Collaborative Method Research Articles

Enhancing Students’ Performance in Integrated Project Course through Collaborative Learning and Open Artificial Intelligence Chatbot

Numerous students faced challenges related to specific concepts, deeming it essential to seek supplementary guidance for the successful completion of their mandatory final semester projects within the Integrated Project Course. This need arose early in the project development phase, and these individuals consistently encountered difficulties in bringing their projects to fruition. The challenges stemmed from the intricate nature of the course material and the inadequacy of suitable guidance. As a result, some students occasionally delivered incomplete and below-par project outcomes. Consequently, this research endeavors to enhance students' final semester project performance by employing collaborative learning methods and integrating an Open Artificial Intelligence Chatbot as interventions. The study was conducted during the session II 2022/2023 in the Department of Information and Communication Technology at Polytechnic Sultan Abdul Halim Mu’adzam Shah, involving 12 students enrolled in the Integrated Project Course and receive score mark below five during their Demonstration One presentation session. Data collection included rubric scores and reflective journals, which were analyzed based on rubric percentages before and after the intervention. The findings of the study indicated an improvement in rubric scores before and after the intervention, signifying an enhancement in students' project-related performance. Additionally, the reflective journal provided insights into the students' learning experiences with both interventions. This research contributes to educators' comprehension of effective methods for elevating students' performance in Integrated Project Course by leveraging contemporary technologies and approaches. Future research initiatives could further diversify teaching and learning methods in Integrated Project through the organization of workshops specifically addressing current technology and knowledge pertinent to the development of final semester projects.

Research on heterogeneous multi-UAV collaborative decision-making method based on improved PPO

Research on heterogeneous multi-UAV collaborative decision-making method based on improved PPO

Reflections on the Collaborative Story Analysis Method to Understand Qualitative Perspectives of Indigenous Syringe Services Program Clients

Many scholars have cautioned that the use of Western research methods is problematic in studies with Indigenous communities given colonialist histories that have exploited Indigenous populations. One solution has been to utilize a Community-Based Participatory Research (CBPR) approach to enhance equity in research partnerships. Employing a CBPR approach, however, does not necessitate the inclusion of Indigenous Research Methods, an additional step that can further benefit studies with their explicit alignment with Indigenous worldviews and values. In a CBPR project aiming to understand Indigenous harm reduction clients' perspectives of barriers and facilitators to opioid use disorder treatment, our research group assembled a multidisciplinary qualitative data analysis team that included diverse tribal community members and academics. Sparse literature was available to guide the use of Indigenous Research Methods for the qualitative data analysis phase of the research. To address this gap, the aims of this process paper are: (1) to describe the implementation of the Collaborative Story Analysis method, and (2) in the Indigenous tradition of honoring and sharing stories, describe our analysis team's experiences and perceptions of implementing this Indigenous Research Method. Through a series of process discussions, the analysis team found that applying the Collaborative Story Analysis method: (1) honored relationships and story, (2) strengthened the depth of analysis, and (3) exhibited tensions when working in a dominant Western culture. Through sharing our team's experiences, the aspiration is that others can use these insights in their own consideration and implementation of an Indigenous Research Method for qualitative data analysis.

HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization.

Existing matrix factorization methods face challenges, including the cold start problem and global nonlinear data loss during similarity learning, particularly in predicting associations between long noncoding RNAs (LncRNAs) and diseases. To overcome these issues, we introduce HPTRMF, a matrix factorization approach incorporating high-order perturbation and flexible trifactor regularization. HPTRMF constructs a high-order correlation matrix utilizing the known association matrix, leveraging high-order perturbation to effectively address the cold start problem caused by data sparsity. Additionally, HPTRMF incorporates a flexible trifactor regularization term to capture similarity information on LncRNAs and diseases, enabling the effective handling of global nonlinear data loss by capturing such data in the similarity matrix. Experimental results demonstrate the superiority of HPTRMF over nine state-of-the-art algorithms in Leave-One-Out Cross-Validation (LOOCV) and Five-Fold Cross-Validation (5-Fold CV) on three data sets.HPTRMF and data sets are available in https://github.com/Llvvvv/HPTRMF.

A multi-objective collaborative planning method for a PV-powered hybrid energy system considering source-load matching

A hybrid system including photovoltaic panels, an air-source heat pump, batteries, and a hot water tank is proposed to satisfy the building’s demands. To discover the optimal energy scheduling scheme, a multi-objective two-stage planning method is developed. In the first stage, considering the seasonal fluctuations of photovoltaic power and the loads, several operation modes are designed. Moreover, the influence of objective functions with and without the energy self-consumption (SCR) and self-sufficiency rate (SSR) on the system configuration is analyzed. The second stage aims to explore the synergetic benefits with demand response, in which the hourly loads are reconstructed accounting for the municipal tariffs. The results show that when accounting for the annual cost (AC) and primary energy (PE), the ideal operation mode is as follows: favor thermal storage during periods of energy surplus and prioritize discharging electricity in winter and heat in summer in cases of insufficient power. When considering the SCR and SSR additionally, storing heat in summer with charging batteries in winter is preferred when excess energy is available. Otherwise, give priority to thermal storage. Furthermore, based on the proposed energy scheduling scheme, the AC, PE, SCR, and SSR are improved by −31.1 %, −19.4 %, +1.5 %, and +11.3 %, respectively.

Research on collaborative multi-UAV localization method based on combination navigation information

In challenging environments, unmanned aerial vehicle (UAV) systems often encounter unstable satellite signals and communication link interference. This paper proposes an integrated navigation method that integrates inertial navigation system (INS), global navigation satellite system (GNSS), and visual navigation system (VNS). Utilizing data from onboard sensors, this method merges relative navigation information from feature tracking of multiple UAVs with each UAV’s absolute navigation data. It includes specially designed transmission rules to reduce data exchange between UAVs. Each UAV uses an adaptive unscented Kalman filter (AUKF) method, which is enhanced into a collaborative AUKF (C-AUKF) using a message passing-based approach. Experiments in a simulated mission scenario revealed that the C-AUKF, in comparison to using extended Kalman filter (EKF), significantly improved flight test performance across the entire testing area, with a cumulative deviation of only 10.22 m, about 0.85% of the total flight distance. These results demonstrate that the proposed method not only meets accuracy requirements for position and velocity in integrated navigation but also significantly enhances multi-UAV navigation precision, particularly in scenarios with global positioning system (GPS) interference.

Object-plane encoding hyperspectral three-dimensional collaborative imaging method

Different from traditional image-plane encoding spectral imaging method, this paper proposes an object-plane encoding hyperspectral three-dimensional collaborative imaging method, which is based on the idea of spectral displacement invariance and multiplexing encoding and decoding. The 4D data of the scene is scanned by coded apertures generated by the projector, and then the encoded data of the scene is obtained. During the reconstruction process, there is no need for phase unwrapping operation or data fusion. The reconstruction result shows excellent performance with a spectral resolution of 3 nm, nearly 120 spectral bands, depth accuracy of 0.25 mm. Compared with the image-plane encoding spectral imaging method, the object-plane encoding spectral imaging method exhibits better anti-interference characteristics, and the reconstruction quality of the object-plane encoding spectral imaging method is better than that of the image-plane encoding spectral imaging method in the scattering environments.

Multi-Stage Coordinated Planning for Transmission and Energy Storage Considering Large-Scale Renewable Energy Integration

Due to the large-scale integration of renewable energy and the rapid growth of peak load demand, it is necessary to comprehensively consider the construction of various resources to increase the acceptance capacity of renewable energy and meet power balance conditions. However, traditional grid planning methods can only plan transmission lines, often resulting in low utilization rates of newly constructed lines. Additionally, static planning methods can only address single-target scenarios and cannot cope with dynamic growth in load and renewable energy. To address these issues, this paper proposes a multi-stage collaborative planning method for transmission networks and energy storage. This method considers the non-line substitution effect of energy storage resources and their characterization methods. It establishes the coupling relationship between resources across different planning stages to achieve coordinated multi-stage planning for transmission networks and energy storage. Based on the IEEE-24 node system and a case study in a northern province of China, the results show that the proposed method reduces investment costs by approximately 30% compared to static planning methods and by about 7.79% compared to conventional grid planning methods. Furthermore, this method can accommodate more renewable energy.

CONCEPT OF APPLICATION OF CONTROL ALGORITHMS FOR MANIPULATION ROBOTS TO PERFORM COMPLEX TECHNOLOGICAL OPERATIONS IN INDUSTRY

The article is devoted to the study of control algorithms for manipulation robots to perform complex technological operations at enterprises in order to optimize working process time. In today’s business environment, in order to maximize efficiency of production management in highly competitive environment, enterprises need to constantly improve work process automating methods using robots and artificial intelligence. Authors of this article take an in-depth look at the real-world challenges that manipulation robots face in performing complex operations in industrial environment. The work discusses various approaches to control manipulation robots. These approaches include adaptive control method, program control method, sensor control method and collaborative control method. Application of artificial intelligence in the control of manipulation robots allows making decisions based on analyzing of large data amounts, learning from experience and adapting to the environment.

Leading impactful research: applying platform thinking to drive collaborative inquiry in the innovation field

PurposeThis study investigates the application of collaborative inquiry within innovation management, employing platform thinking to address challenges of generalizability and relevance. The aim is to integrate Collaborative Inquiry methods, characterized by participatory, diffuse, and reflective practices, to transform research into a tool for impactful change in organizations in the field of innovation management.Design/methodology/approachA longitudinal participatory case study approach focuses on the IDeaLs case—a research platform that collaborated with multiple companies over several years. The data gathered and analyzed comes from the research project within the research platforms over the first two editions and from the research platform management and coordination activities.FindingsThe study introduces the Collaborative Research Platform Approach (CRPA), demonstrating its effectiveness in addressing typical constraints of traditional research methodologies through a real-world application within the IDeaLs case. The findings highlight the CRPA's potential in fostering a dynamic, co-creative research environment that bridges theoretical knowledge with practical applications, thus enhancing both scholarly and organizational outcomes while pursuing a future change within the organizations.Research limitations/implicationsThere are two main research implications. First, it proposes platform thinking as a theoretical lens to read a multi-stakeholder phenomenon in the research domain, confirming its nature of value-creation mechanisms, using it outside the business model and strategic space. Second, it offers a methodological contribution by presenting the CRPA framework.Practical implicationsThe CRPA framework offers organizations a structured approach to managing collaborative research projects that align with both academic rigor and practical relevance. Companies engaged in the study reported enhanced ability to implement actionable insights from research, influencing real-time decision-making processes.Social implicationsBy fostering collaborative engagements across multiple stakeholders, the CRPA promotes a research culture that values inclusivity and practical impact, potentially leading to broader societal benefits through improved innovation management practices.Originality/valueThis paper contributes to the innovation management field by proposing the CRPA, which integrates principles of Platform Thinking with Collaborative Inquiry. This novel approach is designed to improve the applicability and scope of innovation research, offering a robust framework that enhances engagement and utility across academic and business domains. It uses platforms as a theoretical lens to read a multi-stakeholder environment in the research domain.

The interplay of intelligent manufacturing, innovation equilibrium and cost stickiness in the artificial intelligence era

AbstractThis study investigates the impact of intelligent manufacturing methods driven by artificial intelligence (AI) on cost stickiness in Chinese manufacturing enterprises. Leveraging the ABJ model， a regression analysis explores how different AI‐enabled intelligent manufacturing approaches influence cost stickiness through the lens of innovation equilibrium. The sample comprises manufacturing companies listed on China's A‐share market from 2013 to 2021. The findings reveal a negative correlation between intelligent manufacturing adoption and cost stickiness among these firms. Specifically, production‐based intelligent manufacturing exhibits a more significant effect on reducing cost stickiness compared with collaborative intelligent manufacturing methods. Moreover, intelligent manufacturing positively impacts both joint equilibrium innovation and matching equilibrium innovation. While joint equilibrium innovation is negatively associated with cost stickiness, matching equilibrium innovation shows no significant relationship with cost stickiness. The results indicate that innovation equilibrium plays a mediating role in the relationship between AI‐driven intelligent manufacturing and cost stickiness. Overall, this research sheds light on how AI capabilities enabling intelligent manufacturing processes and innovation equilibrium dynamics can help alleviate cost stickiness issues faced by manufacturing enterprises. It highlights the strategic value of adopting AI systems to enhance operational efficiency and cost management flexibility within manufacturing contexts.

Multi‐objective interval planning for 5G base station virtual power plants considering the consumption of photovoltaic and communication flexibility

AbstractLarge‐scale deployment of 5G base stations has brought severe challenges to the economic operation of the distribution network, furthermore, as a new type of adjustable load, its operational flexibility has provided a potential way to promote the consumption and utilization of photovoltaic. In this paper, a multi‐objective interval collaborative planning method for virtual power plants and distribution networks is proposed. First, on the basis of in‐depth analysis of the operating characteristics and communication load transmission characteristics of the base station, a 5G base station of virtual power plants participating in the cellular respiratory demand response model is constructed. In view of the inherent contradiction between system economy and environmental performance, a multi‐objective interval optimization model for collaborative planning of virtual power plants and distribution networks is established with the lowest system investment and operating costs and the lowest carbon emissions as the optimization goals. The established model is transformed into a deterministic optimization problem, which is solved by NSGA‐II algorithm. The modified IEEE‐33 node system is used in the case analysis to analyse the impact of different planning schemes and response characteristics on the system economy. The calculation results verify the effectiveness of the proposed method.

Optimal Weighted Voting-Based Collaborated Malware Detection for Zero-Day Malware: A Case Study on VirusTotal and MalwareBazaar

We propose a detection system incorporating a weighted voting mechanism that reflects the vote’s reliability based on the accuracy of each detector’s examination, which overcomes the problem of cooperative detection. Collaborative malware detection is an effective strategy against zero-day attacks compared to one using only a single detector because the strategy might pick up attacks that a single detector overlooked. However, cooperative detection is still ineffective if most anti-virus engines lack sufficient intelligence to detect zero-day malware. Most collaborative methods rely on majority voting, which prioritizes the quantity of votes rather than the quality of those votes. Therefore, our study investigated the zero-day malware detection accuracy of the collaborative system that optimally rates their weight of votes based on their malware categories of expertise of each anti-virus engine. We implemented the prototype system with the VirusTotal API and evaluated the system using real malware registered in MalwareBazaar. To evaluate the effectiveness of zero-day malware detection, we measured recall using the inspection results on the same day the malware was registered in the MalwareBazaar repository. Through experiments, we confirmed that the proposed system can suppress the false negatives of uniformly weighted voting and improve detection accuracy against new types of malware.

Principles for equitable and resilient tropical peatland restoration in Central Kalimantan, Indonesia

Indonesia's tropical peatlands are crucial global carbon stores but have been heavily degraded in recent decades. We present seven principles for equitable and resilient tropical peatland restoration in Central Kalimantan, Indonesia, host to 19% of Indonesia's tropical peatland area, where local livelihoods, cultural practices, and indigenous social relations remain closely connected. Our collaborative methods employed a Delphi survey and focus group discussions with researchers from various disciplines to develop a shared vision for restoration. This vision served as a boundary object during interviews with diverse stakeholders involved in peatland restoration in Central Kalimantan, allowing for refinement and adaptation of the vision and the development of principles to achieve it. The principles emphasize inclusive and collaborative decision‐making, planning, and implementation; site‐specific approaches adapted to local social and ecological conditions; and ensuring the informed consent of and fair benefit distribution to all local social groups. They also emphasize a holistic, integrated, and long‐term approach to restoration that considers multiple aspects, including hydrological function, vegetation regeneration, fire prevention, locally appropriate livelihood benefits, inclusive governance, and adaptive management practices. These principles serve as a starting point for resilience‐oriented social‐ecological restoration practice and policy formulation, aiming to facilitate equitable, effective, and resilient tropical peatland restoration outcomes.

A Review of Research on Collaborative Assessments in the Open Distance and e-Learning Environment

This paper reviews 38 studies conducted between 2015 and 2022 on collaborative assessments in open-distance and e-learning (ODeL) contexts, focusing on the benefits, types, challenges, and strategies to improve collaborative assessments. This qualitative review aims to investigate collaborative assessments within the ODeL comprehensively. The objectives encompass thoroughly exploring theoretical foundations and empirical evidence to illuminate the pedagogical implications and effectiveness of collaborative assessment methodologies. Employing a systematic literature review approach, various scholarly articles, research papers, and educational studies were scrutinised to synthesise the current landscape. The review shows that effective communication, feedback, and appropriate technology are critical factors in promoting successful collaborative assessments, which can result in improved engagement, motivation, and better learning outcomes. However, challenges such as scheduling difficulties, technology challenges, group dynamics, and assessment quality may arise. Using social constructivism, this paper addresses criticisms of collaborative assessment in the ODeL context, identifies types of collaborative assessments, and presents strategies for implementation and addressing challenges. Based on these findings, recommendations are presented to educators, urging the integration of collaborative assessment methods into open distance and e-learning frameworks, accompanied by pedagogical support and training to optimise their efficacy and promote enriched learning experiences. The paper concludes by suggesting future research areas and recommendations for educators and instructional designers seeking to implement collaborative assessments in ODeL environments. Collaborative assessments can contribute to collaborative learning, providing students with a sense of community, engagement, and responsibility.

Collaborative CRISPR-Cas System-Enabled Detection of Circulating Circular RNA for Reliable Monitoring of Acute Myocardial Infarction.

Acute myocardial infarction (AMI) is one of the major causes of death worldwide, posing significant global health challenges. Circular RNA (circRNA) has recently emerged as a potential diagnostic biomarker for AMI, providing valuable information for timely medical care. In this work, a new electrochemical method for circRNA detection by engineering a collaborative CRISPR-Cas system is developed. This system integrates the unique circRNA-targeting ability with cascade trans-cleavage activities of Cas effectors, using an isothermal primer exchange reaction as the bridge. Using cZNF292, a circulating circRNA biomarker for AMI is identified by this group; as a model, the collaborative CRISPR-Cas system-based method exhibits excellent accuracy and sensitivity with a low detection limit of 2.13 × 10-15 m. Moreover, the method demonstrates a good diagnostic performance for AMI when analyzing whole blood samples. Therefore, the method may provide new insight into the detection of circRNA biomarkers and is expected to have great potential in AMI diagnosis in the future.

Exploring different content creation and display methods for remote collaboration supported by eXtended reality: comparative analysis of distinct task scenarios

AbstractRemote collaboration using eXtended Reality (XR) has been explored to establish a common ground between physically distributed individuals. To achieve usable and impactful solutions, it is paramount to understand how different content creation and display methods contribute to the work effort of each member (remote and on-site). Additionally, explore how various task scenarios influence the collaborative process, specifically examining how activities with unique characteristics and complexities affect remote work. Hence, the question arises, ’How do different display and interaction methods impact the collaborative process within specific task domains?’ In this paper, two user studies with 30 distinct participants each are described, focusing on different content creation support (Laptop Computer; Video Wall & Keyboard; Interactive Projector) and display methods (Hand-Held Device (HHD); HHD & Articulated Support; Head-Mounted Display (HMD)) for remote and on-site collaborators respectively, during scenarios of remote guidance. Plus, different physical tasks were considered for each study (Lego pieces assembly; Tangram puzzle assembly; Maintenance procedures; Resource management in a map; Training activity in a laboratory classroom), to understand which method stands out according to the characteristics of the said tasks. We report the results obtained, suggesting that for the remote part of the collaborative process, using a Video Wall & Keyboard appears to be more suitable for tasks associated with maintenance procedures, learning, and training activities. As for the on-site part, using HMD was considered the better option for maintenance, and learning tasks, closely followed by HHD & Articulated Support.

Towards Sustainable Healthcare: Exploring Factors Influencing Use of Mobile Applications for Medical Escort Services

The aging population is placing pressure on the healthcare system, and the private sector is innovating healthcare through digitalization. Mobile applications for medical escort services (MA-MES) could become a sustainable healthcare tool, assisting adult children in scheduling escorts to accompany their elderly parents to medical appointments. This creates new collaborative methods and service processes for healthcare services. This research applies the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model to identify the intentions of adult children to use MA-MES for their elderly parents. Data were gathered from 350 individuals in the age group of 20 to 59 years and analyzed using structural equation modeling (SEM). The findings indicated that the performance expectancy (PE), effort expectancy (EE), social influence (SI), price value (PV), and perceived trust (PT) significantly increased behavioral intentions (BI). Perceived risk (PR) mediated the relationship between PT and BI, while age moderated the effects of PT on BI. Cohabitation with elderly parents moderated the effects of SI and PT on BI. This research proposes a unique model to predict the intentions of adult children to use MA-MES for their aging parents. It provides insights enabling managers to carry out continuous innovation in MA-MES.

Method for Collaborative Layout Optimization of Ship Equipment and Pipe Based on Improved Multi-Agent Reinforcement Learning and Artificial Fish Swarm Algorithm

The engine room is the core area of a ship, critical to its operation, safety, and efficiency. Currently, many researchers merely address the ship engine room layout design (SERLD) problem using optimization algorithms and independent layout strategies. However, the engine room environment is complex, involving two significantly different challenges: equipment layout and pipe layout. Traditional methods fail to achieve optimal collaborative layout objectives. To address this research gap, this paper proposes a collaborative layout method that combines improved reinforcement learning and heuristic algorithms. For equipment layout, the engine room space is first discretized into a grid, and a Markov decision process (MDP) framework suitable for equipment layout is proposed, including state space, action space, and reward mechanisms suitable for equipment layout. An improved adaptive guided multi-agent Q-learning (AGMAQL) algorithm is employed to train the layout model in a centralized manner, with enhancements made to the agent’s exploration state, exploration action, and learning strategy. For pipe layout, this paper proposes an improved adaptive trajectory artificial fish swarm algorithm (ATAFSA). This algorithm incorporates a hybrid encoding method, adaptive strategy, scouting strategy, and parallel optimization strategy, resulting in enhanced stability, accuracy, and problem adaptability. Subsequently, by comprehensively considering layout objectives and engine room attributes, a collaborative layout method incorporating hierarchical and adaptive weight strategies is proposed. This method optimizes in phases according to the layout objectives and priorities of different stages, achieving multi-level optimal layouts and providing designers with various reference schemes with different focuses. Finally, based on a typical real-world engine room engineering case, various leading algorithms and strategies are tested and compared. The results show that the proposed AGMAQL-ATAFSA (AGMAQL-ATA) exhibits robustness, efficiency, and engineering practicality. Compared to previous research methods and algorithms, the final layout quality improved overall: equipment layout effectiveness increased by over 4.0%, pipe optimization efficiency improved by over 40.4%, and collaborative layout effectiveness enhanced by over 2.2%.

Collaborative optimization of renewable energy power systems integrating electrolytic aluminum load regulation and thermal power deep peak shaving

Addressing renewable energy (RE) curtailment in power systems necessitates a comprehensive strategy leveraging peak regulation resources from both the power and load sides. On the power side, deep peak shaving of thermal power plants can mitigate surplus electricity during periods of high RE production. On the load side, energy-intensive industrial loads, characterized by large capacity and rapid adjustability, can respond effectively to energy deficits when RE is low. To enhance the peak regulation capacity for optimal RE accommodation, this paper proposes a collaborative optimization method combining electrolytic aluminum load (EAL) regulation with thermal power deep peak shaving (DPS). The study is initiated by developing a sophisticated peak regulation model for the electrolytic aluminum load (EAL), considering production characteristics, cost features, and safety constraints. Subsequently, a collaborative optimization model is formulated, integrating EAL regulation with thermal power deep peak shaving (DPS), aiming to minimize societal peak regulation costs (PRC). Applying this model to a practical regional grid in Yunnan Province reveals substantial reductions in RE curtailment rates, effective minimization of total social PRC, and enhanced operational economics of thermal power DPR under varying wind power scenarios. Notably, the proposed approach requires only minor adjustments to the EAL, ensuring production safety is maintained. This research provides valuable insights for the seamless integration of EAL into grid peak regulation services.