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Understanding the asymmetric relationships between satisfaction factors and overall employee satisfaction is critical for airlines if they are to formulate effective strategies for improving job satisfaction. However, little research has been done on this topic. In this study penalty–reward contrast analysis is adopted to analyze 53,718 items of employee-generated data from 225 airlines. The results show that work/life balance, career opportunities, compensation & benefits and senior management are 'basic' factors, and culture & values is a 'performance' factor. Furthermore, the asymmetric impacts of these factors vary with employment status and geographic region. Additionally, the priorities attached to these satisfaction factors by employees of different employment status from different regions are analyzed using 3-dimensional asymmetric impact performance analysis. The obtained results are valuable for formulating strategies to improve employee satisfaction in the airline industry. [ABSTRACT FROM AUTHOR]

Vanadate is expected to be a promising anode material for lithium-ion batteries (LIBs). Herein, a series of Ni doped Zn3V2O8 continuous solid solution has been synthesized through a simple sol–gel method. The effects of Ni-doping on the microstructure of Zn3V2O8 is systematically investigated. The results show that Ni-doping has almost no influence on the crystalline phase structure and morphology, but has influences on the grain size of the samples. With the increase of Ni-doping amount, the grain size of Ni doped Zn3V2O8 gradually increases. Moreover, the conductivity of Ni doped Zn3V2O8 is also improved. Ni doped Zn3V2O8 has exhibited better electrochemical performance than that of Zn3V2O8. In particular, the sample Zn2.4Ni0.6V2O8 can deliver a capacity of 452.3 mAh g−1 at a current density of 1 A g−1 after 600 cycles. This should be attributed to the decrease of band gap, leading to the improvement of conductivity, which is more conducive to the transport of lithium ions and electrons. [ABSTRACT FROM AUTHOR]

Since the 1970s, there has been a long history of assessing the accuracy of remotely sensed land cover types. Various accuracy assessment methods have been proposed, including the method of bootstrap resampling. However, the potential of the bootstrap resampling method to diagnose the accuracy of remote sensing monitoring of land cover has not been fully explored. Here, we used the land cover map classified in the Gannan Tibetan Autonomous Region as an example and investigated the impact of two parameters in the bootstrap resampling method on the assessment of land cover classification accuracy: the number of validation points and the number of bootstrap resampling times. Varying these two parameters can help diagnose the accuracy of remote sensing classifications of land cover. The main conclusions are as follows. First, the number of validation points required to achieve a stabilized accuracy estimate varies among land cover types. It indicates not only the level of homogeneity of the land cover type but also the level of difficulty associated with classifying it. Second, it is not necessary to have a very large number of bootstrap resampling times to estimate the classification accuracy – 50 was sufficient. In summary, we recommend that various numbers of validation points be employed in bootstrap to objectively and comprehensively assess the overall, producer's, and user's accuracies of remote sensing classifications of land cover. [ABSTRACT FROM AUTHOR]

AR, VR Headset for "metaverse" is one of candidates to widen human life experiences with virtual reality. Display requirement for headset is the high resolution as to 3000ppi that needs to enhance outcoupling efficiency of OLED (OLEDoS). We optimized 2D hole arrayed double anode structure for mass production with materials, thickness of anode and interlayer, process variation of etching top anode, and particles in double anodes. Outcoupling efficiency of optimized structure is improved to 1.377 folds than conventional TE‐OLEDs. We hope that this study promotes more approaches to excite SPPs of anode. [ABSTRACT FROM AUTHOR]

Vegetation phenology interacts strongly with climate through the exchange of carbon, water, momentum, and energy between terrestrial ecosystem and atmosphere. These vegetation dynamics in Northern Hemisphere permafrost regions have substantial uncertainties in previous studies, partly due to differences in datasets. Thus, reliable land surface phenology (LSP) retrievals are crucial for understanding the effects of climate change on ecosystems and biosphere‐atmosphere‐hydrosphere interactions. We assessed various LSP datasets at different spatial resolutions for 2001–2014, generated by different methods based on different satellite observations. We also assessed the accuracy of LSP by comparing with CO2 flux phenology. For start of growing season (SOS), the comprehensive evaluation indicated MODIS phenology showed better consistency and higher accuracy with flux‐derived phenology observations (R = 0.54, RMSE = 36.2 days, bias<5 days). For end of growing season (EOS), we cannot conclusively determine which LSP performs best. In the Northern Hemisphere permafrost regions, SOS occurred at 100–150 days (April–May), and EOS occurred at 260–320 days (September–November). During 2001–2014, SOS occurred earlier by 0.33 ± 0.30 days/yr. Significant trends were observed for 6.4%–27.6% of pixels, averaging −1.30 ± 1.16 days/yr. EOS occurred earlier by 0.25 ± 0.43 days/yr, with significant trends averaging −0.52 ± 0.94 days/yr. Among LSPs, variability in EOS trends was significant, with even the direction of trends differing. This study may provide insights into LSP data selection and further understanding of vegetation dynamics and its mechanisms in permafrost regions. Plain Language Summary: To better understand permafrost ecosystem dynamics associated with global change, the changes in vegetation and their spatial patterns in the Northern Hemisphere permafrost regions must be carefully evaluated. Thus, reliable land surface phenology (LSP) retrievals are very important. We therefore collected a total of 13 LSPs to understand how these products perform relative to each other and to ground observations and to quantify their uncertainties. MODIS and different GIMMS products are slightly more consistent overall. Considering different metrics, regions, and parameters, the temporal trends and spatial patterns of phenology indicate an earlier start to the growing season, but the end of the growing season is inconsistent. Bigger vegetation changes are found in the continuous permafrost region. These findings allow us to clarify the applicability of the LSP products, to provide a reference for data selection, and insights into the understanding of vegetation dynamics and their mechanisms in permafrost regions. Key Points: The average start and end of the growing season occurs on 131 ± 18 days and 291 ± 4 days in Northern Hemisphere permafrost regionsComparison of 13 land surface phenology products to CO2 flux phenology indicates that the RMSEs ranges from 27.25ays to 77.84 daysBoth the start and end of the growing season are earlier by 1.30 ± 1.16 and 0.52 ± 0.94 days/yr in Northern Hemisphere permafrost regions [ABSTRACT FROM AUTHOR]

Luminous efficiency is a pivotal factor for assessing the performance of optoelectronic devices, wherein light loss caused by diverse factors is harvested and converted into the radiative mode. In this study, we demonstrate a nanoscale vacuum photonic crystal layer (nVPCL) for light extraction enhancement. A corrugated semi-transparent electrode incorporating a periodic hollow-structure array was designed through a simulation that utilizes finite-difference time-domain computational analysis. The corrugated profile, stemming from the periodic hollow structure, was fabricated using laser interference lithography, which allows the precise engineering of various geometrical parameters by controlling the process conditions. The semi-transparent electrode consisted of a 15 nm thick Ag film, which acted as the exit mirror and induced microcavity resonance. When applied to a conventional green organic light-emitting diode (OLED) structure, the optimized nVPCL-integrated device demonstrated a 21.5% enhancement in external quantum efficiency compared to the reference device. Further, the full width at half maximum exhibited a 27.5% reduction compared to that of the reference device, demonstrating improved color purity. This study presents a novel approach by applying a hybrid thin film electrode design to optoelectronic devices to enhance optical efficiency and color purity. [ABSTRACT FROM AUTHOR]

To forecast the tourism demand across a set of tourist attractions with spatial dependence, a new model is proposed, which has three stages: tourist attraction selection, base predictor generation, and base predictor combination. In stage 1, a method for selecting associated attractions based on multi-dimensional scaling is used to determine the strength of the spatial dependence between each pair of attractions. In stage 2, a hybrid base predictor based on LSTM networks and Autoregressive model is developed, where the LSTM networks are used to capture the spatial dependence among attractions, and the Autoregressive model is used capture the scale of tourist volume at each attraction. In stage 3, a strategy for combining these base predictors is proposed; it can alleviate the overfitting problem of LSTM and improve the stability of forecasts. Finally, the superiority of the model is verified through the data on tourist volumes at 77 attractions in Beijing. [ABSTRACT FROM AUTHOR]

The Two‐Out‐of‐Five method is one of the basic sensory discrimination methods. It is a special case of the 'M+N' method with M = 3 and N = 2. Three variants of the method are discussed in the paper: specified, unspecified, and unspecified with forgiveness. Analytical and simulation‐derived psychometric functions for all three variants of the Two‐Out‐of‐Five method are derived and produced. The performance of the method in both difference and similarity/equivalence tests are explored. It is shown that both difference and similarity/equivalence testing powers for the specified Two‐Out‐of‐Five are larger than those for the 2‐AFC and 3‐AFC. Both difference and similarity/equivalence testing powers for the unspecified Two‐Out‐of‐Five are larger than those for the Triangle and Duo‐trio. Both difference and similarity/equivalence testing powers for the unspecified with forgiveness Two‐Out‐of‐Five are larger than those for the conventional unspecified Two‐Out‐of‐Five and unspecified Tetrads. Tables and R codes are presented and provided for estimations of the probability of correct response, Pc, Thurstonian discriminal distance δ or d', and the B value for estimating the variance of d' for the three variants of the Two‐Out‐of‐Five method. Practical Applications: The Two‐Out‐of‐Five method is a powerful sensory discrimination method. However, this method has not been used widely in sensory and consumer research, partly since the method has not been explored adequately in the literature. This paper explores the Two‐Out‐of‐Five method and shows that the method has potential for wide application. The Two‐Out‐of‐Five method involves a larger number of samples (5) and sensory fatigue may be of concern. Hence, the method may not be suitable for taste and smell tests with strong stimuli. The method is particularly suitable for manual and visual inspection testing where sensory fatigue is of a lesser concern. [ABSTRACT FROM AUTHOR]

A 12‐week longitudinal study with 247 US menopausal women was undertaken to investigate the perceptual efficacy of a tablet formulated with an extract of Siberian rhubarb to provide relief from menopausal symptoms. The 5‐point menopause rating scale (MRS) (0 = none; 1 = mild; 2 = moderate; 3 = severe; 4 = extremely severe) was used to measure symptom intensity. The MRS scale, an ordinal categorical scale, was transformed into an R‐index, a ratio scale, using the frequencies of the MRS categories. Symptom relief was compared to an initial week baseline measure; defined as R‐index = 0.5. An index, A.RDEX, which is based on area under the R‐index curve (AUC) in the range of 0–84 days was used to measure percentage efficacy of symptom relief. Results show that symptom relief occurs through the first 17 days in terms of R‐index = 0.64 for the total score and improves with continued use, reaching an A.RDEX = 0.42 by 84 days, suggesting about 42% relief considering all 11 MRS attributes. Practical Applications: MRS ratings data can be transformed into an R‐index using MRS category frequencies at an initial week and a subsequent week. Functional data analysis for R‐index values provides an advanced statistical model for analysis of perceptual differences throughout longitudinal studies that can examine MRS attribute effects over time. R codes are developed and provided in the paper to transform MRS data into R‐index measures and to conduct functional data analysis for R‐index measures obtained from different time points (weeks or days). [ABSTRACT FROM AUTHOR]

Compared with coarse-grained forecasting, fine-grained tourism demand forecasting is a more challenging task, but research on this issue is very scarce. To address this issue, a decomposition ensemble deep learning model is proposed by integrating CEEMDAN, CNNs, LSTM networks, and AR models. The CEEMDAN can decompose complex tourism demand data into multiple components with simpler characteristics, thereby reducing the complexity of forecasting. The CNNs and LSTM networks can fully capture the locally recurring patterns and the long-term dependencies of the components obtained by CEEMDAN. The AR model can capture the scale of tourism demand data, which can overcome the problem that the output scale of the deep neural networks (i.e., CNNs and LSTM networks) is not sensitive to the scale of the inputs. The effectiveness of the proposed model is verified by comparing with five benchmark models using real-time data on tourist volumes at two attractions. [ABSTRACT FROM AUTHOR]

Quadruped robots working in jungles, mountains or factories should be able to move through challenging scenarios. In this paper, we present a control framework for quadruped robots walking over rough terrain. The planner plans the trajectory of the robot's center of gravity by using the normalized energy stability criterion, which ensures that the robot is in the most stable state. A contact detection algorithm based on the probabilistic contact model is presented, which implements event‐based state switching of the quadruped robot legs. And an on‐line detection of contact force based on generalized momentum is also showed, which improves the accuracy of proprioceptive force estimation. A controller combining whole body control and virtual model control is proposed to achieve precise trajectory tracking and active compliance with environment interaction. Without any knowledge of the environment, the experiments of the quadruped robot SDUQuad‐144 climbs over significant obstacles such as 38 cm high steps and 22.5 cm high stairs are designed to verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Although the piezo‐catalysis is promising for the environmental remediation and biomedicine, the piezo‐catalytic properties of various piezoelectric materials are limited by low carrier concentrations and mobility, and rapid electron‐hole pair recombination, and reported regulating strategies are quite complex and difficult. Herein, a new and simple strategy, integrating phase boundary engineering and defect engineering, to boost the piezo‐catalytic activity of potassium sodium niobate ((K, Na)NbO3, KNN) based materials is innovatively proposed. Tur strategy is validated by exampling 0.96(K0.48Na0.52)Nb0.955Sb0.045O3‐0.04(BixNa4‐3x)0.5ZrO3‐0.3%Fe2O3 material having phase boundary engineering and conducted the defect engineering via the high‐energy sand‐grinding. A high reaction rate constant k of 92.49 × 10−3 min−1 in the sand‐grinding sample is obtained, which is 2.40 times than that of non‐sand‐grinding one and superior to those of other representative lead‐free perovskite piezoelectric materials. Meanwhile, the sand‐grinding sample has remarkable bactericidal properties against Escherichia coli and Staphylococcus aureus. Superior piezo‐catalytic activities originate from the enhanced electron‐hole pair separation and the increased carrier concentration. This study provides a novel method for improving the piezo‐catalytic activities of lead‐free piezoelectric materials and holds great promise for harnessing natural energy and disease treatment. [ABSTRACT FROM AUTHOR]

Natural human–robot interaction requires social robots to have human-like perception of engagement intention. In the multi-person interaction scenario, it's a vital task for social robots to rationally select the main interaction object. Existing studies mostly focus on analyzing whether a person has engagement intention before interaction. However, this qualitative analysis of engagement intention is only applicable in single-person interaction scenarios. When multiple people have the intention to engage with the robot, the robot needs to quantitatively analyze the engagement intention intensity (EII) of all people to make a reasonable interaction decision. In addition, for EII recognition, it is an ideal state that social robots can imitate human social thinking as much as possible. For these purposes, a method that can efficiently recognize the EII by fusing transient features and temporal features is proposed. First, the 3D pose extractor is used to extract the 3D skeleton information which can calculate the transient features including linear distance and body orientation. Second, an improved ConvLSTM network is proposed to effectively identify pedestrian motion states which can reflect temporal information. Finally, based on the proposed two states fusion fuzzy inference system (TSFFIS), the EII can be judged by the three features which are linear distance, body orientation and motion states. Comparative experiments show that our method can effectively identify the EII of different pedestrians relative to the robot. Compared with existing methods, the EII recognition method based on TSFFIS has better performance. [ABSTRACT FROM AUTHOR]

With the rapid advances in information technology, an increasing number of online reviews are posted daily on the Internet. Such reviews can serve as a promising data source to understand customer satisfaction. To this end, in this paper, we proposed a method for modelling customer satisfaction from online reviews. In the method, customer satisfaction dimensions (CSDs) are first extracted from online reviews based on latent dirichlet allocation (LDA). The sentiment orientations of the extracted CSDs are identified using a support vector machine (SVM). Then, considering the existence of complex relationships among different CSDs and the customer satisfaction, an ensemble neural network based model (ENNM) is proposed to measure the effects of customer sentiments toward different CSDs on customer satisfaction. On this basis, to identify the category of each CSD from the customer's perspective, an effect-based Kano model (EKM) is proposed. Finally, an empirical study, which consists of two parts (phones and cameras), is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As a research hotspot in recent years, Ti-based anode materials for lithium ion batteries (LIBs) have advantages including long cycle life and excellent cyclic stability. However, its application is further limited by its poor conductivity and low reversible capacity. Herein, a series of porous hexagonal prism Zn-doped CoTiO3 have been designed and prepared by simple co-precipitation method followed by calcination. The influences of Zn doping concentration on the microstructure and electrochemical performance are further explored. The results indicate that Zn doping has little effect on the microstructure of CoTiO3 and can elevate capacity, cyclic stability, and rate capability. When Zn doping concentration is set as 0.1, the sample Co0.9Zn0.1TiO3 gives the best electrochemical performance. The reversible capacity can be maintained at 430.6 mAh g−1 at a current rate of 0.2 C after 280 cycles. This work can pave an effective strategy for improving the electrochemical performance of other Ti-based anode materials. [ABSTRACT FROM AUTHOR]

Thermally activated delayed fluorescence (TADF) organic molecules are considered the most suitable for blue organic light‐emitting diodes (OLEDs) after extensive research; however, they are plagued by issues of internally guided light and the roll‐off characteristics contributed by triplet exciton‐utilized emission. Thus, this study leverages exciton diffusion guidance and energy extraction to simultaneously achieve optical efficiency enhancement and roll‐off characteristic suppression of mixed‐host blue TADF OLEDs. The array of nanopixels, defined by the inserted nanoscale pixel‐defining layer (nPDL), spatially separates the excitons and polarons, resulting in the exacerbation of triplet quenching by securing exciton diffusion. Furthermore, through the formation of a metal cathode with a corrugated profile, nonradiative energy transfer to the surface plasmon polaritons is capitalized via Bragg diffraction, thereby boosting the emission efficiency. The structure of the nPDL is judiciously determined by finite‐difference time‐domain computational analysis. Consequently, the device with the optimized nPDL demonstrates 88.4%, 118.8%, and 108.8% improvements in external quantum, current, and power efficiencies, respectively, compared to the reference. Moreover, the critical luminance, which quantifies the degree of roll‐off, is improved by 83.7%. This pioneering demonstration of hybridizing the material combination and nanopatterning techniques is expected to provide new insights for designing high‐performance OLEDs. [ABSTRACT FROM AUTHOR]

The sparse hierarchical clustering (SHC) method and the R package "sparcl" are used for consumer segmentation with 247 perimenopausal and postmenopausal US women. The data includes 11 menopause rating scale (MRS) attributes measuring severity of symptoms collected in the first week of a 12‐week extended use menopause tablet perceptual efficacy study. SHC is one of the advanced statistical clustering approaches for high‐dimensional data with potential noise variables. Sparse clustering is an important topic of the actively developing field of statistical learning with sparsity. An advantage of the SHC method is that it simultaneously finds the clusters of subjects and the important clustering variables. It allows clustering the data in low‐dimensional subspaces without dimension reduction of the data before clustering. The SHC method is particularly applicable to consumer segmentation based on high‐dimensional consumer data. Practical Applications: Sparse hierarchical clustering (SHC) provides advantages in consumer segmentation based on high‐dimensional consumer data with potential noise variables. This advanced clustering method finds the relative importance of attributes in clustering, helping to identify homogeneous clusters and enhance understanding of how consumer responses/needs vary among clusters. [ABSTRACT FROM AUTHOR]

To interact with humans more precisely and naturally, social robots need to "perceive" human engagement intention, especially need to recognize the main interaction person in multi-person interaction scenarios. By analyzing the intensity of human engagement intention (IHEI), social robots can distinguish the intention of different persons. Most existing research in this field mainly focus on analyzing whether a person has the intention to interact with the robot while lack of analysis of IHEI. In this regard, this paper proposes an approach for recognizing the engagement intention intensity. Four categories of visual features, including line of sight, head pose, distance and expression of human, are captured, and a CatBoost-based machine learning model is applied to train an optimal classifier for predicting the IHEI on the dataset. The experimental results show that this classifier can effectively predict the IHEI that can be applied into real human–robot interaction scenarios. Moreover, the proposed model is an interpretable machine learning model, where interpretability analysis on the trained classifier has been done to explore the deep associations between input features and engagement intention, thereby providing robust and effective robot social decision-making. [ABSTRACT FROM AUTHOR]

Background: The role and underlying mechanism of liver macrophages and their derived miR-155-5p in hepatic lymphangiogenesis in liver fibrosis remain unclear. Here, we investigated the mechanism by which macrophages and miR-155-5p were involved in lymphangiogenesis during liver fibrosis and cirrhosis. Methods: In vivo, hepatic lymphatic vessel expansion was evaluated; the liver macrophage subsets, proportion of peripherally-derived macrophages and expressions of CCL25, MCP-1, VAP-1 and MAdCAM-1 were documented; and miR-155-5p in the peripheral blood and liver was detected. In vitro, macrophages with miR-155-5p overexpression and inhibition were used to clarify the effect of miR-155-5p on regulation of macrophage polarization and the possible signalling pathway. Results: Hepatic lymphangiogenesis was observed in mice with liver fibrosis and cirrhosis challenged with carbon tetrachloride (CCl4). In the liver, the number of M1 macrophages was associated with lymphangiogenesis and the degree of fibrosis. The liver recruitment of peripherally-derived macrophages occurred during liver fibrosis. The levels of miR-155-5p in the liver and peripheral blood gradually increased with aggravation of liver fibrosis. In vitro, SOCS1, a target of miR-155-5p, regulated macrophage polarization into the M1 phenotype through the JAK1/STAT1 pathway. Conclusion: MiR-155-5p-SOCS1/JAK1/STAT1 pathway participates in hepatic lymphangiogenesis in mice with liver fibrosis and cirrhosis induced by CCl4 by regulating the polarization of macrophages into the M1 phenotype. [ABSTRACT FROM AUTHOR]

Herein, a novel antimony fluoroberyllate, KSb(BeF3)F3, exhibiting a distinctive 1D [BeF3]∞− polyanion chain, has been successfully synthesized using a totally fluorine strategy. Innovatively, by introducing a Be–F bond with a large electronegativity difference and completely eliminating the suspended oxygen atoms in the structure, KSb(BeF3)F3 exhibits a large optical bandgap (5.3 eV). Furthermore, KSb(BeF3)F3 shows a large birefringence (0.11@546 nm), larger than that of other reported alkali/alkaline earth metal oxysalts with tetrahedral units only, showing that the reported compound is a potential practical short-wave ultraviolet birefringent material. First principles theoretical studies reveal that the excellent properties originate from the cooperation of the 1D infinite [BeF3]∞− polyanion chains and SbF6 octahedra. The totally fluorine strategy we adopted not only resulted in a novel structure, which facilitated the diversification of structural types of the antimony-based materials, but also achieved the bandgap tuning of the materials. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) coverage problem is to think about how to maximize the network coverage to obtain reliable monitoring and tracking services with guaranteed quality of service. In this paper, a simplified slime mould algorithm (SSMA) for solving the WSN coverage problem is proposed. In SSMA, we mainly conducted 13 groups of WSNs coverage optimization experiments and compared them with six well-known meta-heuristic optimization algorithms. The experimental results and Wilcoxon rank-sum test show that the proposed SSMA is generally competitive, outstanding performance and effectiveness. We proposed SSMA algorithm could be helpful to effectively control the network nodes energy, improve the perceived quality of services and extend the network survival time. [ABSTRACT FROM AUTHOR]

Zhao et al. [[11]] used the daily MODIS cloud-free snow cover product to calculate various snow phenology variables, including snow cover area (SCA), snow cover onset (SCS), snow cover melt (SCM) and snow cover day (SCD). This Special Issue contains many research highlights related to the remote sensing of mountain snow cover and vegetation and the relationship between vegetation and snow. Climate change has profoundly impacted elements of land surface at high latitudes and elevations, especially snow and vegetation. The time of snow accumulation and melting (snow phenology) is closely related to the vegetation growth condition. [Extracted from the article]

Accurate forecasting of daily tourism demand is a meaningful and challenging task, but studies on this issue are scarce. To address this issue, multisource time series data, relating to past tourist volumes, web search information, daily weather conditions, and the dates of public holidays, are selected as the forecasting variables. To fully capture the relationship between these forecasting variables and actual tourism demand automatically, an ensemble of long short-term memory (LSTM) networks is proposed with a correlation-based predictor selection (CPS) algorithm. The effectiveness of the proposed method is verified in daily tourism demand forecasting for the Huangshan Mountain Area, benchmarked against 11 forecasting methods. This study contributes to the literature by (1) introducing the use of big data in daily tourism demand forecasting, (2) proposing an ensemble of LSTM networks for daily tourism demand forecasting, and (3) providing an effective predictor selection algorithm in ensemble learning. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore how attribute performance and hosts' service quality attributes affect room sales on peer-to-peer (P2P) platforms from the cue congruence perspective. Design/methodology/approach: More than 9.53 million reviews concerning 258,473 listings located in 35 major cities worldwide were collected from Airbnb. Data was collected from December 2019 to December 2020 and was analysed using a generalised linear model. Findings: Results show that when attribute performance and hosts' service quality attributes give positive signals, Airbnb room sales are significantly higher than when the two kinds of cues give inconsistent or negative signals; when attribute performance gives positive signals and hosts' service quality attributes give negative signals, room sales are higher than when the former gives negative signals and the latter give positive signals; surprisingly, when both kinds of cues give negative signals, room sales are higher than when attribute performance gives positive signals and hosts' service quality attributes give negative signals. Research limitations/implications: This paper adds useful insights on understanding of cue congruence (incongruence) effect on room sales of P2P accommodation platforms. This study has practical implications for hosts, online platform managers and guests regarding how to use online strategies and promotions on the Airbnb platform. Originality/value: This study is an early attempt to explore how the combination of attribute performance and hosts' service quality attributes affects Airbnb room sales under the conditions of consistency and inconsistency. [ABSTRACT FROM AUTHOR]

Thiodiglycolic acid (TDGA) is a biomarker for monitoring vinyl chloride exposure. Exploring a facile, rapid and precise analysis technology to quantify TDGA is of great significance. In this research, we demonstrate a fluorescent sensor based on dual-emissive UiO-66 for TDGA detection. This ratiometric fluorescent material named C460@Tb-UiO-66-(COOH)2 was designed and synthesized by introducing organic dye 7-diethylamino-4-methylcoumarin (C460) and Tb3+ into UiO-66-(COOH)2. The as-obtained C460@Tb-UiO-66-(COOH)2 samples showed highly selective recognition, excellent anti-interference and rapid response characteristics for the recognition of TDGA. The detection limit is 0.518 mg·mL−1, which is much lower than the threshold of 20 mg·mL−1 for a healthy person. In addition, the mechanism of TDGA-induced fluorescence quenching is discussed in detail. This sensor is expected to detect TDGA content in human urine. [ABSTRACT FROM AUTHOR]

As an important biomarker in urine, the level of uric acid is of importance for human health. In this work, a Cu(II) functionalized metal–organic framework (Cu2+@Tb-MOFs) is designed and developed as a novel fluorescence probe for wide-range uric acid detection in human urine. The study shows that this fluorescence platform demonstrated excellent pH-independent stability, high water tolerance, and good thermal stability. Based on the strong interaction between metal ions and uric acid, the designed Cu2+@Tb-MOFs can be employed as efficient turn-on fluorescent probes for the detection of uric acid with wide detection range (0~104 µM) and high sensitivity (LOD = 0.65 µM). This probe also demonstrates an anti-interference property, as other species coexisted, and the possibility for recycling. The sensing mechanisms are further discussed at length. More importantly, we experimentally constructed a molecular logic gate operation based on this fluorescence probe for intelligent detection of uric acid. These results suggest the Cu(II) functionalized metal–organic framework can act as a prominent candidate for personalized monitoring of the concentration of uric acid in the human urine system. [ABSTRACT FROM AUTHOR]

Crafting spatially controllable charge transfer channels at the nanoscale level remains an enduring challenge in solar‐to‐chemical conversion technology. Despite the advancements, it still suffers from sluggish interfacial charge transport kinetics and scarcity of strategies to finely modulate charge transport pathways. Herein, this article demonstrates the unexpected charge modulation property of non‐conjugated insulating polymer assisted by a universal layer‐by‐layer self‐assembly tactic. Oppositely charged poly(dimethyl diallyl ammonium chloride) (PDDA) and Ti3C2 MXene quantum dots (MQDs) are periodically attached to the wide bandgap metal oxides (WMOs) to design multilayered heterostructured photoanodes. The intermediate PDDA layer acts as an efficacious charge relay medium to access directional electron flow from WMOs to Ti3C2 MQDs, while Ti3C2 MQDs serve as the electron extractor. Charge transfer cascade is thus stimulated on account of the simultaneous electron‐trapping capabilities of interim PDDA layer and Ti3C2 MQDs, which synergistically favors the conspicuously boosted charge separation over WMOs, affording the WMOs/(PDDA/MQDs)n photoanodes with considerably enhanced photoelectrochemical (PEC) water oxidation performances. Moreover, PEC performances of such photoanodes can be tuned by interface configuration via assembly number and sequence. This work will provide an insightful perspective to craft a directional charge transfer pathway through insulating polymer for solar energy conversion. [ABSTRACT FROM AUTHOR]

This study proposes a hybrid method for producing personalized travel recommendation that better meet travellers'individual needs and also improve their online booking experience. The proposed method integrates multi-attribute collaborative filtering with social network analysis within the framework of large-scale group decision-making. It includes four modules, i.e. identification of online opinion experts, construction of a social network, detection of user communities, and interactively produced of personalized travel recommendation. Specifically, the preliminary user filtering and k-means clustering approach are utilized to identify the online opinion experts for a specific travel recommendation issue. Then, social network construction and its community detection process are adopted to alleviate the sparsity problem. Finally, the travel alternatives are ranked to select recommendations, and this is done interactively with travellers to handle the cold start problem. With the proposed method, a better online booking experience can be achieved for travellers, as they are presented with a more appropriate set of recommended options and so can make better travel decisions. [ABSTRACT FROM AUTHOR]

The solar-to-hydrogen conversion efficiency in photocatalytic water splitting heavily depends on the accumulation of multiple electrons at the catalytically active sites and rapid charge transport/separation. Herein, we demonstrate the construction of 0D-2D nickel-doped and Ti3C2TX MXene (MN)-encapsulated transition metal chalcogenide quantum dot (TMC QD:Ni)/Ti3C2TX MN heterostructures via an elaborate electrostatic self-assembly strategy. The mechanistic studies revealed that the defects induced by atomic-level foreign metal ion doping create a mid-bandgap state, which broadens the optical absorption range and extends the photo-excited carrier lifetime of the TMC QDs. The density functional calculation results verified that Ni2+ ion doping introduces a donor impurity level and increases the density of state at the valence band maximum, leading to a significant increase in the number of active sites and lower energy barrier for photocatalytic hydrogen evolution. The subsequent self-assembly of TMC QDs:Ni on the Ti3C2TX MN framework further accelerates the charge separation and transfer due to the formation of an ideal unidirectional electron migration pathway by Ti3C2TX MN, which functions as an electron-withdrawing mediator. The synergistic effect of Ni2+ ion doping and Ti3C2TX MN decoration significantly decreases the charge transfer resistance at the photosensitizer (TMC QD)/co-catalyst (Ti3C2TX MN) interface and promotes the chemisorption of protons on the catalyst surface, resulting in an excellent solar-to-hydrogen conversion efficiency. Our work provides valuable guidance for the rational design of high-efficiency photocatalysts via precise atomic-level metal ion doping and co-catalyst modulation towards emerging artificial photosynthesis. [ABSTRACT FROM AUTHOR]

This study explores how to select the optimal number of lagged inputs (NLIs) in international tourism demand forecasting. With international tourist arrivals at 10 European countries, the performances of eight machine learning models are evaluated using different NLIs. The results show that: (1) as NLIs increases, the error of most machine learning models first decreases rapidly and then tends to be stable (or fluctuates around a certain value) when NLIs reaches a certain cutoff point. The cutoff point is related to 12 and its multiples. This trend is not affected by the size of the test set; (2) for nonlinear and ensemble models, it is better to select one cycle of the data as the NLIs, while for linear models, multiple cycles are a better choice; (3) significantly different prediction results are obtained by different categories of models when the optimal NLIs are used. [ABSTRACT FROM AUTHOR]

As a typical transition metal oxide anode material, ZnMoO4 can deliver a higher theoretical capacity of 951.6 mAh g−1 due to its variable oxidation state and alloying reaction. However, lower conductivity and huge volumetric expansion restrict its further development. Herein, the modified sol–gel method is applied to synthesize a series of (1-x)ZnMoO4-xZnFe2O4 (x = 0.1, 0.2, 0.3, 0.4, and 0.5) composites. The effects of compound proportion of ZnFe2O4 on the crystal phase structure and morphology for ZnMoO4 have been investigated in detail. All the composites have been conducted on electrochemical measurements, and the results illustrate that the composites display better electrochemical performance than that of ZnMoO4. Especially for 0.6ZnMoO4-0.4ZnFe2O4, it can deliver a capacity of 550.7 mAh g−1 after 500 cycles at a current density of 200 mA g−1. Moreover, it also presents the higher capacity at higher current density. The improvement of electrochemical performance should be attributed to the synergistic effect of ZnFe2O4 and ZnMoO4. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore the dimensions and sub-factors of Airbnb hosts' affinity-seeking strategies. It also aims to build a conceptual framework of hosts' affinity-seeking strategies and their impact on Airbnb guests' feelings of affection toward their host and/or the homestay and their behavioral intentions. Design/methodology/approach: Based on 150,161 Inside Airbnb online reviews in three major US cities, this study uses semantic analysis to explore processes through which hosts' affinity-seeking strategies are constructed. Findings: A conceptual framework is proposed to identify two dimensions (warmth and competency) and their sub-factors ("presenting friendly attitudes," "showing personality traits," "providing service and help" and "promoting social interaction and sharing") of Airbnb homestay hosts' affinity-seeking strategies. The framework shows a positive relationship between these strategies and guests' affection and behavior in response. Practical implications: The research findings provide valuable insights to hosts for improving their affinity and strengthening their competitive advantages. They also offer guidance to destination management organizations on how to build a positive destination image. Originality/value: To the best of the authors' knowledge, this study is the first to conceptualize Airbnb homestay hosts' affinity-seeking strategies. It contributes to the literature by incorporating social cognition theory and service theory in the analysis of these strategies. [ABSTRACT FROM AUTHOR]

Although travelers tend to consider multi-dimensional hotel information when choosing their accommodation, few online travel agency (OTA) websites allow them to express their preferences and expectations for the selection criteria to obtain customized hotel ranking results. The lack of this function makes travelers have to spend extra time and effort in comparing different hotels to make the final decision. To solve this problem, a hotel ranking method considering travelers' preferences and expectations is proposed based on multi-dimensional hotel information. In the method, considering the travelers' actual process of hotel reservation through the OTA website, four types of hotel information (i.e., price, rating, location and text comment) are used. To make full use of these information, text mining, prospect theory and multi-attribute decision-making method are integrated into the proposed method. A case study is given to verify the reliability of the proposed method. The proposed method can be embedded into OTA websites to provide decision support for travelers' hotel reservation, which will reduce the time spent by travelers in hotel search and comparison, thus effectively promote hotel reservation and improve traveler satisfaction. [ABSTRACT FROM AUTHOR]

In recent years, atomically precise metal nanoclusters (NCs) have demonstrated fascinating applicability in solar energy conversion due to their unique atomic stacking arrangements, quantum size effect and abundant active sites. Nevertheless, ultra-short carrier lifetimes and poor stabilities impede the construction of robust metal-NC-based photosystems. Herein, positively charged poly(diallyl-dimethylammonium chloride) (PDDA) and tailor-made negatively charged L -glutathione (GSH)-capped Agx@GSH [Ag9(SG)6, Ag16(SG)9 and Ag31(SG)19] NCs are used as building blocks and alternately deposited on a one-dimensional wide-band-gap metal oxide (MO) substrate using a facile and general layer-by-layer (LbL) assembly strategy to design a highly efficient spatially directional charge transport pathway in multilayered photoanodes. The electrons photoexcited from the periodically stacked Agx@GSH [Ag9(SG)6, Ag16(SG)9 and Ag31(SG)19] NC layers concurrently and unidirectionally migrate to the MO substrate, while the ultrathin PDDA intermediate layer serves as an interfacial electron-withdrawing charge mediator to accelerate the transport of the carriers photoexcited over the neighboring Agx@GSH [Ag9(SG)6, Ag16(SG)9 and Ag31(SG)19] NC layers. Benefiting from the charge transfer enhancement, these LbL-assembled multilayered MO/(PDDA/Agx NCs)n heterostructured photoanodes demonstrate significantly enhanced photoelectrochemical water oxidation performance under visible light irradiation due to the generation of unidirectional and spatially separated tandem charge transfer channels, which remarkably reduces the charge recombination and prolongs the carrier lifetime of the metal NCs. Moreover, we ascertain that this conceptual multilayered nanoarchitecture design and tandem charge transport are universal. [ABSTRACT FROM AUTHOR]

This article explores the two‐interval, two‐alternative forced‐choice (2I2AFC) paradigm in signal detection theory (SDT), which can also be called the paired A‐Not A with AB and BA pairs in the sensory and consumer fields. The article describes how to estimate the parameters and their variances from the psychometric function of the method. Tables and R codes were developed and are provided for the estimations. The performance of the method is evaluated. It shows that the paired A‐Not A with AB and BA pairs is more effective and powerful than the conventional A‐Not A and the A‐Not A with reminder (A‐Not AR) tests. An extension of the method, that is, the ratings of the paired A‐Not A with AB and BA pairs is also explored. A facial shine case study applying this method is provided. Practical applications: As one of the paired versions of sensory discrimination methods, the paired A‐Not A with AB and BA pairs has wide potential applications to detect and measure sensory difference, especially to detect and measure before‐after treatment effects of products. The method can be used to detect both product effects and serving order effects. [ABSTRACT FROM AUTHOR]

Artificial electric field algorithm (AEFA) is a potential global optimization algorithm proposed in recent years and has been successfully applied to various engineering optimizations. However, precocious convergence tends to occur when solving complex engineering optimization problems. To avoid premature convergence to some extent, an artificial electric field algorithm with inertia and repulsion (IRAEFA) is proposed. The IRAEFA algorithm introduces the inertia mechanism and the repulsion between charges, expands the search space, increases the diversity of population, balances the exploration and development ability of the algorithm, and avoids the algorithm falling into the local optimal solution. Finally, the IRAEFA algorithm is used to solve the spherical mining spanning tree (MST) problem, and the results obtained are compared and analyzed with the results of other well-known metaheuristics optimization algorithms. Experimental results show that the proposed algorithm has better performance than other algorithms in solving spherical MST problems. [ABSTRACT FROM AUTHOR]