to achieve SIRST detection with single-point supervision. The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters. To handle this issue, we introduce randomness to the clustering process by adding noise to the input images, and then obtain much more reliable pseudo masks by averaging the clustered results. Thanks to this "Monte Carlo" clustering approach, our method can accurately recover pseudo masks and thus turn arbitrary fully supervised SIRST detection networks into weakly supervised ones with only single point annotation. Experiments on four datasets demonstrate that our method can be applied to existing SIRST detection networks to achieve comparable performance with their fully supervised counterparts, which reveals that single-point supervision is strong enough for SIRST detection. Our code will be available at: https://github.com/YeRen123455/SIRST-Single-Point-Supervision

Efficient Characterizations of Multiphoton States with Ultra-thin Integrated Photonics

Metasurface enables the generation and manipulation of multiphoton entanglement with flat optics, providing a more efficient platform for large-scale photonic quantum information processing. Here, we show that a single metasurface optical chip would allow more efficient characterizations of multiphoton entangled states, such as shadow tomography, which generally requires fast and complicated control of optical setups to perform projective measurements in different bases, a demanding task using conventional optics. The compact and stable device here allows implementations of general positive observable value measures with a reduced sample complexity and significantly alleviates the experimental complexity to implement shadow tomography. Integrating self-learning and calibration algorithms, we observe notable advantages in the reconstruction of multiphoton entanglement, including using fewer measurements, having higher accuracy, and being robust against optical loss. Our work unveils the feasibility of metasurface as a favorable integrated optical device for efficient characterization of multiphoton entanglement, and sheds light on scalable photonic quantum technologies with ultra-thin integrated optics.Comment: 15 pages, 9 figure

Does Economic Agglomeration Lead to Efficient Rural to Urban Land Conversion? An Examination of China’s Metropolitan Area Development Strategy

Urbanization involves expansion of the amount of land covered by urban uses. Rural to urban land conversion (RULC) can satisfy demand for the additional space that growing cities require. However, there can be negative consequences, such as the loss of productive agricultural land and/or the destruction of natural habitats. Considerable interest therefore exists among policy makers and researchers regarding how the efficiency of RULC can be maximized. We used the Gini index and a data envelopment analysis to quantify the relationship between RULC and economic development for 17 metropolitan areas in China. We did this from two perspectives: (i) coordination; and (ii) efficiency. We found that economic agglomeration fosters the coordination of the amount of rural land that is allocated to be converted to urban uses. Similarly, economic agglomeration increases the efficiency of RULC in terms of the processes of socio-economic production. Through production technology innovation and readjustment in the scale of input factors, the productive efficiency of RULC can be promoted. Our findings suggest a need to strictly limit the amount of RULC, design differential land management policies according to location and development level, and adjust RULC allocation between different cities. Further, in harnessing the potential of intensive urban land use and restructuring, production factors, including land, can be enhanced through technological innovation. Research presented in this paper provides insights for areas of the world which are yet to undergo the rapid urbanization that China has experienced, but where it is projected to occur over the coming decades