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Background The effects of electromagnetic pulse (EMP) radiation on cognitive impairment have attracted much attention, but the mechanism is still unclear. Regulation of brain-derived neurotrophic factor (BDNF) gene expression has been found to promote memory formation and neuronal survival. Isoflurane preconditioning (IP) was reported to have a neuroprotective effect. In this study, we verified the protective effect of IP against brain injury induced by EMP exposure and examined the relation of this effect with BDNF gene regulation. Methods Twenty-four hours before EMP exposure, rats were pretreated with 2% inhaled isoflurane for 30 minutes. At 24 hours after EMP injury, the Morris water maze test was carried out. Meanwhile, the other rats were executed and their brain tissues were used for Nissl staining, qRT-PCR, western blot and chromatin immunoprecipitation. Results The Morris water maze results showed that 2% IP improved the spatial learning and memory ability of the rats. The Nissl staining results showed 2% of IP alleviated neuronal damage. Also, we detected the mRNA and protein expression of BDNF, and 2% IP significantly increased the expression of BDNF. We also found the expression level of histone deacetylase 2 (HDAC2) was increased and that EMP exposure significantly decreased H3 acetylation, while 2% IP reversed these phenomena, individually, BDNF transcription was activated, and neurogenesis after EMP exposure was alleviated. Conclusions Our results suggested that 2% of IP alleviates cognitive impairment induced by EMP exposure in rats. Also, the sustained elevated level of BDNF gene transcription may be an essential mechanism for stimulating neurogenesis because of the increased level of HDAC2-dependent H3 acetylation.

The CsI detectors of the High Energy X-ray Telescope of the Hard X-ray Modulation Telescope (HXMT/CsI) can be used for gamma-ray all sky monitoring and searching for the electromagnetic counterpart of gravitational wave source. The instrumental responses are mainly obtained by Monte Carlo simulation with the Geant4 tool and the mass model of both the satellite and all the payloads, which is updated and tested with the Crab pulse emission in various incident directions. Both the Energy-Channel relationship and the energy resolution are calibrated in two working modes (Normal-Gain mode & Low-Gain Mode) with the different detection energy ranges. The simulative spectral analyses show that HXMT/CsI can constrain the spectral parameters much better in the high energy band than that in the low energy band. The joint spectral analyses are performed to ten bright GRBs observed simultaneously with HXMT/CsI and other instruments (Fermi/GBM, Swift/BAT, Konus-Wind), and the results show that the GRB flux given by HXMT/CsI is systematically higher by 7.0 ± 8.8 % than those given by the other instruments. The HXMT/CsI-Fermi/GBM joint fittings also show that the high energy spectral parameter can be constrained much better as the HXMT/CsI data are used in the joint fittings.

PurposeSince business competition has become more intense throughout the world, most enterprises are seeking to engage in business cooperation with other partners in order to enhance their competitive strengths. However, they do not necessarily develop mature information technologies’ (ITs) capabilities and skills internally but rather outsource them to IT providers. Therefore, the benefits received by firms which adopt the approach of business cooperation with IT providers have become an interesting issue for managers and shareholders.Design/methodology/approachThis study adopted an event study methodology for apprising the short-term business value from the stock market. The authors predicted that investors will react as they receive news coverage about the strategy of business cooperation between outsourcing firms and an IT provider, International Business Machines (IBM) Corporation. The authors then collected all news coverage regarding the firms which had announced business cooperation with IBM and observed different types of abnormal returns.FindingsOn analyzing 53 announcements of cooperation with IBM from 2008 to 2016, the authors found that the announcement of business cooperation had a significantly positive influence on companies' market value.Originality/valueTo the best of the authors’ knowledge, this is the first study to investigate the issue for market reaction to the announcement of business cooperation with IBM.

Drug addiction is a serious health problem prevalent worldwide. Currently available therapies including pharmacotherapy and psychotherapy are insufficient to meet the clinical needs for treating drug abuse. Recently, immunotherapy has emerged as a promising approach to treat such drug-use disorders. Pharmacokinetic antagonists are used in immunotherapy, functioning by sequestering the drugs in the periphery but without allowing the drug to cross the blood-brain barrier. This can reduce the toxic and rewarding effects of the drugs, while preventing addiction and facilitating reduced relapse rates. Herein, we update recent developments in the immunotherapeutic strategies to treat abuse of drugs like methamphetamine, heroin and cocaine. In addition, we summarize the drug design used so far and its optimization strategies. Further, we document the efficacy of anti-drug vaccines and monoclonal antibodies, with an aim to promote development of new anti-drug immunotherapies.

Purpose As the application of gamification is gaining great attention and has grown increasingly, thousands of these applications can be easily obtained from mobile phone stores, thus causing intensified competition and discontinuance of use accordingly. Besides, though understanding what factors influence the discontinuance of use of information systems (ISs) is critical for theoretical as well as practical reasons, studies pertaining to the saliency of the final phase, termination of an IS, are still limited. As such, the purpose of this paper is to propose a holistic view to fulfill the above-mentioned research gaps based on the expectation-confirmation model with other salient factors such regret, habit and gamification app values. Design/methodology/approach The context of a fitness gamification app is investigated. A total of 210 valid responses were received, and structural equation modeling was applied for data analysis. Findings The findings of this paper are as follows: among all factors influencing discontinuance intention, regret is the strongest, habit is second and gamification is third; among all factors affecting user satisfaction, gamification app value is the strongest, confirmation is second, perceived usefulness (PU) and perceived ease of use are third and regret is the last one; for factors influencing users’ habits, satisfaction is the strongest, following by PU and frequency of prior use; confirmation negatively influences the degree of regret; and confirmation positively influences PU. Originality/value This study highlights the important determinants influencing users’ discontinuance intentions in the context of gamification apps by incorporating two overlooked factors, regret and habit. Besides, this study suggests that app designers can not only increase user’s perceived value through external cooperation with other alternatives, but can be through internal enhancement with diverse services development as well.

The Internet of Things connects devices or sensors to the Internet for data collection and exchange. These pieces of data provide rich information about production and inventory as well as sales and marketing efforts.

Recently, the Internet of things (IoT) has become most popular topic in various industries. For enterprises, IoT is not just the biggest buzz word but a developing trend, a proven strategy, and an innovative technology. Enterprises conducting IoT reach a turning point, but might also face diverse challenges related to technical or managerial aspects. Various challenges will be encountered, depending on different perspectives, i.e. the industry’s first movers or second movers. Therefore, the impact of implementing conducting IoT will also vary. Previous studies have only focused on background technologies of IoT and applications. Meanwhile, no studies have discussed the impact of IoT implementation on firm performance. This study attempts to fill this gap. From the internal perspective, we want to comprehend the view of managers or shareholders to explore performance after enterprises have adopted IoT. In order to measure such practices have positive impacts on enterprises’ financial performance, this study adopts three approaches to estimate the performance of IoT implementation vis-a-vis financial performance, productivity, and market value. We collected secondary data to perform a quantitative analysis with three dummy variables, including IoT adopters, first movers, and better performers. We hope to provide a complete reference to enterprises through different indicators, which will facilitate assessment of enterprises’ implementation of IoT and also provide a more in-depth understanding of the impact of IoT.

We make the in-orbit calibration to the point-spread functions (PSFs) of the collimators of the Hard X-ray Modulation Telescope with the scanning observation of the Crab. We construct the empirical adjustments to the theoretically calculated geometrical PSFs. The adjustments contain two parts: a rotating matrix to adjust the directional deviation of the collimators and a paraboloidal function to correct the inhomogeneity of the real PSFs. The parameters of the adjusting matrices and paraboloidal functions are determined by fitting the scanning data with lower scanning speed and smaller intervals during the calibration observations. After the PSF calibration, the systematic errors in source localization in the Galactic plane scanning survey are 0.010 deg, 0.015 deg, 0.113 deg for the Low-Energy Telescope (LE), the Medium-Energy telescope (ME) and the High-Energy telescope (HE), respectively; meanwhile, the systematic errors in source flux estimation are 1.8%, 1.6%, 2.7% for LE, ME and HE, respectively., Comment: 15 pages, 27 figures, accepted for publication in JHEAP

Big data has been widely discussed for several years. However, whether the implementation of big data really bring in observable benefits on firm performance remains a critical issue for the top management team. In this study, we investigate the association between big data implementation and the corresponding financial performance, productivity, and market value. Our results demonstrate that big data implementation is positively related to an improvement in financial performance and the market value but such effect is not stronger for first movers. Implications are discussed.

Among the many types of ambiguous and diverse human behaviors, preference ranking and preferred temporal relationships are two human subjective perceptions, and both can be expressed as sequences. For example, information management researchers prefer journal A over B, and this preference can be represented as the ranking sequence: (A > B). When the order of submission is first A and then B, it can be expressed as the temporal sequence: (A → B). In practice, these two preference sequences may be applicable to people with regard to the same items at the same time, which is called “two-dimensional preference” in this study. Based on these concepts, this study defines a novel model and an associated algorithm for mining two-dimensional patterns by combining preference ranking and temporal sequences. The discovered two-dimensional patterns can be categorized into eight types, including consensus, ranking-compromise, temporal-compromise, and conflict patterns. Two experiments in two application areas, namely journal submission and stock purchase, were designed to collect authentic datasets, and demonstrate their managerial meaning. The experimental results show that in most cases, the ordering of the temporal sequences follows the preference ranking sequence, except for several two-dimensional patterns involving high-risk items.

Detecting changes of behaviors or events is crucial when updating existing knowledge in a dynamic business environment. Currently, data analysts can immediately collect data and easily access existing knowledge. However, that knowledge can also rapidly become outdated. This study discusses a form of knowledge, classifiable sequential patterns ( CSP s), defined as s → c , where s is a temporal sequence; c is a class label; and “→” is a sign which implies the sequential relationships between s (cause) and c (effect). If the CSP evolves into another, and the new knowledge is not updated, decision-makers would continue to work with the obsolete CSP . To the authors' knowledge, no study has addressed the topic of change mining in CSPs. To address this research gap, this study proposes a novel change-mining model, SeqClassChange , to identify changes in CSPs. Experiments were conducted with a real-world dataset to evaluate the proposed model.

Temporal pattern mining problems, developed from sequential pattern mining problems, have recently been discussed frequently regarding the gathering of temporal sequences and aggregating them in order to gain insight into consensus decision-making. Existing temporal pattern mining problems reveal only point-based relations; however, in reality, several interval-based circumstances exist, which enable precisely describing temporal relationships. Practical applications include the order and duration of investors purchasing stocks and portfolio management. This study proposes a novel model and its associated algorithm for identifying consensus and conflict patterns from user-provided subjective interval-based temporal sequences. We conducted an experiment on stock investments in the semiconductor industry by drawing on collected authentic datasets and user ratings to demonstrate the model’s effectiveness. The experimental results reveal six consensus patterns and one pair of conflict patterns from the participants’ subjective investment intuitions, which is consistent with common sense concerning the semiconductor stock market.

The relationship between structure and refractive index for SiO2-B2O3- Ta2O5-ZrO2-Na2O system glasses was investigated via Raman spectrum and V-block technology. The results showed that refractive index of the borosilicate glasses is mainly influenced by network structure such as planar [BO3] triangle, [BO4] tetrahedron and [SiO4] tetrahedron. Refractive index decreases from 1.629 to 1.616 when B2O3 content increases from 15 mol% to 50 mol%. Na2O component has a strong preference to provide non-bridging oxygen (NBO) atoms, which not only promotes the conversion of [BO3] to [BO4] unit but also depolymerizes the network structure. The refractive index has the highest value, =1.6264, when Na2O content reaches to 28 mol%. Both ZrO2 and Ta2O5 can promote structure formation of borosilicate glasses and make higher connection degree. However, the refractive index increasing with Ta2O5 addition is quicker than that with ZrO2 addition.

This study proposes a model to detect the change in students' learning performance.This model helps educators to understand learning behaviors in dynamic environments.Experiments are conducted with real-life datasets to evaluate the effectiveness of the model. In recent years, as information technology has become more prevalent, learning management systems have arisen around e-learning and web-based platforms. As a result, huge quantities of data about students' learning process have been recorded and stored. Teachers can apply data-mining techniques to mine students' learning performance. One such technique is association rule mining, which can find correlations between student characteristics and performance. For instance, a rule (Attendance=Middle)?(Gender=Male)?(Semester=Low) indicates that the semester grade of students is at the Low level if their gender is Male and attendance rate is Middle, where Low and Middle are predetermined linguistic terms given by teachers. Teachers can rely on such rules to formulate their teaching strategies. However, these rules may be varied in different semesters because of the change of student characteristics or teaching method of teachers. The above rule is used to describe student behavior during the last semester, yet, within this semester, the rule changes to (Attendance=Low)?(Gender=Female)?(Semester=Low). Without updating this knowledge, teachers might adopt inappropriate teaching strategies for students who are learning in different ways across different semesters. In this study, we propose a new change mining model to discover the change in student learning performance and characteristics on the basis of association rules. We conducted experiments with real-life datasets to evaluate the effectiveness of the proposed model.

// Zhongqian Hu 1, 2, * Xinyi Wang 1, 3, * Haoshu Fang 1 , Yakun Liu 1 , Danlei Chen 1 , Qian Zhang 1 , Xia Liu 1 , Daoyan Wei 1 , Chengkui Qu 1, 4 , Siying Wang 1 1 Department of Pathophysiology, Anhui Medical University, Hefei 230032, China 2 Department of Ultrasound, Zhongda Hospital, Southeast University, Nanjing 210009, China 3 Department of Clinical Medicine, Anhui Medical University, Hefei 230032, China 4 Department of Pediatrics, Aflac Cancer and Blood Disorders Center, Emory University School of Medicine, Atlanta 30322, GA, USA * These authors have contributed equally to this work Correspondence to: Siying Wang, e-mail: sywang@ahmu.edu.cn Keywords: breast cancer, SHP2, D61G mutation, tumor, gain-of-function Received: June 25, 2015 Accepted: November 25, 2015 Published: December 10, 2015 ABSTRACT Background: Evidence suggests that Src homologous protein phosphotyrosyl phosphatase 2 (SHP2) mutations promote cancer development in several solid tumours. In this study, we focused on the in vivo and in vitro effects of an SHP2 mutation on the breast cancer phenotype to determine whether this mutation is correlated with a malignant phenotype. Methods: Mutant PTPN11 cDNA (D61G) was transduced into MDA-MB231 and MCF-7 cells. The effects of the D61G mutation on tumourigenesis and malignant behaviours, such as cell adhesion, proliferation, migration and invasion, were examined. Potential underlying molecular mechanisms, i.e., activation of the Gab1-Ras-Erk axis, were also examined. Results: In vitro experiments revealed that tumour adhesion, proliferation, migration and invasion were significantly increased in the SHP2 D61G mutant groups. Consistently, in vivo experiments also showed that the tumour sizes and weights were increased significantly in the SHP2 D61G-MB231 group (p < 0.001) in association with tumour metastasis. Mechanistically, the PTPN11 mutation resulted in activation of the Ras-ErK pathway. The binding between Gab1 and mutant SHP2 was significantly increased. Conclusion: Mutant SHP2 significantly promotes tumour migration and invasion at least partially through activation of the Gab1-Ras-Erk axis. This finding could have direct implications for breast cancer therapy.

In real-life circumstances, managers often have to face various decision-making problems. Among them, the topic of multi-criteria decision making (MCDM) is one of the most important and complicated problems in the decision-making field. In general, the element of MCDM can consist of three parts: input, output, and the solution approach. The input is expressed as m alternatives with n criteria. It usually assumes that the underlying input data can be represented in a decision table. Correspondingly, the output is an optimal outcome, or a set of outcomes, resolved by different kinds of solution approaches. In this study, we propose a summarization technique to display the decision table as a new summarization table; allowing managers to make decisions more quickly. The proposed technique is to optimize a summarization result so that the degree of information lost is minimum. Since seeking for a minimum result is an NP-hard problem, we applied a genetic algorithm to improve the summarization result. In real-life circumstances, managers have to face various decision-making problems.MCDM is one of the most important problems in the decision-making field.The input data of MCDM can be represented in a decision table.We propose a summarization technique to display it as a new summarization table.

Previous studies exploring adoption in the field of management information systems have focused on the objects of information systems or information technologies. However, since the popularity of smart phones has led to the usage of apps, limited investigations have attempted to understand the usage intention of apps. In this study, a research model is proposed to comprehend how users perceive and adopt a gamification app in the initial adoption phase of the information system life cycle. Since there are various topics within gamification services, we only pinpoint one popular type of service, exercise, as the research subject. The investigation involving 173 usable subjects was conducted to evaluate the model by using structural equation modelling. The result demonstrates that our proposed model explains 56.7% of the variance. The findings have interesting implications with respect to the initial usage of exercise gamification apps, both for researchers and practitioners.

Numerous mobile apps have been developed for making our lives more convenient and improving our quality of life. Health apps are among them. These types of apps are designed to help users for recording their health-related behaviors and to give advice about improving users' physical conditions. However, users frequently do not continue to use these health apps. As a result, the companies of health apps have paid the development cost but cannot get back the benefit from the apps they launch. To find out the reason, this study conducts an empirical investigation and develops a trade-off dual-factor model to dissect the reason why users discontinue use of health apps. The research model is based on the perspectives of information disclosure and expectation-confirmation theory. Users may worry about the disclosure of individual health privacy; however, on the other hand, they enjoy the functions of health apps, proffering various kinds of health-related assistance. The decision of whether or not to continue using this kind of app turns into a trade-off issue. To delve into the determinants, we conduct an online survey and collect 242 qualified responses as our research samples. Structural equation modeling is employed to analyze the samples in this study. The result reveals that our research model explains 31% of the variance. The findings and implications can serve as references for researchers and practitioners.

ObjectiveTo investigate the effect of BMSCs transplantation plus hyperbaric oxygen (HBO) on repair of rat SCI.MethodsSeventy five male rats were divided randomly into five groups: sham, vehicle, BMSCs transplantation group, combination group, 15 rats in each group. Every week after the SCI onset, all animals were evaluated for behavior outcome by Basso–Beattle–Bresnahan (BBB) score and inclined plane test. Axon recovery was examined with focal spinal cord tissue by electron microscope at 6 weeks after the SCI onset. HE staining and BrdU staining were performed to examine the BMSCs and lesion post injury. Somatosensory evoked potential (SEP) testing was performed to detect the recovery of neural conduction.ResultsResults from the behavior tests from combination group were significant higher than rats which received only transplantation or HBO treatment. Results from histopathology showed favorable recovery from combination group than other treatment groups. The number of BrdU+ in combination group were measureable more than transplantation group (P

The needs of long-term care have increased substantially worldwide.We evaluate the system usage performance and continued use intentions of LTCIS.The result demonstrates that our proposed model explains 62.7% of the variance. The needs of long-term care (LTC) have increased substantially worldwide. In particular, Taiwanese society has experienced an increasing aging population because of the sharp decline in the birth rate and advanced medical technology. Some LTC facilities have introduced information technologies to satisfy the substantial demand. Compared with the high level of informatization observed at Taiwanese hospitals, LTC facilities still exhibit low informatization levels despite belonging to the same industry. Therefore, this study evaluated the system use performance and continued use intentions of the long-term care information system (LTCIS) by applying the well-known theories of the task-technology fit, system satisfaction, and postacceptance continuance models used in information systems area. The results showed that users' assessment of whether the LTCIS fulfilled their work and task needs primarily depended on the system quality, locatability of data, timeliness, ease of use, and system-user relationship. These factors also exerted a crucial influence on system use performance and user satisfaction. Moreover, the system use performance and user satisfaction further affected intentions of continued use.

Identification of changes in customer behavior is a major challenge that must be tackled in order to survive in a rapidly changing business environment. For example, because information technology has advanced and data-storage costs have declined, for the purpose of serving customers, numerous enterprises have employed information systems and have directly logged customer behavior in databases. This trend has motivated the development of data mining applications. Fuzzy quantitative sequential pattern mining is a functional data mining technique that is used for discovering customer behavioral patterns over time and determining the quantities of goods or services they purchase. The example term used in shopping ?[(Beer, Low)(Milk, High)] (Cola, Middle)? means that customers will first buy Beer and Milk in Low and High quantities, respectively, and then purchase Cola in Middle quantities on their next shopping trip, where Low, Middle, and High are predefined linguistic terms assigned by managers. A term such as this one provides managers with general and concise knowledge related to customer behavior and allows them to rapidly make decisions in response, especially in a competitive setting. However, literature searches indicate that no previous study has addressed the issue of changes in fuzzy quantitative sequential patterns. The aforementioned example pattern might have been available last year but might not be used this year, and it could have been substituted by ?(Beer, Middle) {(Cola, Low)(Milk, Low)}?. If this knowledge is not renewed, managers might develop inappropriate marketing plans for their products or services and use inventory strategies that are outdated with respect to time and quantities. To solve this problem, we propose a novel change-mining model that can be used for detecting changes in fuzzy quantitative sequential patterns. We conducted experiments in which we used real-world and synthetic datasets in order to evaluate the proposed model. When the pattern change was detected using the real-world dataset, the results showed that the model reveals 3 considerations that can help managers with their handling of products' marketing and production. When we studied the model's scalability by using the synthetic dataset, the results showed that even though all run times increased when parameter values were decreased, the model remained scalable.

The aggregation of individuals’ preferences into a consensus ranking is a group ranking problem which has been widely utilized in various applications, such as decision support systems, recommendation systems, and voting systems. Gathering the comparison of preferences and aggregate them to gain consensuses is a conventional issue. For example, b > c ⩾ d ⩾ a indicates that b is favorable to c, and c (d) is somewhat favorable but not fully favorable to d (a), where > and ⩾ are comparators, and a, b, c, and d are items. Recently, a new type of ranking model was proposed to provide temporal orders of items. The order, b&c → a, means that b and c can occur simultaneously and are also before a. Although this model can derive the order ranking of items, the knowledge about quantity-related items is also of importance to approach more real-life circumstances. For example, when enterprises or individuals handle their portfolios in financial management, two considerations, the sequences and the amount of money for investment objects, should be raised initially. In this study, we propose a model for discovering consensus sequential patterns with quantitative linguistic terms. Experiments using synthetic and real datasets showed the model’s computational efficiency, scalability, and effectiveness.

The aggregation of individuals' preferences into a consensus ranking is a decision support problem which has been widely used in various applications, such as decision support systems, voting systems, and recommendation systems. Especially when applying recommendation systems in business, customers ask for more suggestions about purchasing products or services because the tremendous amount of information available can be overwhelming. Therefore, we have to gather more preferences from recommenders and aggregate them to gain consensuses. For an example of preference ranking, C>A>=D>=B indicates C is favorable to A, A is somewhat favorable but not fully favorable to D, and ultimately D is somewhat favorable but not fully favorable to B, where > and >= are comparators, and A, B, C, and D are items. This shows the ranking relationship between items. However, no studies, to the best of our knowledge, have ever developed a recommendation system to suggest a temporal relationship between items. That is, ''item A could occur during the duration of item B'' or ''item C could occur before item D''. This type of recommendation can be applied to the reading order of books, course plans in colleges, or the order of taking medicine for patients. This study proposes a novel recommendation model to discover closed consensus temporal patterns, where closed means the patterns are only the maximum consensus sequences. Experiments using synthetic and real datasets showed the model's computational efficiency, scalability, and effectiveness.

While improving decisional quality is important to businesses, continued use of DMTs is a critical issue for managerial personnel. This problem mainly concerns the willingness of an individual to participate in the behavior. It can be further defined in a routine-based working behavior. This problem essentially involves three key issues, task fit, technology use, and habit. This study therefore integrates task-technology fit (TTF) model, expectation–confirmation mode (ECM), and habit, to examine the determinants of continued use of DMTs. Prior studies have focused on intention to use DMTs in the first time and only considered part of the three issues for identifying the determinants. 285 respondents from managerial personnel were collected to empirically evaluate this research model. The three issues are all important in influencing continuance use intention of DMTs. In particular, the task-technology fit indicates a direct effect on two factors of the technology use issue, user satisfaction and perceived usefulness, and an indirect effect on continuance use intention. User satisfaction and perceived usefulness, and habit are the key predictors of continuance use intention.

Traditionally, clustering is the task of dividing objects into homogeneous clusters based on their degrees of similarity. As objects are assigned to clusters, users need to manually give descriptions for all clusters. Characterizing clusters by hand can consume a great deal of time of users. In addition, users sometimes have no specific idea as to how to explain the clustering results; thus, they might give inappropriate descriptions. A clustering technique is proposed to discover conjecturable rules, providing descriptions of clusters with a decision tree classification technique. Every cluster in a conjecturable tree is depicted by only one conjecturable rule. However, less-utilized rules are not necessarily trivial. In some real-life circumstances, there might be some clusters which can be depicted by two or more rules, namely, recessive conjecturable rules. For example, customers usually prefer to buy inexpensive red wines; however, on certain occasions, such for a birthday celebration, they will buy expensive wine. Therefore, we know that there are some people who generally belong to a low-value cluster but may simultaneously be assigned to a high-value one. In this study, we propose a new discovery model for mining conjecturable rules to reveal this type of knowledge. The experimental results show that our proposed model is able to discover conjecturable rules as well as recessive rules. The results of sensitivity analysis are also given for practitioners' reference.

In today's business environment, there is tremendous interest in the mining of interesting patterns for superior decision making. Although many successful customer relationship management (CRM) applications have been developed based on sequential pattern mining techniques, they basically assume that the importance of each customer is the same. Previous studies in CRM show that not all customers make the same contribution to a business, and it is indispensible to evaluate customer value before developing effective marketing strategies. Therefore, this study includes the concepts of recency, frequency, and monetary (RFM) analysis in the sequential pattern mining process. For a given subsequence, each customer sequence contributes its own recency, frequency, and monetary scores to represent customer importance. An efficient algorithm is developed to discover sequential patterns with high recency, frequency, and monetary scores. Empirical results show that the proposed method is efficient and can effectively discover more valuable patterns than conventional frequent pattern mining.

Plant light interceptions directly impact on plant photosynthesis, and thus have an impact on plant growth. According to the characteristics of plant photosynthesis and growth and development, we abstract plant growth form as the geometry. Based on solar elevation angle, solar azimuth angle and spacing of plant cultivation, we get the relationship model of plant light interception and planting cultivation form under assumptions. In the simulation environment, we set spindle trees as an example and analyze the best cultivation form of plants under the conditions of the specific latitude.

An experimental setup in dark condition was established to investigate the temperature and bias voltage dependence of the Multi-Pixel Photon Counter (MPPC), one type of the SiPM developed by Hamamatsu. The dark current of an MPPC near room temperature at a given gain can be approximated by an exponential function of temperature which is similar to the behavior of a negative temperature coefficient (NTC) thermistor. According to these facts, a gain control and stabilization circuit for MPPC is developed by using a programmable current sink with temperature compensation. Detailed design and performance analysis results of the circuit in the temperature range from 5.1 degrees C to 33.3 degrees C will be discussed in this paper. (C) 2011 Elsevier B.V. All rights reserved.

We present the low temperature testing of an SCD detector, investigating its performance such as readout noise, energy resolution at 5.9 keV and dark current. The SCD's performance is closely related to temperature, and the temperature range of −80 °C to −50 °C is the best choice, where the FWHM at 5.9 keV is about 130 eV. The influence of the neutron irradiation from an electrostatic accelerator with fluence up to 1 × 109 cm−2 has been examined. We find the SCD is not vulnerable to neutron irradiation. The detailed operations of the SCD and the test results of low temperature are reported, and the results of neutron irradiation are discussed.

A readout system for X-ray CCDs based on an improved architecture is presented; by optimizing several critical circuit blocks along the analog signal chain, the conflict between the readout speed and readout noise is greatly alleviated. Using CCD47-10 as its target CCD, the readout system has achieved 8.6e− readout noise and 142 eV FWHM at 5.9 keV Mn Kα under a pixel rate of 80 kHz. Also its performance of imaging has been investigated.

Significant advances in our understanding of the genetic defects and the pathogenesis of juvenile myelomonocytic leukemia (JMML) have been achieved in the last several years. The information gathered tremendously helps us in designing molecular targeted therapies for this otherwise fatal disease. Various approaches are being investigated to target defective pathways/molecules in this disease. However, effective therapy is still lacking. Development of specific target-based drugs for JMML remains a big challenge and represents a promising direction in this field.

The problem of mining partial periodic patterns is an important issue with many applications. Previous studies to find these patterns encounter efficiency and effectiveness problem. The efficiency problem is that most previous methods were proposed to find frequent partial periodic patterns by extending the well-known Apriori-like algorithm. However, these methods generate many candidate partial periodic patterns to calculate the patterns' supports, spending much time for discovering patterns. The effective problem is that only one minimum support threshold is set to find frequent partial periodic patterns but the results is not practical for real-world. In real-life circumstances, some rare or specific events may occur with lower frequencies but their occurrences may offer some vital information to be referred in decision making. If the minimum support is set too high, the associations between events along with higher and lower frequencies cannot be evaluated so that significant knowledge will be ignored. In this study, an algorithm to overcome these two problems has been proposed to generating redundant candidate patterns and setting only one minimum support threshold. The algorithm greatly improves the efficiency and effectiveness. First, it eliminates the need to generate numerous candidate partial periodic patterns thus reducing database scanning. Second, the minimum support threshold of each event can be specified based in its real-life occurring frequency.

This paper presents both theoretical and experimental studies of the long-term behavior of prestressed old-new concrete composite beams under sustained loads. General differential equations governing the relationship between the incremental deflection and incremental internal forces of the composite beams were deduced in the theoretical study. Closed-form solutions for simply supported composite beams were obtained and validated using test results reported in previous literature on steel-concrete composite beams. The experimental investigation consisted of static long-term load tests carried out on four prestressed old-new concrete composite beams. The behavior of the old-to-new concrete interface, time-dependent deflections, concrete strains, and prestress losses was carefully observed over 260 days. The long-term test program showed that the midspan deflections and concrete strains increased with time because of creep and shrinkage of the new prestressed concrete. The slip strains at the old-to-new concrete interface were found to be relatively small, indicating that the interface bond was sound enough to prevent slip and that the prestressing loads were effectively transferred to the old concrete. The proposed theoretical models predicted the long-term behavior of the prestressed old-new concrete composite beams with an acceptable degree of accuracy. DOI: 10.1061/(ASCE)BE.1943-5592.0000152. © 2011 American Society of Civil Engineers. CE Database subject headings: Composite beams; Creep; Shrinkage; Prestressing; Deflection; Concrete. Author keywords: Old-new concrete composite beam; Long-term deflection; Incremental model; Prestress; Creep; Shrinkage; Prestress loss; Old-to-new concrete interface.

A post-tensioned negative moment connection technique is proposed to strengthen old multiple span simply-supported T-section reinforced concrete (RC) girders. In this technique, old concrete is removed from girder ends and flanges, a diaphragm is placed between the two adjacent girder ends over the interior support and negative moment steel strands are installed in new cast-in-place concrete and post-tensioned to make continuity. A total of six two-span simply-supported T-section RC girders were cast and five of them strengthened using the provision of the proposed post-tensioned negative moment connections. Both laboratory tests and nonlinear finite element (FE) analysis were conducted to investigate the structural performance of the strengthened RC girders. The moment redistribution in the strengthened continuous RC girders was also measured and is discussed. It is shown that the proposed post-tensioned moment connection technique can reliably transfer the negative moment over the support and markedly increase member stiffness and the load-carrying capacity of existing multiple span simply-supported RC girders. The validity of the FE analysis was also demonstrated through comparisons with experimental results.

Steel Fiber Reinforced Self-stressing Concrete (SFRSSC for short) is a new type of high performance cementitious composite with self-expansive performance and high crack resistance. SFRSSC is actually a kind of expansive concrete which self-stresses, namely pre-compressive stresses, are induced by dint of some restrictions generally provided by steel bars and steel fibers to concrete expansion after hydration of expansive cement. As a result of chemical reaction, concrete achieved pre-stresses by itself different from mechanical pre-stressed concrete, so called steel fiber reinforced self-stressing concrete. In this paper the material experiments of 40 SFRSSC prisms are carried out to determine the rule of the self-expansive behavior. According to the theoretical model, a procedure how to determine the self-stress is supplied in order to supply references to the structural applications of steel fiber reinforced self-stressing concrete.

The Low Energy X-ray Instrument (LE) of the Hard X-ray Modulation Telescope (HXMT) uses the Swept Charge Device (SCD) to detect the X-rays in 1−15 keV. The performance of SCD is vulnerable to temperature. We analyzed the thermal condition of LE at different satellite working attitudes with the Finite Element Method (FEM). It is shown that the angle between the sunlight and the normal line of the barrier should be less than 26°, to keep the SCD detectors working in the required temperature range, i.e. −40 °C to −80 °C. We find that the performance of LE is very stable in this temperature range, with a typical energy resolution of 160 eV at 5.9 keV.

Through the experiments of 7 T-section composite beams, steel fiber reinforced self-stressing concrete (SFRSC) as the composite beam in the composite layer was studied under the hogging bending. The tests simulated composite layer tensile strain under the hogging bending of inverted loading composite beams, giving the relationship under the different fatigue stress ratios between fatigue cycles and steel bar’s stress range, crack width, stiffness loss and damage, etc., in composite layer. This article established fatigue life equation, and analyzed SFRSC reinforced mechanism to crack width and stiffness loss. The results show that SFRSC as the composite beam concrete has excellent properties of crack resistance and tensile, can reinforce the fatigue crack width and stiffness loss of composite beams, and improve the durability and in normal use of composite beams in the hogging bending zone.

With advances in information and network technologies, lots of data have been digitized to reveal information for users by the construction of Web sites. Unfortunately, they are both overloading and overlapping in Internet so that users cannot distinguish their quality. To address this issue in education, Hwang, Huang, and Tseng proposed a group decision system to evaluate the quality of educational Web sites by users' and experts' opinions. Their investigative source is solely stemmed from human intention, called the subjective perspective, to make judgments on the quality of Web sites. However, the nature of human beings in making decisions has a gap between intention and behavior. Asking people for eliciting thought is arduous to cause this gap. Human behavior, namely the objective perspective, is the other essential source to obtain human thinking and real doings. For this reason, we can use data mining approaches to acquire the objective source. In this research, we propose an integrated decision model applied in evaluating educational Web sites from the fuzzy subjective and objective perspectives. The former source is extracted by inquiring human opinion using a questionnaire, while the latter is gained automatically by a data mining technique, fuzzy clustering. An empirical study is carried out to validate the model capability.

Platinum-based chemotherapeutics represent a mainstay of cancer therapy, but resistance limits their curative potential. Through a kinome RNAi screen, we identified microtubule-associated serine/threonine kinase 1 (MAST1) as a main driver of cisplatin resistance in human cancers. Mechanistically, cisplatin but no other DNA damaging agents inhibit the MAPK pathway by dissociating cRaf from MEK1, while MAST1 replaces cRaf to reactivate the MAPK pathway in a cRaf-independent manner. We show clinical evidence that expression of MAST1, both initial and cisplatin-induced, contributes to platinum resistance and worse clinical outcome. Targeting MAST1 with lestaurtinib, a recently identified MAST1 inhibitor, restores cisplatin sensitivity, leading to the synergistic attenuation of cancer cell proliferation and tumor growth in human cancer cells and patient-derived xenograft models. SIGNIFICANCE: Activation of proliferative signaling pathways following chemotherapy has been associated with therapeutic resistance. However, relatively little is known of its mechanism. Our findings provide insights into cancer cisplatin resistance by characterizing the role of MAST1 in driving cisplatin resistance and its mode of action in human cancers involving rewiring of the MEK signaling pathway. We offer a clinical strategy to enhance the susceptibility of cancers to cisplatin by identifying the small molecule kinase inhibitor lestaurtinib as a potent MAST1 inhibitor and cisplatin sensitizing agent. These findings not only highlight mechanisms by which MAST1 contributes to the development of cisplatin resistance via the MAPK pathway but also implicate MAST1 as a promising therapeutic target to battle cisplatin resistant cancers.

The special reinforced concrete composite beam consists of a steel fiber reinforced self-stressing concrete composite layer and a reinforced concrete T-beam, and constructional bars are set up at their bonding interface. Fatigue properties of the composite beam under the action of negative moment were experimentally studied. Through inverted loading mode the load-bearing state of a composite beam was simulated under the action of negative moment. With the ratios of constructional bars being 0, 0.082% and 0.164% respectively as parameters, the effects of constructional bars on the properties of composite beam, such as fatigue life, crack propagation, rigidity loss as well as damage behavior of bonding interface, were studied. The mechanism of the constructional bars on the fatigue properties of the composite beams and the restriction mechanism of crack widths and rigidity loss were analyzed. The test results show that the constructional bars can enhance the shear resistance of the bonding interface between composite layer and old concrete beam and restrict expanding of steel fiber reinforced self-stressing concrete, which are beneficial to synergistic action of composite layer and old concrete beam, to reducing the stress amplitude of bars and the crack width of composite layer, and to increasing the durability and fatigue life of the composite beam.

Steel wire wound reinforced flexible pipe in this study mainly consists of multiple anisotropic steel wire wound reinforcement layers and multiple isotropic rubber layers. Based on 3D anisotropic elastic theory, the analytic solutions of stresses and elastic deformations of steel wire wound reinforced rubber flexible pipe under internal pressure are presented. As the adjacent reinforcement layers with wound angle have different radii, the single reinforcement layer shows the effect of tensile-shear coupling. Moreover, the static loading test results of steel wire wound reinforced rubber flexible pipe under internal pressure are basically coincided with the calculated values by present method.

This paper presents experimental and numerical investigations of pre-stressed concrete filled circular steel tube columns by means of expansive (PCFT) under eccentric loading. Conventional concrete filled steel tube (CFT) specimens are also tested in this paper just for comparison purposes. Based on the steel follow-up strengthening and concrete bounding surface constitutive relationships, this paper writes a nonlinear finite element program for calculating complete load mid-height deflection curves of eccentrically loaded PCFT/CFT columns. A good agreement is obtained between the calculated and test results. The results indicate that the eccentrically loaded PCFT columns generally have higher load carrying capacities than the CFT ones when other parameters are kept the same. But by increasing the load eccentricity ratio, the difference of load carrying capacity between them decreases. Other factors such as pre-stress, load eccentricity ratio, and steel ratio, etc. are also investigated.

This paper presents the results of shear tests on 27 self-stressing concrete filled steel tube (SSCFST) specimens and 8 conventional concrete filled steel tube (CFST) specimens. The main parameters in the tests were the self-stress and shear span to depth ratio. The experimental results indicate that the shear capacity of the CFST specimen is lower than that of the SSCFST specimen when 28-day cube strength of concrete core, shear span to depth ratio, yield strength and geometrical dimension of the steel tube are kept the same. The CFST/SSCFST specimen failed in shear when the shear span to depth ratio was smaller than 0.5 and failed in flexure when the shear span to depth ratio was 0.5 or higher. The bond between concrete core and steel tube also affected the shear capacities. Finally, an empirical equation for predicting the shear capacity of a SSCFST short beam is proposed.

The expansion/shrinkage behaviors and bond carrying capacities of 17 short, pre-stressing concrete filled circular steel tube (PCFST) columns by means of expansive cement and three short, conventional concrete filled circular steel tube (CFST) columns are experimentally investigated. The results indicate that both concrete mixes and dimensions of the steel tube have important influence on expansive behaviors of PCFST specimens. The pre-stress in concrete core is a sensitive parameter to the bond strength as well as the load–slip relationship. The current work also indicates that PCFST columns have much higher bond strength than CFST columns and this recommends a new method to improve the bond strength of composite structures. Finally, an empirical equation for predicting the bond strengths of PCFST columns is proposed.

Items sold in a store can usually be organized into a concept hierarchy according to a taxonomy. Based on the hierarchy, sequential patterns can be found not only at the leaf nodes (individual items) of the hierarchy, but also at higher levels of the hierarchy; this is called multiple-level sequential pattern mining. In previous research, taxonomies had crisp relationships between the categories in one level and the categories in another level. In real life, however, crisp taxonomies cannot handle the uncertainties and fuzziness inherent in the relationships among items and categories. For example, the book Alice's Adventures in Wonderland can be classified into the Children's Literature category, but can also be related to the Action &Adventure category. To deal with the fuzzy nature of taxonomy, we apply fuzzy set techniques to concept taxonomies so that the relationships from one level to another can be represented by a value between 0 and 1. Accordingly, a fuzzy multiple-level mining algorithm, the fuzzy multi-level sequential mining algorithm (FMSM), is proposed to extract fuzzy multiple-level sequential patterns from databases. In addition, another algorithm, named the CROSS-FMSM algorithm, is developed to discover fuzzy cross-level sequential patterns. Experiments using synthetic datasets show the algorithms' computational efficiency and scalability, and a real dataset is used to prove the patterns' effectiveness.

A new sort of high performance concrete is introduced which combines most advantages of prestressed concrete and steel fiber concrete, named steel fiber reinforced self-stressing concrete(SFFRSSC for short). Self-stressing concrete is actually a kind of expansive concrete which self-stresses, namely pre-compressive stresses, are induced by dint of some restrictions generally provided by steel bars to concrete expansion after hydration of expansive cement. As a result of chemical reaction, concrete archived prestresses by itself different from mechanical prestressed concrete, so called self-stressing concrete. By distributing short-cut steel fibers into self-stressing concrete at random, prestresses( self-stress) are created in concrete under combined restriction of steel bars and steel fibers. Thank to the pre-stresses tensile strength of concrete are significantly increased as well as cracking strength. In addition, expansive deformation of SFFRSSC can compensate the shrinkage of concrete to decrease shrinkage crack, and the steel fibers play an important role in post-crack behavior. On the other hand, self-stressing concrete can avoid the troubles of construction compared with conventional mechanical prestressed concrete. For purpose of understanding the properties of SFFRSSC, in this paper some researches were carried out to investigate the special expansive behaviors of restrained expansive deformation with restriction of steel bar as well as steel fiber. The test results indicated that steel bar and steel fiber both provide effective restrict to self-stressing concrete as result of forming prestresses in concrete.

With the advances in information technology and the emergence of Internet commerce, analysis of transaction data has become a crucial technique for effective decision-making and strategy formation in business operations. It is especially critical for retail management, in both online and brick-and-mortar stores. Traditional research in mining retail knowledge, however, does not take into account the products' prices and how such settings can affect potential demand. This paper opens a new research dimension by treating products' prices as an important decision variable in mining retail knowledge. To the best of our knowledge, the problem addressed in this paper has never been dealt with in existing research papers. We propose a representation scheme to incorporate price information into historical transaction data. An efficient algorithm is developed to ''dig'' out implicit, yet meaningful, patterns with price information. In addition, an extensive and well-designed experiment is executed, showing that the algorithm is computationally efficient and that the proposed analysis is significant and useful.

A whole of 110 specimens divided into 22 groups were tested with varying the volume fraction of steel fibers and the matrix strength of these specimens. The stress-strain behaviors of four types of steel fiber reinforced concrete (SFRC) under uniaxial tension were studied experimentally. When the matrix strength and the fiber content increase, the tensile stress and tensile strain vary differently according to the fiber type. The mechanisms of reinforcing effect for different type of fiber were analyzed and the stress-strain curves of the specimens were plotted. Some experimental factors for stress or strain of SFRC were given. A tensile toughness modulus Re0.5 was introduced to evaluate the toughness characters of SFRC under uniaxial tension. Moreover, the formula of the tensile stress-strain curve of SFRC was regressed. The theoretical curve and the experimental one fit well, which can be used for references in construction.