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TiO2 crystals are widely used in photocatalytic processes due to their low cost and fabulous catalytic performance. As described in our previous study, three types of TiO2 with the main surfaces of {101}, {001} and {100} were synthesized. In this study, the three types of TiO2 are used to investigate roxithromycin (ROX) photocatalytic degradation kinetics and the pH effect. For photocatalytic degradation, the obtained data have shown that the overall order of optimal degradation is shown as {101} > {001} > {100}. The photooxidation kinetics for {101} facet conforms to first-order kinetics at from pH 5 to pH 10, and most of the photooxidation kinetics for {001} and {100} facets are fitted well with the zero-order and second-order kinetics, respectively. The pH effects are varied to the three types of TiO2, of which {101} has the best degradation effect at pH values 4, 7 and 8, while {001} works best at pH 5 or pH 6, and {100} has a relatively obvious effect at pH 4 and pH 9. The relation between adsorption and oxidation has been tested and proved that the strong adsorption corresponds to the fast oxidation.

MnO2 is one of the most widespread and cheapest materials in nature that can both adsorb arsenic and oxidize arsenite [As(III)] to arsenate [As(V)]. In this study, column β-MnO2 [CM] with the main facet of {110} and pincer β-MnO2 [PM] with the facets of {110} and {101} are synthesized and used to remove arsenic in water under different conditions. For the adsorption process, the experimental data are fitted well with the pseudo second-order kinetic model; the Langmuir model is better than the Freundlich model to describe the adsorption equilibrium isotherms. Furthermore, the As(III) oxidation rate can be denoted by the pseudo zero-order kinetic model and is related to the O2 concentration, the pH value, the light source and the initial concentration of As(III). Finally, the oxidation mechanism is investigated, and the oxidant should be related to O2. It is interesting to find that these two kinds of β-MnO2 exhibit different pH effects for both adsorption and oxidation. For As(III), the adsorption and oxidation abilities of CM follow the order pH 9 > pH 7 > pH 4, whereas the adsorption and oxidation orders of PM are pH 4 > pH 7 > pH 9.

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The massively available data about user engagement with online information service systems provides a gold mine about users' latent intents. It calls for quantitative user behavior modeling. In this paper, we study the problem by looking into users' sequential interactive behaviors. Inspired by the concepts of episodic memory and semantic memory in cognitive psychology, which describe how users' behaviors are differently influenced by past experience, we propose a Long- and Short-term Hawkes Process model. It models the short-term dependency between users' actions within a period of time via a multi-dimensional Hawkes process and the long-term dependency between actions across different periods of time via a one dimensional Hawkes process. Experiments on two real-world user activity log datasets (one from an e-commerce website and one from a MOOC website) demonstrate the effectiveness of our model in capturing the temporal dependency between actions in a sequence of user behaviors. It directly leads to improved accuracy in predicting the type and the time of the next action. Interestingly, the inferred dependency between actions in a sequence sheds light on the underlying user intent behind direct observations and provides insights for downstream applications.

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Temporal sequence data, such as event logs collected from industrial operations, often contain both sequential and time interval information. These two types of information can be used to define periodic patterns. Due to random disturbance, the occurrence of such periodic patterns may be drifted from their expected positions or time, resulting in asynchronous periodic patterns (APPs). The major limitation of existing studies on APPs is their reliance on subjective and case-specific determination of tolerance to the asynchrony. In real-world practice, however, knowledge on such tolerance values may be extremely limited, if not unavailable. To address this limitation, this paper formulates the asynchrony tolerating as a hierarchical clustering of time intervals embedded in the temporal sequence data. The clustering method is improved to balance the trade-off between data similarity and pattern interpretability. Based on the symbol sequence generated by the improved clustering method, APPs are detected with the proposed convolution-based periodicity detection algorithm. The effectiveness of the proposed approach is demonstrated with both numerical simulation experiment and real-world case study.

The integration of quality improvement and manufacturing system management has emerged as a promising research topic in recent years. Since operators’ performance variation can be reflected in product quality, workforce performance evaluation should be conducted with quality-based metrics to improve product quality as well as manufacturing system productivity. In this article, a methodology incorporating regression modeling and multiple comparisons is proposed to aid the performance evaluation. The effects of other impacting factors that contribute to operators’ performance variation are quantified with a robust zero-inflated Poisson regression model. The model coefficients are analyzed with multiple hypothesis tests to identify underperforming machines. Two statistical charts used in multiple comparisons are adopted for identifying underperforming operators. A case study with data from a real-world production system and a simulation experiment are presented to demonstrate the proposed methodology.

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) collaboratively play central roles in intelligence gathering and control in urban/border surveillance and crowd control. In this paper, we first propose a comprehensive planning and control framework based on dynamic-data-driven, adaptive multi-scale simulation (DDDAMS). We then discuss proposed algorithms enabling DDDAMS capability based on several methods such as 1) Bayesian-based information aggregation/disaggregation, 2) dynamic information updating based on observation/simulation, 3) temporal and spatial data fusion for enhanced performance, 4) multi-resolution strategy in temporal tracking frequency, and 5) cached intelligent observers. Finally, preliminary results based on the proposed framework, algorithms, and testbeds are discussed.

By using the "Six climatic Qi"-wind, heat, fire, dampness, dryness, and cold-to classify diseases, to mark the innate property of viscerae, and by using the theory of engendering-restraining-overwhelming-rebellion of the six climatic Qi and its conversion to elucidate the new theme of "extreme hyperactivity might conversely overcoming the Qi it is to be converted", and expound the specificity that fire-heat is apt to combine and assimilate with wind, dampness, and dryness. Liu Wansu proposed a complete "Theory of Six Climatic Qi for Mechanism of Diseases". By inheriting the main points of Liu's such theory, Zhang Yuansu studied the theory of prescription and medication in accordance with the "Six climatic Qi cause diseases" and classified prescriptions and medicines on the basis of "Six climatic Qi", thus, developed Liu's such theory as the guideline for syndrome differential diagnosis to directly handle clinical practice. In the meantime, he also promoted the theory of visceral syndrome differential diagnosis, embodying the mutual complementary property of theories for syndrome differentiation by both viscera and six climatic Qi.

With the explosion of big data, companies both small and large are increasingly motivated to make data-driven decisions. For web-based companies in particular online controlled experiments or A/B tests have become essential scientific tools for decision-making. Large scale organizations like Google, Amazon, eBay, Facebook, LinkedIn, Yahoo, and Microsoft have built mature systems and support for controlled experiments and have helped to popularize the methodology of A/B testing for guiding product development. In e-Commerce, A/B tests are used extensively to understand how customers respond to new features and to use statistics at scale to make decisions rather than relying on the highest paid person's opinion. However, unlike other web-based businesses, e-Commerce exhibits particular qualities and behaviors where generalized controlled experimentation guidelines are not nuanced enough to provide meaningful insight into decision-making. Specific functions of an e-Commerce website present further complexity in testing, especially in essential and highly visible functions like search. Per week, more than 260 million customers visit Walmart's retail units and e-Commerce websites. A single decision can result in not only large financial repercussions but also cascading effects across such an expansive customer base that traverses between digital and physical purchase points. For the retail sector at large, e-Commerce is a new business as most operations are still primarily physical. The transformation to web-focused purchase points is burgeoning but the application of data-driven decision-making remains difficult. Generalized guidelines from technology companies have not been able to fully serve the problems specific to e-Commerce. In this paper, we discuss our experiences in running controlled experiments at WalmartLabs Search and specific guidelines in bias, test design, measurement, analysis, financial constraint and decision-making for e-Commerce. We share examples and key lessons from our work in each of these focal areas where we believe the reader will benefit from interpreting real results. Our work provides the growing e-Commerce analytics community with guidelines for running experiments within their own retailers and highlights sector specific challenges in the application of controlled experiments.

As a book with significant historical materials for the research of impediment disease not to be ignored, Shen nong ben cao jing (Shennong's Classic of Materia Medica) contains, among its 365 kinds of materia medica, 75 kinds of medicines related to impediment disease. Among the latter ones, 51 are related to "damp impediment", "cold-damp impediment", "wind-cold-damp impediment", "wind impediment", "wind-damp impediment", and "cold impediment"; 16 are related to "laryngeal impediment", "general impediment", and "blood impediment"; 8 are related to "muscular impediment", "stomach impediment", "hernia-conglomeration impediment", "internal consumptive-thirst impediment", "wilting impediment", and "hemiplegic impediment". To systematically analyze the properties, tastes, and effects of these medicines and its related knowledge, and then further explore the overall recognition and their treating experience of the physicians at that period would not only enrich the historical research on impediment disease, but also offer much help and reference to the understanding and treatment of impediment for contemporary clinicians.

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) collaboratively play important roles in crowd tracking for applications such as border patrol and crowd surveillance. Dynamic data-driven application systems (DDDAS) paradigm has been developed for these applications to take advantage of real-time monitoring data. In the DDDAS paradigm, one crucial step in crowd surveillance is crowd dynamics modeling, which is based on multi-resolution crowd observation data collected from both UAVs and UGVs. Data collected from UAVs capture global crowd motion but have low resolution while those from UGVs have high resolution information of local crowd motion. This paper proposes an information–aggregation approach for crowd dynamics modeling by incorporating multi-resolution data, where a grid-based method is developed to model crowd motion with UAVs’ low-resolution global perception, and an autoregressive model is employed to model individuals’ motion based on UGVs’ detailed perception. A simulation experiment is provided to illustrate and demonstrate the effectiveness of the proposed approach.

eCommerce is one of the popular electronic services available in the vast Internet world. eCommerce endpoints, also called eCommerce websites, in general, are a composition of several web pages. Within eCommerce endpoints, there exist specific web page types that are abnormal in their consumption of information and user behavior called focal spots. Finding a focal spot is key for understanding and improving the human interaction interface on eCommerce endpoints. In order to make business decisions concerning these focal spots, decision analytics teams are employed to identify focal spots with abnormal consumer perception and to address areas in which to expand business. We propose a methodology for transforming user activity data into useful business analytics to find focal spots if any. In this work, we developed a prototype of a one-stop solution for non-technical users to understand customer response analysis on a given eCommerce endpoint. The proposed system, ‘eCommerce Consumer Analytics Tool (eCCAT)’, consists of a data extraction and automated analysis component and a visualization component. The interactive tool further provides a way to find a page in the eCommerce endpoint with an extreme key performance indicator.

Turbochargers are now commonly used in modern automotive engines, which increase the density of air entering the engine to produce more power. This device not only greatly improves the degree of engine efficiency, but also reduces the pollutant emissions. However, one of the important issues which must be considered is the noise from turbochargers. This noise can radiate either after propagation through the intake and exhaust ducts or via induced vibrations in the turbocharger housing. Here the in duct sound will be studied and normally this cause problems mainly on the intake side where there is less silencing available. In particular noise from high frequency tones as well as surge noise phenomena can cause noise problems.The main aerodynamic noise from centrifugal compressors are blade tone noise, buzz-saw noise and flow separation (or surge) noise. For instance the tones occurring at blade passing frequencies can be a problem for large turbochargers,e.g., as found on trucks, where they lie in the range around 10 kHz. The most efficient way to achieve quieter machines is the reduction of the sources, therefore it is important to be able to measure and investigate the sound generation ofturbo chargers.In this thesis work the sound generated has been investigated using a dedicated turbocharger acoustic test rig. The main purpose is to investigate methods for estimating the in-duct acoustic power using a micro-phone array. Three differen tmodels are proposed to estimate the power in the propagating acoustic modes and to suppress the flow noise. In order to test the proposed methods experiments have been performed on an automotive compressor at four different operating conditions. The results are compared with the literature in order to identify the dominating sound generation mechanisms.