Numerical Terms Research Articles

Investigation of Low-Temperature Hydrogen Permeability of Surface Modified Pd–Cu Membranes

The Pd60%Cu40% membranes were modified with nanostructured coatings to intensify low-temperature (25–100°C) hydrogen transport. Classical palladium black and filamentous particles were applied as surface modifiers by electrodeposition. The experiment results confirmed significant reducing of surface limitations by modifying layer application on both surfaces of the developed membranes of the Pd60%Cu40% alloy. The study of the developed membranes in the low-temperature hydrogen transport processes demonstrated high and stable flux up to 0.36 mmol s–1 m–2, as well as high hydrogen permeability up to 1.33 × 10–9 mol s–1 m–2 Pa–0.5. In numerical terms, the values of the membranes of the Pd60%Cu40% alloy modified with nanofilaments were up to 1.3 and 3.9 times higher compared to membranes modified with classical black and uncoated ones, respectively. The developed Pd60%Cu40% membranes also demonstrated a high level of H2/N2 selectivity – up to 3552. The strategy of surface modification of palladium-based membranes can shed new light on the development and manufacturing of high-performance and selective membranes for ultrapure hydrogen evolution devices.

Floods risk determination through a fuzzy logic system in developing countries. Case study Magdalena River, Colombia

Natural disasters around the world, and specifically floods, can generate great economic, en- vironmental, and human life losses in short periods of time. Early warning systems have been developed as a tool to mitigate the impact of these floods, nevertheless, most of this information is dispersed. In the particular case of the Colombian context, the Institute of Hydrology, Meteorology and Environmental Studies of Colombia (IDEAM) has developed a platform called Famine Early Warning Systems Network (FEWS), which presents data of interest for the different stations in the basins of the territory, such as: level, flow, and rainfall. From the height level of water table data of each station, the platform presents three possible alerts: yellow, orange, and red, which are obtained y comparing with reference or threshold levels associated ith ach station. However, the method currently used to determine the alerts does not consider the ariation of he water table level as a function of ime, or the degree of danger represented y a value close to or ending to flood threshold levels. This work proposed a contribution to determine the flood risk level, based on the development of a fuzzy system, taking as inputs the water table level and the level variation as a function of time, to obtain as output the flood risk level in numerical and linguistic terms. For the determination of the risk level, the system applies inference logic rules, involving operations between fuzzy sets represented through mathematical membership functions. Finally, the operation of the fuzzy system was validated using data from the FEWS platform for the hydrological stations of the Bajo Magdalena basin. This project aims to serve as a reference to improve early warning systems of developing couries

Study on microstructure evolution, mechanical properties and deformation mechanism of Ti-6Al-4V alloy by hydraulic tension on-line warm rolling

In this research, Ti-6Al-4 V alloy thin strip with ultra-high tensile strength and good ductility combination was successfully prepared by a new high-efficiency short-flow on-line warm rolling processing method. To explore the intricate relationships between microstructure evolution, mechanical characteristics, and the deformation mechanism during on-line warm rolling of the Ti-6Al-4 V alloy, various analytical methods were employed, including OM, SEM, XRD, EPMA, EBSD, and TEM. When the rolling reduction escalated from a Warm Rolling (WR) rate of 70–90%, there was a concurrent increase in both the alloy's strength and ductility. In numerical terms, the ultimate tensile strength, yield strength, and total elongation rose from 1241 MPa, 1117 MPa, and 5.3% respectively at WR-70%, to 1366 MPa, 1214 MPa, and 8.2% at WR-90%. At WR-90%, the high strength of the resulting Ti-6Al-4 V alloy thin strip is ensured through a distinct combination of material adaptations. These include grain refinement, high-density dislocation accumulation, and the presence of both partial fine recrystallization and ultrafine grains. The initiation of the pronounced basal slip system {0001}< 11–20 > coupled with the pyramidal system {11−22}< 11–2–3 > plays a pivotal role in retaining ductility even under conditions of elevated strength. The amalgamation of grain refinement, dense dislocation accumulation, and the juxtaposition of partial fine recrystallization with ultrafine grains, in tandem with the prevailing basal slip system {0001}< 11–20 > and the active pyramidal system {11−22}< 11–2–3 > , ensures the preservation of commendable ductility while simultaneously achieving high strength. This successful creation of the high-performance Ti-6Al-4 V alloy thin strip provides crucial parameters and theoretical guidance for the industrial application of the hydraulic tension on-line warm rolling method for titanium alloys and other similarly difficult-to-deform metals.

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems.

Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users' preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users' opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using 'TripAdvisor reviews', 'TMDB 5000 movies', and an 'Amazon dataset'. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past.

A Fuzzy-AHP Analysis to the Determination of Weights of the Main Obstacles of RMG in Industry 4.0 Application for Bangladesh

The movement of the Fourth Industrial Revolution is touching the manufacturing and processing industries in Bangladesh. The research uses the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) geometric mean technique, a Multi Criteria Decision Making (MCDM) methodology, to identify, analyze, and prioritize the key obstacles to Industry 4.0 implementation in Bangladesh’s Ready Made Garments (RMG) industries. Another triangular type Fuzzy-AHP extent analysis approach is applied to evaluate the minimum degree of possibilities by using fuzzy appropriateness indices and to determine the weights of assessment criteria. Pairwise comparisons are used to collect 11 experts’ preferences in verbal and numerical terms from different industries. The four main obstacles identified from related review studies are used as input variables in the Fuzzy-AHP methods to measure the intensity level of obstacles. The results have shown that the main four obstacles for Industry 4.0 are “Lack of Top Management Commitment and Owners’ Willingness” (40.6%), “Lack of Ability to Meetup Initial Investment” (30.8%), “Lack of Technical Knowledge and Education” (17.8%), and “Availability of Cheaper Labor” (10.8%). In order to avoid a null weight criterion using Fuzzy-AHP possibility extent, the weight values evaluated using Fuzzy-AHP geometric mean method are considered for decision making. The opinions or ratio scales collected from industry experts are verified with the consistency ratio checking technique.

Developing a virtual reality healthcare product based on data-driven concepts: A case study

The rapid development of artificial intelligence (AI) and big data technologies has profoundly changed the way of human life and work, and in the healthcare field, the related product design is moving towards the trend of digitalization, intelligence, and personalization. The growing demands for personalised healthcare have contributed to a boom in product design theories in related fields. However, it is difficult to measure product users' preferences in precise numerical terms to make strategic decisions in product design. To fill this gap, this study proposes a framework for virtual reality (VR) healthcare product design based on a data-driven concept and develops a VR virtual roaming system for system users guided by this theory. The data-driven concept incorporates AI and big data technologies into product design, helping to shift the design of medical products towards a data-centred approach. Ultimately, an ergonomic experiment was conducted and a case study was carried out to assess the user's experience using brain function data to enable data-driven VR value-added services. This study provides a new perspective on rehabilitation product design, opening the way for better personalised healthcare delivery.

Features of accumulation and variability of stable strontium in some organs and tissues of landrace pigs

The authors in the article demonstrated the results of assessing the level of stable strontium in the skeletal muscles, liver, kidneys and spleen of Landrace pigs at the end of the technological cycle. The studies were performed on clinically healthy animals raised in a large pig farm in the Altai Territory. The conditions of keeping the animals corresponded to those typical for meat fattening up to 100 kg. Feeding was carried out with complete feed. Elemental analysis of samples of parenchymal organs and muscle tissue was performed using inductively coupled plasma atomic emission spectral analysis. Data processing was performed using Microsoft Office Excel and the R programming language in the data analysis environment RStudio version 2022.07.2+576 (RStudio, PBC). The distribution did not correspond to normal in all cases. The dispersions are not homogeneous. Based on the mean value and median, an increasing ranked series of strontium content in organs and tissues were established: liver = skeletal muscles &lt; spleen &lt; kidneys, in numerical terms: 1: 1: 3.7: 4.7 and 1: 1: 3.9: 5.7 respectively. Medians for stable strontium in the liver, kidney, spleen, and skeletal muscle were 0.024; 0.130; 0.089 and 0.023 mg/kg, respectively. Reference intervals for the liver are 0.004-0.043 and for the spleen - 0.030-0.145 mg/kg. The most significant range of variability is characteristic of the kidneys of pigs. A considerable uniformity is typical of the liver and skeletal muscles. Using the Kruskal-Wallis test, it was established that the accumulation of stable strontium significantly differs in the examined organs and tissues of pigs (H = 68.9, df = 3, p &lt; 0.0001). Pairwise comparison showed significant differences for the kidney-liver, spleen-liver, skeletal muscle-kidney, and skeletal muscle-spleen pairs. Two clusters were identified according to the similarity of strontium accumulation: liver and skeletal muscles, kidneys and spleen. The calcium-strontium ratio, taking into account medians, for skeletal muscles was 1:1833, and for the liver - 1:1870. The established results can serve as an approximate physiological norm for the concentration of stable strontium in individual structures of the organism of Landrace pigs under the conditions of Western Siberia.

Generalized neural closure models with interpretability

Improving the predictive capability and computational cost of dynamical models is often at the heart of augmenting computational physics with machine learning (ML). However, most learning results are limited in interpretability and generalization over different computational grid resolutions, initial and boundary conditions, domain geometries, and physical or problem-specific parameters. In the present study, we simultaneously address all these challenges by developing the novel and versatile methodology of unified neural partial delay differential equations. We augment existing/low-fidelity dynamical models directly in their partial differential equation (PDE) forms with both Markovian and non-Markovian neural network (NN) closure parameterizations. The melding of the existing models with NNs in the continuous spatiotemporal space followed by numerical discretization automatically allows for the desired generalizability. The Markovian term is designed to enable extraction of its analytical form and thus provides interpretability. The non-Markovian terms allow accounting for inherently missing time delays needed to represent the real world. Our flexible modeling framework provides full autonomy for the design of the unknown closure terms such as using any linear-, shallow-, or deep-NN architectures, selecting the span of the input function libraries, and using either or both Markovian and non-Markovian closure terms, all in accord with prior knowledge. We obtain adjoint PDEs in the continuous form, thus enabling direct implementation across differentiable and non-differentiable computational physics codes, different ML frameworks, and treatment of nonuniformly-spaced spatiotemporal training data. We demonstrate the new generalized neural closure models (gnCMs) framework using four sets of experiments based on advecting nonlinear waves, shocks, and ocean acidification models. Our learned gnCMs discover missing physics, find leading numerical error terms, discriminate among candidate functional forms in an interpretable fashion, achieve generalization, and compensate for the lack of complexity in simpler models. Finally, we analyze the computational advantages of our new framework.

Designing the Optimal Configuration of a Small Power System for Autonomous Power Supply of Weather Station Equipment

Autonomous power systems serving remote areas with weather stations with small settlements are characterized by a fairly high cost of generating electricity and the purchase and delivery of fuel. In addition, diesel power plants require regular maintenance, have a relatively short service life during continuous operation and produce a large amount of emissions into the environment. This article discusses various methods of placing solar panels in the space for the autonomous power supply of weather station equipment. The principles of these methods are described and their advantages and disadvantages are outlined. The optimal algorithms of functioning for photomodules are described and their comparison regarding the main, significant parameters is carried out. The choice of the most effective algorithm for use at a weather station is made. The effective positioning of solar panels is also calculated, and positioning conditions are determined depending on the territorial location and various environmental conditions. Simulation of the power supply system of a weather station consisting of solar panels, batteries and inverters is performed. As a result, a practical example of the application of the method of selecting the optimal composition of equipment for a hybrid power system of a weather station territorially located in Siberia with different configurations of equipment is considered. In numerical terms, it was possible to reduce the cost of power equipment operation by more than 60% with a fairly low payback period of 5.5 years and an increased reliability of the power system, which is very important for autonomous power systems of northern weather stations.

Владикавказское мещанское училище (Часть 2)

The article is a continuation (2nd part) of the study published in the previous issue of the history of the Vladikavkaz petty-bourgeois school. The 2nd part contains some statistical data on the number of students and teachers, on the need to expand the staff, caused by an ever-increasing number of people wishing to send their children to the school of representatives of the petty-bourgeois class of Vladikavkaz. The problem of creating conditions for classes that meet the requirements is reflected in the points indicated by the educational authorities, which will also be given in the publication. The program of the school was identical to the programs of city schools, as evidenced by the excerpts from the approved program given in the work, developed for this school on a general basis and approved by the inspector of public schools in the Terek region. The women’s department opened at the philistine school, despite the fact that it is often referred to as the women’s philistine school, in the historical sense is considered as part of the philistine school, which is the reason for the consideration in the work of two parts - the men’s and women’s departments - sometimes called the women’s philistine school, in within the framework of one - the Vladikavkaz petty-bourgeois school. In this part of the study, critical moments in the life of the school will also be shown, at which there was a real threat of its closure, caused by some distrust and even a temporary fading of interest in the school on the part of the townspeople - which looks rather paradoxical against the backdrop of a shortage of student places a few years earlier. From the moment when the school in administrative, cultural, educational and numerical terms could stand on a par with other city educational institutions, the question arose and the expansion of its building, which is briefly mentioned in the work, since the data for a detailed study of the issue of building construction have not yet been identified.

Analysis of dissipation operators that damp spurious modes while maintaining discrete approximate geostrophic equilibriums for the B-grid staggered scheme on triangular meshes

We consider the lowest order - so called B-grid - discretization on triangular cells which is common in the ocean or atmosphere simulation science, with pressure field at the vertices of the triangles and the velocity field at the cell centers. It is known that this discretization presents spurious modes due to a too large number of velocities unknowns and that these modes should be damped; since preservation of approximate geostrophic equilibrium (GE) may be of primary importance in the simulations, we propose and analyze damping operators that, when applied to the linearized constant coefficient configuration, exactly preserve discrete versions of the GEs. We also analyze their effects in more general configurations and their possible coupling with more standard damping operators. These strategies cover triangular variants of the Low Froude (LF) scheme [1], the Apparent Topography (AT) scheme [2,3], as well as a new modification of it. The LF scheme has no numerical diffusion terms in the pressure equation and damps the normal velocity jumps between adjacent cells in the momentum equation. In the AT scheme, numerical diffusion is added in the pressure equation in such a way that it does not impact the discrete GE. However, since the AT scheme alone cannot be proved to be damping through energy estimates and does not preserve the space which is orthogonal to the discrete GE, we also study what we call the Modified Apparent Topography (MAT) that satisfies these important properties. We also suggest semi-implicit time discretizations that preserve these properties. Extensions to the full non-linear equations are also provided. Numerical experiments illustrate the performance of the new schemes.

General mechanisms for stabilizing weakly compressible models.

Many weakly compressible models with intrinsic mechanisms for stabilizing computation have been proposed to simulate incompressible flows. The present paper analyzes several weakly compressible models to establish general mechanisms that incorporate them into a unified and simple framework. It is found that all these models contain some identical numerical dissipation terms, mass diffusion terms in the continuity equation, and bulk viscosity terms in the momentum equation. They are proven to provide general mechanisms for stabilizing computation. Referring to the general mechanisms and the computational procedures of the lattice Boltzmann flux solver, two general weakly compressible solvers for isothermal flows and thermal flows are proposed. They can be directly derived from standard governing equations and implicitly introduce those numerical dissipation terms. Detailed numerical investigations demonstrate that the two general weakly compressible solvers have good numerical stability and accuracy for both isothermal and thermal flows, which validates the general mechanisms further and the general approach of constructing general weakly compressible solvers.

Эвристическая оценка качества услуг на примере операторов мобильной связи

A heuristic approach is proposed and considered in assessing the quality of information services on the example of mobile operators. The characteristics of the services of mobile operators can be divided into positive and negative. The former characteristics are directly proportional to the preferred choice of mobile communication users, and the latter are inversely proportional. For example, ceteris paribus, preference is given to a lower cost for the use &#x0D; of mobile communications. The existing characteristics of the services of telecom operators are displayed in the tables in numerical terms of the corresponding dimension. Every mobile operator has such reporting spreadsheets, which are usually available in the Internet. In this regard, there arises a problem of determining a more preferable operator by given numerical values of the characteristics of services. In the proposed work, there has been made an attempt to determine the quality of mobile communications based on statistical indicators and input quality markers determined on the basis of heuristic assumptions. Bringing the numerical values of the characteristics of communication services to relative units determines the arithmetic average level of each of the characteristics of mobile communication services. According to this average level, generalized markers are assigned that characterize the quality of the services provided by the corresponding mobile operators. Packing the generated numeric markers into a container makes it possible to rank mobile operators by the values of quality markers. It helps to determine which of the telecom operators will be more preferable for the user.

A Trapezoidal Fuzzy Number-Based VIKOR Method with Completely Unknown Weight Information

Multi-attribute group decision-making plays an important role in modern politics, economy, culture, and life. The multi-attribute decision-making process is limited by policymakers’ experience, and knowledge of uncertainty factors, such as weight information, is difficult to directly provide. Moreover, many types of complex rescue information are difficult to accurately describe in numerical terms, which reduces the accuracy and objectivity of the decision results, although the fuzzy theory to solve these problems provides a suitable tool. In order to solve the above problems, a new VIKOR decision method based on trapezoidal fuzzy numbers (TFNs) is proposed. Firstly, the expert weight is calculated by the distance measurement method, the criterion weight is calculated by the deviation maximization method, and then the VIKOR method is used to solve the problem. In order to verify the effectiveness and feasibility of this method, it is applied to the emergency alternative selection problem. Finally, the symmetry analysis of the method is carried out by contrast experiment and sensitivity test.

Efficient dissipation-based nonlinear solver for multiphase flow in discrete fractured media

Solving nonlinear equation systems resulting from the Fully Implicit Method (FIM) remains a challenge for numerical simulation of multiphase flow in subsurface fractured media. The Courant numbers can vary orders of magnitude across discrete fracture-matrix (DFM) models because of the high contrasts in the permeability and length-scale between matrix and fracture. The standard Newton solver is usually unable to converge for big timestep sizes.Limited research has been conducted on nonlinear solver techniques for multiphase flow and transport in fractured media. We make an extension of a new dissipation-based continuation (DBC) method to DFM models. Our goal is to prevent timestep cuts and maintain efficient time-stepping for FIM. The DBC algorithm builds a homotopy of the discretized conservation equations through the addition of numerical dissipation terms. We introduce a continuation parameter for controlling the dissipation and ensuring that accuracy of the computed solution will not be reduced. Under the nonlinear framework of DBC, general dissipation operators and adaptive strategies are developed to provide the optimal dissipation matrix for the multiphase hyperbolic system.We assess the new nonlinear solver using multiple numerical examples. Results reveal that the damped-Newton solver suffers from serious restrictions on timestep sizes and wasted iterations. In contrast, the DBC solver provides excellent computational performance. The dissipation operators are able to successfully resolve main convergence difficulties. We also investigate the impact of star-delta transformation which removes the small cells at fracture intersections. Moreover, we demonstrate that an aggressive time-stepping does not affect the solution accuracy.

Barriers to gender equality in STEMM: do leaders have the gender competence for change?

PurposeScience, technology, engineering, mathematics and medicine (STEMM) workplaces worldwide remain stubbornly resistant to gender equality initiatives. Leaders are vital to driving change, but the extent to which their capabilities lead to change remains unknown. This article examines STEMM leaders' gender competence to achieving transformative changes in gender inequality.Design/methodology/approachThis article examines the capability of STEMM leaders to act as change agents through an in-depth, qualitative analysis of perceptions of gender inequality, sexual harassment, sex discrimination and gender bias within their organisations. Findings are analysed using a customised tripartite gender competence schema, comprising commitment, knowledge and method (or know-how).FindingsThe findings suggest that while STEMM leaders may express a commitment to addressing gender inequality, misapprehensions about the nature and scope of the problem are likely to hamper efforts. Two key misapprehensions standout: a tendency to frame gender inequality in primarily numerical terms; and recourse to blaming external factors beyond STEMM for gender inequality in STEMM.Originality/valueThis article makes an original contribution by examining the gender competence of leaders in STEMM organisations, which has not been previously researched. The findings extend understanding of the salience of leaders' capabilities to lead change by identifying key gaps and misapprehensions in STEMM leaders' understanding of the nature and scope of the problem.

Copper content in parenchymatous organs of landrace pigs

Results of the evaluation of copper levels in the liver, kidneys, lungs, and spleen of Landrace pigs are presented. Studies were performed on clinically healthy six-month-old animals bred in a large pig farm in Altai Krai. Animal housing conditions were standard, typical for meat-fattening feeding was used. The authors performed an elemental analysis of samples of parenchymal organs using atomic absorption spectrometry with flame and electrothermal atomization. The data were processed using Microsoft Office Excel and the R programming language in the RStudio data analysis environment version 2022.07.2+576 (RStudio, PBC). It was found that the distribution in the case of copper accumulation in the kidneys and lungs was different from usual, and the dispersions were not homogeneous. Based on the mean value and median, the authors established an increasing ranked series of the copper content in the organs: spleen &lt; lungs &lt; kidneys &lt; liver, in numerical terms: 1: 1.1: 6.4: 7.5. Median copper values in the liver, kidneys, lungs, and spleen were 6.25; 5.02; 0.88; and 0.83 mg/ kg, respectively. More excellent uniformity is characteristic of copper accumulation in the lungs and spleen. Using the Kraskell-Wallis criterion, we found that the collection of copper varies significantly in the organs under study (H = 80.03, df = 3, p &lt; 0.0001). Pairwise comparison showed significant differences for pairs: “liver - lungs” (p &lt; 0.0001), “kidneys - lungs” (p &lt; 0.0001), “liver - spleen” (p &lt; 0.0001), “kidneys - spleen” (p &lt; 0.0001). Cluster analysis revealed two groups: “lungs - spleen” and “liver - kidneys,” which differ significantly in the level of copper accumulation. The data obtained can be used to calculate the corresponding standard values of the copper content in the liver, kidneys, lungs, and spleen of Landrace pigs under the conditions of Western Siberia.

Is Human Being Computable, or Anthropology of Artificial Intelligence. Methodological Aspect

The article analyzes the anthropological paradigms of the mainstream approaches to AI development. The article concludes that the principle of computability (quantifiability) first introduced in the early stages of AI development (namely by A.Turing) is still paramount. The article showcases the way this principle is rooted in what Heidegger described as treating humans and the state of being itself as a consumable and ultimately destructible commodity. One tries to quantify the state of being in order to consume it. As a consequence, humanity approaches the creation of a “thinking machine” as an exercise in self-imitation. The article highlights the deficiencies of creating AI via copying human patterns. The article also suggests a viable alternative — a mediatory method, developed by culturally-historical psychology. The latter perceives the thought process not as a brain activity but as a objective act, mediated through sings and speech. This way of thinking leads to a new strategy of AI development — as a smart helping hand for humans. This, however, requires an overhaul in medical, educational, governmental and many other social practices. The author concludes that any AI development is bound to start with a social engineering process, renovating the principles, governing day to day lives. This is why the development of AI is not a purely technological task. Is affects a deeper level of human existence, unfit to be interpreted in numerical terms.

HOW CAN EMOTION-AI HELP UNDERSTAND TRANSLATOR TRAINEES’ TECHNOLOGY LEARNING EXPERIENCES?

The present study examines the effectiveness of Sentiment Analysis, also known as Emotion-AI, in analysing translator trainees’ learning narratives regarding their experiences with translation memory systems (TMs). Students were asked to describe how they learned and whether the experience was pleasant or unpleasant. The narrative texts were then automatically analysed with Sentiment Analysis, and the emotional component was quantified into a Sentiment score which encompasses both the polarity, i.e., positive vs. negative, and the magnitude (in numerical terms) of emotion. The results showed that narratives about pleasant learning experiences had significantly higher scores than those about unpleasant ones, indicating that Sentiment Analysis can be used to identify learners’ emotions while using technology. Our findings suggest that automatic emotion detection tools can be used in combination with human judgments for data triangulation. Keywords: Sentiment Analysis, translation memory, emotions, human-computer interaction

Forgiveness Meaning in the Lyric of Gandong Song: Linguistics Point of View

This study aims to describe the memorable experience with the conflict, the feelings of the Maluku people, and the influence of the meaning of the lyrics of the Gandong song when listening to the Gandong song, in the context of sustainability of the community’s life Orang Basudara in Maluku. This study used the qualitative method. The primary data were collected using questionnaires with closed and open-ended questions. While literature reviews were about the meaning of the lyrics was collected as secondary data. There were 224 respondents in total, which consist of 53 male respondents and 165 female respondents in the gender category, with 6 people choosing not to identify their gender, and also all from other different backgrounds. Further, the Gandong song, which is a Malukan people's rhythm song, is very popular and frequently sung during conflict and post-conflict times. Subsequently, the result revealed that the respondent listened to the song on several occasions. The lyrics contain a range of word classes, including nouns, verbs, adverbs, adjectives, pronominal phrases, and numerical terms. The term Gandong appears more frequently than other terms. Linguistically means that the song gives stressing on the Gandong relationship as a pearl of important local wisdom for all people living in Maluku. The majority of respondents (99.6 % of 224 respondents) have ever listened to the Gandong song, and it has influenced their feelings about the conflict in Maluku as a reminder of a harmonious life (82. 2%), drawing back from revenge (9, 9%), and 4% of people accepted it to forget the conflict, while the other respondents will forget the feeling of mutual suspicion and forgive rudeness.