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This study explores a supply chain product-inventory problem with advance sales under the omni-channel strategies (physical and online sales channels) based on the brand owner's business model and develops corresponding models that have not been proposed in previous studies. In addition, because the brand owner is a member of the supply chain, and has different handling methods for defective products or products returned by customers in various retail channels, defective products or returned products are included in the supply chain models to comply with actual operating conditions and fill the research gap in the handling of defective/returned products. Regarding the mathematical model's development, we first clarify the definition of model parameters and relevant data collection, and then establish the production-inventory models with omni-channel strategies and advance sales. The primary objective is to determine the optimal production, delivery, and replenishment decisions of the manufacturer, physical agent, and online e-commerce company in order to maximize the joint total profits of the entire supply chain system. Further, this study takes the supply chain system of mobile game steering wheel products as an example, uses data consistent with the actual situation to demonstrate the optimal solutions of the models, and conducts sensitivity analysis for the proposed model. The findings reveal that increased demand shortens the replenishment cycle and raises order quantity and shipment frequency in the physical channel, similar to the online channel during normal sales. However, during the online pre-order period, higher demand reduces order quantity and cycle length but still increases shipment frequency. Rising ordering or fixed shipping costs lead to higher order quantity and cycle length in both channels, but variable shipping costs in the online channel reduce them. Market price increases boost order quantity and frequency in the online channel, while customer return rates significantly impact inventory decisions. [ABSTRACT FROM AUTHOR]

Norfloxacin (NOR) and levofloxacin (LEV) are the two most frequently used fluoroquinolones (FQs) in clinic. Their residues seriously endanger the ecosystem and human health. Due to their similarity in structure and properties, it is urgent to develop an efficient and sensitive strategy for detection and differentiation. Herein, we synthesized a novel ratiometric fluorescent sensor for the first time by combining N, S co-doped carbon dots (CDs) and the precursors of Tb-MOFs through a facile one-pot method. The introduction of CDs effectively facilitated the energy transfer between Tb3+ and FQs, overcoming the limitation that single Tb-MOFs could not identify similar antibiotics. Specifically, the presence of NOR resulted in reverse signal response through the inner filter effect and antenna effect. The synergistic effect of these two mechanisms contributed to achieving signal amplification accompanied by a distinguishable color transition. The limit of detection (LOD) was 0.036 μM. Different from NOR, the addition of LEV reduced the electron density of the system, weakened the coordination ability of Tb3+ with LEV, and induced a single signal response with Tb3+ fluorescence intensity as a reference signal (LOD = 0.383 μM). Furthermore, the method proved to be rapid and visual, allowing for the straightforward analysis of FQs residues in water, food matrices, and biological samples with satisfactory precision. By integrating N, S-CDs@Tb-MOFs with flexible substrates, the paper-based sensor facilitated the visual quantitative determination of FQs by reading RGB values. The developed sensor presents a promising strategy for the identification and real-time monitoring of antibiotics. [Display omitted] • A novel ratiometric fluorescent sensor was developed by in-situ encapsulation. • The introduction of CDs can trigger a dual-mode sensing strategy to identify FQs. • The method can simultaneously realize the fluorescent and visual detection of FQs. • The sensor exhibited reliability and practicability in real samples. • The paper-based device shows great potential for visual determination of FQs. [ABSTRACT FROM AUTHOR]

Designing high-performance flame retardants for poly (L-lactic acid) (PLA) materials and exploring a simple and scalable strategy have been hot topics in research. In this work, a novel and highly efficient flame retardant, that is, 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) decorated urchin-like NiCo-based bimetallic hydroxide (NiCo-BH@DOPO), was synthesized and incorporated into PLA to prepare PLA and NiCo-BH@DOPO (PLA/NiCo-BH@DOPO) composite. Benefiting from the DOPO organic modification, NiCo-BH@DOPO had superb hydrophobicity and presented excellent dispersion in the PLA matrix. When 20 wt% NiCo-BH@DOPO was added, the LOI value of PLA/NiCo-BH@DOPO composites reached 33.2 %, passed the V-0 level of UL-94 grade, and its maximum peak heat release rate (PHRR) and total heat release (THR) were reduced by 13.2 % and 17.3 %, respectively, compared with PLA/NiCo-BH composites. Furthermore, the residue of PLA/NiCo-BH@DOPO at 800 °C reached 19.8 wt% and the T 10% (temperature at 10 % weight loss) increased by 33 °C. More importantly, the residual PLA/NiCo-BH@DOPO char exhibits a significantly reduced presence of large cracks compared to PLA/NiCo-BH, indicating a more compact formation of residual char. NiCo-BH@DOPO endowed PLA with outstanding flame retardancy, thermal stability and carbonization properties, which were owing to the multi-coordinating effect transition metal (NiCo-BH) catalyzed the char formation to form a char layer barrier and DOPO free radicals captured to inhibit the combustion reaction chain. This investigation provided a facile strategy for the novel multi-function NiCo-based bimetallic hydroxide flame retardant, expanding NiCo-BH potential applications in PLA. • Flame retardant containing abundant transition metals (NiCo-BH@DOPO) was first reported for flame retardancy. • The (NiCo-BH@DOPO) exhibited exceptional hydrophobicity and multifunctional flame retardancy • The PLA/NiCo-BH@DOPO composites exhibited excellent flame retardant and mechanical performances. [ABSTRACT FROM AUTHOR]

An integrated multistage supply chain inventory model containing a single manufacturer and multiple retailers is proposed to consider deteriorating raw materials and finished products with imperfect production and inspection systems. The main purpose is to jointly determine the manufacturer's production and delivery strategies and the retailers' replenishment strategies to maximize the integrated total profit. First, the individual total profit functions of the manufacturer and multiple retailers are established and are integrated to form the total profit function of the supply chain system. Then, to address the model complexity, an algorithm is proposed to obtain the optimal solution. Several practical numerical examples are presented to demonstrate the solution procedure, and a sensitivity analysis is performed on the major parameters. From the numerical results, several findings that differ from those in the previous literature were observed. First, retailers with larger market scale, better cost control, and/or inspection capabilities guarantee higher individual and integrated total profits. Second, increasing the deterioration rates of raw materials and finished products has different effects on the order quantity of raw materials. Third, the manufacturer's shipping strategy is rigid and not easily changed except for significant changes (increase or decrease by 20%) to the production rate or selling price. The performance of the proposed model has several meaningful management implications. [ABSTRACT FROM AUTHOR]

The increasing use of biodegradable plastics may result in more serious pollution of microplastics which often coexist with biochar in soil, this will affect how organic pollutants move and transform in the soil. This work investigated the effect of biodegradable polybutylene adipate- co -terephthalate (PBAT) coexistence with biochars produced at temperatures of 400 and 700 °C (W4 and W7) on soil bacterial communities and phenanthrene degradation. The results showed that coexistence of PBAT and biochar paticles greatly boosted the relative abundance of Nocardioides while decreased the relative abundance of Sphingomonas as compared to soils with a single addition of PBAT or biochar. Changes in soil Eh values were the most influential factor in bacterial communities (more than 40% contribution). The degradation ratio of phenanthrene when PBAT coexisted with W7 (39.6 ± 3.6%) was not significantly different from the treatment with a single W7 addition (35.0 ± 2.3%, P ＞0.05), and was related to phenanthrene degradation in the adsorbed state of W7 in soil. In contrast, the degradation ratio of phenanthrene in PBAT coexisting with W4 (35.1 ± 3.5%) was intermediate between that of single PBAT (49.8 ± 0.9%) and W4 (13.7 ± 5.8%) treatments. This was primarily due to changes in the experiment's initial bioavailable phenanthrene content. Furthermore, after the introduction of earthworms, phenanthrene degradation ratio in coexistence treatments were very similar to that described above in the absence of earthworms. Except for two treatments that contain W7, phenanthrene degradation ratio in the other treatments was increased by the presence of earthworms (up to 23%), which is related to the enhanced relative abundance of polycyclic aromatic hydrocarbon-degraders. Our findings indicated that PBAT coexistence with high-temperature or low-temperature biochar had a completely different impact on bacterial communities and phenanthrene degradation in soil. [Display omitted] • The most critical factor impacting soil bacteria is changed in soil Eh value (>40%). • The coexistence of PBAT MPs and biochar has a substantial impact on soil bacteria. • Phenanthrene degradation is affected when PBAT coexists with various biochar. • When mixed with PBAT, phenanthrene degradation is half that of solely W4 added. • When mixed with PBAT, phenanthrene degradation is like to solely W7 added. [ABSTRACT FROM AUTHOR]

Soil composition varies considerably in nature, so it is vital to investigate the mechanism of the effect of various soil parameters on biochar sorption capacity. In this study, two biochars (W4 and W7) were derived from wheat straw at temperatures of 400 and 700 °C and were incubated with three different soils. Changes in biochar surface features by aging in the soils and the consequent impact on phenanthrene sorption were examined. The results showed that the effect of adding biochar on phenanthrene sorption capacity (K oc) varied by soil. When biochar was freshly mixed with soil, the K oc value in soil with higher clay content was more dramatically altered by biochar, which is due to clay particles adhering to the biochar surface. Moreover, the K oc value was significantly decreased by the addition of W4 but increased by the addition of W7 in general. After aging, most of the K oc value decreased. The greatest decrease in K oc value was observed in biochar and soil composed with the highest clay content for W4 (24–63%), as well as soil composed with the highest organic matter content for W7 (46–64%). This is because the surface polarity and micropores of biochar dropped the most rapidly in these mixes, resulting in a significant decrease in hydrophobic and pore-filling properties. The results revealed that the impact of biochar-soil interactions on phenanthrene sorption is related to not only biochar properties but also soil clay particles, soil organic matter content and pH. The findings of the study can be utilized to assess the efficacy of biochar application in soil remediation for various features. [Display omitted] • Surface characteristic of biochar was increased by the adhesion of clay particles. • Change in Phe K oc among different soils was related to adhesion of clay particles. • K oc value was more obviously affected by W7 added than by W4 added in general. [ABSTRACT FROM AUTHOR]

Objective: Dementia is a significant medical and social issue in most developed countries. Practical tools for predicting the progression of degenerative dementia are highly valuable. Machine learning (ML) methods facilitate the construction of effective models using real-world data, which may include missing values and various integrated datasets. Method: This retrospective study analyzed data from 679 patients diagnosed with degenerative dementia at Fu Jen Catholic University Hospital, who were evaluated by neurologists, psychologists and followed for over two years. Predictive variables were categorized into demographic (D), clinical dementia rating (CDR), mini-mental state examination (MMSE), and laboratory data value (LV) groups. These categories were further integrated into three subgroups (D-CDR, D-CDR-MMSE, and D-CDR-MMSE-LV). We utilized the extreme gradient boosting (XGB) model to rank the importance of variables and identify the most effective feature combination via a step-wise approach. Result: The D-CDR-MMSE-LV model combination showed robust performance with an excellent area under the receiver operating characteristic curve (AUC) and the highest sensitivity value (84.66). Employing both demographic and neuropsychiatric variables, our prediction model achieved an AUC of 83.74. By incorporating additional clinical information from laboratory data and applying our proposed feature selection strategy, we constructed a model based on eight variables that achieved an AUC of 85.12 using the XGB technique. Conclusion: We established a machine-learning model to monitor the progression of dementia using a limited, real-world clinical dataset. The XGB technique identified eight critical variables across our integrated datasets, potentially providing clinicians with valuable guidance. [ABSTRACT FROM AUTHOR]

The suitable preload design of cylindrical roller bearing with triple-lobe raceway can avoid effectively bearing sliding damage. To realize the preload characteristics analysis of bearing, the quasi-static numerical model of cylindrical roller bearing with triple-lobe raceway is established and solved by the improved Newton-Raphson method. Taking a cylindrical roller bearing with triple-lobe raceway as an example, the effects of mounting radial clearance, outer raceway waveform value, radial load and rotational speed on maximum contact load and preload of the bearing are analyzed. The results show that the preload of cylindrical roller bearing with triple-lobe raceway can be controlled by adjusting the mounting radial clearance and outer triple-lobe raceway waveform value according to the radial load and rotational speed conditions. The bearing temperature trend predicted by the preload characteristic is verified by the test. It indicates the numerical model can effectively guide the preload quantitative design of cylindrical roller bearing with triple-lobe raceway. [ABSTRACT FROM AUTHOR]

In order to explore the influence of carboxylic acid substituents on the structures and properties of coordination polymers, three new coordination polymers (CPs) of [Zn2(L)2(IP)2] (1), [Zn(L)(HIP)] ⋅ 3H2O (2) and [Zn(L)(NIP)] ⋅ H2O (3) [H2IP=isophthalic acid, H2HIP=5‐hydroxyisophthalic acid, H2NIP=5‐nitroisophthalic acid] were designed and synthesized under hydrothermal conditions with a multi‐torsion‐centered N‐donor ligand of N,N′‐bis(4‐methylenepyridin‐4‐yl)‐1,4‐naphthalene dicarboxamide (L). All three CPs have similar 4,4‐connected two‐dimensional structures exhibiting different structural details, which is induced by uncoordinated‐substituents and discussed systematically. The title CPs 1 and 2 show multi‐functional fluorescent responses towards metal ion (Fe3+), anions (MnO4−, CrO42− and Cr2O72−) and pesticide (2,6‐dichloro‐4‐nitroaniline). The corresponding detection limits are 6.1×10−5 M−1, 3.7×10−4 M−1, 5.5×10−4 M−1, 3.0×10−4 M−1 and 6.7×10−5 M−1 for CP 1, 2.8×10−5 M−1, 4.1×10−4 M−1, 3.0×10−4 M−1, 4.1×10−4 and 2.8×10−5 M−1 for CP 2. [ABSTRACT FROM AUTHOR]

Special attention has recently been paid to the high-salt formation in Qianjiang depression as its abundant oil resources with ultra-low permeability and porosity. However, the complex pore structure characteristic of the high-salt formation has not been well understood so far because of its pore size ranging from nanometer to micrometer scale. This paper aims to investigate the complex pore structure characteristic of the target high-salt formation based on an integrated experimental approach involving X-ray diffraction (XRD), scanning electron microscope (SEM), mercury injection capillary pressure (MICP), casting lamella observation and nuclear magnetic resonance (NMR) techniques. XRD analysis results indicate that the target formation mainly includes clastic mineral, carbonate mineral and salt mineral, and the content of them are 32.16%, 36.59%, and 24.65%, respectively. The results of SEM and casting lamella observation indicate that storage space of target formation can be divided into intercrystal pores, solution pores, and fractures. Solution pores have great impact on oil production, which includes dolomite dissolved pore and glauberite dissolved pore. Based on the MICP test results, the samples can be divided into pore type and fracture-pore type. The average pore radius of pore type samples and fracture-pore type samples are 0.0144 μm and 0.40 μm, respectively. Pore size distribution (PSD) results from MICP indicate that single peaks appeared in PSD curves of pore type samples and multi-peaks appeared in PSD curves of fracture-pore type samples. Permeability contribution peaks of pore type samples range from 0.01 to 0.03 μm, and permeability contribution peaks of fracture-pore type samples range from 1 to 10 μm. Comparing the permeability contribution of pore type samples and fracture-pore type samples reveals that mesopores and macropores make the vast majority of permeability contribution of the two kind type samples. A method to get permeability contribution of pore throats from PSD results has been introduced in this paper based on T2 NMR test. Comparing the PSD results from MICP and T2 NMR test results shows that T2 NMR test can identify smaller pore throat. [ABSTRACT FROM AUTHOR]

The usual numerical simulation of the oil-water two-phase water cone is difficult to popularize because of its complicated method and the large amount of calculations and data involved. Based on the assumption that oil is supplied horizontally at the outer radius of the oil zone, some simplified water cone calculation methods ignoring the lifting process of the oil-water interface have been proposed. These are not in line with the actual conditions in the oilfield. Therefore, to overcome the shortcomings of the previously developed calculation methods, this paper provides a quick and simple calculation method, which takes into account the influence of liquidity ratio, reservoir heterogeneity, degree of drilling, fluid production rate, and well spacing. In the framework of the new prediction method, the initial limit production, water breakthrough time, water rising content, anhydrous recovery, and final recovery characteristics are calculated. The results can be used to study the elect of various factors such as flow ratio and drilling degree on the development electiveness of bottom water reservoirs and to select the best development plan for bottom water reservoirs. [ABSTRACT FROM AUTHOR]

Maintenance therapy after first autologous transplant (autoSCT) improves progression-free survival (PFS) and overall survival (OS) in multiple myeloma (MM). However, efficacy of maintenance therapy after second autoSCT is unknown. We retrospectively evaluated outcomes of 111 adult MM patients who underwent second autoSCT between January 2000 and December 2018. Lenalidomide up to 15 mg daily or subcutaneous bortezomib 1.3 mg/m2 every 2 weeks was considered maintenance therapy. Outcomes were compared among three groups: no-maintenance (n = 73), lenalidomide (n = 23), and bortezomib maintenance (n = 15). At a median follow-up of 58 months from second autoSCT for survival, 3-year PFS and OS for no-maintenance, lenalidomide, and bortezomib maintenance were 11.2%, 29.9%, and 0%, respectively; and 58.5%, 83.3%, and 67.5% respectively. Lenalidomide maintenance was associated with improved PFS (HR 0.46, p = 0.009) and OS (HR 0.25, p = 0.009) compared to no-maintenance. Lenalidomide maintenance therapy after second autoSCT appears to prolong PFS and OS. [ABSTRACT FROM AUTHOR]

Three new zinc(II)/cobalt(II) coordination complexes [Zn(HBTB)(L)0.5] (1), [Zn(HCPPA)(L)] (2), and [Co(HCPPA)(L)]·2H2O (3) [H3BTB = 1,3,5‐tri(4‐carboxylphenyl)benzene, H3CPPA = 5‐(4‐carboxyl‐phenoxy)‐isophthalic acid, L = N1,N4‐bis(3‐pyridyl)naphthaldiamide] were solvothermally synthesized. The structural characterization reveals that complex 1 represents a 3D coordination framework with a binodal 3,4‐connected {4.102}2{4.105}2 topology constructed from the 2D [Zn(HBTB)]n polymeric double‐layers and the bidentate L ligands. Complex 2 is a 1D metal‐organic chain derived from the dinuclear [Zn2(HCPPA)2] loops and [Zn2(L)2] loops. Complex 3 possesses a binodal 3,5‐connected {42.6}{43.6.84.102} topological 2D layered architecture based on the [Co2(HCPPA)2] ribbon chains and the bidentate L ligands. For 2 and 3, their adjacent chains or layers are respectively stacked into 3D supramolecular architectures via the hydrogen bonds. Moreover, the fluorescent and fluorescent sensing activities towards small solvent molecules of coordination complexes 1 and 2, the photocatalyitc properties of 1–3 towards organic dyes were studied. [ABSTRACT FROM AUTHOR]

Layered double hydroxide (LDH) can be used as a flame retardant (FR) for polypropylene (PP) because of its excellent flame retardancy and smoke-suppression effects. However, the agglomeration of LDH seriously affects its dispersity in the PP matrix, thus limiting its application as a FR polymer. This study aimed to enhance the dispersion of LDH in the PP matrix for improving its flame retardancy performance. Herein, a reactive triazine-based organic framework (TOF) was designed and formed by the in situ polymerisation of cyanuric chloride and melamine, and then the LDH surface was covered with TOF to obtain a novel core–shell FR (LDH@TOF), with TOF as the 'shell' and LDH as the 'core'. Subsequently, LDH@TOF was melted into the PP matrix as nanofillers to prepare PP and LDH@TOF composites (PP/LDH@TOF). As expected, the PP/LDH@TOF composites exhibited superior flame-retarding performance (limiting oxygen index of 29.2% and UL-94 V -0 rating) and favourable smoke-suppression performance at 20 wt% of LDH@TOF loading. Moreover, the peak heat release rate, total heat release and total smoke production of PP/LDH@TOF composites decreased by 29.9%, 12.1% and 12.4%, respectively, compared with those of PP and LDH composites. The outstanding flame retardancy properties of the PP/LDH@TOF composites were attributed to the better dispersibility of LDH@TOF in the PP matrix, the synergy of the physical barrier effect of nanosheets and the dense carbonisation acceleration of LDH@TOF. The findings of this study provide a highly efficient approach for functionalising LDH for expanding its application value of PP composites. [Display omitted] • Novel organic covalent structure TOF@LDH modified by triazinyl organic covalent skeleton are synthesised. • TOF@LDH has well dispersion and outstanding thermostability in PP. • TOF@LDH enhances the charring capability, flame retardancy and smoke suppression of PP. [ABSTRACT FROM AUTHOR]

Ratiometric fluorescent sensors can suppress the interference of factors unrelated to analysis due to their built-in self-calibration characteristics, which exhibit higher sensitivity and more obvious visual detection in the process of qualitative and quantitative analysis. Herein, we constructed a ratiometric fluorescence probe based on fluorescent/colorimetric dual-mode method for the determination of arginine by encapsulating rhodamine B in-situ into UiO-66-NH 2 MOFs (UiO-66-NH 2 @RhB). The as-prepared probe showed dual-emission characteristics under a single excitation wavelength. The fluorescence intensity of UiO-66-NH 2 was increased significantly by arginine, while the emission peak intensity of rhodamine B remained stable, resulting in a single-signal response with fixed reference. Furthermore, the practicality of the presented sensor was successfully validated by quantitative detection of arginine in human serum. More significantly, paper-based sensors for arginine detection were devised by using carboxymethyl cellulose modified filter papers. Under the irradiation of ultraviolet light, the paper-based sensors would produce obvious color variation from lightpink to bluish violet. This work provided a convenient and efficient method for on-site detection of arginine. Schematic illustration of in-situ encapsulation strategy of ratiometric fluorescence probe UiO-66-NH 2 @RhB and paper-based sensor for visual detection of arginine. [Display omitted] [ABSTRACT FROM AUTHOR]

Sales forecasting is a critical task for computer retailers endeavoring to maintain favorable sales performance and manage inventories. In this study, a clustering-based forecasting model by combining clustering and machine-learning methods is proposed for computer retailing sales forecasting. The proposed method first used the clustering technique to divide training data into groups, clustering data with similar features or patterns into a group. Subsequently, machine-learning techniques are used to train the forecasting model of each group. After the cluster with data patterns most similar to the test data was determined, the trained forecasting model of the cluster was adopted for sales forecasting. Since the sales data of computer retailers show similar data patterns or features at different time periods, the accuracy of the forecast can be enhanced by using the proposed clustering-based forecasting model. Three clustering techniques including self-organizing map (SOM), growing hierarchical self-organizing map (GHSOM), and K-means and two machine-learning techniques including support vector regression (SVR) and extreme learning machine (ELM) are used in this study. A total of six clustering-based forecasting models were proposed. Real-life sales data for the personal computers, notebook computers, and liquid crystal displays are used as the empirical examples. The experimental results showed that the model combining the GHSOM and ELM provided superior forecasting performance for all three products compared with the other five forecasting models, as well as the single SVR and single ELM models. It can be effectively used as a clustering-based sales forecasting model for computer retailing. [ABSTRACT FROM AUTHOR]

In this study, a self-designed laminar rhizobox divided into six separate compartments was used to investigate the degradation gradient of pyrene in the rhizosphere of submerged aquatic plant Potamogeton crispus L. ( P. crispus ). At the end of a 30-day experiment, P. crispus significantly increased the dissipation of pyrene by 16.7-24.5% within the distance of 4 mm from root surface. Concentrations of 1-OH-pyrene, a hydroxylated metabolite of pyrene, were 1.4-15.5 times higher in unplanted treatment than in planted treatment and were positively correlated to pyrene concentrations. At the same time, aerobic bacterial biomass indicated by phospholipid fatty acid content and polycyclic aromatic hydrocarbon (PAH)-degrading bacterial population were both significantly enhanced by P. crispus within the distance of 8 mm from root surface. Sediment redox potential within the distance of 5 mm from root surface was also obviously improved. Pearson analysis indicated that dissipation ratios of pyrene were positively correlated to PAH-degrading bacterial populations and aerobic bacterial biomasses. Based on the rhizosphere gradient zones of different parameters influenced by P. crispus as well as the correlation analysis, it could be concluded that oxygen is the limiting factor in remediating pyrene in sediments. [ABSTRACT FROM AUTHOR]

Ovarian cancer is the second leading cause of deaths among gynecologic cancers in the world. Approximately 90% of women with ovarian cancer reported having symptoms long before a diagnosis was made. Literature shows that recurrence should be predicted with regard to their personal risk factors and the clinical symptoms of this devastating cancer. In this study, ensemble learning and five data mining approaches, including support vector machine (SVM), C5.0, extreme learning machine (ELM), multivariate adaptive regression splines (MARS), and random forest (RF), were integrated to rank the importance of risk factors and diagnose the recurrence of ovarian cancer. The medical records and pathologic status were extracted from the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrated that the integrated C5.0 model is a superior approach in predicting the recurrence of ovarian cancer. Moreover, the classification accuracies of C5.0, ELM, MARS, RF, and SVM indeed increased after using the selected important risk factors as predictors. Our findings suggest that The International Federation of Gynecology and Obstetrics (FIGO), Pathologic M, Age, and Pathologic T were the four most critical risk factors for ovarian cancer recurrence. In summary, the above information can support the important influence of personality and clinical symptom representations on all phases of guide interventions, with the complexities of multiple symptoms associated with ovarian cancer in all phases of the recurrent trajectory. [ABSTRACT FROM AUTHOR]

In order to clarify the effect of Potamogeton crispus L. ( P. crispus ) on bioavailability and biodegradation activity of pyrene in aged and unaged sediments, model calculation based on experimental results was carried out. During a 36-day experiment, the dissipation ratio of pyrene was increased by planting but decreased by aging. P. crispus improved the dissipation more significantly in aged sediments (45.9%) than in unaged sediments (17.6%). Results derived from a two-compartment desorption model showed that the decrease of rapidly desorbing fraction of pyrene was in the order of aged sediments without plant (A) > unaged sediments without plant (U) > unaged sediments with plant (UP) > aged sediments with plant (AP). Moreover, the results of biodegradation kinetic model showed that the first-order biodegradation coefficient was in the order of AP > UP > U and A, which was consistent with that of sediment redox potential. These modeling results indicated that planting could enhance the bioavailability (73.9%) and biodegradation activity (277%) of pyrene more significantly in aged sediments as compared to unaged sediments (13.1% and 150%, respectively), which should be the key reasons leading to more significant dissipation increment of pyrene in aged sediments by P. crispus . [ABSTRACT FROM AUTHOR]

Microplastics and biochar normally coexist in soil. In this study, two microplastics of different polarities (nonpolar polyethylene (PE) and polar polybutylene adipate-co-terephthalate (PBAT)) and two wheat straw biochars produced at 400 (W4) and 700 °C (W7) were selected to investigate the sorption behaviors of phenanthrene in soil where microplastics and biochar coexisted. The results showed that the presence of PE more significantly weakened the adhesion of soil particles onto biochar than the presence of PBAT. Meanwhile, the presence of biochar enhanced the soil particle attachment on the microplastic surface. As a result, the sorption behavior of phenanthrene was significantly different in soil where biochar coexisted with microplastics of different polarities. The K oc values of PE-biochar-soil mixtures at C e = 0.005 C s were up to 42 % lower than those of PBAT-biochar-soil mixtures, which is related to lower micropore area of particles isolated from the former. However, at C e = 0.05 C s and 0.5 C s , the K oc values of PE-biochar-soil mixtures were up to 1.4 times higher than those of PBAT-biochar-soil mixtures because of a more significant reduction in biochar surface polarity when it coexisted with nonpolar PE. [Display omitted] • The presence of biochar (W) increased the soil particle adhesion to microplastics. • Nonpolar PE more obviously reduced soil particle adhesion to W than polar PBAT. • Surface polarity and S mic of PE-W-soil were lower than those of PBAT-W-soil. • K oc values of PE-W-soil at C e = 0.005 C s were lower than those of PBAT-W-soil. • K oc values of PE-W-soil at C e = 0.05 and 0.5 C s were higher than those of PBAT-W-soil. [ABSTRACT FROM AUTHOR]

In this study, Cu-Sn-S nanoinks were synthesized by combining chelating polyetheramine to Cu, Sn, S powders of various concentrations. X-ray diffraction patterns indicate that nanoinks synthesized at low concentrations are composed almost entirely of binary phases SnS and Cu 2 S. Synthesizing nanoinks at higher concentrations decreased the quantity of binary phase and led to the appearance of ternary phase Cu 4 SnS 4 . Following sulfurization, single phase Cu 2 SnS 3 (CTS) thin film was obtained from nanoinks of low concentration; however, impurities, such as Cu 2 S were detected in the thin film obtained from nanoinks of high concentration. This can be attributed to the fact that lower concentrations reduce the reactivity of all the elements. As a result, the SnS phase reacted more readily and more rapidly, resulting in the early formation of a stoichiometric CTS thin film during sulfurization. Under these reaction conditions, Cu 2 S and SnS transform into CTS and thereby prevent the formation of unwanted phases of Cu 2 S and Cu 4 SnS 4 . Raman spectra revealed that second phase Cu 2 S phase remained in the high-concentration samples, due to an increase in reactivity due to the participation of a greater proportion of the copper in the reaction. The surface microstructure of low-concentration samples display closely packed Cu 2 SnS 3 grains with a flat morphology and an atomic composition ratio of Cu:Sn:S = 34.69:15.90:49.41, which is close to stoichiometric. Hall measurement revealed that low-concentration sample has superior electrical properties; i.e., a hole concentration of 5.23 × 10 17 cm −3 , mobility of 14.2 cm 2 /V-s, and optical band-gap energy of 1.346 eV, which are suitable for thin-film solar cells. [ABSTRACT FROM AUTHOR]

Sales forecasting has long been crucial for companies since it is important for financial planning, inventory management, marketing, and customer service. In this study, a novel clustering-based sales forecasting scheme that uses an extreme learning machine (ELM) and assembles the results of linkage methods is proposed. The proposed scheme first uses the K-means algorithm to divide the training sales data into multiple disjointed clusters. Then, for each cluster, the ELM is applied to construct a forecasting model. Finally, a test datum is assigned to the most suitable cluster identified according to the result of combining five linkage methods. The constructed ELM model corresponding to the identified cluster is utilized to perform the final prediction. Two real sales datasets of computer servers collected from two multinational electronics companies are used to illustrate the proposed model. Empirical results showed that the proposed clustering-based sales forecasting scheme statistically outperforms eight benchmark models, and hence demonstrates that the proposed approach is an effective alternative for sales forecasting. [ABSTRACT FROM AUTHOR]

This paper reports on the reaction mechanism of Cu 2 ZnSnSe 4 (CZTSe) nanoink via a solvent-thermal reflux method using copper (Cu), zinc (Zn), tin (Sn), and selenium (Se) powders as precursors and polyetheramine as a reaction solvent. The formation of CZTSe nanoparticles in polyetheramine began with the formation of binary phase CuSe and CuSe 2 due to the strong catalysis provided by polyetheramine. Finally, ternary crystals of Cu 2 SnSe 3 transformed into well-dispersed nanocrystals of Cu 2 ZnSnSe 4 . The size of the crystals was shown to decrease with reaction time due to the emulsification effect of the polyetheramine epoxy group. The PH value-reaction time curves for single Cu, Zn elements and CZTSe from all participants elements reacted together have a relationship just reversed each other and both multistage feature were observed, which indicates that the CZTSe reaction was dominated by copper and zinc elements. The PH-temperature mechanism demonstrates that the reaction was controlled by the formation of metal-amine complexes, especially, after heating the PH-time variation manner is the same for pure element and all four elements reacted together. To the best of our knowledge, this is the first study on the mechanism underlying CZTSe formation based on the reactivity and stability of reaction species. [ABSTRACT FROM AUTHOR]

Abstracts Wheat straw biochars produced at 400 and 700 °C (BC400 and BC700) were used to investigate their effects on the growth of Vallisneria spiralis and the dissipation of phenanthrene and pyrene in sediments. Biochar amendment inhibited the growth of V. spiralis to some extent at the end of 54-day experiment, and the inhibition was stronger by BC700 than by BC400. In unplanted sediments, PAH dissipation was significantly retarded by BC400 but slightly increased by BC700 although bioavailable fraction of the contaminants extracted by butanol was more significantly reduced by the addition of BC700. It is possible that the contaminants sorbed to BC700 are still bioavailable to bacteria. Planting V. spiralis significantly enhanced the dissipation of phenanthrene and pyrene in biochar-unamended sediments, but showed only small effect on the dissipation in biochar-amended sediments. Moreover, redox potential was much higher in planted sediments than in unplanted sediments no matter whether the biochars were amended or not, suggesting that oxygen is not the limiting factor for the degradation in biochar-amended sediments. [ABSTRACT FROM AUTHOR]

Disappearance of phenanthrene and pyrene (on the basis of butanol-extractable fraction) from sediment with different submerged macrophytes, Hydrilla verticillata (H), Myriophyllum verticillatum (M), Vallisneria spiralis (V) and Potamogeton crispus L. (P), was investigated. A relationship between the remedial effect and the bacterial community structure was analyzed using PCR–DGGE. During a 54-day experimental period, removal efficiencies of the two contaminants were the highest in V treatment, followed by M treatment, H and P treatments, and then unplanted treatment. Moreover, only a small part of the removal increments was caused by plant accumulation, indicating that the enhanced removal was mainly due to microbial degradation. DGGE profile analysis indicated that bacterial community in sediments was relatively stable after planting. PAH-degrading bacteria were widely distributed in all of the sediment samples but species differences among the samples were obvious. Moreover, the DGGE band-based similarity between V. spiralis and the other three plants was the highest in M treatment, followed by H treatment, and then P treatment, which is similar to the trend of removal efficiencies of the two contaminants. Therefore, it can be concluded that the difference between the four submerged macrophytes in the removal of phenanthrene and pyrene from sediment might be largely due to the changes in the microbial community structure. [ABSTRACT FROM AUTHOR]

Background Dipeptidyl peptidase ( DPP)-4 inhibitors and sulfonylureas may have different effects on islet function. We designed a new two-step hyperglycemic clamp to further compare the effects of sitagliptin, saxagliptin, and glimepiride on β-cell function and the incretin effect. Methods The present study was a four-way cross-over open label randomized study. Twelve healthy male subjects were administered a single dose of sitagliptin (100 mg), saxagliptin (5 mg), glimepiride (2 mg) or blank control 2 h before undergoing a two-step hyperglycemic clamp (Step 1: only intravenous glucose was administered; Step 2: i.v. glucose loading was combined with oral glucose consumption). Two-phase insulin secretion, glucagon secretion, and incretin levels were measured during the clamp. Results In Step 1, with i.v. glucose only, there were no differences between the effects of the three drugs on insulin secretion, except that saxagliptin increased second-phase insulin secretion more than glimepiride ( P = 0.007). In Step 2, oral glucose consumption led to an approximate two fold increase in insulin secretion and both gliptins significantly increased first-phase insulin secretion compared with glimepiride ( P = 0.003 for both). Saxagliptin further increased second-phase insulin secretion compared with glimepiride ( P = 0.005) and sitagliptin ( P < 0.001). Both gliptins significantly decreased glucagon secretion and increased active glucagon-like peptide-1 ( GLP-1) compared with glimepiride, especially in Step 2. Conclusions The two-step hyperglycemic clamp appears to be a precise method to assess β-cell function by taking the effect of incretins into consideration. The oral glucose consumption adds to the i.v. glucose infusion, amplifying the differences in the effects of DPP-4 inhibitors and glimepiride on insulin secretion. [ABSTRACT FROM AUTHOR]

The proposed model is based on a three-stage multinational supply chain production-inventory model, considering the deterioration of both raw materials and finished products. The application of cross-border carbon policy combinations is an innovation of this study, and we provide assumptions for two situations. [Display omitted] • A cross-border production-inventory model for deteriorating items is developed. • Two carbon policy combinations in a global supply chain system are considered. • Comparison between proposed model and previous related studies is provided. • The concavity of the optimal solution has been verified by theoretical analysis. • Numerical examples and sensitivity analysis are done to gain managerial insights. This study is the first to propose a cross-border production-inventory model for deteriorating items under different carbon emission policy combinations by considering a global supply chain system with a single manufacturer from abroad and a single domestic retailer. An integrated three-stage supply chain that includes material supply, manufacturer's production and delivery, and retailer's ordering and sales is designed. Two carbon policy combinations are considered: (1) the manufacturer faces a carbon cap-and-trade policy, and the retailer faces a carbon tax policy; (2) the manufacturer faces a carbon tax policy and the retailer faces a carbon cap-and-trade policy. The main purpose is to determine the optimal material supply, production, and delivery strategies for the manufacturer and the optimal replenishment strategies for the retailer to maximize the joint total profit of the supply chain in each situation, while observing the solutions' carbon reduction benefits. This study uses mathematical programming to find optimal solutions for the manufacturer and the retailer, with numerical examples presented to demonstrate the solution procedures for comparing different carbon policy combinations. The results show that under different policy combinations, fluctuations in carbon prices, carbon taxes, and exchange rates have different effects on the profits of the entire supply chain and each member, as well as the carbon reduction's social benefits. This is expected to provide enterprise or supply chain decision makers, especially in multinational enterprises, with valuable references for opting for a green supply chain. [ABSTRACT FROM AUTHOR]

To investigate the mechanism of submerged macrophyte–bacteria interactions on the removal of phthalic acid esters from surface water, experiments with and without Potamogeton crispus L. were performed. A two-compartment (i.e., water and plant) kinetic model was developed. The model adequately described the variation of dibutyl phthalate (DBP) and di-2-ethylhexyl phthalate (DEHP) in the plant-water system by providing the first-order rate constants of plant uptake ( k 1 ) and release ( k 2 ), microbial degradation in water ( k 3 ) and plant degradation ( k 4 ). During 10-d incubation, the presence of P. crispus enhanced the removal of DBP and DEHP from water by 6.3% and 22.4%. Compared with the experiment without P. crispus , biodegradation of DBP in water with P. crispus decreased by 8.3% because of plant uptake even though k 3 increased by 30%. 21.4% of DBP transferred from water to plants, of which only small amount (5.1%) retained in the plant and the rest (94.9%) was degraded. Different from DBP, biodegradation of DEHP in water with P. crispus was a slightly higher than that without P. crispus . 25.5% of DEHP transferred from water to plants, of which a large portion (73.3%) retained in the plant and the rest (26.7%) was degraded. This finding reveals that the enhancement of DBP removal from surface water is mainly related to faster degradation in the plant, whereas it is mainly related to higher plant accumulation for DEHP. [ABSTRACT FROM AUTHOR]

This study applied advanced machine learning techniques, widely considered as the most successful method to produce objective to an inferential problem of recurrent cervical cancer. Traditionally, clinical diagnosis of recurrent cervical cancer was based on physician's clinical experience with various risk factors. Since the risk factors are broad categories, years of clinical study and experience have tried to identify key risk factors for recurrence. In this study, three machine learning approaches including support vector machine, C5.0 and extreme learning machine were considered to find important risk factors to predict the recurrence-proneness for cervical cancer. The medical records and pathology were accessible by the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrate that C5.0 model is the most useful approach to the discovery of recurrence-proneness factors. Our findings suggest that four most important recurrence-proneness factors were Pathologic Stage, Pathologic T, Cell Type and RT Target Summary. In particular, Pathologic Stage and Pathologic T were important and independent prognostic factor. To study the benefit of adjuvant therapy, clinical trials should randomize patients stratified by these prognostic factors, and to improve surveillance after treatment might lead to earlier detection of relapse, and precise assessment of recurrent status could improve outcome. [ABSTRACT FROM AUTHOR]

Abstract: Since computer products are highly replaceable and consumer demand often changes dramatically with the invention of new computer products, sales forecasting is therefore always crucial for computer product sales management. When constructing a sales forecasting model, discussing and understanding the important predictor variables can help focus on improving sales management efficacy. Aiming at to select appropriate predictor variable and construct effective forecasting model, this study combines variable selection method and support vector regression (SVR) to construct a hybrid sales forecasting model for computer products. In order to evaluate the feasibility and performance of the proposed approach, this study compiles the weekly sales data of five computer products including Notebook (NB), Liquid Crystal Display (LCD), Main Board (MB), Hard Disk (HD), and Display Card (DC) from a computer product retailer as the illustrative example. The experimental results indicate that the proposed hybrid sales forecasting scheme can not only provide a better forecasting result than the four competing models in terms of forecasting error, but also exhibit the capability of identifying important predictor variables. Furthermore, useful information can be provided by discussing the identified predictor variables for the five different computer products, thereby increasing sales management efficacy. [Copyright &y& Elsevier]

Abstract: This study investigates the effect of different types of SiO2 on the electronic and surface properties of pentacene films. Developing better contacts on dielectrics is one of the main challenges for pentacene-based transistor technology. The water contact angle variation indicates more hydrophobic thermally grown SiO2 surfaces than sol–gel SiO2 surfaces, suggesting that the thermally grown SiO2 dielectric enables a better molecular arrangement as the pentacene layer is deposited. It is found that the carrier mobility in pentacene on thermally grown SiO2 dielectrics is higher than that in pentacene on sol–gel SiO2 dielectrics. The Hall-effect analysis by using the polaron theory revealed that the enhanced carrier mobility is due to the increased spacing between molecules. [Copyright &y& Elsevier]

Background and aims: Plant growth-promoting rhizobacteria (PGPR) have been widely studied for agricultural applications. One aim of this study was to isolate cadmium (Cd)-tolerant bacteria from nodules of Glycine max (L.) Merr. grown in heavy metal-contaminated soil in southwest of China. The plant growth-promoting (PGP) traits and the effects of the isolate on plant growth and Cd uptake by legume and non-legume plants in Cd-polluted soil were investigated. Methods: Cd-tolerant bacteria were isolated by selective media. The isolates were identified by 16S rRNA gene and phylogenetic analysis. The PGR traits of the isolates were evaluated in vitro. Cd in soil and plant samples was determined by ICP-MS. Results: One of the most Cd-tolerant bacteria simultaneously exhibited several PGP traits. Inoculation with the PGPR strain had positive impacts on contents of photosynthesis pigments and mineral nutrients (Fe or Mg) in plant leaves. The shoot dry weights of Lolium multiflorum Lam. increased significantly compared to uninoculated control. Furthermore, inoculation with the PGPR strain increased the Cd concentrations in root of L. multiflorum Lam. and extractable Cd concentrations in the rhizosphere, while the Cd concentrations in root and shoot of G. max (L.) Merr. significantly decreased. Conclusions: This study indicates that inoculation with Cd-tolerant PGPR can alleviate Cd toxicity to the plants, increase Cd accumulation in L. multiflorum Lam. by enhancing Cd availability in soils and plant biomass, but decrease Cd accumulation in G. max (L.) Merr. by increasing Fe availability, thus highlighting new insight into the exploration of PGPR on Cd-contaminated soil. [ABSTRACT FROM AUTHOR]

Crude oil is the most important nonrenewable energy resource and the most key element for the world. In contrast to typical forecasts of oil price, this study aims at forecasting the demand of imported crude oil (ICO). This study proposes different single stage and two-stage hybrid stages of forecasting models for prediction of ICO in Taiwan. The single stage forecasting modeling includesmultiple linear regression (MLR), support vector regression (SVR), artificial neural networks (ANN), and extreme learning machine (ELM) approaches. While the first step of the two-stage modeling is to select the fewer but more significant explanatory variables, the second step is to generate predictions by using these significant explanatory variables. The proposed two-stage hybrid models consist of integration of different modeling components. Mean absolute percentage error, root mean square error, and mean absolute difference are utilized as the performance measures. Real data set of crude oil in Taiwan for the period of 1993-2010 and twenty-three associated explanatory variables are sampled and investigated. The forecasting results reveal that the proposed two-stage hybrid modeling is able to accurately predict the demand of crude oil in Taiwan. [ABSTRACT FROM AUTHOR]

One of the major activities of financial firms and private investors is to predict future prices of stocks. However, stock index prediction is regarded as a challenging task of the prediction problem since the stock market is a complex, chaotic and nonlinear dynamic system. As stock markets are highly dynamic and exhibit wide variation, it may be more realistic and practical that assumed the stock index data are a nonlinear mixture data. In this study, a hybrid stock index prediction model by utilizing nonlinear independent component analysis (NLICA), support vector regression (SVR) and particle swarm optimization (PSO) is proposed. In the proposed model, first, the NLICA is used to deal with the nonlinearity property of the stock index data. The proposed model utilizes NLICA to extract features from the observed stock index data. The features which can be used to represent underlying/hidden information of the data are then served as the inputs of SVR to build the stock index prediction model. Finally, PSO is applied to optimize the parameters of the SVR prediction model since the parameters of SVR must be carefully selected in establishing an effective and efficient SVR model. In order to evaluate the performance of the proposed approach, the closing indexes of the Taiwan stock exchange capitalization weighted stock index, Shanghai stock exchange composite index and Bombay stock exchange index are used as illustrative examples. Experimental results showed that the proposed hybrid stock index prediction method significantly outperforms the other six comparison models. It is an efficient and effective alternative for stock index forecasting. [ABSTRACT FROM AUTHOR]

Abstract: To study the effects of submerged hydrophytes on the fate of dibutyl phthalate (DBP) and di-2-ethylexyl phthalate (DEHP) in the aquatic environment, a Potamogeton crispus L. (P. crispus) microcosm was constructed. A four-compartment (i.e., water, plant, non-rhizosphere and rhizosphere sediments) level IV fugacity model was established and applied to the simulation experiments in the microcosm. Data obtained from model calculations were in good agreement with those from the experiments. Results of the model calculations showed that the total residues of DBP and DEHP in the microcosm with P. crispus were 39.7% and 19.8% lower than those in the microcosm without P. crispus. The overall biodegradation fluxes of DBP and DEHP in the microcosm with P. crispus increased by 4.7% and 12.3%, respectively, and meanwhile, advective outflow decreased. In the presence of P. crispus, a large fraction of loaded DBP and DEHP (17.7% and 29.0%) transferred to plants, and then to the rhizosphere. 4.8% and 28.0% of loaded DBP and DEHP were removed by biodegradation in P. crispus, and the remaining 12.9% and 1.0% were by biodegradation in rhizosphere sediment which was 3.6% of the total sediment. This finding demonstrates that P. crispus can substantially reduce the accumulation of phthalic acid esters (PAEs) in the experiment system and enhance the removal of PAEs. The enhancement of PAE removal is related to the biodegradation of PAEs in P. crispus, especially for the more hydrophobic DEHP. For the less hydrophobic DBP, biodegradation in the rhizosphere also plays a key role. In addition to nutrient uptake from sediment, transport process between P. crispus and the rhizosphere has also a significant influence on the distribution and fate of PAEs in the aquatic environment. [Copyright &y& Elsevier]

Abstract: Experiments in 5 surface waters with different levels and compositions of nitrogen were performed to investigate the effects of nitrogen on the removal of dibutyl phthalate (DBP) from surface water in the presence of Potamogeton crispus L., a submerged aquatic plant. The removal of DBP using P. crispus with (Group A) and without (Group B) associated microorganisms was studied and compared with that in the non-autoclaved surface water samples (Group C). The first-order rate constants for DBP loss from the aqueous medium in Groups A, B, and C were in the range of 0.016–0.227d−1, 0.008–0.112d−1 and 0.015–0.204d−1, respectively, and were mostly in the order of Group A>Group C>Group B. In Group B, a positive link between NO3 −-N concentration and rate constant for DBP loss, as well between NO3 −-N concentration and plant activity (chlorophyll a, soluble protein and nitrate reductase) was found. In addition, at the end of 10-day incubation, only small amount of DBP taken up by P. crispus was retained in P. crispus, which was no more than 11.5%, suggesting that DBP in P. crispus was mostly degraded. Different from Group B, rate constant for DBP loss in Group C increased with the increase of total inorganic N (TIN) concentration. The rate constants for DBP loss in Group A were 5.9–45.9% higher than those in Group C, and the enhancement was significantly related to the NO3 −-N:TIN ratio. The most possible reason is that surface waters with higher NO3 −-N:TIN ratio facilitate the activity of P. crispus, and consequently, removal of DBP in the presence of P. crispus is more significantly enhanced. [Copyright &y& Elsevier]

A hybrid forecasting model that integrates ensemble empirical model decomposition (EEMD), and extreme learning machine (ELM) for computer products sales is proposed. The EEMD is a new piece of signal processing technology. It is based on the local characteristic time scales of a signal and could decompose the complicated signal into intrinsic mode functions (IMFs). The ELM is a novel learning algorithm for single-hidden-layer feedforward networks. In our proposed approach, the initial task is to apply the EEMD method to decompose the original sales data into a number of IMFs. The hidden useful information of the original data could be discovered in those IMFs. The IMFs are then integrated with the ELM method to develop an effective forecasting model for computer products sales. Experimental results from three real computer products sales data, including hard disk, display card, and notebook, showed that the proposed hybrid sales forecasting method outperforms the four comparative models and is an effective alternative for forecasting sales of computer products. [ABSTRACT FROM AUTHOR]

The monitoring of a multivariate process with the use of multivariate statistical process control (MSPC) charts has received considerable attention. However, in practice, the use of MSPC chart typically encounters a difficulty. This difficult involves which quality variable or which set of the quality variables is responsible for the generation of the signal. This study proposes a hybrid scheme which is composed of independent component analysis (ICA) and support vector machine (SVM) to determine the fault quality variables when a step-change disturbance existed in a multivariate process. The proposed hybrid ICA-SVM scheme initially applies ICA to the Hotelling T² MSPC chart to generate independent components (ICs). The hidden information of the fault quality variables can be identified in these ICs. The ICs are then served as the input variables of the classifier SVM for performing the classification process. The performance of various process designs is investigated and compared with the typical classification method. Using the proposed approach, the fault quality variables for a multivariate process can be accurately and reliably determined. [ABSTRACT FROM AUTHOR]

Abstract: Stock index forecasting is one of the major activities of financial firms and private investors in making investment decisions. Although many techniques have been developed for predicting stock index, building an efficient stock index forecasting model is still an attractive issue since even the smallest improvement in prediction accuracy can have a positive impact on investments. In this paper, an efficient cerebellar model articulation controller neural network (CAMC NN) is proposed for stock index forecasting. The traditional CAMC NN scheme has been successfully used in robot control due to its advantages of fast learning, reasonable generalization capability and robust noise resistance. But, few studies have been reported in using a CMAC NN scheme for forecasting problems. To improve the forecasting performance, this paper presents an efficient CMAC NN scheme. The proposed CMAC NN scheme employs a high quantization resolution and a large generalization size to reduce generalization error, and uses an efficient and fast hash coding to accelerate many-to-few mappings. The forecasting results and robustness evaluation of the proposed CMAC NN scheme were compared with those of a support vector regression (SVR) and a back-propagation neural network (BPNN). Experimental results from Nikkei 225 and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) closing indexes show that the performance of the proposed CMAC NN scheme was superior to the SVR and BPNN models. [Copyright &y& Elsevier]

Abstract: Effective recognition of control chart patterns (CCPs) is an important issue since abnormal patterns exhibited in control charts can be associated with certain assignable causes which affect the process. Most of the existing studies assume that the observed process data which needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data could be mixture patterns, which consist of two basic CCPs combined together. In this study, a hybrid scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for CCPs recognition. The proposed hybrid ICA-SVM scheme initially applies an ICA to the mixture patterns in order to generate independent components (ICs). The hidden basic patterns of the mixture patterns can be discovered in these ICs. The ICs can then serve as the input variables of the SVM for building a CCP recognition model. Experimental results revealed that the proposed scheme is able to effectively recognize mixture control chart patterns and outperform the single SVM models, which did not use an ICA as a preprocessor. [Copyright &y& Elsevier]

Abstract: The forecasting of stock price is one of the most challenging tasks in investment/financial decision-making since stock prices/indices are inherently noisy and non-stationary. In this paper, an integrated independent component analysis (ICA)-based denoising scheme with neural network is proposed for stock price prediction. The proposed approach first uses ICA on the forecasting variables to generate the independent components (ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables. The reconstructed forecasting variables will contain less noise information and are served as the input variables of the neural network model to build the forecasting model. The TAIEX closing index and Nikkei 225 opening index are used as illustrative examples to evaluate the performance of the proposed model. Experimental results show that the proposed model outperforms the integrated wavelet denoising technique with BPN model, the BPN model with non-filtered forecasting variables, and a random walk model. [Copyright &y& Elsevier]

Abstract: This study was conducted in Haihe River, China, in order to assess the ability of Potamogeton crispus L. (P. crispus), a submerged plant, to accumulate phthalic acid esters (PAEs). Dibutyl phthalate (DBP) and di-2-ethylexyl phthalate (DEHP) were measured in samples of water, sediment and P. crispus plant from March to May 2008. The results showed that the highest levels of DBP and DEHP in above-ground tissues appeared during the blooming period of P. crispus, which was consistent with the results obtained with the water samples. Regression analysis reveals that concentrations of PAEs in above-ground tissues were mainly influenced by PAE concentrations in the water of Haihe River. Enrichment of DBP and DEHP in above-ground tissues was observed, with bioconcentration factors (BCFs) of 4.82–83.65Lkg−1 for DBP and 6.71–93.70Lkg−1 for DEHP. The distribution pattern of DBP in roots, surface and near root sediments was different from that of DEHP. Concentrations of DBP in near root sediments were lower than those in roots and comparable to those in surface sediments, while concentrations of DEHP in roots were lower than those in near root sediments but higher than those in surface sediments. [Copyright &y& Elsevier]

Hexachlorocyclohexane (HCH) concentrations in sediments and sediment trap fluxes of particulate organic carbon and HCHs were measured bi-weekly from March 31 to October 18, 2006 in an urban eutrophic lake in Tianjin, China, in order to investigate sedimentation and seasonal variation of HCHs in sediments. HCH concentrations (dry weight basis) ranged from 2.2 to 20.2 ng/g (mean 7.7 ng/g) in surface sediments and from 26.6 to 972.7 ng/g (mean 187.0 ng/g) in settling particles, respectively. A clear seasonal variation in HCH sedimentation and HCH concentrations in sediments was observed. The maximal HCH deposition occurred following a spring phytoplankton bloom. The average flux of HCHs to sediment was approximately 21-fold higher in April to mid-June as compared to late June to October. This was attributed to the high vertical fluxes at the end of the spring phytoplankton bloom. The maximum values of HCH concentrations in sediments were observed in mid-June to late July. Concentrations of HCHs in sediments from the eutrophic lake were well-correlated with organic carbon contents in sediments. The annual sediment trap flux of HCHs in the eutrophic lake, which was estimated using data obtained in the eutrophic lake, was 117 μ g/m2 yr, about 72% of which was attributed to the sedimentation corresponding to spring bloom phytoplankton deposition in late May to mid-June. The high sediment trap flux of HCHs in the eutrophic lake was related to serious local contamination. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a machine vision approach for automatic detection of micro defects in periodically patterned surfaces and, especially, aim at thin film transistor liquid crystal display (TFT-LCD) panels. The proposed method is based on an image reconstruction scheme using independent component analysis (ICA). ICA is first applied to a faultless training image to determine the de-mixing matrix and the corresponding independent components (ICs). The ICs representing the global structure of the training image are then identified and the associated row vectors of those ICs in the de-mixing matrix are replaced with a de-mixing row representing the least structured region of the training image. The reformed de-mixing matrix is then used to reconstruct the TFT-LCD image under inspection. The resulting image can effectively remove the global structural pattern and preserve only local anomalies. A number of micro defects in different TFT-LCD panel surfaces are evaluated with the proposed method. The experiments show that the proposed method can well detect various ill-defined defects in periodically patterned surfaces. [Copyright &y& Elsevier]

Influences of major nutrients (N, P) on the biodegradation and bioconcentration of dibutyl phthalate (DBP) and di-2-ethylexyl phthalate (DEHP) by Chlorellavulgaris in lake water were investigated in this work. Our study demonstrated that nutrient addition obviously influenced biodegradation rate constants and apparent bioconcentration factors (BCFs) of DBP and DEHP in Chlorellavulgaris. The effects of P addition on biodegradation were less pronounced than the effects of N addition as a result of N-limitation status of phytoplankton in the lake water, while addition of both N and P more greatly affected biodegradation than addition of N or P. BCFs of DBP and DEHP decreased with increasing algal exudate as measured by dissolved organic carbon (DOC) and a strong correlation between BCFs and DOC was obtained. The results indicate that DOC plays an important role in the bioconcentration of DBP and DEHP. [ABSTRACT FROM AUTHOR]

Thin film transistor liquid crystal displays (TFT-LCDs) have become increasingly popular and dominant as display devices. Surface defects on TFT panels not only cause visual failure, but result in electrical failure and loss of LCD operational functionally. In this paper, we propose a global approach for automatic visual inspection of micro defects on TFT panel surfaces. Since the geometrical structure of a TFT panel surface involves repetitive horizontal and vertical elements, it can be classified as a structural texture in the image. The proposed method does not rely on local features of textures. It is based on a global image reconstruction scheme using the singular value decomposition (SVD). Taking the image as a matrix of pixels, the singular values on the decomposed diagonal matrix represent different degrees of detail in the textured image. By selecting the proper singular values that represent the background texture of the surface and reconstructing the matrix without the selected singular values, we can eliminate periodical, repetitive patterns of the textured image, and preserve the anomalies in the restored image. In the experiments, we have evaluated a variety of micro defects including pinholes, scratches, particles and fingerprints on TFT panel surfaces, and the result reveals that the proposed method is effective for LCD defect inspections. [ABSTRACT FROM AUTHOR]

Photodegradation of pentachlorophenol (PCP) in aquatic surface microlayer (SM) was examined under natural sunlight. SM samples were collected using a glass plate, along with corresponding subsurface waters. PCP was added to samples of the SM and subsurface water. When all samples were exposed to the same natural sunlight conditions, the first order photodegradation rates of PCP in the SM water samples were different from those of PCP in corresponding subsurface water samples. The difference was correlated with the enrichment of dissolved organic carbon (DOC) in SM compared to the corresponding subsurface waters. Enhanced photodegradation in SM may be due to increased abundance of photoreactants related to the UV-absorbing properties of dissolved organic materials in the SM as the result of photo-induced reactions. Effects of environmental factors, pH and salinity, on photodegradation process of PCP in SM were also evaluated. [ABSTRACT FROM AUTHOR]

Investigation of di-2-ethylhexyl phthalate (DEHP) in the surface microlayer (SM) and subsurface water (SSW) of a small eutrophic lake was carried out from April to June 2002. Results obtained from the field samples showed that tens to several hundred μg/L of DEHP was found in each sample of SM and SSW, indicating that the lake has been polluted by DEHP. Linear regression analysis showed that concentrations of DEHP were more strongly correlated with sampling temperature than with chlorophyll a concentrations. Correlation between DEHP concentrations and total phosphor concentrations was also obtained. Enrichment factors (EFs) of DEHP in the SM comparing with the corresponding SSW were ranging over 0.85–2.12 with an average of 1.35. DEHP EFs were significantly related to both enrichment of chlorophyll a in the SM and sampling temperature. [Copyright &y& Elsevier]