Modeling the complexation properties of mineral-bound organic polyelectrolyte: An attempt at comprehension using the model system alumina/polyacrylic acid/M (M = Eu, Cm, Gd)

International audienceThis paper contributes to the comprehension of kinetic and equilibrium phenomena governing metal ion sorption on organic-matter-coated mineral particles. Sorption and desorption experiments were carried out with Eu ion and polyacrylic acid (PAA)-coated alumina colloids at pH 5 in 0.1 M NaClO4 as a function of the metal ion loading. Under these conditions, M interaction with the solid is governed by sorbed PAA (PAAads). The results were compared with spectroscopic data obtained by time-resolved laser-induced fluorescence spectroscopy (TRLFS) with Cm and Gd. The interaction between M and PAAads was characterized by a kinetically controlled process: after rapid metal adsorption within less than 1 min, the speciation of complexed M changed at the particle surface till an equilibrium was reached after about 4 days. At equilibrium, one part of complexed M was shown to be not exchangeable. This process was strongly dependent on the ligand-to-metal ratio. Two models were tested to explain the data. In model 1, the kinetically controlled process was described through successive kinetically controlled reactions that follow the rapid metal ion adsorption. In model 2, the organic layer was considered as a porous medium: the kinetic process was explained by the diffusion of M from the surface into the organic layer. Model 1 allowed a very good description of equilibrium and kinetic experimental data. Model 2 could describe the data at equilibrium but could not explain the kinetic data accurately. In spite of this disagreement, model 2 appeared more realistic considering the results of the TRLFS measurements

Quantitative description and local structures of trivalent metal ions Eu(III) and Cm(III) complexed with polyacrylic acid

The trivalent metal ion (M(III) = Cm, Eu)/polyacrylic acid (PAA) system was studied in the pH range between 3 and 5.5 for a molar PAA-to-metal ratio above 1. The interaction was studied for a wide range of PAA (0.05 mg L−1–50 g L−1) and metal ion concentrations (2×10−9–10−3 M). This work aimed at 3 goals (i) to determine the stoichiometry of M(III)–PAA complexes, (ii) to determine the number of complexed species and the local environment of the metal ion, and (iii) to quantify the reaction processes. Asymmetric flow-field-flow fractionation (AsFlFFF) coupled to ICP-MS evidenced that size distributions of Eu–PAA complexes and PAA were identical, suggesting that Eu bound to only one PAA chain. Time-resolved laser fluorescence spectroscopy (TRLFS) measurements performed with Eu and Cm showed a continuous shift of the spectra with increasing pH. The environment of complexed metal ions obviously changes with pH. Most probably, spectral variations arose from conformational changes within the M(III)–PAA complex due to pH variation. Complexation data describing the distribution of complexed and free metal ion were measured with Cm by TRLFS. They could be quantitatively described in the whole pH-range studied by considering the existence of only a single complexed species. This indicates that the slight changes in M(III) speciation with pH observed at the molecular level do not significantly affect the intrinsic binding constant. The interaction constant obtained from the modelling must be considered as a mean interaction constant

Evaluasi perilaku seismik struktur rangka beton bertulang yang direncanakan dengan cara kapasitas dan cara alternatif kapasitas di wilayah 1 dan 2 peta gempa Indonesia

Perencanaan struktur dengan cara kapasitas membutuhkan waktu yang relatif panjang dibandingkan dengan cara biasa, dan dapat menghasilkan kekuatan kolom yang berlebihan jika pada struktur tersebut dibebani beban gravitasi sebagai beban dominan. Guna menghindari hal ini dapat digunakan cara alternatif dimana balok dan kolom luar tetap direncanakan dengan cara kapasitas, sedangkan kolom dalam direncanakan dengan cara biasa, atau dengan kata lain diijinkannya terjadi sendi plastis pada balok, kolom dalam, kolom luar lantai dasar bagian bawah, dan kolom luar lantai paling atas. Dalam studi ini dipaparkan aplikasi cara alternatif perencanaan kapasitas pada struktur rangka lima tingkat dengan empat bentangan, dan sepuluh tingkat dengan dua bentangan, yang terletak di wilayah 1 dan 2 peta gempa Indonesia. Untuk mengetahui perbedaan perilaku struktur yang direncanakan dengan cara alternatif kapasitas, dan perilaku struktur yang direncanakan dengan cara kapasitas, dilakukan analisa riwayat waktu dengan bantuan program komputer Ruaumoko, dan rekaman gempa yang dibuat dengan memodifikasi rekaman Gempa Pacoima Dam 1971 S16K guna disesuaikan dengan spektrum respons percepatan gempa Indonesia untuk periode ulang 200 tahun di wilayah 1 dan 2 peta gempa Indonesia. Perilaku seismik pada perencanaan struktur rangka beton bertulang yang direncanakan berdasarkan cara alternatif kapasitas, dan cara kapasitas menghasilkan simpangan struktur yang terjadi di lantai satu dan dua pada struktur lima tingkat di wilayah 1 melampaui simpangan maksimum yang disyaratkan, namum mekanisme terjadinya sendi plastis sesuai dengan yang direncanakan, serta daktilitas dan kapasitas geser pada balok dan kolom portal yang tersedia cukup untuk memancarkan energi gempa. Dengan demikian keuntungan dari alternatif perencanaan kapasitas, adalah disamping membutuhkan waktu perencanaan yang pendek, juga lebih sedikit pemanfaatan tulangan kolom. Selain keuntungan diatas, perlu diperhatikan jumlah kolom interior yang direncanakan dengan cara biasa, dan perlu adanya pembatasan terhadap besaran gaya-gaya dalam rencana pada kolom luar, yang disesuaikan dengan persyaratan pada SKSNIT-15-1991-03

Forecasting Short-Term Returns on Tennis Betting Exchange Markets Using Deep Learning

In this work, we propose a regressional framework, built on the work ”Deep Order Flow Imbalance: Extracting Alpha at Multiple Horizons from the Limit Order Book” by Kolm, et al. (2023), for predicting short term returns of odds on binary betting exchange markets. Using the framework, we apply five different deep learning models that leverage order book data from tennis betting exchanges during the calendar month of July 2023 with the purpose of examining the predictive capabilities of deep learning models in this setting. We train each model on either raw limit order book states or order flow. The models predict the returns of the best available odds returns on five different short term time horizons on the four order book sides, back and lay for each of the two players in a given tennis match. Applying windowing, for each vector prediction we use the 100 latest market messages consisting of 81 features (odds and volumes per the ten first levels in the order book and time delta between market messages) in the case of the raw limit order book state and 41 features (order book flow per the ten first levels in the order book and time delta between market messages) in the case of the order book flow. All code is written in Python and run on Google Colab, leveraging cloud computing, off-the-shelf models and popular libraries, TensorFlow and Keras, for data processing and pipelining, model implementation, training and testing. The models are evaluated relative to a benchmark in the form of a naive predictor based on the average odds returns on the training set. The models do not converge towards an optimal parameter composition duringtraining, indicating low predictive capabilities of the input data. Despite this, we generally find all models to outperform the benchmark on the lay order book sides and while some perform better than others, we see similar relative performance distributions within each model across horizon-order book side combinations. To enhance discussion and suggest the direction of future research we examine relationships between key game characteristics such asthe variation of odds returns and the accuracy of predictions on a given market

Distribution de l'Europium entre l'acide polymaléique en solution ou adsorbé sur l'alumine et Bacillus subtilis

Afin de comprendre le comportement des radionucléïdes avec un système aquifère naturel, les interactions dans un système quaternaire constitué de systèmes de référence bien caractérisés sont étudiées, avec l'europium comme élément métallique, l'acide polymaléïque comme modèle des substances humiques, l'alumine comme phase minérale et Bacillus subtilis comme support biologique. L'étude est réalisée à pH=5 dans 0,1 mol/L de NaClO4. Une question fondamentale posée est de savoir dans quelles mesures les paramètres issus de l'étude quantitative des systèmes de référence Eu/APM, Eu/APM-Al2O3 et Eu/Bacillus subtilis, permettent de quantifier la distribution de Eu dans des systèmes multi-substratsEu/APM/Bacillus subtilis and Eu/APM-Al2O3/Bacillus subtilis. Les données expérimentales sont décrites à partir d'un modèle de type Langmuir ou d'un modèle de complexation de surface et sont confirmées par des analyses de spéciation en surface par spectrofluorimétrie laser résolue en temps. L'étude du système Eu/APM montre une similitude avec le système Eu/substances humiques par rapport à la force d'interaction et par la nature des environnements de Eu. Lorsque APM est adsorbé sur Al2O3, il ne présente pas les mêmes propriétés de complexation vis à vis de Eu. Notamment, pour les fortes concentrations en Eu, il y a formation d'un complexe ternaire dans lequel Eu est à la fois lié à une fonction carboxylique de APM et une fonction aluminol de Al2O3. Enfin, pour le système Eu/B subtilis, Eu est lié à une fonction carboxylique et une fonction phosphate. Pour le système APM/Eu/bactérie, les systèmes de référence sont réversibles et les paramètres déduits des sous-systèmes permettent de quantifier la distribution de Eu dans le système global. Dans le système APM-Al2O3/Eu/bactérie, l'équilibre Eu/APM-Al2O3 n'est pas réversible dû à une diffusion de Eu dans la couche de APM adsorbée, le rendant ainsi non disponible.NANTES-BU Sciences (441092104) / SudocSudocFranceF

Forecasting Short-Term Returns on Tennis Betting Exchange Markets Using Deep Learning

In this work, we propose a regressional framework, built on the work ”Deep Order Flow Imbalance: Extracting Alpha at Multiple Horizons from the Limit Order Book” by Kolm, et al. (2023), for predicting short term returns of odds on binary betting exchange markets. Using the framework, we apply five different deep learning models that leverage order book data from tennis betting exchanges during the calendar month of July 2023 with the purpose of examining the predictive capabilities of deep learning models in this setting. We train each model on either raw limit order book states or order flow. The models predict the returns of the best available odds returns on five different short term time horizons on the four order book sides, back and lay for each of the two players in a given tennis match. Applying windowing, for each vector prediction we use the 100 latest market messages consisting of 81 features (odds and volumes per the ten first levels in the order book and time delta between market messages) in the case of the raw limit order book state and 41 features (order book flow per the ten first levels in the order book and time delta between market messages) in the case of the order book flow. All code is written in Python and run on Google Colab, leveraging cloud computing, off-the-shelf models and popular libraries, TensorFlow and Keras, for data processing and pipelining, model implementation, training and testing. The models are evaluated relative to a benchmark in the form of a naive predictor based on the average odds returns on the training set. The models do not converge towards an optimal parameter composition duringtraining, indicating low predictive capabilities of the input data. Despite this, we generally find all models to outperform the benchmark on the lay order book sides and while some perform better than others, we see similar relative performance distributions within each model across horizon-order book side combinations. To enhance discussion and suggest the direction of future research we examine relationships between key game characteristics such asthe variation of odds returns and the accuracy of predictions on a given market