Search results for: refined

Authors: B. C. Biermann

Abstract:

This in-depth exploration investigates the transformative integration of urban vibrancy into contemporary interior design through the strategic incorporation of vivid wall pieces. Bridging the gap between public dynamism and private tranquility, this study delves into the nuanced methodologies, creative processes, and profound impacts of this innovative approach. Drawing inspiration from street art's dynamic language and the timeless allure of natural beauty, these artworks serve as conduits, orchestrating a dialogue that challenges traditional boundaries and redefines the relationship between external chaos and internal sanctuaries. The fusion of urban vibrancy with contemporary interior design represents a paradigm shift, where the inherent dynamism of public spaces harmoniously converges with the curated tranquility of private environments. This paper aims to explore the underlying principles, creative processes, and transformative impacts of integrating vivid wall pieces as instruments for bringing the "outside in." Employing an innovative and meticulous methodology, street art elements are synthesized with the refined aesthetics of contemporary design. This delicate balance necessitates a nuanced understanding of both artistic realms, ensuring a synthesis that captures the essence of urban energy while seamlessly blending with the sophistication of modern interior design. The creative process involves a strategic selection of street art motifs, colors, and textures that resonate with the organic beauty found in natural landscapes, creating a symbiotic relationship between the grittiness of the streets and the elegance of interior spaces. This groundbreaking approach defies traditional boundaries by integrating dynamic street art into interior spaces, blurring the demarcation between external chaos and internal tranquility. Vivid wall pieces serve as dynamic focal points, transforming physical spaces and challenging conventional perceptions of where art belongs. This redefinition asserts that boundaries are fluid and meant to be transcended. Case studies illustrate the profound impact of integrating vivid wall pieces on the aesthetic appeal of interior spaces. Urban vibrancy revitalizes the atmosphere, infusing it with palpable energy that resonates with the vivacity of public spaces. The curated tranquility of private interiors coexists harmoniously with the dynamic visual language of street art, fostering a unique and evolving relationship between inhabitants and their living spaces. Emphasizing harmonious coexistence, the paper underscores the potential for a seamless dialogue between public urban spaces and private interiors. The integration of vivid wall pieces acts as a bridge rather than a dichotomy, merging the dynamism of street art with the curated elegance of contemporary design. This unique visual tapestry transcends traditional categorizations, fostering a symbiotic relationship between contrasting worlds. In conclusion, this paper posits that the integration of vivid wall pieces represents a transformative tool for contemporary interior design, challenging and redefining conventional boundaries. By strategically bringing the "outside in," this approach transforms interior spaces and heralds a paradigm shift in the relationship between urban aesthetics and contemporary living. The ongoing narrative between urban vibrancy and interior design creates spaces that reflect the dynamic and ever-evolving nature of the surrounding environment.

Keywords: Art Integration, Contemporary Interior Design, Interior Space Transformation, Vivid Wall Pieces

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Authors: Albert W. K. Tan

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This research consolidates a comprehensive array of publications from peer-reviewed journals, case studies, and seminar reports focused on reverse logistics and network design. By synthesizing this secondary knowledge, our objective is to identify and articulate key decision factors crucial to reverse logistics network design for e-commerce. Through this exploration, we aim to present a refined mathematical model that offers valuable insights for companies seeking to optimize their reverse logistics operations. The primary goal of this research endeavor is to develop a comprehensive framework tailored to advising organizations and companies on crafting effective networks for their reverse logistics operations, thereby facilitating the achievement of their organizational goals. This involves a thorough examination of various network configurations, weighing their advantages and disadvantages to ensure alignment with specific business objectives. The key objectives of this research include: (i) Identifying pivotal factors pertinent to network design decisions within the realm of reverse logistics across diverse supply chains. (ii) Formulating a structured framework designed to offer informed recommendations for sound network design decisions applicable to relevant industries and scenarios. (iii) Propose a mathematical model to optimize its reverse logistics network. A conceptual framework for designing a reverse logistics network has been developed through a combination of insights from the literature review and information gathered from company websites. This framework encompasses four key stages in the selection of reverse logistics operations modes: (1) Collection, (2) Sorting and testing, (3) Processing, and (4) Storage. Key factors to consider in reverse logistics network design: I) Centralized vs. decentralized processing: Centralized processing, a long-standing practice in reverse logistics, has recently gained greater attention from manufacturing companies. In this system, all products within the reverse logistics pipeline are brought to a central facility for sorting, processing, and subsequent shipment to their next destinations. Centralization offers the advantage of efficiently managing the reverse logistics flow, potentially leading to increased revenues from returned items. Moreover, it aids in determining the most appropriate reverse channel for handling returns. On the contrary, a decentralized system is more suitable when products are returned directly from consumers to retailers. In this scenario, individual sales outlets serve as gatekeepers for processing returns. Considerations encompass the product lifecycle, product value and cost, return volume, and the geographic distribution of returns. II) In-house vs. third-party logistics providers: The decision between insourcing and outsourcing in reverse logistics network design is pivotal. In insourcing, a company handles the entire reverse logistics process, including material reuse. In contrast, outsourcing involves third-party providers taking on various aspects of reverse logistics. Companies may choose outsourcing due to resource constraints or lack of expertise, with the extent of outsourcing varying based on factors such as personnel skills and cost considerations. Based on the conceptual framework, the authors have constructed a mathematical model that optimizes reverse logistics network design decisions. The model will consider key factors identified in the framework, such as transportation costs, facility capacities, and lead times. The authors have employed mixed LP to find the optimal solutions that minimize costs while meeting organizational objectives.

Keywords: reverse logistics, supply chain management, optimization, e-commerce

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Authors: Ananya Pappu, Sneha Mishra

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Introduction: The South Asian population experiences unique health challenges that predisposes this demographic to cardiometabolic diseases at lower BMIs. South Asians may therefore benefit from recommendations specific to their cultural needs. Here, we focus on current BMI guidelines for Asians with a discussion of South Asian dietary practices and culturally tailored interventions. By integrating traditional dietary practices with modern nutritional recommendations, this manuscript aims to highlight effective strategies to improving health outcomes among South Asians. Background: The South Asian community, including individuals from India, Pakistan, Bangladesh, and Sri Lanka, experiences high rates of cardiovascular diseases, cancers, diabetes, and strokes. Notably, the prevalence of diabetes and cardiovascular disease among Asians is elevated at BMIs below the WHO's standard overweight threshold. As it stands, a BMI of 25-30 kg/m² is considered overweight in non-Asians, while this cutoff is reduced to 23-27.4 kg/m² in Asians. This discrepancy can be attributed to studies which have shown different associations between BMI and health risks in Asians compared to other populations. Given these significant challenges, optimizing lifestyle management for cardiometabolic risk factors is crucial. Tailored interventions that consider cultural context seem to be the best approach for ensuring the success of both dietary and physical activity interventions in South Asian patients. Adopting a whole food, plant-based diet (WFPD) is one such strategy. The WFPD suggests that half of one meal should consist of non-starchy vegetables. In the South Asian diet, this includes traditional vegetables such as okra, tindora, eggplant, and leafy greens including amaranth, collards, chard, and mustards. A quarter of the meal should include plant-based protein sources like cooked beans, lentils, and paneer, with the remaining quarter comprising healthy grains or starches such as whole wheat breads, millets, tapioca, and barley. Adherence to the WFPD has been shown to improve cardiometabolic risk factors including weight, BMI, total cholesterol, HbA1c, and reduces the risk of developing non-alcoholic fatty liver disease (NAFLD). Another approach to improving dietary habits is timing meals. Many of the major cultures and religions in the Indian subcontinent incorporate religious fasting. Time-restricted eating (TRE), also known as intermittent fasting, is a practice akin to traditional fasting, which involves consuming all daily calories within a specific window. TRE has been shown to improve insulin resistance in prediabetic and diabetic patients. Common regimens include completing all meals within an 8-hour window, consuming a low-calorie diet every other day, and the 5:2 diet, which involves fasting twice weekly. These fasting practices align with the natural circadian rhythm, potentially enhancing metabolic health and reducing obesity and diabetes risks. Conclusion: South Asians develop cardiometabolic disease at lower BMIs; hence, it is important to counsel patients about lifestyle interventions that decrease their risk. Traditional South Asian diets can be made more nutrient-rich by incorporating vegetables, plant proteins like lentils and beans, and substituting refined grains for whole grains. Ultimately, the best diet is one to which a patient can adhere. It is therefore important to find a regimen that aligns with a patient’s cultural and traditional food practices.

Keywords: BMI, diet, obesity, South Asian, time-restricted eating

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Lauren B. Birney

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The envisioned long-term outcome of this three-year research, and implementation plan is for 1) teachers and students to design and build their own computational models of real-world environmental-human health phenomena occurring within the context of the “Human Harbor” and 2) project researchers to evaluate the degree to which these integrated Computer Science (CS) education experiences in New York City (NYC) public school classrooms (PreK-12) impact students’ computational-technical skill development, job readiness, career motivations, and measurable abilities to understand, articulate, and solve the underlying phenomena at the center of their models. This effort builds on the partnership’s successes over the past eight years in developing a benchmark Model of restoration-based Science, Technology, Engineering, and Math (STEM) education for urban public schools and achieving relatively broad-based implementation in the nation’s largest public school system. The Billion Oyster Project Curriculum and Community Enterprise for Restoration Science (BOP-CCERS STEM + Computing) curriculum, teacher professional developments, and community engagement programs have reached more than 200 educators and 11,000 students at 124 schools, with 84 waterfront locations and Out of School of Time (OST) programs. The BOP-CCERS Partnership is poised to develop a more refined focus on integrating computer science across the STEM domains; teaching industry-aligned computational methods and tools; and explicitly preparing students from the city’s most under-resourced and underrepresented communities for upwardly mobile careers in NYC’s ever-expanding “digital economy,” in which jobs require computational thinking and an increasing percentage require discreet computer science technical skills. Project Objectives include the following: 1. Computational Thinking (CT) Integration: Integrate computational thinking core practices across existing middle/high school BOP-CCERS STEM curriculum as a means of scaffolding toward long term computer science and computational modeling outcomes. 2. Data Science and Data Analytics: Enabling Researchers to perform interviews with Teachers, students, community members, partners, stakeholders, and Science, Technology, Engineering, and Mathematics (STEM) industry Professionals. Collaborative analysis and data collection were also performed. As a centerpiece, the BOP-CCERS partnership will expand to include a dedicated computer science education partner. New York City Department of Education (NYCDOE), Computer Science for All (CS4ALL) NYC will serve as the dedicated Computer Science (CS) lead, advising the consortium on integration and curriculum development, working in tandem. The BOP-CCERS Model™ also validates that with appropriate application of technical infrastructure, intensive teacher professional developments, and curricular scaffolding, socially connected science learning can be mainstreamed in the nation’s largest urban public school system. This is evidenced and substantiated in the initial phases of BOP-CCERS™. The BOP-CCERS™ student curriculum and teacher professional development have been implemented in approximately 24% of NYC public middle schools, reaching more than 250 educators and 11,000 students directly. BOP-CCERS™ is a fully scalable and transferable educational model, adaptable to all American school districts. In all settings of the proposed Phase IV initiative, the primary beneficiary group will be underrepresented NYC public school students who live in high-poverty neighborhoods and are traditionally underrepresented in the STEM fields, including African Americans, Latinos, English language learners, and children from economically disadvantaged households. In particular, BOP-CCERS Phase IV will explicitly prepare underrepresented students for skilled positions within New York City’s expanding digital economy, computer science, computational information systems, and innovative technology sectors.

Keywords: computer science, data science, equity, diversity and inclusion, STEM education

Procedia PDF Downloads 40