Translating Prepositions from Russian Legal Texts Into English: An Analysis of the Corresponding Interference Zones for Teaching Purposes

Various aspects of prepositions translation have been primarily investigated in the framework of translation theory. Applied research is mostly focused on translating particular groups of prepositions against the background of plain language. Legal translation researchers have not yet comprehensively analysed peculiarities of translating Russian prepositions used in legal texts into English. The paper is an attempt to investigate the difficulties which Russian learners can encounter when translating prepositions from Russian commercial contracts into English. Methods employed include language typology comparison, continuous sampling technique, language corpus data analysis as applied to language error forecast and prevention. The material selected for analysis – Russian commercial contracts – is chosen in accordance with the principles of professionalism, globalization, specialisation as well as graduates’ employment opportunities. The author develops a classification of prepositions drawing upon their structural, grammar and semantic functions in the texts of Russian commercial contracts. The findings reveal negative interference zones that can potentially cause preposition errors. Feasibility of the forecast is confirmed by the analysis of real learners’ errors. The research concludes that modelling legal translation teaching which takes into account potential interference zones for students can contribute to shifting focus to problem zones while teaching, raising students’ awareness, and therefore acting as propedeutics of the corresponding errors.

Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion

Harmful algal blooms (HABs) often cause great harm to fishery production and the safety of human lives. Therefore, the detection and prediction of HABs has become an important issue. Machine learning has been increasingly used to predict HABs at home and abroad. However, few of them can capture the sudden change of Chl-a in advance and handle the long-term dependencies appropriately. In order to address these challenges, the Long Short-Term Memory (LSTM) based spatial-temporal attentions model for Chlorophyll-a (Chl-a) concentration prediction is proposed, a model which can capture the correlation between various factors and Chl-a adaptively and catch dynamic temporal information from previous time intervals for making predictions. The model can also capture the stage of Chl-a when values soar as red tide breaks out in advance. Due to the instability of the current Chl-a concentration prediction model, the model is also applied to make a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors and provide a reference to describe the range of marine disasters. The data used in the experiment is retrieved from Fujian Marine Forecasts Station from 2009 to 2011 and is combined into 8-dimension data. Results show that the proposed approach performs better than other Chl-a prediction algorithms (such as Attention LSTM and Seq2seq and back propagation). The result of error prediction also reveals that the error forecast method possesses established advantages for red tides prevention and control.

Using a Nonlinear Forcing Singular Vector Approach to Reduce Model Error Effects in ENSO Forecasting

Nonlinear forcing singular vector (NFSV)-based assimilation is adopted to determine the model tendency errors that represent the combined effect of different kinds of model errors; then, an NFSV-tendency error forecast model is formulated. This error forecast model is coupled with an intermediate complex model (ICM) and makes the ICM output closer to the observations; finally, an NFSV-ICM forecast model for ENSO is constructed. The competing aspect of the NFSV-ICM is to consider not only model errors but also the interaction between model errors and initial errors because of the mathematical nature of the NFSV-tendency errors. Based on the prediction experiments for tropical SSTAs during either the training period (1960–96; i.e., when the NFSV-ICM is formulated) or the cross-validation period (1997–2016), the NFSV-ICM is determined to have a much higher forecast skill in predicting ENSO that, specifically, extends the skillful predictions of ENSO from a lead time of 6 months in the original ICM to a lead time of 12 months. The higher skill of the NFSV-ICM is especially reflected in the predictions of SSTAs in the central and western Pacific. For the well-known spring predictability barrier (SPB) phenomenon that greatly limits ENSO forecasting skill, the NFSV-ICM also shows great abilities in suppressing its negative effect on ENSO predictions. Although the NFSV-ICM is presently only involved with the NFSV-related assimilation of SSTs, it has shown its usefulness in predicting ENSO. It is clear that the NFSV-based assimilation approach is effective in dealing with the effect of model errors on ENSO forecasts.

DEMAND FORECASTING PADA INDUSTRI JUS SIAP MINUM

Industri makanan dan minuman merupakan salah satu sektor penting perekonomian Indonesia. Namun, sifatnya yang perishable membuat peramalan permintaan bagi produk ini menjadi suatu hal yang krusial. Karena skenario ini, maka perusahaan yang bergerak pada ndustri makanan dan minuman memerlukan sebuah metode peramalan permintaan yang bisa memberikan tingkat akurasi tinggi dan cepat menanggapi perubahan terhadap permintaan. Sehingga tujuan penelitian adalah menentukan metode peramalan terhadap permintaan dengan tingkat error terkecil. Dalam kasus industri minuman jus siap minum, peramalan permintaan bukan hanya sekedar pemenuhan permintaan konsumen semata, tapi lebih kearah efisiensi perencanaan produksi dan efisiensi pengaturan raw material. Dalam studi kasus yang dilakukan pada salah satu industri jus siap minum didapat dengan menggunakan metode peramalan Holt winter didapat penurunan tingkat error forecast dengan MAPE mencapai 6%.Keyword: Demand forecasting, Holt Winter, Industri jus, MAPE

Box-Jenkins transfer function framework applied to saving-investment nexus in the South African context

This paper studied the relationship between investment and savings in South Africa for the period 1990 quarter 1 to 2014 quarter 3. The unit root test confirmed the non-stationarity of the series prior to first differencing. The correlation coefficient and the model assessing a full capacity mobility hypothesis were significant and passed all the diagnostic examinations. The estimated parameter provided evidence of imperfect capital mobility. ARIMAX (5, 1, 0) out-performed all the five models and was used for pre-whitening process. This model was later used to produce a two year forecasts of investment. The error forecast measure provided enough evidence to conclude that ARIMAX (5, 1, 0) provided valid forecasts. These results are recommended when embarking on future saving-investment plans in South Africa.