Search Results (877)

Machine learning (ML) algorithms have promoted the development of predictive modeling of mineral prospectivity, enabling data-driven decision-making processes by integrating multi-source geological information, leading to efficient and accurate prediction of mineral exploration targets. However, it is challenging to conduct ML-based mineral prospectivity mapping (MPM) in under-explored areas where scarce data are available. In this study, the Narigongma district of the Qiangtang block in the Himalayan–Tibetan orogen was chosen as a case study. Five typical alterations related to porphyry mineralization in the study area, namely pyritization, sericitization, silicification, chloritization and propylitization, were extracted by remote sensing interpretation to enrich the data source for MPM. The extracted alteration evidences, combined with geological, geophysical and geochemical multi-source information, were employed to train the ML models. Four machine learning models, including artificial neural network (ANN), random forest (RF), support vector machine and logistic regression, were employed to map the Cu-polymetallic prospectivity in the study area. The predictive performances of the models were evaluated through confusion matrix-based indices and success-rate curves. The results show that the classification accuracy of the four models all exceed 85%, among which the ANN model achieves the highest accuracy of 96.43% and a leading Kappa value of 92.86%. In terms of predictive efficiency, the RF model outperforms the other models, which captures 75% of the mineralization sites within only 3.5% of the predicted area. A total of eight exploration targets were delineated upon a comprehensive assessment of all ML models, and these targets were further ranked based on the verification of high-resolution geochemical anomalies and evaluation of the transportation condition. The interpretability analyses emphasize the key roles of spatial proxies of porphyry intrusions and geochemical exploration in model prediction as well as significant influences everted by pyritization and chloritization, which accords well with the established knowledge about porphyry mineral systems in the study area. The findings of this study provide a robust ML-based framework for the exploration targeting in greenfield areas with good outcrops but low exploration extent, where fusion of a remote sensing technique and multi-source geo-information serve as an effective exploration strategy. Full article

Microgrids are becoming an innovative piece of modern energy systems as they provide locally sourced and resilient energy opportunities and enable efficient energy sourcing. However, microgrid operations can be greatly affected by sudden environmental changes, deviating demand, and unexpected outages. In particular, extreme climatic events expose the vulnerability of microgrid infrastructure and resilience, often leading to increased risk of system-wide outages. Thus, successful microgrid operation relies on timely and accurate outage predictions. This research proposes a data-driven machine learning framework for the optimized operation of a microgrid and predictive outage detection using a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) architecture that reflects inherent temporal modeling methods. A time-aware embedding and masking strategy is employed to handle categorical and sparse temporal features, while mutual information-based feature selection ensures only the most relevant and interpretable inputs are retained for prediction. Moreover, the model addresses the challenges of experiencing rapid power fluctuations by looking at long-term learning dependency aspects within historical and real-time data observation streams. Two datasets are utilized: a locally developed real-time dataset collected from a 5 MW microgrid of Maple Cement Factory in Mianwali and a 15-year national power outage dataset obtained from Kaggle. Both datasets went through intensive preprocessing, normalization, and tokenization to transform raw readings into machine-readable sequences. The suggested approach attained an accuracy of 86.52% on the real-time dataset and 84.19% on the Kaggle dataset, outperforming conventional models in detecting sequential outage patterns. It also achieved a precision of 86%, a recall of 86.20%, and an F1-score of 86.12%, surpassing the performance of other models such as CNN, XGBoost, SVM, and various static classifiers. In contrast to these traditional approaches, the RNN-LSTM’s ability to leverage temporal context makes it a more effective and intelligent choice for real-time outage prediction and microgrid optimization. Full article

Background: Midwifery leadership is central to delivering safe, high-quality maternity care. Yet despite sustained investment in leadership development and governance frameworks, UK national reviews consistently identify leadership as a weakness. Understanding why this persists is vital to achieving meaningful improvement. Objective: This paper offers a reflective commentary on the challenges of midwifery leadership in the UK, drawing on national evidence, leadership theory, and professional experience. Methods: A reflective commentary approach was adopted, informed by over 30 years of practice across clinical, academic, and national improvement roles. The discussion integrates insights from national maternity inquiries, academic literature, international comparisons, and leadership theories including compassionate, courageous, and adaptive leadership. Findings: Structural and cultural barriers—including workforce shortages, rising clinical complexity, tensions between midwifery- and medically led models of care, and punitive governance systems—limit the effectiveness of midwifery leadership. These conditions erode psychological safety, fuel attrition, and constrain succession planning. Reflection on professional experience highlights the impact of these dynamics on leaders’ ability to act with confidence and influence. Evidence also points to the value of relational, values-based behaviours—compassion, courage, adaptability, and systems thinking—in enhancing resilience and outcomes. International examples show that supportive policy environments and greater autonomy enable midwifery leadership to thrive. Conclusions: Midwifery leadership requires both individual capability and structural support. Practical priorities include dismantling punitive cultures, embedding Safety-II approaches, investing in leadership development, and enabling professional autonomy. Without such systemic reform, the ambitions of the NHS Long Term Plan will remain at risk, regardless of individual leaders’ skills. Full article

The scaling of microwave plasma technologies from successful laboratory demonstrations to larger industrial applications usually involves an increase in microwave power. This upgrade is accompanied by a higher electron density (and electric conductivity) of the plasma that often limits the power efficiency of the device. In this paper, we address this issue through a focused computational study of electromagnetic characteristics of a microwave system containing plasma. Our approach employs finite-different time-domain analysis supported by a simple model which characterizes the plasma medium using plasma frequency and the frequency of electron-neutral collisions. Based on experimental data for electron density with respect to power, the plasma frequency is generated as a linear function of power, thus enabling a direct understanding of how frequency characteristics of the reflection coefficient and patterns of the electric field may vary for different power levels in a variety of plasma scenarios. For a cavity modeled after conventional plasma applicators, computational results illustrate complex behavior of the field with respect to power. When the power is increased, energy efficiency may decrease, remain low, or increase depending on where the operating frequency stands with respect to the system’s resonances. The proposed modeling approach identifies the system parameters which are most impactful in tuning the system to resonance, thus informing the design variables for subsequent computer-aided design of the scaled system. Full article

Current practices in the construction industry could negatively affect the long lifecycle of building management due to the lack of information and stakeholder management. The purpose of this paper is to identify the critical success factors (CSFs) of integrating BIM models into facility management systems (FMS). This paper conducted a series of semi-structured interviews with industry experts in the FM sector. It used a structured questionnaire to identify the hierarchy arrangement of the identified CSFs using statistical analogies. The findings demonstrated a robust consistency with significant correlation, alongside a strong correlation established using Spearman’s rank correlation coefficient and strong agreement using Kendall coefficient. Additionally, the Relative Importance Index (RII) was employed to prioritize factors according to the professionals’ assessments, yielding the subsequent impact ranking: (1) define the OIR, AIR, and FM information requirements; (2) acquire correct files, data, and formats; and (3) update of information requirements during the defect liability period (DLP). These findings would help in assisting the management of information during FM operations by establishing clear guidelines to be added into the EIR in the early project initiation stages for a successful integration of BIM-FMS for more efficient life cycle management, operation, and maintenance by the FM. Full article

With the rapid expansion of digital knowledge platforms and intelligent information systems, organizations and communities are producing a vast number of unstructured knowledge data, including annotated corpora, technical diagrams, collaborative whiteboard content, and domain-specific multimedia archives. However, knowledge sharing across institutions is hindered by privacy risks, high communication overhead, and fragmented ownership of data. Federated learning promises to overcome these barriers by enabling collaborative model training without exchanging raw knowledge artifacts, but its success depends on motivating data holders to undertake the additional computational and communication costs. Most existing incentive schemes, which are based on non-cooperative game formulations, neglect unstructured interactions and communication efficiency, thereby limiting their applicability in knowledge-driven scenarios. To address these challenges, we introduce SC-NBTI, a smart contract and Nash bargaining-based incentive framework for federated learning in knowledge collaboration environments. We cast the reward allocation problem as a cooperative game, devise a heuristic algorithm to approximate the NP-hard Nash bargaining solution, and integrate a probabilistic gradient sparsification method to trim communication costs while safeguarding privacy. Experiments on the FMNIST image classification task show that SC-NBTI requires fewer training rounds while achieving 5.89% higher accuracy than the DRL-Incentive baseline. Full article

This study investigates the multifaceted factors influencing the success of government-funded construction projects and addresses the challenges posed by climate-induced flooding, proposing integrated solutions encompassing structural vulnerability, system capacity, and organizational preparedness. By examining the challenges faced by coastal infrastructure, such as aging infrastructure, sea-level rise, and extreme weather events, this research seeks to identify strategies that enhance resilience and minimize the impact of flooding on coastal communities. The study presents a systematic review of 80 scholarly articles integrating quantitative and qualitative findings. Utilizing the PRISMA guidelines, the review highlights structural analysis, hydraulic modeling, and organizational surveys, to assess the resilience of coastal infrastructure systems. The results of this study offer actionable insights for policymakers, infrastructure managers, and coastal communities, facilitating informed decision-making and promoting climate-resilient development. Coastal regions around the world are increasingly vulnerable to climate-induced hazards such as sea level rise, storm surges, and intense flooding events. Among the most at-risk assets are transport infrastructure and buildings, which serve as the backbone of urban and regional functionality. This research paper presents a multidimensional assessment framework that integrates structural vulnerability, system capacity, and organizational preparedness to evaluate the resilience of coastal infrastructure. Drawing upon principles of resilience such as robustness, redundancy, safe-to-fail design, and change-readiness, the study critically reviews and synthesizes existing literature, identifies gaps in current assessment models, and proposes a comprehensive methodology for resilience evaluation. By focusing on both transport systems and building infrastructure, the research aims to inform adaptive strategies and policy interventions that enhance infrastructure performance and continuity under future climate stressors. Full article

Six years after its deployment, SatShipAI, an operational platform combining AI models with Sentinel-1 SAR imagery and AIS data, has provided robust maritime surveillance around Denmark. A meta-analysis of archived outputs, logs, and manual reviews shows stable vessel detection and classification performance over time, including successful cross-sensor application to X-band SAR data without retraining. Key operational challenges included orbit file delays, nearshore detection limits, and emerging infrastructure such as wind farms. The platform proved particularly valuable for detecting offshore “dark” vessels beyond AIS coverage, informing maritime security, traffic management, and emergency response. These findings demonstrate the feasibility, resilience, and adaptability of long-term AI–geospatial systems, offering practical guidance for future autonomous monitoring infrastructure. Full article

The present study examines the role of ChatGPT as a travel advisor in influencing tourists’ decision-making in regard to destination visit intentions. Grounded in the Information Systems Success (ISS) model, this study explores three primary relationships: (1) the effect of information quality on users’ trust in ChatGPT’s travel recommendations, (2) the impact of trust in ChatGPT’s travel recommendations on destination visit intentions, and (3) the moderating role of destination images in the relationship between information quality and trust. This research employed a quantitative research design, collecting data from 528 Indonesian ChatGPT users. The findings show that information quality does not significantly shape users’ trust in ChatGPT’s travel advice, contradicting the classical ISS-Model prediction. In contrast, trust in ChatGPT’s travel recommendations exerts a significant positive effect on destination visit intentions, and the destination image fails to moderate the information–quality–trust link. This study provides practical guidance for Destination Management Organizations (DMOs), travel agencies, and policymakers seeking to optimize AI-driven tourism marketing by focusing on interactive storytelling and personalized engagement rather than solely focusing on information quality. Full article

In light of a Volatile, Uncertain, Complex and Ambiguous (VUCA) world, the need for employee adaptability is a critical capacity to navigate challenges and facilitate employees thriving in organizations. One important capacity, systems intelligence, captures employees’ ability to think, adapt and act effectively in interactions with systems. In a three-wave longitudinal study, we examine the relationship between systems intelligence (SI), job crafting (JC), and job performance (JP) over time. We employ the job demands-resources model to demonstrate that SI increases JP, hypothesizing that job resources, as manifested in JC, act as mediator between personal resources (SI) and JP. Data were collected from employees in Pakistan working across the banking, telecommunications, information technology, and engineering sectors. In the first wave, 303 participants completed the survey using validated self-report measures, followed by 212 in the second wave, and 99 in the third wave, each two months apart. Our findings show that systems intelligence at Time 1 was positively related to job performance at Time 3 but not Time 2. We found no significant association of SI at Time 1 with JC at Time 2 or Time 3. JC at Time 2 did not mediate the effects of SI at Time 1 on JP at Time 3. However, JC (T1 & T2) had a significant positive effect on JP (T2 & T3). Overall, our findings suggest that the pathways from systems intelligence and job crafting to job performance are independent. This dual pathway to performance has important theoretical implications as well as practical implications for organizations. Organizations can improve team and individual productivity by fostering systems intelligence and promoting job crafting behaviours. This research directs the attention of leaders and HR functions to the value of tailored interventions in developing these abilities and achieving long-term success and adaptive performance in the workforce. Full article

Floods are among the most destructive natural disasters and can devastate human life, infrastructure, and mobility in urban areas. It is necessary to develop a simulation model suitable for disaster management to prepare for flooding and facilitate rapid response interventions. The advantage of a three-dimensional (3D) geographic information system (GIS) is that it allows researchers to perform more successful spatial analyses than traditional two-dimensional (2D) systems. In this study, real-time 3D flood simulations were created for the Bozkurt district of Kastamonu, Türkiye, integrating GIS and game engine technologies. Land use land cover (LU/LC) map, digital elevation model (DEM), soil properties and climate data of the study region constitute the input data for the hydrological model. DEM and building footprints are also used to create 3D models of the buildings in the region. Through the Soil and Water Assessment Tool (SWAT) analysis, a hydrological model that included environmental factors such as precipitation, runoff, and soil erosion was created. The average flow rate for the same period, obtained from flow monitoring stations in the Bozkurt district, was 4.64 m³/s, while the flow rate obtained with the SWAT+ model was 4.12 m³/s. Using the flow parameters obtained with SWAT, 3D flood models were developed on Unreal Engine (UE). The flood simulation created with UE and the flood disaster experienced in 2021 in the region were compared on an area basis. The obtained simulation accuracy was 88%. Full article

This article examines the current statutory care and protection landscape in Aotearoa New Zealand (Aotearoa), focusing on the operations of Waikato-Tainui, a post-treaty settlement entity operating on behalf of the Waikato tribe (iwi), within this complex colonial context to safeguard and nurture mokopuna (descendants) and whaanau (families). Waikato-Tainui supports indigenous mokopuna within a fundamentally flawed settler-colonial care and protection system while concurrently reimagining an indigenous-led model rooted in ancestral wisdom and knowledge systems. Mokopuna Ora (Thriving descendants) is an indigenous whaanau-led and mokopuna-centred care and protection initiative that has been piloted, tested, researched, evaluated, and expanded over the past eleven years within the current settler colonial system. Drawing from deep empirical ancestral wisdom, the authors reimagine a new approach, building a roadmap for mokopuna and whaanau success. Ki Tua o Ngaku Mokopuna is presented as a cultural practice framework encapsulating Waikato ancestral wisdom and knowledge. While still in its early implementation stages, its development has been generations in the making, belonging to Waikato paa (communal meeting places) and hapuu (sub-tribes). Beyond a tool for frontline staff, this framework offers a vision, measures of success, and standards of excellence to inform theory and practice. This work addresses continuous indigenous resistance against negative colonial impacts, reflecting a shared indigenous experience and system of care and protection. In contemporary Aotearoa, the neo-colonial challenge is exacerbated by the current right-wing coalition Government and its ideological stance. The swift and extensive legislative reforms driven by harmful racist ideology are unprecedented, facilitating the exploitation of people, Papatuuaanuku (the earth mother), and te taiao (the natural world) for corporate gain and profit. Maaori tribes, organisations, sub-tribes, families, and individuals are actively countering these racist ideologies, legislations, strategies, policies, funding decisions, and operational practices. This ongoing colonial violence is met with the strength of ancestral knowledge and wisdom, envisioning a future where mokopuna thrive. The framework represents indigenous love, growth, prosperity, and abundance amidst enduring colonial harm and ideological warfare. Full article

Nowadays, Internet of Things (IoT) application systems are broadly applied to various sectors of society for efficient management by monitoring environments using sensors, analyzing sampled data, and giving proper feedback. For their fast deployment, we have developed Smart Environmental Monitoring and Analysis in Real Time (SEMAR) as an integrated IoT application server platform and implemented the input setup assistance service using prompt engineering and a generative AI model to assist connecting sensors to SEMAR with step-by-step guidance. However, the current service cannot assist in connections of the sensors not learned by the AI model, such as newly released ones. To address this issue, in this paper, we propose an extension to the service for handling unlearned sensors by utilizing datasheets with four steps: (1) users input a PDF datasheet containing information about the sensor, (2) key specifications are extracted from the datasheet and structured into markdown format using a generative AI, (3) this data is saved to a vector database using chunking and embedding methods, and (4) the data is used in Retrieval-Augmented Generation (RAG) to provide additional context when guiding users through sensor setup. Our evaluation with five generative AI models shows that OpenAI’s GPT-4o achieves the highest accuracy in extracting specifications from PDF datasheets and the best answer relevancy (0.987), while Gemini 2.0 Flash delivers the most balanced results, with the highest overall RAGAs score (0.76). Other models produced competitive but mixed outcomes, averaging 0.74 across metrics. The step-by-step guidance function achieved a task success rate above 80%. In a course evaluation by 48 students, the system improved the student test scores, further confirming the effectiveness of our proposed extension. Full article

Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collaboration (HMC). Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments, where adaptive autonomy and intuitive interaction are essential. Traditional UAV autonomy has relied mainly on visual perception or preprogrammed planning, offering limited adaptability and explainability. This study introduces a novel reference architecture, the multimodal AI–HMC system, based on which a dedicated UAV use case architecture was instantiated and experimentally validated in a controlled laboratory environment. The architecture integrates VLM-powered reasoning, real-time depth estimation, and natural-language interfaces, enabling UAVs to perform context-aware actions while providing transparent explanations. Unlike prior approaches, the system generates navigation commands while also communicating the underlying rationale and associated confidence levels, thereby enhancing situational awareness and fostering user trust. The architecture was implemented in a real-time UAV navigation platform and evaluated through laboratory trials. Quantitative results showed a 70% task success rate in single-obstacle navigation and 50% in a cluttered scenario, with safe obstacle avoidance at flight speeds of up to 0.6 m/s. Users approved 90% of the generated instructions and rated explanations as significantly clearer and more informative when confidence visualization was included. These findings demonstrate the novelty and feasibility of embedding VLMs into UAV systems, advancing explainable, human-centric autonomy and establishing a foundation for future multimodal AI applications in HMC, including robotics. Full article

Cities are significant contributors to climate change, environmental degradation, and resource depletion. To address these challenges, sustainable strategies in building design, construction, and management are essential, and digitalisation through the integration of Building Information Modelling (BIM) and Life Cycle Assessment (LCA) can enable it. However, the environmental benefits of BIM–LCA integration remain underexplored, limiting broader practical adoption. This study systematically reviews 80 case studies (2015–2025) to assess how recent applications address known barriers and to identify enablers of successful BIM–LCA workflows. The analysis highlights a growing alignment between technological, regulatory, and methodological advancements and practical implementation needs, especially as technical barriers are increasingly overcome. Nevertheless, systemic challenges related to institutional, behavioural, and socio-economic factors persist. From a stakeholder perspective, four thematic drivers were identified: material circularity and resource efficiency; integration with complementary assessment tools; energy-performance strategies for comfort and efficiency; and alignment with international certification systems. The study offers a stakeholder-oriented framework that demonstrates the multi-level value of BIM–LCA integration not only for environmental impact assessment but to support informed decision-making and reduce resource consumption. These insights aim to bridge the gap between academic research and practical implementation, contributing to the advancement of sustainable practices in the built environment. Full article