Food quality is defined as a collection of properties that differentiate each unit and influences acceptability degree of food by users or consumers. Owing to the nature of food, food quality prediction is highly significant after specific periods of storage or before use by consumers. However, the accuracy is the major problem in the existing methods. Hence, this paper presents a BEFA_DRNN approach for accurate food quality prediction using dairy products. Firstly, input data is fed to data normalisation phase, which is performed by min-max normalisation. Thereafter, normalised data is given to feature fusion phase that is conducted employing DNN with Canberra distance. Then, fused data is subjected to data augmentation stage, which is carried out utilising oversampling technique. Finally, food quality prediction is done wherein milk is graded employing DRNN. The training of DRNN is executed by proposed BEFA that is a combination of BES and FA. Additionally, BEFA_DRNN obtained maximum accuracy, TPR and TNR values of 93.6%, 92.5% and 90.7%.

To explore the computer-aided translation technology based on the intelligent algorithm of edge computing. This paper presents the research on computer-aided translation technology based on edge computing intelligent algorithm. In the K-means computer edge algorithm, it analyses the traditional way of average resource allocation and the way of virtual machine allocation. In the process of online solution, we have a more detailed understanding of the data information at the edge, and also avoid the connection relationship between network users and the platform, which has a certain impact on the internal operation efficiency of the system. The network user group is divided into several different types of existence through K-means computer algorithm, and various information resources are counted according to their own characteristics. Computer-aided translation technology can significantly improve the quality of translation, improve the translation efficiency, and reduce the translation cost.

With the development of urban construction, its spatial evolution is also influenced by behavioural actors such as enterprises, residents, and environmental factors, leading to some decision-making behaviours that are not conducive to urban public space and environmental design. At the same time, some cities are vulnerable to various factors such as distance factors, transportation factors, and human psychological factors during the construction of public areas, resulting in a decline in the quality of urban human settlements. Urban public space is the guarantee of urban life. For this, in order to standardise urban public space and improve the quality of urban living environment, the standardisation of the environment of urban public space is required. The rapid development of intelligent algorithms and fuzzy control provides technical support for the environmental design of urban public spaces. Through the modelling of intelligent algorithms and the construction of fuzzy space, it can meet the diverse.

Currently, Android is a booming technology that has occupied the major parts of the market share. However, as Android is an open-source operating system there are possibilities of attacks on the users, there are various types of attacks but one of the most common attacks found was malware. Malware with machine learning (ML) techniques has proven as an impressive result and a useful method for malware detection. Here in this paper, we have focused on the analysis of malware attacks by collecting the dataset for the various types of malware and we trained the model with multiple ML and deep learning (DL) algorithms. We have gathered all the previous knowledge related to malware with its limitations. The machine learning algorithms were having various accuracy levels and the maximum accuracy observed is 99.68%. It also shows which type of algorithm is preferred depending on the dataset. The knowledge from this paper may also guide and act as a reference for future research related to malware detection. We intend to make use of Static Android Activity to analyse malware to mitigate security risks.

In today's world, malware has become a part and threat to our computer systems. All electronic devices are very susceptible/vulnerable to various threats like different types of malware. There is one subset of malware called ransomware, which is majorly used to have large financial gains. The attacker asks for a ransom amount to regain access to the system/data. When dynamic technique using machine learning is used, it is very important to select the correct set of features for the detection of a ransomware attack. In this paper, we present two novel algorithms for the detection of ransomware attacks. The first algorithm is used to assign the time stamp to the features (API calls) for the ordering and second is used for the ordering and ranking of the features for the early detection of a ransomware attack.

Nostalgia has an intrinsic association with consumer behaviour. Retrieval of memories drives emotions among consumers and reinforces experience-led buying decisions. Despite nostalgia, and consumption being a common practice at various times in life, issues regarding the nostalgia stimuli on customers' perceptions and buying decisions remain less explored. This article aims at exploring the consumption pattern of adult consumers by analysing the influence of nostalgic behaviour referring to the autobiographic memories and social motivations. It describes the purchase intentions and consumption pattern among adult consumers in the context of self-reference criteria based on nostalgic memories and social motivations. This article offers constructive understanding on establishing relationship between nostalgic memories and consumption pattern over the temporal framework and establishing the brand loyalty and hedonic satisfaction. It contributes to the existing literature by critically examining the theoretical concepts and empirical findings of previous studies on perceptions of consumers on nostalgic emotions and their role in making buying decisions.

Badminton, as a fast and highly technical sport, requires high accuracy in identifying athletes' swing movements. Accurately identifying different swing movements is of great significance for technical analysis, coach guidance, and game evaluation. To improve the recognition accuracy of badminton players' swing movements, this text is based on an improved dense trajectory algorithm to improve the accuracy of recognising badminton players' swing movements. The features are efficiently extracted and encoded. The results on the KTH, UCF Sports, and Hollywood2 datasets demonstrated that the improved algorithm achieved recognition accuracy of 94.2%, 88.2%, and 58.3%, respectively. Compared to traditional methods, the innovation of research lies in optimised feature extraction methods, efficient algorithm design, and accurate action recognition. These results provide new ideas for the research and application of badminton swing motion recognition.

Action recognition plays a vital role in analysing human body behaviour and has significant implications for research and education. However, traditional recognition methods often suffer from issues such as inaccurate time and spatial feature vectors. Therefore, this study addresses the problem of inaccurate recognition of aerobic gymnastics action image data and proposes a visualised three-dimensional convolutional neural network algorithm-based action recognition model. This model incorporates unsupervised visualisation methods into the traditional network and enhances data recognition capabilities through the introduction of a random noise perturbation enhancement algorithm. The research results indicate that the data augmented with noise perturbation achieves the lowest mean square error, reducing the error value from 0.3352 to 0.3095. The use of unsupervised visualisation analysis enables clearer recognition of human actions, and the algorithm model is capable of accurately recognising aerobic movements. Compared to traditional algorithms, the new algorithm exhibits higher recognition accuracy and superior performance.

In order to improve recommendation coverage, a personalised recommendation method for English online learning resources based on improved collaborative filtering algorithm is studied to enhance the comprehensiveness of personalised recommendation for learning resources. Use matrix decomposition to decompose the user English online learning resource rating matrix. Cluster low dimensional English online learning resources by improving the K-means clustering algorithm. Based on the clustering results, calculate the backfill value of English online learning resources and backfill the information matrix of low dimensional English online learning resources. Using an improved collaborative filtering algorithm to calculate the predicted score of learning resources, personalised recommendation of English online learning resources for users based on the predicted score. Experimental results have shown that this method can effectively backfill English online learning resources, and the resource backfilling effect is excellent, and it has a high recommendation coverage rate.

In order to overcome the problems of low evaluation accuracy and long evaluation time in traditional teaching quality evaluation methods, a method of English distance information teaching quality evaluation based on decision tree classification algorithm is proposed. Firstly, construct teaching quality evaluation indicators under different roles. Secondly, the information gain theory in decision tree classification algorithm is used to divide the attributes of teaching resources. Finally, the rough set theory is used to calculate the index weight and establish the risk evaluation index factor set. The result of teaching quality evaluation is obtained through fuzzy comprehensive evaluation method. The experimental results show that the accuracy rate of the teaching quality evaluation of this method can reach 99.2%, the recall rate of the English information teaching quality evaluation is 99%, and the time used for the English distance information teaching quality evaluation of this method is only 8.9 seconds.

In order to overcome the problems of large error, low evaluation accuracy and long evaluation time in traditional evaluation methods of ideological and political education, this paper designs a quantitative evaluation method of ideological and political education achievements based on collaborative filtering algorithm. First, the evaluation index system is constructed to divide the teaching achievement evaluation index data in a small scale; then, the quantised dataset is determined and the quantised index weight is calculated; finally, the collaborative filtering algorithm is used to generate a set with high similarity, construct a target index recommendation list, construct a quantitative evaluation function and solve the function value to complete the quantitative evaluation of teaching achievements. The results show that the evaluation error of this method is only 1.75%, the accuracy can reach 98%, and the time consumption is only 2.0 s, which shows that this method can improve the quantitative evaluation effect.

In order to overcome the problems of poor evaluation accuracy and long evaluation time in traditional customer relationship evaluation methods, this study proposes a new customer relationship evaluation method for e-commerce platform based on decision tree algorithm. Firstly, analyse the connotation and characteristics of customer relationship; secondly, the importance of customer relationship in e-commerce platform is determined by using decision tree algorithm by selecting and dividing attributes according to the information gain results. Finally, the decision tree algorithm is used to design the classifier, the weighted sampling method is used to obtain the training samples of the base classifier, and the multi-period excess income method is used to construct the customer relationship evaluation function to achieve customer relationship evaluation. The experimental results show that the accuracy of the customer relationship evaluation results of this method is 99.8%, and the evaluation time is only 51 minutes.

In order to improve the classification accuracy and online allocation accuracy of teaching resources and shorten the allocation time, this paper proposes a new online allocation method of college ideological and political curriculum teaching resources based on differential search algorithm. Firstly, the feedback parameter model of teaching resources cleaning is constructed to complete the cleaning of teaching resources. Secondly, according to the results of anti-interference consideration, the linear feature extraction of ideological and political curriculum teaching resources is carried out. Finally, the online allocation objective function of teaching resources for ideological and political courses is constructed, and the differential search algorithm is used to optimise the objective function to complete the online allocation of resources. The experimental results show that this method can accurately classify the teaching resources of ideological and political courses, and can shorten the allocation time, with the highest allocation accuracy of 97%.

Multi-criteria inventory classification (MCIC) is popularly used to aid managers in categorising the inventory. Researchers have used numerous mathematical models and approaches, but few resorted to unsupervised machine-learning techniques to address MCIC. This study uses the expectation-maximisation (EM) algorithm to estimate the parameters of the Gaussian mixture model (GMM), a popular unsupervised machine learning algorithm, for ABC inventory classification. The EM-GMM algorithm is sensitive to initialisation, which in turn affects the results. To address this issue, two different initialisation procedures have been proposed for the EM-GMM algorithm. Inventory classification outcomes from 14 existing MCIC models have been given as inputs to study the significance of the two proposed initialisation procedures of the EM-GMM algorithm. The effectiveness of these initialisation procedures corresponding to various inputs has been analysed toward inventory management performance measures, i.e., fill rate, total relevant cost, and inventory turnover ratio.

An algorithm learning tool was developed for an introductory computer science class in a specialized science and technology high school in Japan. The tool presents lessons and simple visualizations that aim to facilitate teaching and learning of fundamental algorithms. Written tests and an evaluation questionnaire were designed and implemented along with the learning tool among the participants. The tool’s effect on the learning performance of the students was examined. The differences of the two types of visualizations offered by the tool, one with more input and control options and the other with fewer options, were analyzed. Based on the evaluation questionnaire, the scales with which the tool can be assessed according to its usability and pedagogical effectiveness were identified. After using the algorithm learning tool there was an increase in the posttest scores of the students, and those who used the visualization with more input and control options had higher scores compared to those who used the one with limited options. The learning objectives used to evaluate the tool correlated with the test performance of the students. Properties comprised of learning objectives, algorithm visualization characteristics, and interface assessment are proposed to be incorporated in evaluating an algorithm learning tool for novice learners.

Aim/Purpose: The aim of this study was to analyze various performance metrics and approaches to their classification. The main goal of the study was to develop a new typology that will help to advance knowledge of metrics and facilitate their use in machine learning regression algorithms Background: Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. A performance metric can be defined as a logical and mathematical construct designed to measure how close are the actual results from what has been expected or predicted. A vast variety of performance metrics have been described in academic literature. The most commonly mentioned metrics in research studies are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), etc. Knowledge about metrics properties needs to be systematized to simplify the design and use of the metrics. Methodology: A qualitative study was conducted to achieve the objectives of identifying related peer-reviewed research studies, literature reviews, critical thinking and inductive reasoning. Contribution: The main contribution of this paper is in ordering knowledge of performance metrics and enhancing understanding of their structure and properties by proposing a new typology, generic primary metrics mathematical formula and a visualization chart Findings: Based on the analysis of the structure of numerous performance metrics, we proposed a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set. For each component, implementation options have been identified. The suggested new typology has been shown to cover a total of over 40 commonly used primary metrics Recommendations for Practitioners: Presented findings can be used to facilitate teaching performance metrics to university students and expedite metrics selection and implementation processes for practitioners Recommendation for Researchers: By using the proposed typology, researchers can streamline development of new metrics with predetermined properties Impact on Society: The outcomes of this study could be used for improving evaluation results in machine learning regression, forecasting and prognostics with direct or indirect positive impacts on innovation and productivity in a societal sense Future Research: Future research is needed to examine the properties of the extended metrics, composite metrics, and hybrid sets of metrics. Empirical study of the metrics is needed using R Studio or Azure Machine Learning Studio, to find associations between the properties of primary metrics and their “numerical” behavior in a wide spectrum of data characteristics and business or research requirements

Aim/Purpose: In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background: The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the time between webpage visits, differences among users, and the time spent on a page or on each part of the page. Therefore, webpage access prediction draws substantial effort of the web mining research community in order to obtain valuable information and improve user experience as well. Methodology: CPT+ is a complex prediction algorithm that dramatically offers more accurate predictions than other state-of-the-art models. The integration of the importance of every particular page on a website (i.e., the PageRank) regarding to its associations with other pages into CPT+ model can improve the performance of the existing model. Contribution: In this paper, we propose an approach to reduce prediction space while improving accuracy through combining CPT+ and PageRank algorithms. Experimental results on several real datasets indicate the space reduced by up to between 15% and 30%. As a result, the run-time is quicker. Furthermore, the prediction accuracy is improved. It is convenient that researchers go on using CPT+ to predict Webpage access. Findings: Our experimental results indicate that PageRank algorithm is a good solution to improve CPT+ prediction. An amount of though approximately 15 % to 30% of redundant data is removed from datasets while improving the accuracy. Recommendations for Practitioners: The result of the article could be used in developing relevant applications such as Webpage and product recommendation systems. Recommendation for Researchers: The paper provides a prediction model that integrates CPT+ and PageRank algorithms to tackle the problem of complexity and accuracy. The model has been experimented against several real datasets in order to show its performance. Impact on Society: Given an improving model to predict Webpage access using in several fields such as e-learning, product recommendation, link prediction, and user behavior prediction, the society can enjoy a better experience and more efficient environment while surfing the Web. Future Research: We intend to further improve the accuracy of webpage access prediction by using the combination of CPT+ and other algorithms.

Aim/Purpose: The clustering techniques are normally considered to determine the significant and meaningful subclasses purposed in datasets. It is an unsupervised type of Machine Learning (ML) where the objective is to form groups from objects based on their similarity and used to determine the implicit relationships between the different features of the data. Cluster Analysis is considered a significant problem area in data exploration when dealing with arbitrary shape problems in different datasets. Clustering on large data sets has the following challenges: (1) clusters with arbitrary shapes; (2) less knowledge discovery process to decide the possible input features; (3) scalability for large data sizes. Density-based clustering has been known as a dominant method for determining the arbitrary-shape clusters. Background: Existing density-based clustering methods commonly cited in the literature have been examined in terms of their behavior with data sets that contain nested clusters of varying density. The existing methods are not enough or ideal for such data sets, because they typically partition the data into clusters that cannot be nested. Methodology: A density-based approach on traditional center-based clustering is introduced that assigns a weight to each cluster. The weights are then utilized in calculating the distances from data vectors to centroids by multiplying the distance by the centroid weight. Contribution: In this paper, we have examined different density-based clustering methods for data sets with nested clusters of varying density. Two such data sets were used to evaluate some of the commonly cited algorithms found in the literature. Nested clusters were found to be challenging for the existing algorithms. In utmost cases, the targeted algorithms either did not detect the largest clusters or simply divided large clusters into non-overlapping regions. But, it may be possible to detect all clusters by doing multiple runs of the algorithm with different inputs and then combining the results. This work considered three challenges of clustering methods. Findings: As a result, a center with a low weight will attract objects from further away than a centroid with higher weight. This allows dense clusters inside larger clusters to be recognized. The methods are tested experimentally using the K-means, DBSCAN, TURN*, and IDCUP algorithms. The experimental results with different data sets showed that IDCUP is more robust and produces better clusters than DBSCAN, TURN*, and K-means. Finally, we compare K-means, DBSCAN, TURN*, and to deal with arbitrary shapes problems at different datasets. IDCUP shows better scalability compared to TURN*. Future Research: As future recommendations of this research, we are concerned with the exploration of further available challenges of the knowledge discovery process in clustering along with complex data sets with more time. A hybrid approach based on density-based and model-based clustering algorithms needs to compare to achieve maximum performance accuracy and avoid the arbitrary shapes related problems including optimization. It is anticipated that the comparable kind of the future suggested process will attain improved performance with analogous precision in identification of clustering shapes.

Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

Aim/Purpose: The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background: The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology: In our work, we analyze various machine learning algorithms and implement K-NN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution: To the researchers’ best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings: An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendation for Researchers: With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Future Research: Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages.

Qinghua WU,Weitong LI, Vol.E107-D, No.11, pp.1422-1432
Multi-focus image fusion involves combining partially focused images of the same scene to create an all-in-focus image. Aiming at the problems of existing multi-focus image fusion algorithms that the benchmark image is difficult to obtain and the convolutional neural network focuses too much on the local region, a fusion algorithm that combines local and global feature encoding is proposed. Initially, we devise two self-supervised image reconstruction tasks and train an encoder-decoder network through multi-task learning. Subsequently, within the encoder, we merge the dense connection module with the PS-ViT module, enabling the network to utilize local and global information during feature extraction. Finally, to enhance the overall efficiency of the model, distinct loss functions are applied to each task. To preserve the more robust features from the original images, spatial frequency is employed during the fusion stage to obtain the feature map of the fused image. Experimental results demonstrate that, in comparison to twelve other prominent algorithms, our method exhibits good fusion performance in objective evaluation. Ten of the selected twelve evaluation metrics show an improvement of more than 0.28%. Additionally, it presents superior visual effects subjectively.
Publication Date: 2024/11/01

KUALA LUMPUR: The shift towards subscription-based services is gaining traction in Malaysia, aligning with a broader global trend that redefines how consumers access products.

This model provides an appealing option for many Malaysians, particularly young families and newlyweds, who face rising living costs.

Offering high-quality appliances on a subscription basis eases the financial burden of ownership, allowing consumers to enjoy premium products without the pressure of a large upfront investment.

One notable brand offering subscription-based home appliance services is the South Korean brand, LG.

LG Malaysia product director of subscription business Hojin Jung said the introduction of the LG Rent Up Subscription in Malaysia is a natural progression of the company’s commitment to providing innovative and accessible solutions tailored to the evolving needs of modern consumers.

“LG Rent Up Subscription is inspired by our success with subscription models in South Korea, where we saw significant growth, driven by increasing demand for convenience and affordability.

“Recognising similar trends here, we noticed a growing interest in flexible ownership models in Malaysia, spurred by the need for more cost-effective solutions amidst rising living expenses and fuelled by shifting consumer preferences.

“Since its launch in March 2024, the market response has been encouraging. We have seen growing inquiries from customers who have signed up for our water purifier subscription model and are now exploring subscriptions for other high-demand appliances such as refrigerators, washing machines and TVs.

“This shift highlights a changing mindset in how Malaysians approach home appliance ownership – especially among younger, urban consumers who prioritise access over ownership, seeking premium products without the upfront financial commitment,” Hojin told SunBiz.

He said urbanisation and the desire for more sustainable, convenience-focused living have made subscription services an attractive option.

“By offering top-tier technology on a subscription basis, we make high-end living more accessible while emphasising affordability and environmental responsibility. LG’s Rent Up Subscription model meets Malaysians’ evolving needs, allowing them to enjoy premium technology without the burden of ownership,” he said.

Hojin said the subscription model is gaining popularity among young Malaysians, especially urban professionals and families facing high living costs and limited space.

This trend, he said, reflects a growing shift toward a ‘sharing economy,‘ where access to energy-efficient appliances without the financial strain of ownership is valued.

LG Rent Up Subscription’s launch saw a strong uptake in Kuala Lumpur and major cities, where 40% of tech-savvy millennials prefer renting to stay updated with technology affordably.

Elaborating on the model further, Hojin said that although subscription services share similarities across markets, the Malaysian context has distinct differences.

“In South Korea, for example, the rental model for water purifiers is well-established, with over 70% market penetration. Malaysia, meanwhile, is still in its early phase, but consumer awareness is rising quickly. Moreover, this trend is not isolated to Malaysia. LG is actively preparing to introduce the subscription model in other markets, including Taiwan and Thailand, by year-end.”

Touching on the vision for LG Rent Up in Malaysia, Hojin said the LG Rent Up Subscription is just the beginning of a transformative journey in how it engages with consumers in Malaysia.

“As we look ahead, we plan to expand our subscription offerings to include a wider array of smart home appliances and electronics, reflecting the growing demand for connected living solutions.

“Our vision for LG Rent Up Subscription is to enhance the customer experience by offering seamless integration with our LG ThinQ technology, which already empowers our appliances to be more intuitive and user-friendly. This will allow our customers to enjoy a smart, responsive lifestyle, further elevating the convenience and efficiency of their homes,” he explained.

Hojin said that as the subscription economy continues to evolve, particularly among tech-savvy and environmentally conscious consumers, LG Rent Up Subscription aims to play a pivotal role in making premium technology more accessible.

“Our ultimate goal is to foster a circular economy model in which subscribing to high-quality appliances reduces the financial burden on consumers and contributes to sustainability by extending product lifecycles and minimising waste.

“The more we enhance our subscription model, the more committed we are to making innovative technology more attainable. We ultimately aim to enrich the lives of our customers while promoting responsible consumption and environmental stewardship,” Hojin said.

Atanas Skatov’s body flown by a Pakistani military helicopter to the nearby city of Skardu

Group of Russian climbers were trapped after an Avalanch hit the Gasherbrum IV mountain in Gilgit-Baltistan region.

All water samples collected from the scenic city of Gilgit have been found to be contaminated

Polling to take place in three phases due to non-provision of additional security forces

An aerial view of the Port of Odense, Denmark on October 15, 2024. — AFP

At a shipyard on the North Sea, workers in luminescent vests are building dozens of massive, hollow concrete boulders, each the size of an apartment block.

These are to be floated out to sea and sunk...

Panniers strapped to their haunches, a team of horses waded collar-deep through North Sea waters — hauling wide nets along the Belgian coast as cawing seagulls swirled all around.

In the saddle, clad head to toe in yellow oilskins, riders steered them parallel with the...

The 14-year-old boy, a resident of Skardu, was held at an isolation centre where he tested positive for COVID-19

Rescue efforts underway near the site of the accident in GB on November 12, 2024. — Reporter

This North Baltimore neighborhood is just inside the city line, but it’s got the cloistered feel of an affluent suburban hamlet. High-end consignment boutiques, beauty salons, and restaurants bring well-heeled locals to Sulgrave Avenue in Mount Washington Village, a quiet world away from the traffic and sirens of downtown.

We visit Baltimore Clayworks, where artist Sam Wallace teaches a pottery technique he learned as a kid in Jamaica. We talk with the crew at The Mount Washington Tavern about romance, oyster shucking, and a major fire that put the place out of business for a year. And we drop in at The Village Vet, where the staff cares for ailing animals and the worried humans that come along with them.

Tamara Carrasco setzt sich für unabhängiges Katalonien ein. Sie wurde verhaftet und sitzt seit Monaten im Hausarrest

jungeWelt

27-12-2018       

Von Krystyna Schreiber, Barcelona

Am Morgen des 10. April 2018 wird die 35jährige Sozialarbeiterin Tamara Carrasco y García durch lautes Klopfen an der Wohnungstür geweckt. Als sie öffnet, stehen draußen ein Dutzend Beamte der spanischen paramilitärischen Guardia Civil in Tarnfarbenuniformen und mit Maschinengewehren. Sie haben einen Durchsuchungsbefehl gegen sie und einen Haftbefehl wegen des Verdachts auf Rebellion, Aufruhr und Zugehörigkeit zu einer terroristischen Organisation. Tamara ist sich keiner Schuld bewusst: »Mehr als einen Strafzettel oder einer Anzeige wegen Ungehorsam habe ich nie riskiert«, erzählt sie im Gespräch mit junge Welt in einer kleinen Bücherei im Zentrum ihres Heimatortes Viladecans, einem Städtchen unweit der katalanischen Metropole Barcelona.
 

Die Guardia Civil durchsucht die 70 Quadratmeter große Wohnung vier Stunden lang. Die Beamten beschlagnahmen eine gelbe Trillerpfeife, ein Plakat mit der Aufschrift »Freiheit und Demokratie«, ein Foto des inhaftierten Vorsitzenden der Kulturvereinigung Òmnium Cultural, Jordi Cuixart, ein kaputtes Handy sowie einen Speicherstick. Tamara steht am Fenster und hört, wie die Medien vor ihrem Haus über sie berichten. »Die Journalisten waren zur gleichen Zeit wie die Polizei da und wussten mehr Details als ich.« Wie die meisten Spanier ihres Alters ist Tamara mit den Fernsehübertragungen der Festnahmen mutmaßlicher ETA-Terroristen aufgewachsen. Als die Guardia Civil sie fragt, ob sie beim Verlassen des Hauses ihr Gesicht verdecken wolle, lehnt sie ab. Sie will nicht das von damals bekannte Bild liefern. Noch heute hat sie das Blitzlichtgewitter vor Augen, als sie über den Platz vor ihrem Haus abgeführt wird. Sie ist überzeugt, dass man ihre Festnahme für die Medien inszeniert hat.
 

Tamara wird nach Madrid gebracht und verbringt dort zwei Tage in einer fünf Quadrameter großen Zelle. Nach den spanischen Sonderbestimmungen bei Verfahren wegen Terrorismusverdachts darf sie keinen Anwalt sprechen. Tamara schweigt. Dennoch wird sie zum Verhör gezwungen. Der einzige Anruf, den sie machen darf, richtet sich an Freunde, nicht die Familie. Tamara ist für ein unabhängiges Katalonien, ihr Vater für die Einheit Spaniens. Oft kracht es deshalb bei Familienfeiern. Zurück in ihrer Zelle sagt sich Tamara immer wieder: »Ich habe nichts Schlechtes getan, ich bin eine starke Frau.« In dem Moment hat sie Angst. »Nur jemand, der in so einer Zelle eingeschlossen ist, kann das nachvollziehen.«

Fingierte Anklage

Am dritten Tag wird sie in Handschellen dem Richter der Audiencia Nacional, einem Sondergericht für besonders schwere Straftaten, vorgeführt. Dort trifft sie ihren Anwalt, den die Eltern organisiert haben. Vor Gericht beantwortet sie nur seine Fragen. Als der Staatsanwalt die Anschuldigungen verliest, erkennt sich Tamara nicht wieder. Sie habe einen Anschlag auf die Kaserne der Guardia Civil in Barcelona geplant. Später stellt sich heraus, dass sich der Vorwurf auf ein Bild von Google Maps stützt, das sie sich als Wegbeschreibung zu einer Demonstration ausgedruckt hatte. Sie wird als Koordinatorin der »Komitees zur Verteidigung der Republik« (CDR) bezeichnet, die als terroristische Organisationen dargestellt werden, und soll sich der Anstiftung zu Straftaten, zum Beispiel zur Blockade von Autobahnen am Osterwochenende, schuldig gemacht haben. »Wer etwas über die CDR weiß, kennt ihre horizontale Struktur. Es gibt keine Koordinatoren«, erklärt Tamara jW gegenüber.
 

Im Dokument der Staatsanwaltschaft taucht auch Adrià Carrasco auf, der am gleichen Tag wie Tamara verhaftet werden sollte, aber flüchten konnte und sich nach Belgien absetzte. »Die Guardia Civil ging wegen des gleichen Nachnamens davon aus, dass wir miteinander verwandt wären, dabei kennen wir uns gar nicht«, erzählt Tamara. Der Verteidigung platzt der Kragen. Das sei ein politischer Prozess ohne juristische Grundlage, schimpft ihr Anwalt. Der Staatsanwalt droht ihm daraufhin mit einer Klage wegen Befangenheit. Als der Schlagabtausch zwischen den Juristen aus dem Ruder läuft, beendet der Richter die Anhörung. Eine Viertelstunde danach ist Tamara gegen Auflagen auf freiem Fuß.
 

Erst später wird ihr bewusst, was diese Vorschriften für sie bedeuten. Tamara darf Viladecans nur verlassen, um zu ihrer Arbeitsstelle in Barcelona zu fahren, und sie muss sich wöchentlich beim Ortsgericht melden. Die Guardia Civil überwacht sie. Nach wenigen Wochen lässt sich Tamara krankschreiben, der psychologische Druck ist zu hoch. Ihre Familie und viele Freunde wohnen in anderen Orten und kommen sie besuchen. Als sich ihre Mutter ein Bein bricht und nicht zu ihrer Tochter fahren kann, beantragt Tamara, sie besuchen zu dürfen. Der Antrag wird abgelehnt.

Anfangs hat die Repression gegen Tamara auch auf die CDR einschüchternde Wirkung. Im gesamten Gebiet des Baix Llobregat, in dem Viladecans liegt, finden keine Aktionen mehr statt. Die Aktivisten haben Angst und wollen der Anklage keine Argumente gegen Tamara und Adrià liefern. »Es ist wie eine Welle, die sich nicht nur gegen dich richtet, sondern sich auf dein gesamtes Umfeld ausbreitet. Bis vor kurzem wurde ich sehr streng bewacht – und damit alle, die mich umgeben«, erklärt Tamara.

Kein Gericht zuständig

Anfang November entscheidet der Richter, dass es gegen Tamara keine Belege für Rebellion, Terrorismus und Aufruhr gibt. Dennoch wird der Hausarrest nicht aufgehoben. Anwalt Benet Salelles erläutert im Telefongespräch die absurde Situation: »Es gibt eine endgültige juristische Entscheidung. Die Audiencia Nacional sagt, dass sie nicht zuständig ist, weil sie keine Indizien für die Anschuldigungen sieht. Damit geht der Fall an die allgemeine Justiz. Das heißt, der Fall wird dem Gericht übergeben, in dessen Einzugsbereich die untersuchten Vorfälle stattgefunden haben. Aber man weiß nicht, welche Taten meiner Mandantin vorgeworfen werden.« Da ihr keine konkreten Taten zugeordnet werden konnten, schickte die Audiencia Nacional den Fall gleichzeitig an die Gerichte in vier Bezirken: Lleida, Girona, Barcelona und Tarragona. »Tamara lebt in keinem der vier, es ist der totale Unsinn«, formuliert ihr Anwalt sein Unverständnis. Es werde wahrscheinlich Monate dauern, bis sich ein Gericht für zuständig erklärt und dann eventuell den inzwischen achtmonatigen Hausarrest gegen sie aufhebt.
 

Ihr Anwalt glaubt, dass hinter dieser Situation eine klare Absicht steckt. »Wir kennen das Phänomen der CDR, und wir denken, dass man das Konzept des Terrorismus nicht auf sie anwenden kann. Sie sind Ausdruck des friedlichen Widerstands. Aber der Staat will ein Bild aufrechterhalten, das in den 1990er Jahren im Baskenland gewirkt hat und stellt Parallelen her, die nicht der Wirklichkeit entsprechen. Wenn Politiker und Juristen die CDR mit Kale borroka (gewalttätige Straßenaktionen von Anhängern der baskischen Unabhängigkeitsbewegung; jW) vergleichen, mischen sie bewusst Konzepte. Ich glaube, dass es sich hierbei um eine Operation des Staates handelt, mit der versucht wird, die Realität in Katalonien zu ändern.«
 

Aus Tamaras Sicht hat ihre Situation auch Positives bewirkt. Früher lagen Tochter und Vater ideologisch weit auseinander. Inzwischen zweifelt ihre Familie am System. »Mein Vater ist weiterhin für Spanien, aber er versteht jetzt, warum ich tue, was ich tue«, sagt Tamara fast stolz. Viele Bewohner ihres Orts Viladecans seien eher dafür, alles in Spanien so zu lassen, wie es ist. Doch sie erhält von allen Seiten Unterstützung. »Viele Nachbarn, die prospanisch eingestellt sind, habe mir ihre Solidarität bekundet, denn sie wissen, dass ich keiner Fliege etwas zuleide tue.«

Inzwischen würden immer mehr Menschen verstehen, dass es nicht nur um die Unabhängigkeit Kataloniens geht, sondern um die Grundrechte. »Wenn man ein Störfaktor ist, wird das Maulkorbgesetz angewendet«, kritisiert Tamara. Aus Angst zu Hause zu bleiben ist für sie aber keine Option. Sie will jetzt erst recht kämpfen: »Es ist eine Frage meiner Würde.« Und sie ist wütend. »Drei Tage lang war ich die meistgehasste Person Spaniens. Ich habe mehr als 300 Morddrohungen auf meinem Handy erhalten. Ich wurde zu einer öffentlichen Person gemacht. Adrià musste ins Exil gehen. Wir hatten keinen Gerichtsprozess, um uns verteidigen zu können.« Und sie glaubt, dass die Repression Katalonien der Unabhängigkeit möglicherweise näherbringen könnte. Das erste, was sie in einem unabhängigen Katalonien ändern würde, sei das Strafrecht. »Wenn wir eine Republik gründen, müssen wir sicherstellen, dass keinem Menschen das widerfährt, was mir passiert.«

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