Closed‐form approximations for spread options in Lévy markets

We provide new closed‐form approximations for the pricing of spread options in three specific instances of exponential Lévy markets, ie, when log‐returns are modeled as Brownian motions (Black‐Scholes model), variance gamma processes (VG model), or normal inverse Gaussian processes (NIG model). For the specific case of exchange options (spread options with zero strike), we generalize the well‐known Margrabe formula (1978) that is valid in a Black‐Scholes model to the VG model under a homogeneity assumption.     

Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains

Operational forecasting in supply chain management supports a variety of short-term planning decisions, such as production scheduling and inventory management. In this respect, improving short-term forecast accuracy is a way to build a more agile supply chain for manufacturing companies. Demand forecasting often relies on well-established univariate forecasting methods to extrapolate historical demand. Collaboration across the supply chain, including information sharing, is suggested in the literature to improve upon the forecast accuracy of such traditional methods. In this paper, we review empirical studies considering the use of downstream information in demand forecasting and investigate different modeling approaches and forecasting methods to incorporate such data. Where empirical findings on information sharing mainly focus on point-of-sale data in two-level supply chains, this research empirically investigates the added value of using sell-through data originating from intermediaries, next to historical demand figures, in a multi-echelon supply chain. In a case study concerning a US drug manufacturer, we evaluate different methods to incorporate this data and consider both time series methods and machine learning techniques to produce multi-step ahead weekly forecasts. The results show that the manufacturer can effectively improve its short-term forecast accuracy by integrating sell-through data into the forecasting process and provide useful insights as to the different modeling approaches used. The conclusion holds for all forecast horizons considered, though it is most pronounced for one-step ahead forecasts. Therefore, our research provides a clear incentive for manufacturers to assess the forecast accuracy that can be achieved by using sell-through data.     

Ion mobility spectrometry of talarozole, a new azole drug, in cleaning quality control

Ion mobility spectrometry (IMS) gains increased pharmaceutical interest as an analytical technique for the verification of equipment cleaning. Using a fractional factorial design, we developed an IMS method for talarozole, which represents a new generation of retinoic acid metabolism blocking agents (RAMBA) that can be used for the treatment of different dermatological diseases, such as psoriasis and acne. Using a Smiths Detection Ionscan-LS and the optimal IMS settings obtained, talarozole showed a drift time of 16.648 ms, corresponding to a reduced ion mobility K₀ of 1.072 cm²V^?1s^?1. Total analysis times below 1 min were achieved. Talarozole was well separated in the plasmagram from other azole compounds and the limit of detection was found to be 43 ng/ml. Swab samples collected from steel and glass plates were successfully analyzed, thereby showing that IMS is indeed a suitable technique for the quantitative analysis of talarozole in cleaning quality control.     

QUENCHING PROBLEM IN SOME SEMILINEAR WAVE EQUATIONS

This article proves a quenching result of the solutions for some semi-linear wave equations.     

Dijet angular distributions at \sqrt{s}=14 TeV

We present a Monte Carlo study of dijet angular distributions at $\sqrt{s}=14$  TeV. First we perform a next-to-leading order QCD study; we calculate the distributions in four different bins of dijet invariant mass using different Monte Carlo programs and different jet algorithms, and we also investigate the systematic uncertainties coming from the choice of the parton distribution functions and the renormalization and factorization scales. In the second part of this paper, we present the effects on the distributions coming from a model including gravitational scattering and black hole formation in a world with large extra dimensions. Assuming a 25% systematic uncertainty, we report a discovery potential for the mass bin 1<M _jj <2 TeV at 10 pb^?1 integrated luminosity.     

A novel UPLC–MS–MS method for simultaneous determination of seven uremic retention toxins with cardiovascular relevance in chronic kidney disease patients

Chronic kidney disease (CKD) is a devastating illness characterized by accumulation of uremic retention solutes in the body. The objective of this study was to develop and validate a simple, rapid, and robust UPLC–MS–MS method for simultaneous determination, in serum, of seven organic acid uremic retention toxins, namely uric acid (UA), hippuric acid (HA), indoxylsulfate (IS), p-cresylglucuronide (pCG), p-cresylsulfate (pCS), indole-3-acetic acid (IAA), and 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid (CMPF). Isotopically labeled internal standards (d₅-HA; 1,3-¹⁵N₂-UA, and d₅-IAA) were used to correct for variations in sample preparation and system performance. Separation on a C18 column was followed by negative electrospray ionization and tandem mass spectrometric detection. Accuracy was below the 15 % threshold. Within-day precision varied from 0.60 to 4.54 % and between-day precision was below 13.33 % for all compounds. The applicability of the method was evaluated by analyzing 78 serum samples originating both from healthy controls and from patients at different stages of CKD. These results were compared with those obtained by use of conventional HPLC–PDA–FLD methods. A good correlation was obtained between both methods for all compounds.     

A mathematical model of enterprise competitive ability and performance through a particular Emden-Fowler Equation

In this paper we work with the ordinary diffential equation u′′ u3 = 0 and obtain some interesting phenomena concerning blow-up, blow-up rate, life-spann, zeros and critical points of solutions to this equation.     

Paving the way to conformationally unravel complex glycopeptide antibiotics by means of Raman optical activity

It is crucial for fundamental physical chemistry techniques to find their application in tackling real-world challenges. Hitherto, Raman optical activity (ROA) spectroscopy is one of the examples where a promising future within the pharmaceutical sector is foreseen, but has not yet been established. Namely, the technique is believed to be able to contribute in investigating the conformational behaviour of drug candidates. We, herein, strive towards the alignment of the ROA analysis outcome and the pharmaceutical expectations by proposing a fresh strategy that ensures a more complete, reliable, and transferable ROA study. The strategy consists of the treatment of the conformational space by means of a principal component analysis (PCA) and a clustering algorithm, succeeded by a thorough ROA spectral analysis and a novel way of estimating the contributions of the different chemical fragments to the total ROA spectral intensities. Here, vancomycin, an antibiotic glycopeptide, has been treated; it is the first antibiotic glycopeptide studied by means of ROA and is a challenging compound in ROA terms. By applying our approach we discover that ROA is capable of independently identifying the correct conformation of vancomycin in aqueous solution. In addition, we have a clear idea of what ROA can and cannot tell us regarding glycopeptides. Finally, the glycopeptide class turns out to be a spectroscopically curious case, as its spectral responses are unlike the typical ROA spectral responses of peptides and carbohydrates. This preludes future ROA studies of this intriguing molecular class.

Raman optical activity tackles the complex conformational space of glycopeptide antibiotics.