This paper assesses the contribution that board games can make as decision support tools to offer stakeholders another option to better navigate the complexity and wicked nature of urban challenges using more novel participatory techniques. Using the example of Birmingham, the design, play and evaluate phases of the ‘Urban Placemakers’ game are described and analysed with respect to synergies between key literatures on games and public participation. Using the Urban Placemakers game in a workshop setting to identify and explore the problems facing urban areas, complements traditional approaches to participation and policy-making, but provides additionality through creating more accessible and enjoyable end-user experiences through which policy-focused research models and supporting outputs can be co-developed with stakeholders. The core ingredients of co-design and co-production within the Urban Placemakers game ensure that academic rigour, policy relevance and pragmatism intersect. This convergence space provides a safe hypothetical fertile space for thinking and deliberation that enables players to discuss ‘wicked’ urban problems outside usual agency restrictions, yielding insights to challenges championing innovation and social learning in a fun setting. Whilst playing the game was an enjoyable experience for the majority of participants, it also helped the research team better understand the urban interdependencies within their own work packages and research and was used to help prioritise a set of indicators to explore and diagnose the problems facing the city of Birmingham. This use of a game board approach was found to be a valuable additional method for engaging with urban problems in innovative ways that were grounded in co-creation, play and fun with a computer nowhere in sight.
Nimmy Mariam Abraham, Stana Zivanovic, Genevieve Williams
Abstract Jumping on vibrating platforms is described not only by the frequency of jumping (FoJ) but also by the timing of key events in a cycle of jumping relative to vibrations. This study aimed to capture timing and efficiency-related adaptations during jumping on vertically vibrating platforms. Whole body kinematic and kinetic data were collected as ten participants jumped on a sinusoidally vibrating platform of 2.0, 2.4 and 2.8 Hz at 2 m/s2. FoJ matched platform frequency, and audio cues were provided to time the jump landing at four positions relative to the platform position: reference position on its way down, lowest position, reference position on its way up, and highest position. FoJ, jump timing, impact factor, contact ratio, mechanical work and leg stiffness were calculated for each jump cycle. Results confirmed that the impact factor, contact ratio, mechanical work, and leg stiffness are timing-dependent. Results also showed that despite being cued, participants adjusted their timing to take off from a higher platform position during its downward motion and land at a lower position during the upward motion while maintaining FoJ. Importantly, participants tended towards efficiency by employing jump timings related to lower energy input, appropriate contact ratio and lower forces. This study provides evidence of jump timing behaviour relative to platform motion being dominated by the efficiency of jumping. Practically, it may be crucial to consider this aspect when estimating human-induced loads on lively assembly structures.
The Wain River protected forest serves not only as a watershed but also holds a critical role in sustaining hydrological functions. The Sultan of Kutai Kertanegara Sultanate initiated ecological engineering efforts in 1934 by designating this area as a protected forest. However, rapid urbanization has led to a decline in local wisdom, posing a threat and intensifying pressure on the Wain River protected forest. The practices of local wisdom applied by the community within the Wain River protected forest area significantly impact the forest's sustainability. Despite their diminishing influence, they still uphold ancestral guidance in forest conservation. Wain River is also a buffer zone forest for the new capital city of Indonesia at Penajam Paser Utara. Utilizing the Multidimensional Scaling (MDS) method, it was determined that the role of local wisdom in managing the Wain River protected forest falls under a category of weak sustainability, scoring 68.034 percent. Major influencing factors include land-use conversion and economic concerns, with the economic dimension scoring a sustainability rate of 62.83 percent. To foster the traditional agricultural economy, efforts are needed to maximize the utilization of Community Forests (CF) and capitalize on environmental services while ensuring the preservation of the Wain River protected forest.
In the maintenance planning of civil aircraft, the initial maintenance tasks based on MSG-3 standard for civil aircraft are set conservatively due to the lack of usage data support, resulting in problems such as excessive maintenance and poor economy in practice. An optimization method for civil aircraft maintenance tasks based on the guidance of IP 44 and the process of Boeing statistical analysis for scheduled maintenance optimization(SASMO)is proposed. Firstly, the characteristics of obvious operational impact faults based on their impact categories are sorted out. Secondly, all planned and unplanned maintenance data related to this category of maintenance tasks are collected. Then, a maintenance event chain to fuse multi-source maintenance data into a failure cycle and conduct statistical analysis is established, and the failure probability curve of the maintenance project is fitted. Finally, the optimization suggestions for maintenance task intervals are given based on engineering analysis and comprehensive evaluation. The results show that the optimization method for civil aircraft maintenance tasks proposed in this paper can reduce the grounding time, improve the maintenance efficiency, and reduce the maintenance cost.
Urban leisure consumption activities (ULCA) are key indicators of urban vitality, and analyzing their spatio-temporal distribution is essential for understanding the structure and dynamics of urban spaces. This study conducts a quantitative analysis of leisure consumption behavior in the main urban area of Chongqing based on multi-source data (including mobile signaling data, POI data, and traffic analysis zone) and proposes a novel spatio-temporal layout analysis method. The study focuses on the spatial layout of ULCA venues, the spatio-temporal distribution of activity behaviors, and their interrelationships. An autoencoder model is employed to predict behavioral patterns and visualize leisure consumption dynamics. Specifically, five key indicators—land diversity, population density, transportation accessibility, environmental livability, and site convenience—are defined to quantify and evaluate urban leisure consumption vitality. Subsequently, spatio-temporal trends of leisure consumption are analyzed using the autoencoder, and trajectory predictions of human behavior are performed to reveal vitality patterns across different areas of Chongqing. Finally, cluster analysis and tag cloud visualizations are used to identify ULCA hotspots and explore their spatial characteristics. The results indicate significant spatio-temporal variation in ULCA distribution, reflecting disparities in urban vitality across regions. This study provides valuable data support for urban planning and leisure facility design, while also offering new perspectives and methodological insights for future urban vitality research.
Abstract Effective dynamic agricultural planning is crucial for optimising resource allocation and ensuring income stability, yet conventional methods often face limitations in adapting to the complex and variable conditions of mountainous regions, particularly under fluctuating climate and market pressures. Therefore, this study introduces a novel multi-stage dynamic optimization framework specifically designed for crop planning in such challenging terrains. This framework is underpinned by a sophisticated model integrating advanced monitoring systems with a Hybrid Simulated Annealing Genetic Algorithm (H-SAGA), further enhanced by neural network-driven real-time predictions. The H-SAGA component optimises planting strategies by synergistically combining global exploration (SA) and local refinement (GA) capabilities, while the neural network dynamically adjusts revenue forecasts based on climatic and market data, significantly improving the model’s responsiveness and adaptability. We rigorously evaluated the applicability and effectiveness of this model through extensive simulations across 7,290 mu (1,201 acres) of diverse farmland in mountainous Northern China. The results demonstrate that the proposed H-SAGA approach consistently achieves 5–10 percentage points higher profit increment ratios than other benchmark optimization algorithms (such as GA, SA, PSO, and ACO), alongside faster convergence and notable robustness against environmental and economic variability. This research establishes an integrated “monitoring-modelling-decision” paradigm, driven by multi-source data and machine learning, offering a practical and robust tool that provides valuable guidance for enhancing resource allocation efficiency and promoting sustainable precision agriculture in complex topographical regions, thereby holding significant reference value for optimising agricultural production nationwide.
Alexandra Mazak-Huemer, Christian Huemer, Michael Vierhauser
et al.
With the increasing significance of Research, Technology, and Innovation (RTI) policies in recent years, the demand for detailed information about the performance of these sectors has surged. Many of the current tools are limited in their application purpose. To address these issues, we introduce a requirements engineering process to identify stakeholders and elicitate requirements to derive a system architecture, for a web-based interactive and open-access RTI system monitoring tool. Based on several core modules, we introduce a multi-tier software architecture of how such a tool is generally implemented from the perspective of software engineers. A cornerstone of this architecture is the user-facing dashboard module. We describe in detail the requirements for this module and additionally illustrate these requirements with the real example of the Austrian RTI Monitor.
Jannatul Bushra, Md Habibor Rahman, Mohammed Shafae
et al.
Reverse engineering can be used to derive a 3D model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing reverse engineering techniques can be readily applied, but parts produced with additive manufacturing can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to minimize distortions of reverse-engineered additively manufactured components. This framework employs iterative finite element simulations to predict geometric distortions to minimize errors between the predicted and measured geometrical deviations of the key dimensional characteristics of the part. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion additive manufacturing. This paper presents a remanufacturing process that combines reverse engineering and additive manufacturing, leveraging geometric feature-based part compensation through process simulation. Our approach can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during reverse engineering. We evaluate the merits of STL-based and CAD-based approaches by quantifying the errors induced at the different steps of the proposed approach and analyzing the influence of varying part geometries. Using the proposed CAD-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method.
Allysson Allex Araújo, Marcos Kalinowski, Matheus Paixao
et al.
[Background] Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. SE workers face increasing pressure to develop both technical and interpersonal skills, as modern software development emphasizes collaborative work and complex team interactions. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. [Objective] This paper analyzes SE students' self-perceptions of their EI after a two-month cooperative learning project, using Mayer and Salovey's four-ability model to examine how students handle emotions in collaborative development. [Method] We conducted a case study with 29 SE students organized into four squads within a project-based learning course, collecting data through questionnaires and focus groups that included brainwriting and sharing circles, then analyzing the data using descriptive statistics and open coding. [Results] Students demonstrated stronger abilities in managing their own emotions compared to interpreting others' emotional states. Despite limited formal EI training, they developed informal strategies for emotional management, including structured planning and peer support networks, which they connected to improved productivity and conflict resolution. [Conclusion] This study shows how SE students perceive EI in a collaborative learning context and provides evidence-based insights into the important role of emotional competencies in SE education.
R. Soares, N. M. Siqueira, Molamma P Prabhakaram
et al.
Over nearly 70 years, polymers have revolutionized the global economy, manufacturing and, mainly, the fields which require biocompatible materials, as food technology and packaging, controlled drug delivery, tissue engineering, regenerative medicine, wound dressing, anti-allergy textiles, and personal care. While new high-performance polymers were produced from fossil-based sources to meet the functional performance demands of new applications, Earth has been polluted by the operation of factories that released CO2 to the atmosphere during the production of synthetic polymers. At the same time, biocompatible and biodegradable alternatives were being required to meet specific needs of a range of applications. In this paper, we reviewed the use of electrospun/electrospray bio-based and natural polymers in the last ten years in food technology and smart packaging, food additives, antimicrobial packaging, enzyme immobilization, tissue engineering, drug delivery, wound dressing, anti-allergy fibers from milk, and faux meat. Also, we reviewed the use of ionic liquids and click chemistry techniques as alternatives for modification and functionalization of electrospun/electrospray bio-based and natural polymers.
Wolf-Peter Schill is Deputy Head of the Energy, Transportation, Environment Department at the German Institute for Economic Research (DIW Berlin), where he leads the research area Transformation of the Energy Economy. He engages in open-source power sector modeling, which he applies to economic analyses of renewable energy integration, energy storage, and sector coupling. He holds a diploma in environmental engineering and a doctoral degree in economics from Technische Universitat Berlin.
Abstract Hydrogen is identified as the most promising zero-carbon fuel of the future. Naturally, in this regard, the hydrogen evolution reaction (HER), being a first critical step of the hydrogen technology and economy, attracts much attention. Conventionally, noble metals have been used as the electrocatalyst for HER, which in part holds back the hydrogen technology to become a large scale and heavily distributed energy technology. This has encouraged scientists to study cost-effective strategies for HER. Transition metal disulfides, being a low-cost material system with a great degree of engineering versatility, have recently emerged as a potential candidate that can significantly promote hydrogen evolution. Several studies have demonstrated that the control and manipulation of the structure and morphology of these materials can improve their proton reduction performance. This review covers many of the decisive factors and strategies to advance transition metal sulfides for HER applications.
Data mining is widely used in engineering and science, On the contrary, it is used in finance and marketing applications to resolve the challenges in the respective fields. Data mining based decision support system enhances the organization performance by analysing the ground reality. Turbulent economy is common for every organization due to the competition, cost, tax pressures, etc., Privatization, Globalization and liberalization drags the organization more into a competitive environment. In order to balance the competition and withstand to achieve desired gain proper marketing strategies are need to planned and executed. Marketing decision support system helps to reduce the organization burdens in analysing and strategical planning through its efficient data mining approach. This research work proposed a data mining based decision support system using decision tree and artificial neural network as a hybrid approach to estimate the marketing strategies for an organization.
Abstract The deposition of textile waste into landfill has reached an unsustainable level and raises serious environmental issues across the world. Transforming textile waste into fiber reinforcement in cementitious composites offers a sustainable resolution toward a circular textile economy. This article presents a comprehensive review of environmental concerns, recycling routes for textile waste, together with an in-depth review of the engineering properties of concrete incorporating recycled textiles. In general, the incorporation of these recycled fibers from textile waste enhances strain capacity, crack control, durability, and energy absorption of concrete via dual effects: bridging action (direct mechanism) and refinement of pore distribution (indirect effect). An improvement in compressive strength can be achieved by the utilization of a small dosage of recycled fibers or recycled fiber fabrics in concrete (strength < 40 MPa). Finally, the cost and environmental benefits for eco-efficient building application are also evaluated to draw the attention of researchers toward these potentially recyclable waste materials.
Subspace clustering of high-dimensional data is a hot issue in the field of unsupervised learning.The difficulty of subspace clustering lies in finding the appropriate subspaces and corresponding clusters.At present,the most existing subspace clustering algorithms have the drawbacks of high computational complexity and difficulty in parameter selection because the number of subspaces combinations is very large and the algorithmic execution time is very long for high-dimensional data.Also,the diffe-rent datasets and application scenarios require different parameter inputs.Thus,this paper proposes a new subspace clustering algorithm named sub-I-nice to recognize all clusters in subspaces.First,the sub-I-nice algorithm randomly divides the original dimensions into groups to build subspaces.Second,I-niceMO algorithm is used to recognize clusters in each subspace.Finally,the newly-designed ball model is designed to construct subspace clustering ensemble.The persuasive experiments are conducted to validate the clustering performances of sub-I-nice algorithm on synthetic datasets with noise.Experimental results show that the sub-I-nice algorithm has better accuracy and robustness compared to the other three representative clustering algorithms,thereby confirming the rationality and effectiveness of the proposed algorithm.
Large Language Models (LLMs) have shown prominent performance in various downstream tasks and prompt engineering plays a pivotal role in optimizing LLMs' performance. This paper, not only as an overview of current prompt engineering methods, but also aims to highlight the limitation of designing prompts based on an anthropomorphic assumption that expects LLMs to think like humans. From our review of 50 representative studies, we demonstrate that a goal-oriented prompt formulation, which guides LLMs to follow established human logical thinking, significantly improves the performance of LLMs. Furthermore, We introduce a novel taxonomy that categorizes goal-oriented prompting methods into five interconnected stages and we demonstrate the broad applicability of our framework. With four future directions proposed, we hope to further emphasize the power and potential of goal-oriented prompt engineering in all fields.
Ishmam Abid, S. M. Zuhayer Anzum Fuad, Mohammad Jabed Morshed Chowdhury
et al.
The circular economy has the potential to increase resource efficiency and minimize waste through the 4R framework of reducing, reusing, recycling, and recovering. Blockchain technology is currently considered a valuable aid in the transition to a circular economy. Its decentralized and tamper-resistant nature enables the construction of transparent and secure supply chain management systems, thereby improving product accountability and traceability. However, the full potential of blockchain technology in circular economy models will not be realized until a number of concerns, including scalability, interoperability, data protection, and regulatory and legal issues, are addressed. More research and stakeholder participation are required to overcome these limitations and achieve the benefits of blockchain technology in promoting a circular economy. This article presents a systematic literature review (SLR) that identified industry use cases for blockchain-driven circular economy models and offered architectures to minimize resource consumption, prices, and inefficiencies while encouraging the reuse, recycling, and recovery of end-of-life products. Three main outcomes emerged from our review of 41 documents, which included scholarly publications, Twitter-linked information, and Google results. The relationship between blockchain and the 4R framework for circular economy; discussion the terminology and various forms of blockchain and circular economy; and identification of the challenges and obstacles that blockchain technology may face in enabling a circular economy. This research shows how blockchain technology can help with the transition to a circular economy. Yet, it emphasizes the importance of additional study and stakeholder participation to overcome potential hurdles and obstacles in implementing blockchain-driven circular economy models.
Abstract In recent years, rapid population growth and industrialization have increased the use of natural resources and the production of waste. To develop a circular economy, it is necessary to study and promote alternative long-term solutions for waste disposal, such as reuse and recovery. Wastewater treatment plants (WWTPs) can be an important part of circular sustainability if re-oriented to function as a water resource recovery facilities (WRRFs). In this context, biological sewage sludge (SS) can be treated in order to produce more stabilized residues: biosolids (BS). This paper aims to review the possible alternatives to reuse the BS in order to increase matter recovery. Around 250 papers, reviews, books and conference proceedings have been examined. Authors explored the application of BS on land, such as soil amendment/fertilizer both in agriculture and for interventions on abandoned mine sites, and on engineering fields, in partial or total substitution of virgin materials. The reuse of BS as adsorbent materials and as a source of phosphorus is also discussed.