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WOS: 000430761500026, Intrinsic high nonlinearity in experimentally measured laser scattering intensities poses significant difficulties in analyzing various molecular and optical properties of nematic liquid crystals (NLCs). In this respect, as we theoretically proved in a previous paper, universal nonlinear function approximator layered feedforward neural network (LFNN) can be applied to construct consistent empirical physical formulas (EPFs) for nonlinear physical phenomena. The novelty of this paper is that, by using our previous conference paper data (literature data or simply data for short) for He-Ne probe laser illumination nonlinear scattering intensities of dye-doped NLCs with ultraviolet pump laser-driven Fredericksz threshold (FT) shifts, we constructed definitive LFNN-EPFs for these illumination intensities of nonlinear scattering exhibiting FT shifts. The dyes used in the literature data were methyl red (MR) azo and disperse red (DR) anthraquinone. The LFNN-EPFs fitted the data very well. Moreover, magnificent LFNN test set forecastings over previously unseen data confirmed the consistent LFNN-EPFs inferences of the intensities of scattering. The LFNN-EPFs properly extracted the FT threshold shifts, as well as revealing the intensity dependencies on the kind of dye used. We, therefore, conclude the LFNN consistently infers nonlinear physical laws governing the NLC scattering data. Provided that sufficient scattering intensity data is available, these nonlinear physical laws embedded in LFNN-EPFs may potentially be useful for investigating various NLC molecular structure parameters in molecular nonlinear optics domain. This knowledge may be applicable in developing new optical materials. (C) 2017 Elsevier GmbH. All rights reserved.

Gama ışınlarının multipolariteleri ve nükleerdurumların spinleri, genelliklle, nükleer reaksiyonlarla oluşturulan hizalanmışdurumlardan yayılan gama ışınlarının açısal dağılımı ile incelenir. Geçişlerinfarklı multipolarite değerleri için, dağılım farklı özellikler göstermektedir.Dağıtım, farklı spinler ve çok kutupluluklar için literatürdeki tablolanmışkatsayılarve açısal dağılım formülü kullanılarak elde edilir. Bununla birlikte,bu katsayılar r katlı tensör çarpımları içerir ve yapıları oldukça doğrusalolmayan şekildedir. Dahası, bu katsayıların hesaplanması karmaşık integralleriçerdiğinden, daha büyük r değerleri için açıkça ele alınması çok zordur. Bubağlamda, daha önceki bir çalışmamızla teorik olarak ispatlandığımız gibi,doğrusal olmayan fiziksel fenomenler için, tutarlı, ampirik fiziksel formüller(EPF'ler) oluşturmak için, evrensel doğrusal olmayan bir katmanlı beslemelisinir ağı (LFNN) kullanılabilir. Bu makalede, nükleer durumların tamsayıspinlerine ve geçişlerin dipol ve kuadrupol multipolaritelerine odaklanarak,uygun LFNN'leri inşa ederek katsayıları tutarlı bir şekilde tahmin ettik.LFNN-EPF'ler, literatür katsayısı verisini çok iyi bir şekilde fitledi. Ayrıca,daha önce görülmemiş veriler üzerinde yapılan LFNN test seti tahminleri, katsayılarınbelirlenmesi için tutarlı LFNN-EPF'leri doğrulamıştır. Bu bağlamda, LFNN'nin,gama ışınlarının açısal dağılımını yöneten doğrusal olmayan fiziksel yasalaratutarlı bir şekilde uyduğu sonucuna varabiliriz. Bu da, geleneksel katsayıhesaplama yöntemleri ile elde edilmesi zor olan bir sonuçtur., Multipolarities of gamma rays andspins-parities of nuclear states are usually investigated by the angulardistribution of gamma rays emitted from aligned states formed by nuclearreactions. For different multipolarities of the transitions, the distributionshows different characteristics. The distribution is obtained by using angulardistribution formula which has literature tabulated coefficients for differentspins and multipolarities. However,these coefficients involve -fold tensor products and they are highly nonlinear in nature.Furthermore, as the calculation of these coefficients implicitly involveshighly complicated integral quantities, they are very difficult to handleexplicitly for larger values. In this respect, aswe theoretically proved in a previous paper, universal nonlinear functionapproximator layered feedforward neural network (LFNN) can be applied toconstruct consistent empirical physical formulas (EPFs) for nonlinear physicalphenomena. In this paper, by concentrating on the integer spins of nuclearstates and dipole and quadrupole type multipolarities of the transitions, weconsistently estimated the coefficients by constructing suitable LFNNs. TheLFNN-EPFs fitted the literature coefficient data very well. Moreover, magnificentLFNN test set forecastings over previously unseen data confirmed the consistentLFNN-EPFs for the determination of coefficients. In this sense, we can conclude that the LFNNconsistently infers nonlinear physical laws governing the angular distributionof gamma rays, which are otherwise difficult to obtain by conventionalcoefficient calculation methods.

Nuclear shape transition has been actively studied in the past decade. In particular, the understanding of this phenomenon from a microscopic point of view is of great importance. Because of this reason, many works have been employed to investigate shape phase transition in nuclei within the relativistic and non-relativistic mean field models by examining potential energy curves (PECs). In this paper, by using layered feed-forward neural networks (LFNNs), we have constructed consistent empirical physical formulas (EPFs) for the PECs of 38-66Ti calculated in Hartree-Fock-Bogoliubov (HFB) method with SLy4 Skyrme forces. It has been seen that the PECs obtained by neural network method are compatible with those of HFB calculations.

WOS: 000311660700003, Gamma ray tracking is an efficient detection technique in studying exotic nuclei which lies far from beta stability line. To achieve very powerful and extraordinary resolution ability, new detectors based on gamma ray tracking are currently being developed. To reach this achievement, the neutron-gamma discrimination in these detectors is also an important task. In this paper, by suitable layered feedforward neural networks (LFNNs), we have constructed novel and consistent empirical physical formulas (EPFs) for some highly nonlinear detector counts measured in neutron-gamma discrimination. The detector counts data used in the discrimination was actually borrowed from our previous paper. The counts used here had been originally measured versus the following parameters: energy deposited in the first interaction points, difference in the incoming direction of initial gamma rays, and finally figure of merit values of the clusters determined by tracking. The LFNN-EPFs are of explicit mathematical functional form. Therefore, by various suitable operations of mathematical analysis, these LENN-EPFs can be used to derivate further physical functions which might be potentially relevant to neutron-gamma discrimination performance of gamma ray tracking. (C) 2012 Elsevier Ltd. All rights reserved.

WOS: 000297189700007, In this paper, we realized two objectives. Firstly, birefringence of azo and anthraquinone dye-doped nematic liquid crystal (NLC) molecules was investigated by applied electric field dependent laser scattering intensities. The birefringence was essentially calculated from ordinary and extraordinary ray phase difference, which is determined from the measured intensities corresponding to parallel and perpendicular orientations of analyzer to polarizer. The birefringence was found to be dependent on both applied voltage and the kind of the doping dye. As the second objective, by nonlinear universal function approximator layered feedforward neural network (LFNN), we constructed explicit form of empirical physical formulas (EPFs) for experimentally measured dye-doped NLC nonlinear scattering intensities. Excellent LFNN test set predictions over yet-to-be measured experimental data proved that the constructed LFNN-EPFs estimated the measured intensities consistently. The correlation coefficients assessing the goodness of predictions were about r = 0.998 for all cases. The LFNN-EPFs also extracted the intensity dependency on the kind of dye used. When theoretical and LFNN-EPFs intensities are compared, we conclude that given certain experimental conditions, theoretical and LFNN-EPFs predictions are in excellent agreement. In this sense, we can say that the physical laws embedded in the birefringence scattering data can be consistently extracted by LFNN. Therefore, judging from the consistent extraction of the molecular dependencies of pure and doped NLC intensities, we predict that the LFNN-EPFs can help to identify unknown molecular structural parameters in liquid crystal extracts. More concretely, by suitable mathematical operations such as differentiation, integration, minimization on these intensity LFNN-EPFs, some useful information into the charge distributions of the LC molecules can be gained. (C) 2011 Elsevier B.V. All rights reserved.

WOS: 000276667400022, Among other significant obstacles, inherent nonlinearity in experimental physical response data poses severe difficulty in empirical physical formula (EPF) construction. In this paper, we applied a novel method (namely layered feedforward neural network (LFNN) approach) to produce explicit nonlinear EPFs for experimental nonlinear electro-optical responses of doped nematic liquid crystals (NLCs). Our motivation was that, as we showed in a previous theoretical work, an appropriate LFNN, due to its exceptional nonlinear function approximation capabilities, is highly relevant to EPF construction. Therefore, in this paper, we obtained excellently produced LFNN approximation functions as our desired EPFs for above-mentioned highly nonlinear response data of NLCs. In other words, by using suitable LFNNs, we successfully fitted the experimentally measured response and predicted the new (yet-to-be measured) response data. The experimental data (response versus input) were diffraction and dielectric properties versus bias voltage; and they were all taken from our previous experimental work. We conclude that in general, LFNN can be applied to construct various types of EPFs for the corresponding various nonlinear physical perturbation (thermal, electronic, molecular, electric, optical, etc.) data of doped NLCs. (C) 2010 Elsevier B.V. All rights reserved., CUBAP (Cumhuriyet University-Bilimsel Arastirma Projeleri Birimi) [F-250], This work has been supported by CUBAP (Cumhuriyet University-Bilimsel Arastirma Projeleri Birimi) Project no. F-250. Also we thank to permission given by the authors (some are also the co-authors here) of the paper of literature data [13] used in this paper.

In this study, it was aimed to determine the effects of high temperature on the live weight, food consumption, taking benefit from the food, living strength, some carcass characteristics and the respective effects on the living strength. By this purpose, 584 quails at the age of 1 week were separated into two groups; one of the groups was identified as the control group to be placed under variable temperature (18-24 C), and the other group (trial group) was kept under 35 C. As a result of the research, the differences o o between the control and trial groups regarding to the live weight, carcass weight and carcass productivity, food consumption and the values of taking benefit from the food were found significant. (P

WOS: 000232070300010, We theoretically establish that, contrary to superficial observation, constructing an empirical physical formula (or physical law interchangeably) to explain the physical phenomenon is inherently full with several serious obstacles. We theoretically show that an appropriate layered feedforward neural network (LFNN) is relevant to overcome significantly these obstacles. To this purpose, we first form a five element set of obstacles pertaining to the empirical physical formula construction. Second, we show that a suitably chosen LFNN can overcome each of the five obstacles, because the LFNN arbitrarily accurately estimates the unknown empirical physical formula whether the experimental variables are deterministic or probabilistic. To offer a general approach, we treat the LFNN that uses the non-parametric method of sieves estimation. The method allows one to increase properly the number of hidden neurons with growing sample size. Finally, to support our theory, we present some simulation LFNN results with large sample size. Here we use artificial rather than real data simply in order not to prefer any specific physical equation. (c) 2005 Elsevier B.V. All rights reserved.

In this study, it was aimed to determine the effects of higher temperatures on the blood serum values, egg productivity, egg weight and the thickness of eggshell of the Japanese quails. By this purpose, two temperature groups were arranged by consisting of control (18-24 C) and trial (35 C) groups. In the end o o of the research, it was observed that the higher temperature increased some of the blood serum values such as glucose, Na, triglyceride (P

WOS: 000317545400015, PubMed ID: 23295214, The optimized geometrical structure, vibrational and electronic transitions, chemical shifts and nonlinear optical properties of 2-(4-hydroxyphenylazo)benzoic acid (HABA) compound were presented in this study. The ground state geometrical structure and vibrational wavenumbers were carried out by using density functional (DFT/B3LYP) method with 6-311++G(d,p) basis set. The vibrational spectra of title compound were recorded in solid state with FT-IR and FT-Raman spectrometry in the range of 4000-400 cm(-1) and 4000-10 cm(-1); respectively. The fundamental assignments were done on the basis of the recorded spectra and total energy distribution (TED) of the vibrational modes. The H-1 and C-13 NMR spectra were recorded in deuterated DMSO solution, and gauge-invariant atomic orbitals (GIAOs) method was used to predict the isotropic chemical shifts. The UV-Vis absorption spectra of the compound were observed in the range of 200-800 nm in ethanol, methanol and water solvents. To investigate the nonlinear optical properties, the polarizability, anisotropy of polarizability and molecular first hyper-polarizability were computed. A detailed description of spectroscopic behaviors of compound was given based on the comparison of experimental measurements and theoretical computations. (C) 2012 Elsevier B.V. All rights reserved.

WOS: 000324298700022, Proper selection of neutron exit channels following heavy-ion reactions is important in nuclear structure physics. A knowledge of detector counts versus number of neutron interaction points per event can be useful in this selection. In this paper, we constructed layered feedforward neural networks (LFNNs) consistent empirical physical formulas (EPFs) to estimate the detector counts versus number of neutron interaction points per event. The LFNN-EPFs are of explicit mathematical functional form. Therefore, by various suitable operations of mathematical analysis, these LFNN-EPFs can be used to derivate further physical functions which might be potentially relevant to neutron exit channel selection. (C) 2013 Elsevier Ltd. All rights reserved.

WOS: 000309635100002, The gamma-ray tracking technique is a highly efficient detection method in experimental nuclear structure physics. On the basis of this method, two gamma-ray tracking arrays, AGATA in Europe and GRETA in the USA, are currently being tested. The interactions of neutrons in these detectors lead to an unwanted background in the gamma-ray spectra. Thus, the interaction points of neutrons in these detectors have to be determined in the gamma-ray tracking process in order to improve photo-peak efficiencies and peak-to-total ratios of the gamma-ray peaks. In this paper, the recoil energy distributions of germanium nuclei due to inelastic scatterings of 1-5 MeV neutrons were first obtained by simulation experiments. Secondly, as a novel approach, for these highly nonlinear detector responses of recoiling germanium nuclei, consistent empirical physical formulas (EPFs) were constructed by appropriate feedforward neural networks (LFNNs). The LFNN-EPFs are of explicit mathematical functional form. Therefore, the LFNN-EPFs can be used to derive further physical functions which could be potentially relevant for the determination of neutron interactions in gamma-ray tracking process. (c) 2012 Elsevier Ltd. All rights reserved.

WOS: 000301913400010, PubMed ID: 22306452, Being directly related to the electric charge distributions in a molecule, the vibrational spectra intensities are both experimentally and theoretically important physical quantities. However, these intensities are inherently highly nonlinear and of complex pattern. Therefore, in particular for unknown detailed spatial molecular structures, it is difficult to make ab initio intensity calculations to compare with new experimental data. In this respect, we very recently initiated entirely novel layered feedforward neural network (LFNN) approach to construct empirical physical formulas (EPFs) for density functional theory (OFT) vibrational spectra of some molecules. In this paper, as a new and far improved contribution to our novel molecular vibrational spectra LFNN-EPF approach, we constructed LFFN-EPFs for absorbances and intensities of 6-choloronicotinic acid (6-CNA) molecule. The 6-CNA data, borrowed from our previous study, was entirely different and much larger than the vibrational intensity data of our formerly used LFNN-EPF molecules. In line with our another previous work which theoretically proved the LFNN relevance to EPFs, although the 6-CNA DFT absorbance and intensity were inherently highly nonlinear and sharply fluctuating in character, still the optimally constructed train set LFFN-EPFs very successfully fitted the absorbances and intensities. Moreover, test set (i.e. yet-to-be measured experimental data) LFNN-EPFs consistently and successfully predicted the absorbance and intensity data. This simply means that the physical law embedded in the 6-CNA vibrational data was successfully extracted by the LFNN-EPFs. In conclusion, these vibrational LFNN-EPFs are of explicit form. Therefore, by various suitable operations of mathematical analysis, they can be used to estimate the electronic charge distributions of the unknown molecule of the significant complexity. Additionally, these estimations can be combined with those of theoretical OFT atomic polar tensor calculations to contribute to the identification of the molecule. (C) 2012 Elsevier B.V. All rights reserved.

WOS: 000299148200089, Vibrational intensities are both experimentally measured and theoretically estimated important physical quantities which are directly related to distributions of the electric charges in a molecule. In this paper, as a novel approach, by a layered feedforward neural network (LFNN), empirical physical formulas (EPFs) were constructed for density functional theory (DFT) vibrational spectra intensities of N-(2-methylphenyl) and N-(3-methylphenyl) methanesulfonamides. The spectral data was obtained from our previous study. Although the DFT spectral data was inherently extremely difficult-to-fit (sparse frequency intervals, highly nonlinear and sharply fluctuating intensities), still the optimally constructed LFFN-EPFs succeeded in fitting this data to medium and higher level of satisfaction. Moreover, LFNN-EPFs test set (i.e. yet-to-be measured experimental data) intensity predictions were also moderate to higher level. This briefly means that the general tendency of the intensity data was consistently estimated by the LFNN to an acceptable degree. In conclusion, provided that vibrational spectral data measured over sufficiently dense frequency intervals are available for any unknown molecule of significant complexity, suitable LFNN-EFFs can be constructed. Then, by various mathematical tools such as differentiation, integration, minimization, these vibrational LFNN-EFFs can be used to estimate the electronic charge distributions of the molecule. Moreover, these estimations can be compared and combined with those of theoretical DFT atomic polar tensor calculations to contribute to the identification of the molecule. (C) 2011 Elsevier B.V. All rights reserved.

WOS: 000288340700016, The aim of this paper is two-fold. Firstly, static laser light-scattering amplitude measurements in azo-dye doped nematic liquid crystals (NLCs) were made versus scattering angle, temperature and applied bias voltage. Three NLC parameters were determined: the elastic constant ratios K-11/K-22 by regression, phase transition temperatures, and Freedericksz voltages from the graphs. They were all doping ratio dependent. Secondly, as a novel approach, by a nonlinear universal function approximator layered feedforward neural network (LFNN) we constructed an explicit form of empirical physical formulas (EPFs) for theoretically unknown nonlinear azo-dye doped NLC scattering amplitude functions. Excellent LFNN test set (i.e. yet-to-be measured experimental data) predictions prove that the constructed LFNN-EPPs estimate unknown amplitude functions consistently. The LFFN-EPFs, too, confirmed the doping-ratio dependency. Also, comparing LFNN and regression amplitude fits, the LFNN fits were significantly better. In conclusion, physical laws embedded in the physical data can be consistently extracted by LFNN. One major potential application in the nonlinear optics domain is that these LFNN-EPFs, by differentiation, integration, minimization, etc., can be used to obtain further NLC scattering amplitude related molecular structural physical quantities. This could in turn help us to develop new nonlinear optical materials. (C) 2011 Elsevier B.V. All rights reserved.

Artificial neural networks (ANN) have their origins in efforts to produce computer models of the information processing that takes place in the brain. They have found application in a wide variety of fields such as image analysis of facial features, traffic management of underground station platforms, hand-writing verification of cheques, stock market predictions, etc. They have also been applied to computer-aided molecular design, notably protein structure prediction, and more recently ANN have been used to perform statistical tasks such as discriminant analysis and multiple linear regression in the investigation of Quantitative Structure-Activity Relationships (QSAR). We have begun a study of the properties of ANN when used to perform such multivariate statistical analyses. The most popular network used in QSAR-type applications is the multi-layer feed-forward network, also known as the back propagation multi-layer perceptron (MLP). The approaches of MLP and multiple linear regression to modelling are discussed. In order to give some insight into the operation of MLP networks we have carried out experiments with artificial data. Finally, we report two examples of MLP in computer-aided design, a QSAR analysis and the prediction of secondary protein structure.

Bu çalışma küınes içi iklimselfaktöderin )'ul12l1rtaverimi, )"uınurta a,ğırlığı ve yem tüketimine etkisini inceleme/; amacı)'la yapılmıştır. FıratÜniversitesi Deneme Çifli,~indeki aynı kiimes içindeki kafeslerde Mart - Ha.ciran döneminde i22 gün süre"yle i52 tek, 28,18 dörtlü olarak bC)'azyumurtacıticari hibrit tavuk barındırılmıştır.Araştırma dö'neminde kiimes içi sıcaklık, oransal nem l'e atmosfer basıncına ait ortalama de/ferler en az l'e en çok sınırlar arasında sırası ile 20 oC(9 "C - 29 OC), % 61 (% 39 - °0 99) ve 783 mb (748 mb- 789 mb) olarak elde edilmiştir.Gruplarda en düşük lH? en yüksek )'umurta verimi,. tek barındırılanlardaMa)'ıs ve Mart a)'larında % 83 - % 88 dö'l'Iliibarındırılardarda Alart ve Ma"vısaylarında (j~ 78 -- % 87 olarak saptanınıştır. Her iki grupta da a..ylararasıfarklar sırasl)'la P < 0.05 ve P < 0.0 i dÜzeylerinde önemli bulunmuştur.Dörtlii barındınlım grubun yumurta veriıni ile sıcaklık araSlı/da saptanan-0.2 i ::l 0.08 negatif korelas"vonP < 0.05 te iinemli bulunmuştur.Yumurta verimi ile nem dü,7.Cj'iarasında, tek barındırılanlarda 0.2 i::!:: 0.08; dö'rtlü bamıdınlanlarda -0.58 -+ 0.07 olarak saptanan korelas)'ondeğerleri P < 0.0 i de (inemli bulunmuştur. Yumurta ı'erimi ile atmosfer basıncı arasındaki sırasiyle saptanan 0,72 :+: 0.06 ue 0.26 ~L::0.09 korelasyonlar P < 0,01 de ö'nemli bulunmuştur,Yalnu:.ca tekli grupta incelmen )'lllIuırta ağırlık ortalamalarının a)'lararası farkları önemli bulunmamıştır, Yumurta aif,ırlığı ile sıcaklık, oransalnem l'e atmosfer basl1IClarasında sırasl)"a,. -0.50 ::!:: 0,08, -0.28 ::i:: 0.09ve 0.62 ::!:: 0.07 olarak saptanan korelasyonlar P < 0.01 de ö'nemli bulunmuştur.