Hasil untuk "Machine design and drawing"

Ali Razizadeh

Abstract With the rapid advancement of emerging technologies, video games have become a platform for experiencing interactive, dynamic, and personalized narratives. This study aims to examine how four core technologies—Artificial Intelligence, Virtual Reality, Augmented Reality, and Machine Learning—have influenced the evolution of interactive storytelling in video games. The central research question investigates the mechanisms through which these technologies contribute to redefining narrative structure and experience, as well as the implications they pose for narrative design in digital environments. This study adopts a conceptual-theoretical analysis grounded in interdisciplinary theoretical sources, a purposive sampling of prominent video games, and the construction of a conceptual model. Selected games were chosen for their distinct implementation of emerging technologies and analyzed based on indicators such as responsiveness, dynamism, and personalization. To ensure analytical coherence, a conceptual model was developed across four levels: technology, mechanism, outcome, and narrative. This model was constructed through a deductive approach, drawing on theoretical literature from interactive narrative, human-computer interaction, and technology applications in game design. The findings indicate that artificial intelligence enhances decision-driven and responsive narrative structures; virtual reality augments the immersive and embodied dimensions of narrative; augmented reality integrates storytelling with the user’s physical environment; and machine learning enables adaptive narratives generated from real-time user data. These results suggest that the future of interactive storytelling lies not in isolated technologies, but in the synergistic interplay of multiple technological dimensions. Extended Abstract: Introduction In recent years, video games have undergone a paradigmatic shift, evolving from linear storytelling systems into highly dynamic, personalized, and participatory narrative experiences. This transformation has been primarily driven by the integration of emerging digital technologies such as Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Machine Learning (ML). These technologies have not only enriched the audiovisual and interactive aspects of gameplay but have also significantly redefined the very architecture of narrative construction within digital environments. The central aim of this study is to explore how these four technologies contribute, individually and collectively, to the transformation of interactive storytelling in video games. Unlike earlier forms of interactive narrative that often relied on predefined branches and simple user input, the current generation of games features advanced mechanisms that respond in real time to user behavior, emotions, preferences, and even spatial contexts. This study does not merely focus on gameplay mechanics or technological capabilities but critically analyzes the narrative logic that emerges from the interplay between user agency and system adaptability. The primary research question is: Through what conceptual and technical mechanisms do emerging digital technologies shape the evolution of interactive narrative structures in video games, and what implications does this hold for future narrative design? Methodology This study is conceptual and analytical in nature, employing a qualitative and deductive approach rooted in interdisciplinary perspectives from media studies, game studies, and narrative theory. Instead of relying on empirical fieldwork or user-centered ethnography, it focuses on conceptual modeling, theoretical synthesis, and the purposive analysis of selected video games. The research first establishes its theoretical foundation through a review of diverse frameworks, including interactive narrative theory, human-computer interaction models, and studies on technological narrative design. This theoretical grounding enables the formulation of analytical dimensions for examining how emerging technologies interact with narrative design principles. Building on this foundation, a conceptual model was developed to map the influence of four key technologies—Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Machine Learning (ML)—across three intermediary levels: mechanisms, outcomes, and narrative transformation. The model follows the systematic conceptual analysis method, where abstract concepts are extracted, clustered, and organized within a causal-analytical hierarchy. To operationalize the model, a purposive sample of exemplary games was selected, ensuring diversity in technological integration, narrative depth, and cultural relevance. Representative titles such as Detroit: Become Human (AI), Half-Life: Alyx (VR), Pokémon Go (AR), and Shadow of Mordor (ML) were analyzed in terms of narrative responsiveness, branching complexity, user immersion, and personalization capacity. Finally, the theoretical insights and case analyses were coded according to the conceptual model, allowing for a systematic synthesis of how each technology contributes to specific modes of narrative transformation. Findings The study reveals four primary mechanisms through which emerging technologies transform the structure of interactive narratives. Artificial Intelligence enhances narrative reactivity by enabling systems to track player decisions, behavioral tendencies, and emotional responses, allowing storylines to adapt dynamically in real time. Games such as Detroit: Become Human exemplify this mechanism through intricate decision trees that produce substantial variations in plot and character behavior. AI also enriches non-playable characters with memory, affective sensitivity, and learning capabilities, turning them into active participants in the narrative experience rather than passive elements of the game environment. Virtual Reality reimagines storytelling as an embodied and multisensory experience rather than a sequence of scripted events. In Half-Life: Alyx, players are not mere observers but physically immersed participants whose movements, gaze, and spatial interactions directly shape the rhythm and flow of the story. This transformation shifts narrative logic from a linear timeline to spatial exploration, inviting users to uncover meaning through physical presence and sensory engagement. Augmented Reality introduces a new form of contextual storytelling by overlaying digital narratives onto the user’s real-world environment. Games like Pokémon Go and The Walking Dead: Our World integrate location-based elements, transforming ordinary urban spaces into dynamic narrative settings. In this way, the player’s movement, geographical location, and temporal context become integral components of the unfolding story, creating a fluid relationship between fiction and lived experience. Machine Learning contributes a layer of algorithmic adaptability, allowing narratives to evolve continuously based on player interaction and performance. By analyzing behavioral patterns and preferences, ML systems tailor storylines and adjust difficulty or complexity in real time. In Shadow of Mordor, for instance, enemies adapt to previous encounters and refine their strategies, resulting in a personalized narrative loop unique to each player. ML also enables predictive storytelling, adjusting future developments according to historical player behavior. Although each mechanism functions independently, the study highlights that their most profound narrative impact arises from their convergence. The combination of AI and ML, for example, enables emotionally responsive and adaptive storylines, while the fusion of VR and AR creates hybrid narrative environments that blend digital imagination with physical reality. Together, these technologies mark a shift toward interactive narratives that are not only dynamic and immersive but also deeply personalized and contextually aware. Discussion and Conclusion The study highlights a fundamental transformation in the nature and structure of narrative within digital games. Interactive narratives have evolved from static, pre-scripted forms into dynamic, living systems that are flexible, responsive, and deeply sensitive to user context. The four examined technologies—AI, VR, AR, and ML—collectively contribute to this evolution by diversifying narrative pathways and enabling stories to be co-created by players in real time. This shift carries important implications for both game design and narrative theory. From a design perspective, narrative creation must move beyond linear scripting toward procedural and modular frameworks in which designers construct systems of potential rather than fixed storylines. This approach requires a deeper synthesis between narrative logic and technological affordances, positioning the designer as an architect of narrative possibilities. From a theoretical standpoint, traditional models of plot, character, and closure must be reconsidered in light of user-centered and data-driven storytelling. Theories such as reader-response criticism and narrative immersion require redefinition when the player assumes the dual role of participant and co-narrator. Furthermore, this research offers a conceptual framework for understanding how narrative meaning now emerges from the interplay of agency, context, and computation. It demonstrates that technologies like Artificial Intelligence and Machine Learning primarily operate at the structural and content levels, determining how stories evolve through decision-making algorithms. Conversely, Virtual and Augmented Reality function at the experiential level, reshaping how narratives are perceived, embodied, and spatially situated by the user. In conclusion, the study contends that the future of interactive storytelling lies not in the isolated use of individual technologies but in their synergistic integration. The most engaging and innovative narrative systems will arise from designs that harmonize reactivity, immersion, contextualization, and personalization. As a path for future research, the study encourages the examination of the ethical and cultural implications surrounding AI-driven and data-dependent narrative design. Additionally, it calls for empirical investigations into how diverse user groups experience and interpret these emerging storytelling forms, thereby bridging conceptual modeling with lived narrative experience.

Arsić Dušan, Nikolić Ružica R., Arsić Aleksandra et al.

The rotary excavator is a complex machine system, the main part of the ECS (excavator-conveyer-spreader) system, used in open-pit mining. Such a machine’s service life can last for decades, it generally operates in the harsh exploitation conditions, which requires that its vital structures must be continuously controlled and well maintained. Damage or fracture of parts or assemblies of a rotary excavator can be caused by influence of various manufacturing, construction, exploitation conditions or environmental factors. Analysis of those eventual failures can be performed by various methods, out of which the most suitable are the failure analysis methods, for example the fault-tree analysis (FTA), the Ishikawa fishbone (cause-and-effect) diagrams or the failure modes and effects analysis (FMEA). In this article are presented results of the fault tree analysis of possible causes of rotary excavator’s parts, as well as measures to prevent their damages and/or fractures and to extend the service life of an excavator as a whole. The model of the organizational system for the rotary excavator’s maintenance is given, as well.

Pranav K. Jha, Manoj K. Jha

Connected and Autonomous Vehicles (CAVs) remain vulnerable to cyberattacks due to inherent security gaps in the Controller Area Network (CAN) protocol. We present a structured Python (3.11.13) framework that repairs structural inconsistencies in a public CAV dataset to improve the reliability of machine learning-based intrusion detection. We assess the effect of training data volume and compare Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers across four attack types: DoS, Fuzzy, RPM spoofing, and GEAR spoofing. XGBoost outperforms RF, achieving 99.2 % accuracy on the DoS dataset and 100 % accuracy on the Fuzzy, RPM, and GEAR datasets. The Synthetic Minority Oversampling Technique (SMOTE) further enhances minority-class detection without compromising overall performance. This methodology provides a generalizable framework for anomaly detection in other connected systems, including smart grids, autonomous defense platforms, and industrial control networks.

Abderahman Rejeb, Karim Rejeb, Edit Süle et al.

The electric vehicle (EV) battery supply chain plays a critical role in promoting sustainable transportation and tackling scarce resources, environmental costs, and supply chain vulnerabilities. The current study aims to conduct an extensive literature review of the EV battery supply chain given its importance for developing sustainable and efficient EVs. Using keyword co-occurrence and article co-citation analyses, this study analyses more than 681 publications from 2005 to 2024 and sourced from the Scopus database. Findings show that the number of articles increased considerably after 2020, which can be attributed to the global focus on decarbonization, electromobility, and circular economy practices. The review identifies important themes such as sustainability challenges, critical materials management, reverse logistics, and policy-driven frameworks for closed-loop supply chains. The findings from this study highlight a multidimensional approach where the integration of technologies, innovative policies, and collaborative actions can contribute to the resilience and sustainability of EV battery supply chains. It offers practical insights for stakeholders, strategic directions to maximize EV battery lifecycle management, and outlines the pathways to reach carbon neutrality in the transportation sector. By identifying the intellectual structure of this emerging field, the study contributes to academic discourse and informs the formulation of practical strategies to advance sustainable mobility.

Akop Antonyan, Aleksandr Klimov, Andrey Buchkin et al.

The electric traction drive is increasingly being applied as a device providing traction force on driving wheels. This is due to its reliable torque transmission to the driving wheels, step-less regulation of the traction force on the driving wheels depending on the driving conditions, and increased design capabilities. In terms of power, the electric traction drive has maximum torque at low speeds, which internal combustion engines lack. This property of the electric drive is not applied in urban vehicles, as not all passengers are comfortable with intensive acceleration. In modern vehicles with an electric traction drive, the maximum acceleration can be limited by software, which is the focus of this study. This paper aims to develop an algorithm capable of recognizing when the permissible longitudinal acceleration exceeds the limit and generating an action to maintain the acceptable acceleration level. The electric traction drive of a large-class electric bus was used as a control object. An algorithm and a control law are hereby developed, which reduce longitudinal acceleration using PI control. Both simulation modeling and full-scale tests on the electric bus were carried out to evaluate the performance and efficiency of the algorithm. In this paper, the authors also introduce the cumulative velocity concept and prove the operability and efficiency of the developed method.

Liping Ye, Na Fan, Junhui Zhang et al.

The air–ground integrated Internet of Vehicles (IoV), which incorporates unmanned aerial vehicles (UAVs), is a key component of a three-dimensional intelligent transportation system. Task offloading is crucial to improving the overall efficiency of the IoV. However, blackhole attacks and false-feedback attacks pose significant challenges to achieving secure and efficient offloading for heavily loaded roadside units (RSUs). To address this issue, this paper proposes a reputation-aware, multi-objective task offloading method. First, we define a set of multi-dimensional Quality of Service (QoS) metrics and combine K-means clustering with a lightweight Proximal Policy Optimization variant (Light-PPO) to realize fine-grained classification of offloading data packets. Second, we develop reputation assessment models for heterogeneous entities—RSUs, vehicles, and UAVs—to quantify node trustworthiness; at the same time, we formulate the RSU task offloading problem as a multi-objective optimization problem and employ the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to find optimal offloading strategies. Simulation results show that, under blackhole and false-feedback attack scenarios, the proposed method effectively improves task completion rate and substantially reduces task latency and energy consumption, achieving secure and efficient task offloading.

Sari Diana Puspita, Hartini Sri, Azzahra Faradhina et al.

Environmental issues have become an important consideration to be included in business operations. One of the main environmental problems in the wood industry is the high production of wood waste and increasing scarcity and cost of raw materials. For this reason, companies need to utilize wood waste to reduce material costs and, at the same time, reduce the impact of waste on the environment. Converting wood waste into products that can be sold will increase its economic value. This research aims to identify the types of waste from a furniture company and reduce waste by designing various products made from wood waste. Wood chips are wood waste that have the potential to be reused. Waste wood chips from the materials station can be used to create bedside table products. The bedside table was chosen because of its high selling price, and the company could make it with its existing resources. Apart from that, the company still needs to expand its variety of bedside tables. The bedside table was designed using the TRIZEE method. TRIZEE is a method that combines eco-efficiency with 40 TRIZ principles, which can reduce environmental impacts in alignment with company goals. The design process resulted in 4 bedside table variations. Production capacity is estimated to produce 56 bedside tables per month. If scrap waste is successfully used as bedside table material. Apart from saving raw materials, the company will be able to reduce wood waste and gain greater profits from waste utilization.

C. Moskowitz, M. Welch, M. Jacobs et al.

Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described.

M. Eisenstein

Protein engineers are drawing on rapidly evolving machine learning tools, deep reservoirs of data, and the structure-predicting firepower of AlphaFold2 to pursue more sophisticated de novo protein designs.

Péter Bocz, Nándor Liegner, Ákos Vinkó et al.

On behalf of MÁV Hungarian State Railways Ltd., the authors carried out a research and development (R&D) project on behalf of the Budapest University of Technology and Economics, Department of Highway and Railway Engineering, on the subject of “Research and investigation of the causes of gauge narrowing by finite-element modeling in running track and turnout, and under operational and laboratory conditions”. The main objective of the research was to investigate the causes of localized defects of gauge narrowing in railway tracks based on machine and manual track measurements, laboratory measurements, and theoretical considerations. The measures proposed as a consequence of identifying the causes could significantly contribute to reducing the number and extent of local defects in the future. Furthermore, the research aims to develop new theories in less scientifically mature areas and provide procedures and instructions that professional engineers and practitioners can easily apply. The main areas of research, which are not exhaustive, are as follows: (i) the evaluation of the measurement results provided by track geometry measuring and recording cars; (ii) on-site investigations in the railway track in terms of gauge and rail profile measurements; and, based on these, (iii) the selection of concrete sleepers, which were removed from the track and subjected to more detailed geometrical investigations in the laboratory, together with the components of the rail reinforcement; (iv) the track–vehicle connection, tight running in straight and curved track sections under track confinement; (v) modeling of the stability and deflection of the rail when the rail fastenings lose part of their supporting function; and (vi) finite element modeling of the concrete sleepers under operating conditions such as slow deformation of the concrete, temperature variation effects, and lateral support on the ballast. In the already-narrowed track section, the tight vehicle running is not the cause of the track gauge narrowing but a consequence, so it is not investigated in this paper.

Peter Eades, Niklas Gröne, Karsten Klein et al.

Celtic knots are an ancient art form often attributed to Celtic cultures, used to decorate monuments and manuscripts, and to symbolise eternity and interconnectedness. This paper describes the framework CelticGraph to draw graphs as Celtic knots and links. The drawing process raises interesting combinatorial concepts in the theory of circuits in planar graphs. Further, CelticGraph uses a novel algorithm to represent edges as Bézier curves, aiming to show each link as a smooth curve with limited curvature.

Tim Hegemann, Alexander Wolff

Orthogonal graph drawing has many applications, e.g., for laying out UML diagrams or cableplans. In this paper, we present a new pipeline that draws multigraphs orthogonally, using few bends, few crossings, and small area. Our pipeline computes an initial graph layout, then removes overlaps between the rectangular nodes, routes the edges, orders the edges, and nudges them, that is, moves edge segments in order to balance the inter-edge distances. Our pipeline is flexible and integrates well with existing approaches. Our main contribution is (i) an effective edge-nudging algorithm that is based on linear programming, (ii) a selection of simple algorithms that together produce competitive results, and (iii) an extensive experimental comparison of our pipeline with existing approaches using standard benchmark sets and metrics.

M. A. Ibrahim, Z. Assaad, Elizabeth T. Williams

Intelligent highly-automated systems (HASs) are increasingly being created and deployed at scale with a broad range of purposes and operational environments. In uncertain or safety-critical environments, HASs are frequently designed to seamlessly co-operate with humans, thus, forming human-machine teams (HMTs) to achieve collective goals. Trust plays an important role in this dynamic: humans need to be able to develop an appropriate level of trust in their HAS teammate(s) to form an HMT capable of safely and effectively working towards goal completion. Using Autonomous Ground Vehicles (AGVs) as an example of an HAS used in dynamic social contexts, we explore interdependent teaming and communication between humans and AGVs in different contexts and examine the role of trust and communication in these teams. Drawing on lessons from the AGV example for the design of an HAS used for an HMT more broadly, we argue that trust is experienced and built differently in different contexts, necessitating context-specific approaches to designing for trust in such systems.

Xien Thomas, L. Powell, Seth Polsley et al.

From an early age, children begin developing critical motor skills, such as fine motor control, that contribute significantly to reading, writing, drawing, and more, all of which are important for communication and school readiness. Pediatricians can evaluate a child’s motor skills using activities and questionnaires. Sometimes these involve adults drawing with their child, but it can be difficult to fully evaluate a child’s drawings through a handful of sketches from limited direct assessments. We propose creating a sketching system that will collect free-form drawing data from parents and children that can then automatically differentiate a child’s sketch from an adult’s using only the pen strokes of their drawing. In this paper, we describe our study that collected sketches from 14 children aged 2 to 5 and 25 adults over 18. We contribute a machine learning classifier based on sketch recognition features from free-hand drawings capable of distinguishing children’s sketches from those made by adults with an F-measure of 0.906. These results indicate the potential of creating sketch-based applications for assessing children’s fine motor development.

Waleed Abd El-khalik

Thermal comfort prediction within sustainable built environments stands as a pivotal challenge intertwining human well-being and environmental sustainability. This paper presents a pioneering framework leveraging machine learning methodologies to advance predictive models for thermal comfort. Drawing upon a comprehensive dataset sourced from ASHRAE field studies and the RP-884 database, comprising 107,463 entries, our study unfolds a novel approach to enhancing thermal comfort predictions. The integration of diverse physiological parameters, environmental data, and occupant preferences forms the foundation of our machine learning-driven framework. Through meticulous analysis and model development, our approach not only refines predictive accuracy but also underscores adaptability across varying environmental contexts. The study contributes not only to the discourse on thermal comfort prediction but also emphasizes the crucial nexus between sustainable design, occupant well-being, and energy efficiency.

Mr. Rupak Kumar Gogoi

Abstract: A finite-state machine (FSM) is a computational mathematical model. For design and analysis, circuits and system operations can be represented in a variety of ways. FSM is one of the methods for using a drawing to represent the operations of many circuits and systems in electronic engineering, computer engineering, and so on. In terms of design, the finite state machine is a very simple machine. It is made up of a set of input symbols, output symbols, and states that must be designed. Furthermore, a function of input and output symbols with the current state to give the next state must be present. To simulate the Finite State machine, a software simulator is implemented in this paper using the Visual Basic programming language (in terms of design and operation).Several general examples are represented in this model. However, the software can be used to teach students about FSM and how it works.

Pandey Gaurav Kumar, Patel Praveen Bhai, Kumar Abhishek et al.

The present experimental investigation aims to analyse the effect of various machining parameters, such as pulse peak current (Ion), pulse on time (Ton), pulse off time (Toff) and spark voltage (SV) on the surface roughness (SR) and material removal rate(MRR) by using continuous traveling of both wire electrode (i.e. brass wire and zinc-coated brass wire). The present work also analyses the effect of types of wires, such as brass wire and zinc-coated brass wire used during machining of Titanium alloy (Ti-6Al-4V) on Surface roughness (SR) and material removal rate (MRR). This work studies the correlation between various response parameter such SR and MRR by using same machining parameter by for both wires.

Hariastuti Ni Luh Putu, Lukmandono

This article describes in detail the elements of value creation through the transformations and flexibility, which is carried out in the implementation of sustainable manufacturing. The purpose of this study is to generate the criteria or elements that build the sustainable value creation process through a literature review analysis. The overall classification of sustainable manufacturing implementation discussed shows several essential factors that support this. The process of review studies on selected papers strengthens the classification carried out to obtain the necessary elements of sustainable value creation. The value created can later be a hallmark of the company's superiority to survive the market competition. Besides, the role of partnerships, such as collaboration indicates a positive influence in generating value creation to increasing the company's competitive rate. In addition, the importance of partnership processes such as collaboration and cooperation between stakeholders, is needed to generate value creation to increase the company's competitive level. The partnership process is one of the critical factors in creating sustainable value in achieving sustainable manufacturing in the future.

Martin Nöllenburg, Manuel Sorge, Soeren Terziadis et al.

The splitting number of a graph $G=(V,E)$ is the minimum number of vertex splits required to turn $G$ into a planar graph, where a vertex split removes a vertex $v \in V$, introduces two new vertices $v_1, v_2$, and distributes the edges formerly incident to $v$ among its two split copies $v_1, v_2$. The splitting number problem is known to be NP-complete. In this paper we shift focus to the splitting number of graph drawings in $\mathbb R^2$, where the new vertices resulting from vertex splits can be re-embedded into the existing drawing of the remaining graph. We first provide a non-uniform fixed-parameter tractable (FPT) algorithm for the splitting number problem (without drawings). Then we show the NP-completeness of the splitting number problem for graph drawings, even for its two subproblems of (1) selecting a minimum subset of vertices to split and (2) for re-embedding a minimum number of copies of a given set of vertices. For the latter problem we present an FPT algorithm parameterized by the number of vertex splits. This algorithm reduces to a bounded outerplanarity case and uses an intricate dynamic program on a sphere-cut decomposition.

J. Biddle

Abstract Recent scholarship in philosophy of science and technology has shown that scientific and technological decision making are laden with values, including values of a social, political, and/or ethical character. This paper examines the role of value judgments in the design of machine-learning (ML) systems generally and in recidivism-prediction algorithms specifically. Drawing on work on inductive and epistemic risk, the paper argues that ML systems are value laden in ways similar to human decision making, because the development and design of ML systems requires human decisions that involve tradeoffs that reflect values. In many cases, these decisions have significant—and, in some cases, disparate—downstream impacts on human lives. After examining an influential court decision regarding the use of proprietary recidivism-prediction algorithms in criminal sentencing, Wisconsin v. Loomis, the paper provides three recommendations for the use of ML in penal systems.