Photocatalytic oxidation of low concentration 2,4-D solution with new TiO2 fiber catalyst in a continuous flow reactor.

Environmental pollution by low concentrations of 2,4-Dichlorophenoxyacetic acid (2,4-D) is a concern these days due to ever increasingly stringent regulations. Photocatalysis with immobilized TiO2 fiber is a promising oxidation method. Laboratory experiments on photocatalytic degradation of 0.045 mmol l(-1) 2,4-D with the world's first high-strength TiO2 fiber catalyst were carried out in a continuous flow reactor in which the degradations were, in general, similar to those with high 2,4-D concentrations investigated elsewhere. Degradation and mineralization of 2,4-D were significantly enhanced with no initial pH adjustments. The rate constants for total organic carbon (TOC) without pH adjustment were about two-fold bigger than the pH adjustment cases. CO2 gas measurement and carbon mass-balance were carried out for the first time, where about 34% organic carbon converted into CO2 gas during four-hour oxidation. 2,4-Dichlorophenol (2,4-DCP), phenol, benzyl alcohol and two unknowns (RT = 2.65 and 3.78 min.) were detected as aromatic intermediates while Phenol was the new aromatic in HPLC analysis. Dechlorination efficiencies were high (> 70%) in all the cases, and more than 90% efficiencies were observed in chloride mass balance. Bigger flow rates and solution temperature fixed at 20 degrees C without pH adjustment greatly enhanced 2,4-D mineralization. These results can be an important basis in applying the treatment method for dioxin-contaminated water and wastewater.     

Water matrix effect on UV photodegradation of perfluorooctanoic acid

The widespread detection of perfluorinated compounds (PFCs) in the water environment has been a concern for the last several years, while effluents from wastewater treatment facilities are the major sources of these compounds. Even advanced oxidation technologies (AOTs) are not useful for mineralization of the compounds due to their very high stability. Photochemical techniques using particularly vacuum UV (VUV) have been found to be very promising in this regard. But the use of VUV in UV-based AOTs has still not progressed much. Moreover, the impact of water quality on PFCs photomineralization is unknown. This investigation aimed to assess photomineralization potentials of perfluorooctanoic acid (PFOA) in ultrapure water (UPW), tap water (TW), surface water and treated wastewater effluent using a reactor setup enabling maximum utilization of VUV emission of low pressure lamp in laboratory batch experiments. Neya River water (NRW) and the Nakahama Wastewater Treatment Plant Effluent (NWWTPE) represented surface water and treated wastewater effluent respectively. Also, tests were carried out in 50% diluted NRW and NWWTPE. PFOA photomineralization in terms of PFOA removal, defluorination and total organic carbon (TOC) removal are discussed. The usefulness of the method for PFOA mineralization in organic-rich wastewaters, and further research needs are also highlighted.     

Managing inventory with two suppliers under yield uncertainty and risk aversion

In this paper, we consider a single-product single-period inventory model in which the retailer can source from two suppliers. The primary supplier is cheaper but unreliable in the sense that it generates supply yield uncertainty, whereas the secondary supplier is perfectly reliable but more expensive. The reliable supplier's capacity is fixed and the retailer cannot order more than the quantity reserved in advance. We study the problem in the context of a risk-averse retailer who has to determine the optimal order quantity from the primary supplier and the optimal reserved quantity from the secondary supplier. We develop the model in the perspective of a low risk averse retailer and quantify the risk via an exponential utility function. We show by numerical experiments how the resulting dual sourcing strategies differ from those obtained in the risk-neutral analysis. We also examine the sensitivity of some model-parameters on the optimal decisions.     

Human Capital,Organizational Orientations and Performance: Evidence From the Restaurant Industry

ABSTRACT

The study examines how human capital combines with and influences organizational orientations to determine performance. Specifically, this study investigates small restaurant businesses’ ability to effectively exploit intangible resources and capabilities, rather than tangible resources, in order to produce sustainable competitive advantage. A structured questionnaire was used to survey owners/managers of small independent restaurants in the United States. The results demonstrate that human capital, market orientation and entrepreneurial orientation serve as intangible resources and capabilities that can augment the competitive position of independent restaurants, and thereby improve performance. Human capital also combines with and influences the adoption of market orientation and subsequent performance.     

Optimal Pricing and Order-Up-To S Inventory Policy with Expected Utility of the Present Value Criterion

The article determines pricing and order-up-to level S inventory decisions over an infinite planning horizon from the point of view of a risk-averse decision maker. The demand is assumed to be stochastic but influenced by the selling price which is a decision variable. Shortages are allowed and backordered partially. We calculate the present value of the cash flow over the entire planning horizon and incorporate the notion of risk aversion into the model using a concave utility function. We numerically demonstrate the model and investigate the impact of different model-parameters on the optimal decisions. It is observed that the optimal selling price for a risk-averse decision maker is not less than the optimal selling price of a risk-neutral decision maker while the optimal order level for the risk-averse decision maker is always less than that of the risk-neutral decision maker.     

Biogeography based optimization for mining rules to assess credit risk

Financial institutions, by and large, rely on the use of machine learning techniques to improve the classic credit risk assessment model for reduction of costs, delivery of faster decisions, guaranteed credit collections, and risk mitigations. As such, several data mining and machine learning approaches have been developed for computation of credit scores over the last few decades. Moreover, the existing rule-based classification algorithms tend to generate a number of rules with a large number of conditions in the antecedent part. However, these algorithms fail to demonstrate high predictive accuracy while balancing coverage and simplicity. Thus, it becomes quite a challenging task for the researchers to generate an optimal rule set with high predictive accuracy. In this paper, we present an effective rule based classification technique for the prediction of credit risk using a novel Biogeography Based Optimization (BBO) method. The novel BBO in the context of rule mining is named as locally and globally tuned biogeography based rule-miner (LGBBO-RuleMiner). This is applied for discovering optimal rule set with high predictive accuracy from the dataset containing both the categorical and continuous attributes. The performance of the proposed algorithm is compared against a variety of rule-miners such as OneR (1R), PART, JRip, Decision Table, Conjunctive Rule, J48, and Random Tree, along with some meta-heuristic based rule mining techniques by considering two credit risk datasets obtained from University of California, Irvine (UCI) repository. It is found from the comparative study that the proposed rule miner in ten independent runs of ten-fold cross validation outperforms all of the aforesaid algorithms in terms of predictive accuracy, coverage, and simplicity.     

Transaction costs and the collection of information: presale measurement on private timber sales

Measurement efforts to reduce the uncertainty concerning theattributes of heterogeneous goods may simply redistribute wealthand result in social waste. Individuals bearing the cost ofsuch distributional measurement have incentives to develop buyingand selling practices that limit such measurement. We examine,both theoretically and empirically, the determinants of thelevel of distributional measurement efforts in a competitiveauction framework. The empirical application, which uses a sampleof private timber sales, provides strong support for the implicationsof the theoretical model of presale measurement.     

Local costs of distribution,international trade costs and micro evidence on the law of one price

This paper connects trade flows to deviations from the law of one price (LOOP) in a structural model of trade and retailing. It accounts for the observed cross-country dispersion in prices of goods, based on retail price survey data, by focusing on two sources of goods market segmentation — (i) international trade costs, and (ii) non-traded input costs of distribution. I find that a multi-sector Ricardian trade model, ala Eaton–Kortum, augmented with a distribution sector, can account for the average price dispersion for a basket of goods fully and generates 70% of the variation in price dispersion across goods within the basket. While tradability of goods is important in explaining the average price dispersion for the basket of goods, distribution costs are important in explaining why, within the basket, some goods show more price dispersion than others.     

Locally minimax test of independence in elliptically symmetrical distributions with additional observations

Summary LetX=(X _ij )=(X ₁, ...,X _n )’,X’ _i =(X _i1, ...,X _ip )’,i=1,2, ...,n be a matrix having a multivariate elliptical distribution depending on a convex functionq with parameters, 0,σ. Let ϱ²=ϱ ₂ ^-2 be the squared multiple correlation coefficient between the first and the remainingp ₂+p ₃=p−1 components of eachX _i . We have considered here the problem of testingH ₀:ϱ²=0 against the alternativesH ₁:ϱ ₁ ^-2 =0, ϱ ₂ ^-2 >0 on the basis ofX andn ₁ additional observationsY ₁ (n ₁×1) on the first component,n ₂ observationsY ₂(n ₂×p ₂) on the followingp ₂ components andn ₃ additional observationsY ₃(n ₃×p ₃) on the lastp ₃ components and we have derived here the locally minimax test ofH ₀ againstH ₁ when ϱ ₂ ^-2 →0 for a givenq. This test, in general, depends on the choice ofq of the familyQ of elliptically symmetrical distributions and it is not optimality robust forQ.     

Locally minimax tests for a multinormal data problem

LetX be ap-normal random vector with unknown mean and unknown covariance matrix and letX be partitioned asX=(X ₍₁₎ ,X ₍₂₎ , ...,X _(r) ) whereX _(j) is a subvector of dimensionp _j such that _j=1 ^r p _j =p. We show that the tests, obtained by Dahel (1988), are locally minimax. These tests have been derived to confront H_o: =0 versusH ₁: 0 on the basis of sample of sizeN, X ₁, ..., X_N, drawn fromX andr additional samples of sizeN _j, U _i ^(j) , i=1, ..., N_j, drawn fromX ₍₁₎, ...X _(r) respectively. We assume that the (r+1) samples are independent and thatN _j>p _j forj=0, 1, ..., r (N _oN andp _op). Whenr=2 andp=2, a Monte Carlo study is performed to compare these tests with the likelihood ratio test (LRT) given by Srivastava (1985). We also show that no locally most powerful invariant test exists for this problem.