7264 research outputs found
Sort by
WATER ACCOUNTING IN KAZAKHSTAN'S MINING
No full textMining activities can substantially influence water resources, and water availability is critically important, especially in regions subject to water scarcity. Therefore, gaining publicly available data on water usage within Kazakhstan’s mining sector and understanding its water use can provide valuable insights into broader industry practices. The objective of this study is to utilize the International Council on Mining and Metals (ICMM) water accounting framework to evaluate water use in a mining company in Kazakhstan. Particularly, this research aims to shed light on practices in water management at Kazakhstan’s mines and identify water-related risks. It was found that the Exploration Point mine site has successfully achieved a considerable level of water reuse during its operations, where water reuse grew by 8.74% annually, with an average reuse rate of 82% of the water used for operations. However, a substantial proportion of water is still entrained in waste, along with an excessive reliance on freshwater intake. According to these results, various barriers to water management were identified, and suggestions for improvement were proposed. Our research demonstrates presence of opportunities to optimize water consumption at the mine site. Water accounting and reporting based on the ICMM framework promotes greater transparency and improves social and environmental performance of a mine
PASSIVE UPPER LIMB EXOSKELETON: MECHANISM DESIGN AND PROTOTYPE DEVELOPMENT
No full textAn exoskeleton is a wearable robotic device, powered with passive or active
systems, that allows limbs or trunk movement with increased strength and/or
endurance. This is in line with the philosophy of Industry 4.0, in which humans
can be assisted by technological devices in difficult or unsafe tasks. Various
passive upper limb exoskeletons have been developed in the last years for indus
trial applications. In particular, occupational tasks that require postures with
elevated arms or overhead works, and hence represent a high risk factor for mus
culoskeletal disorders, are considered. Passive exoskeleton devices give a fixed
contribution, independently from the external applied load. Usually they are de
signed to compensate, partially or totally, the gravity forces acting on the limb
or on the trunk. However only few studies investigated effectiveness, usability,
comfort, drawbacks and biomechanical strains associated to the use of upper
limb exoskeleton in power augmentation tasks. The proposed project aims to
develop an assistive exoskeleton designed for surgeons in clinical settings. The
assistive functionality will be achieved through gravity compensation, utilizing
passive 4-bar mechanisms. A key advantage of such an exoskeleton should lie in
its reliance on a mechanism that eliminates the need for electrical components
and actuators. Consequently, the device should be lightweight and relatively
simple to manufacture. The primary mechanical components of the exoskeleton
will consist of 3D printed linkages and springs
THE BEAR AND THE PIPE DREAM? DIVERSIFYING KAZAKHSTAN’S OIL EXPORTS FROM RUSSIA
No full textTraditionally, Kazakhstan transported most of its oil exports to Europe through Russian territory. Russia’s war
with Ukraine had a profound impact on Kazakhstan’s oil exports. The conflict jeopardised the stability of the
established oil export route, due to the European Union’s (EU’s) sanctions against Russia’s oil sector and
Russia’s strategic use of its pipeline networks as a tool to exert pressure on the Kazakh government.
Consequently, disruptions in Kazakh oil exports occurred intermittently throughout 2022 and 2023. In
response to these challenges, the Kazakh government actively sought alternative oil export routes, with the
development of the so-called Middle Corridor emerging as the most viable option. However, the progress of
the Middle Corridor faced its own set of obstacles. In this context, what actions could the Kazakh government
take to mitigate the adverse consequences of the war on its oil sector?
This case study examines the impact of the Russia-Ukraine War on Kazakh oil exports, followed by an
exploration of the alternatives to the main traditional export channel of the Caspian Pipeline Consortium (CPC)
Pipeline running through Russia, and the resulting implications for Kazakhstan’s foreign policy. It concludes
with a discussion of potential longer-term economic and foreign policy strategies for Kazakhstan
PATH PLANNING IN SLAM
No full textSimultaneous Localization and Mapping (SLAM) is a technology that helps an
autonomous robot to localize itself in the unknown environment creating a map
and further navigating without any human interference. Within the SLAM tech-
nology we are able to make detailed maps of the environment both in outdoor and
indoor spaces where GPS signals are not available. Overall, SLAM is highly effi-
cient technology that can help robots to properly navigate, inspect and monitor
industrial environments autonomously through proper path planning. Identify-
ing the shortest collision-free path and bypassing obstacles quickly is done by
path-planning that applies different algorithms and their combinations. Due to
the challenging environments, sensor limitations and discrepancies in localization
estimation, it can be hard for robots to efficiently bypass and arrive at the desired
destination. Therefore in our thesis we aim to familiarize ourselves with the path
planning algorithms to fit the turtlebot. Our research focuses on individually ex-
amining the pivotal path planning algorithms ( Djikstra, A* and RRT) to test
it in the application of turtlebot to identify which algorithm is suitable for this
robotic device. The research is done by analyzing existing literature on path-
planning to further apply the knowledge in an experiment. It was found that
despite the better intrinsic capabilities of A* and RRT algorithms, the Dijkstra
appears to be the most advisable algorithm for the turtlebot )
DEVELOPMENT OF AN OMNI-DIRECTIONAL BALL LAUNCHER
No full textTo excel in football, a player must enhance both technical and tactical abilities. This improvement is achieved through repeated practice of ball-
handling positions. A study introduces a design for a football throwing machine, capable of consistently directing and propelling the ball for
thorough educational assessment. The
machine features a ball loading canister to accommodate multiple balls, and
two polyurethane throwing wheels
mounted on a horizontally fixed body.
Each wheel’s speed can be adjusted
independently. Horizontal ball movement is controlled by the difference
of speeds at each motor, and vertical ball movement is controlled by the
lifter mechanism, while electronic systems manage velocity, direction, trajec-
tory. Speed and direction control operation are controlled with two sepa-
rate DC motor speed controllers. At
this project we used the universal motors which can run from DC and AC
power source. However, we using the
DC 36V power source with charge controller mounted into the device frame
BREAKING THE MOLD: EXPLORING FEMALE FANS' ONLINE REACTIONS TO THE REPRESENTATION OF WOMEN IN K-DRAMAS
No full textKorean dramas have gained enormous popularity throughout the world, particularly among female viewers, thanks to their compelling stories and cinematography. However, the number of research papers exclusively focusing on “Korean drama studies” remains limited, which is perhaps partly due to the relative youth of this field in English. The present thesis investigates gender dynamics, specifically the representation of women in three internationally recognized Korean dramas set in different years: Boys Over Flowers (2009), The Heirs (2013), and Business Proposal (2022). The paper aims to analyze the evolution in the portrayal of female characters, also touching on feminist activism on the Internet, known as cyberfeminism. Using the three K-dramas as the research object, the paper also explores female fan reactions to the heroines by examining fan comments on social media pages and movie websites. As a research methodology, the study employs an interpretive, contextualized, and descriptive approaches. Based on the content analysis of the comments the thesis argues that older K-dramas uphold patriarchal values and reinforce traditional gender roles which portray female characters as dependent and resilient in the face of abuse. The study highlights a contrast in contemporary K-dramas, which demonstrate progress in the representation of women and consider the fans’ opinions. The analysis of audience reception of female characters in K-dramas is conducted through the lens of Cultivation Theory, questioning susceptibility to transmitted media messages. Lastly, the paper also applies the concept of “cyborg” to address the new transformed images of female characters in contemporary K-dramas, which notably diverge from previous representations
RISK-SENSITIVE LQR PROBLEMS WITH EXPONENTIAL NOISE
No full textThis thesis is about optimal control of Markov Decision Processes and solving risk-sensitive cost minimization and reward maximization problems, specifically, the Linear Quadratic Regulator (LQR) problem with Average-Value-at-Risk criteria. The problem is solved for different risk levels, different random noises (theoretical and sampled), and using different methods: analytical and approximate dynamic programming. The obtained results were analyzed and discussed for the presence of certain patterns and trends. The results show that approximate dynamic programming is a very accurate method for solving risk-sensitive LQR problems with exponential noise
FAST FOOD STORE WEBSITE
No full textOur project involved developing an advanced online application specifically for a fast-food restaurant, utilising the capabilities of modern technologies like PostgreSQL, Vue.js, and Spring Boot. Our main goal was to design an intuitive user interface that would enable customers to browse menu options, place orders with ease, and have strong administration features for managing users.
Our initiative was inspired by the realisation that the food sector was changing and that accessibility and digitalization were becoming more and more important. We worked to create a dynamic and responsive interface that not only meets but above the expectations of restaurant administrators and patrons by combining PostgreSQL, Vue.js, and Spring Boot.
Our design philosophy placed the user experience first. The platform's user-friendliness and aesthetically pleasing interface are the result of our painstaking attention to detail in every facet of its design. We created a smooth, interactive surfing experience with Vue.js that enables users to easily browse menu items and make wise selections.
Furthermore, our solution goes beyond just features that interact with customers. We created a full administrative backend that enables restaurant personnel to effectively manage user accounts, track orders, and optimise operations by utilising the powerful features of Spring Boot. The backbone of the platform is this administrative suite, which facilitates smooth coordination between front-end and back-end operations.
Our project's integration of PostgreSQL, a robust and capable database management system, was one of its key elements. We guaranteed data integrity, scalability, and efficiency by utilising PostgreSQL—essential elements for a mission-critical application like a fast-food ordering system. PostgreSQL offers a strong basis for efficiently storing and retrieving data, whether it is for managing customer profiles, tracking order history, or evaluating sales patterns.
In summary, our idea is the result of a careful design process combined with state-of-the-art technologies, with the goal of completely changing the digital environment of the fast-food sector. We have developed a user-centric platform that not only meets but exceeds the expectations of contemporary customers and restaurant operators by utilizing the capabilities of Spring Boot, Vue.js, and PostgreSQL
ML-BASED PREDICTION AND GENERATION OF FLUORESCENT MOLECULES AND THEIR PROPERTIES
No full textThis report provides an in-depth overview of the “ML-based Prediction and Generation of
Fluorescence Molecules and Their Properties”, which dives into the usage of machine learning techniques
to accelerate the discovery process of new fluorescence molecules and predict their properties efficiently.
This project aims to reduce the time and costs associated with discovery and property prediction of
organic compounds.
A key component of our work was the use of generative models, including Transmol and
Chemical Language Model and the usage of deep learning architectures for property prediction. The
results of our approaches have been highly promising. We achieved nearly state-of-art performance in
predicting molecular properties and constructed a database of newly sampled molecules. It is worth
mentioning that with ensemble learning techniques, we attained an score of 0.956 for absorption max,
2
0.901 for emission max, and 0.73 for quantum yield. It demonstrates the high performance of our models.
During working on this project, we have learned different domains, such as RDKit for
cheminformatics, various molecular representations, and the integration of generative models with
classical machine learning algorithms to improve property prediction and synthesis.
Finally, we have developed a database containing newly sampled molecules and with their
properties, as predicted by our best performing model
DIMENSIONALITY REDUCTION IN DESIGN OPTIMIZATION
No full textThe engineering design of complex free-form surfaces such as turbine blades and ship
hulls presents significant challenges due to the intricate geometries involved. This
study investigates the application of dimensionality reduction techniques to optimize
the design process of such surfaces, with a focus on enhancing computational efficiency
and maintaining high design quality. We propose a novel approach that integrates geometric moments with Karhunen-Loeve Expansion (KLE) to form a reduced-dimensional design space that retains essential shape characteristics while reducing computational demands. Our methodology leverages the collection of geometric moments to enrich the latent space representation, which is crucial for capturing the physical and geometric nuances of the designs. By employing this approach, we aim to address the curse of dimensionality often encountered in engineering optimization tasks. The findings suggest that incorporating geometric moments noticeably improves the quality of the resultant design space by maintaining variance and enhancing the validity of the designs without extensive computational resources. This research not only contributes to the academic field by providing a feasible solution to a common problem in engineering design but also suggests practical applications in optimizing design processes across various industries