Design Intelligence (United States)

Political conflict and mass violence are major threats to security stability, especially in developing countries such as Indonesia. Civil mobilization through demonstrations is often considered an expression of social dissatisfaction, but its role as an early indicator of political violence escalation has not been analyzed quantitatively across countries and time. This study aims to explore the temporal relationship between an increase in demonstrations, the escalation of political violence, and the number of fatalities, as well as to develop an Early Warning Index as a counterintelligence tool. Using cross-country ACLED panel data from 1997 to 2026, the study applies a fixed effects model with one and two period lags, logarithmic transformation, and Granger causality tests to assess temporal causal relationships. The results show that a surge in demonstrations in the previous period significantly predicts an increase in political violence (coefficient = 0.592, t = 11.62, p < 0.001) and fatalities (coefficient = 0.475, t = 6.90, p < 0.001). The Granger causality test confirmed that demonstrations systematically led to an escalation in violence and fatalities. The Early Warning Index shows significant variations in risk, with maximum values indicating high social pressure that can serve as an early signal of conflict escalation. These findings fill a gap in quantitative research on the role of demonstrations as a leading indicator of political conflict. The index provides counterintelligence decision makers with an analytical tool for proactively detecting the risk of destabilization, particularly in the pre-election period or when sensitive identity-based issues arise.

The accelerating convergence of geopolitical instability and rapid digital transformation poses unprecedented challenges for national resilience, particularly in countries undergoing societal transition. Motivated by Ukraine’s dual struggle to defend against Russian aggression while advancing digital infrastructure, this paper examines how civil-military resource trade-offs affect the ability to maintain essential services and defend against cyber threats during a crisis. We identify institutional, financial, technological, and social barriers to cyber resilience, highlighting how resource allocation can enhance or undermine adaptive capacity. Building on Conservation of Resources (COR) theory, we propose a strategic framework for resource allocation that prioritizes the prevention of cascading loss across military and civilian systems. By conceptualizing resources as interdependent assets, we show how poorly managed trade-offs can trigger long-term vulnerability, while synergistic investments can generate resilience “gain spirals.” We argue that national resilience requires integrated governance, anticipatory planning, and whole-of-society engagement to avoid destabilizing feedback loops. Our framework offers actionable guidance for governments navigating the complex terrain of digital resilience, security policy, and economic development in an increasingly volatile world.

This study provides an in-depth study advanced machining processes and their optimization using various machine learning algorithms.The study focuses on key machining parameters such as cutting speed (m/min), feed rate (mm/rev), and cutting depth (mm), and rotation speed (RPM), investigating their effects on surface roughness (Ra) in manufacturing operations.This research addresses emerging challenges in modern manufacturing, particularly in the processing of advanced engineering materials for the aerospace, automotive, and precision industries.These algorithms were selected for their ability to manage complex and non-linear relationships in manufacturing data and for their proven performance in predictive modeling.The study explores how these methods can overcome traditional limitations in process planning and optimization, especially in situations where conventional empirical models are inadequate.Special attention is paid to the theoretical foundations of each algorithm, in which linear regression serves as a basic model, random forest regression provides improved predictive capabilities through ensemble learning, and support vector regression provides robust optimization through its -insensitive loss function approach.The research also explores the important relationship between machine parameter optimization and surface quality, emphasizing the importance of parameter optimization in achieving desired surface properties while maintaining production efficiency.This study advances the field by providing a structured methodology machine parameter optimization, particularly relevant to computer-aided process planning and advanced manufacturing processes.These findings have significant implications for industries requiring high-precision manufacturing, providing insights into How can machine learning methods be used effectively?optimize machining processes, reduce production costs, and improve surface quality in modern manufacturing operations.

This study provides an in-depth study advanced machining processes and their optimization using various machine learning algorithms. The study focuses on key machining parameters such as cutting speed (m/min), feed rate (mm/rev), and cutting depth (mm), and rotation speed (RPM), investigating their effects on surface roughness (Ra) in manufacturing operations. This research addresses emerging challenges in modern manufacturing, particularly in the processing of advanced engineering materials for the aerospace, automotive, and precision industries.These algorithms were selected for their ability to manage complex and non-linear relationshipsin manufacturing data and for their proven performance in predictive modelling. The study explores how these methods can overcome traditional limitations in process planning and optimization, especially in situations where conventional empirical models are inadequate.Special attention is paid to the theoretical foundations of each algorithm, in which linear regression serves as a basic model, random forest regression provides improved predictive capabilities through ensemble learning, and support vector regression provides robust optimization through its ε-insensitive loss function approach. The research also explores the important relationship between machine parameter optimization and surface quality, emphasizing the importance of parameter optimization in achieving desired surface properties while maintaining production efficiency.This study advances the field by providing a structured methodology machine parameter optimization, particularly relevant to computer-aided process planning and advanced manufacturing processes. These findings have significant implications for industries requiring high-precision manufacturing, providing insights into How can machine learning methods be used effectively? optimize machining processes, reduce production costs, and improve surface quality in modern manufacturing operations.

Introduction: This study introduces a novel approach that combines Combining Machine Learning methods with Multi-Criteria Decision-Making to help supply chain stakeholders identify the best model for delay prediction. Unlike conventional approaches that integrate MCDM and ML into a single system, this paper uses MCDM to evaluate different ML classifiers to improve decision-making. Additionally includes a sensitivity study to assess the method's resilience to other MCDM approaches, providing a thorough solution for accurate delay prediction in dynamic supply chain settings. Research significance: This study is noteworthy for being the first to combine Supply chain support through machine learning and multi-criteria decision-making stakeholders in predicting delays. By evaluating different ML classifiers through MCDM and performing a sensitivity analysis, it provides a robust and interpretable decision-making framework that improves supply chain management efficiency. Alternative: Decision Tree,Random Forest,Ada Boost,And Bagging. Evaluation Preference: Accuracy,Precision,Recall,Log Loss. Result: The bagging method achieved the highest rank, while the Decision Tree received the lowest rank. Conclusion: According to the MOORA approach, bagging holds the highest value for machine learning in supply chain applications.

World Health Organization (WHO) tahun 2020, menyatakan bahwa hanya sekitar 41% bayi yang berusia 0-6 bulan di seluruh dunia diberikan ASI secara eksklusif. Semakin tinggi pendidikan seseorang, semakin mudah pula mereka menerima informasi dan pada akhirnya pengetahuan yang dimilikinya tentang pemberian ASI eksklusif semakin baik. Tujuan dalam penelitian ini adalah untuk mengetahui karakteristik dan pengetahuan ibu tentang ASI Eksklusif Kabupaten Deli Serdang tahun 2024. Populasi dalam penelitian ini adalah seluruh ibu dengan bayi berusia 0-6 bulan di Desa Sihuik-huik Kecamatan Angkola Selatan Tahun 2024, dan jumlah sampel 98 responden dengan teknik total sampling. Adapun hasil dari penelitian ini adalah mayoritas umur ibu adalah 20-35 tahun yaitu sebanyak 63 ibu (64,3%), mayoritas pendidikan ibu adalah SMA yaitu sebanyak 58 ibu (59,2%), mayoritas pekerjaan ibu adalah ibu rumah tangga yaitu sebanyak 62 ibu (63,3%), dan mayoritas ibu memiliki 1 anak yaitu sebanyak 55 ibu (56,1%). Berdasarkan hasil penelitian didapatkan bahwa mayoritas ibu memiliki pengetahuan kurang yaitu sebanyak 41 ibu (41,8%) dan minoritas ibu memiliki pengetahuan baik yaitu sebanyak 24 ibu (24,5%). Dengan adanya penelitian ini diharapkan ibu lebih mendapatkan informasi tentang pentingnya ASI Eksklusif.

Explanation of an AI agent requires knowledge of its design and operation. An open question is how to identify, access, and use this design knowledge for generating explanations. Many AI agents used in practice, such as intelligent tutoring systems fielded in educational contexts, typically come with a User Guide that explains what the agent does, how it works, and how to use the agent. However, few humans actually read the User Guide in detail. Instead, most users seek answers to their questions on demand. This chapter describes a question-answering agent (AskJill) that uses the User Guide for an AI-based interactive learning environment (VERA) to automatically answer users' questions and thereby explain VERA's domain, functioning, and operation. This chapter also presents a preliminary assessment of AskJill in VERA.

In this paper, we introduce a novel Dense D2C-Net, an unobtrusive display-to-camera (D2C) communication scheme that embeds and extracts additional data via visual content through a deep convolutional neural network (DCNN). The encoding process of Dense D2C-Net establishes connections among all layers of the cover image, and fosters feature reuse to maintain the visual quality of the image. The Y channel is employed to embed binary data owing to its resilience against distortion from image compression and its lower sensitivity to color transformations. The encoder structure integrates hybrid layers that combine feature maps from the cover image and input binary data to efficiently hide the embedded data, while the addition of multiple noise layers effectively mitigates distortions caused by the optical wireless channel on the transmitted data. At the decoder, a series of 2D convolutional layers is used for extracting output binary data from the captured image. We conducted experiments in a real-world setting using a smartphone camera and a digital display, demonstrating superior performance from the proposed scheme compared to conventional DCNN-based D2C schemes across varying parameters such as transmission distance, capture angle, display brightness, and camera resolution.

ABSTRACT: Crackers were formulated with pulse flours – Chickpea and Pea along with the incorporation of Anethum Graveolens L. microgreens to develop an end product that is rich in protein, fibre, and micronutrients, gluten-free and low in glycemic index. Microgreens are tiny greens that are often harvested in an immature state that are loaded with innumerable benefits thus, Anethum Graveolens L. was utilized as a part of the study. The main goal was to develop a gluten-free snack that was rich in micronutrients due to the lack of availability of such products in the market. The crackers were inflicted to different analyses that included – sensory analysis, physicochemical analysis, proximate analysis, antioxidant analysis (DPPH Radical Scavenging Activity-DRSA and Ferric Reducing Antioxidant Power- FRAP) , texture analysis, microbial analysis, and shelf-life analysis. Results exhibited that the crackers had a remarkable consumer acceptance, were nutrient-dense, had potential antioxidant activity, minimal microbial activity, and had an extended shelf-life. The crackers weighed 3.0±1.0g per unit and the average pH of the crackers was 6.09±0.34. They were rich in protein (28.4g), dietary fibre (3.99g) micronutrients, high in antioxidants (DRSA-21.4% and FRAP-575.71 µM Fe(II)/g), and had low fat (9.19g) content per 100g of the crackers. Thus, the Anethum graveolens L. microgreen incorporated pulse-based gluten-free crackers are an ideal snack with phenomenal potential.