Search Results (524)

The mineral system model formalizes the critical geological processes and mappable parameters that control ore formation, which can then be translated into spatial predictors used as input features in machine learning (ML)-based mineral prospectivity mapping (MPM). In most MPM studies, exploration evidence features are indeed derived from the mineral system model of the targeted deposit type. However, not all features produced in this way are necessarily informative or favorable for prospectivity analysis. This challenge can be addressed by using feature selection frameworks to identify the most relevant features before applying ML and deep learning (DL) algorithms for mathematical integration. To address this need, this study employs an unsupervised variational autoencoder (VAE) framework to evaluate and rank exploration evidence layers. The VAE quantifies feature importance through a systematic strategy that measures the sensitivity of reconstruction-error components, mean squared error (MSE), mean absolute error (MAE), and Kullback–Leibler (KL) divergence, to individual feature variations. In this way, the VAE ranks the exploration features and helps to identify those that are the most useful for prospectivity mapping. The proposed approach was applied to a real geo-dataset from a porphyry copper district in Iran. Based on the conceptual model of porphyry copper mineralization, 15 evidence layers were generated, including proximity to phyllic, argillic, propylitic, iron oxide, and silicification alteration zones; proximity to intrusive rocks, faults, and fault intersections; and geochemical maps of Cu, Mo, Sb, Pb, Zn, As, and W. The VAE-based ranking indicated that evidence layers related to hydrothermal alterations, intrusive rocks, and faults were the most influential exploration features, whereas geochemical evidence layers showed lower relative importance. Based on this evaluation, two modeling scenarios were considered: in the first, all available features were used, and in the second, only the features selected by the VAE framework were included. In both cases, the final prospectivity model was produced by an autoencoder (AE). For comparison, the prediction-area (P–A) plots of the two prospectivity models were generated using 14 known mineral occurrences as positive ground-truth labels, indicating that the model based on the selected features achieved a higher prediction rate (80%) than the model based on all features (72%). These results demonstrate that the evidence layers derived from the mineral system approach can benefit from unsupervised VAE-based evaluation, leading to improved performance of the prospectivity modeling. Full article

With the increasing complexity of unmanned aerial vehicle (UAV) missions in complex obstacle environments, cooperative hunting of maneuvering ground targets by UAV swarms has become an important problem for multi-agent autonomous decision-making. This paper focuses on a simulated three-UAV hunting scenario in a two-dimensional obstructed environment, where UAVs must search for, approach, encircle, and continuously track a target while avoiding static obstacles under local observation. To address the problem of multi-UAV cooperative hunting of dynamic targets in complex obstacle environments, this paper proposes a curriculum learning (CL)-based Multi-Agent Proximal Policy Optimization algorithm, termed CL-MAPPO. Specifically, a three-stage progressive training curriculum is designed to overcome the challenges of low exploration efficiency, slow environmental adaptation, and difficult convergence of cooperative hunting policies faced by multi-agent deep reinforcement learning in hunting tasks, thereby gradually enhancing the cooperative hunting capability of UAVs in complex environments. Curriculum I employs fixed obstacles and a stationary target position to train the UAVs’ basic obstacle avoidance and target search abilities. Curriculum II introduces randomly generated obstacles and target positions to improve the UAVs’ adaptability to varying environments. Curriculum III further incorporates a dynamic target, prompting the UAVs to learn effective hunting strategies against maneuvering targets. The simulation experiment includes ablation experiments against MAPPO without curriculum learning and comparative simulations against MADDPG and MADQN, using reward convergence curves and trajectory visualizations to evaluate the training results. The results show that, under the same training episodes in the ablation experiment, CL-MAPPO reaches a higher and more stable reward level than vanilla MAPPO, indicating improved learning efficiency without increasing model complexity. In the comparative experiment, the CL-MAPPO algorithm achieved a higher success rate in cooperative hunting. These simulation experiments verify the effectiveness and superiority of the CL-MAPPO algorithm in multi-agent cooperative hunting tasks. Full article

Train-induced ground vibrations can propagate into pile foundations, potentially causing undesirable vibration in nearby buildings, laboratories housing vibration-sensitive equipment, and manufacturing facilities for high-precision processes. This paper presents an innovative method for predicting building vibration from free-field ground vibration measurements at locations away from the tracks during train pass-bys. The proposed method accounts for site-specific soil profiles and train-soil-pile-structure interactions and is implemented in four steps. In Step 1, train-induced vibration transmission into the ground is estimated using an axisymmetric finite element model that simulates wave propagation through layered soils from the tracks to free-field ground locations. Step 2 estimates free pile head vibration using a three-dimensional finite-element model that captures the ground-borne transmission of track inputs through soil layers to the pile. Step 3 estimates vibration at the junction of the pile head and depot column base using a finite-element model to estimate the pile head impedance and an analytical impedance model for the depot structures supported by the pile. In Step 4, estimates of column-base vibration that transmits into over-track buildings are compared to measured column-base vibration levels obtained during train pass-bys. The method was applied at a metro depot in China, where tracks were in close proximity to columns supporting over-track buildings. Ground and column base vibration levels were measured during multiple train pass-bys. The estimated vibration levels at the base of depot columns closely agreed with the measured vibration levels at the columns during six-car train pass-bys. It demonstrated the potential effectiveness of this hybrid method for assessing vibration transmission into structures atop existing railway tracks. By integrating field measurements, finite element simulations, and analytical impedance models, the proposed hybrid method provides a framework for evaluating the transmission of the train-induced vibration to nearby building structures. Full article

Background/Objectives: Alignment philosophy in total knee arthroplasty (TKA) may affect joints beyond the knee. Mechanical alignment (MA) targets a neutral mechanical axis, whereas kinematic alignment (KA) aims to restore native alignment and joint line obliquity (JLO). This study compares the effects of MA and KA on hip and ankle radiographic parameters and investigates the propagation of coronal correction along the lower limb. Methods: A retrospective comparative study evaluated 63 TKAs performed for varus deformity (KA: n = 32; MA: n = 31). Pre- and postoperative full-length standing radiographs were used to calculate changes (Δ), defined as the difference between postoperative and preoperative values, in hip offsets, mechanical and arithmetic hip–knee–ankle angles (mHKA, aHKA), medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), JLO, and ankle ground-referenced angles. Between-group differences and correlations were analysed. Interobserver reliability was assessed for all variables. Results: MA produced significantly greater limb correction than KA (ΔmHKA: 8.89° vs. 4.82°, p < 0.001), primarily due to increased tibial valgus correction (ΔMPTA: 6.26° vs. 2.41°, p < 0.001). JLO increased substantially with MA (+4.10°) but was preserved with KA (+0.30°, p < 0.001). MA resulted in significant valgus shifts at the ankle (ground talar dome angle (GTDA) −3.01°, ground tibial plafond angle (GTPA) −3.02°; p = 0.006 for both), whereas KA produced no significant ankle changes. Correlation analysis demonstrated limited knee–ankle biomechanical coupling, with a moderate negative correlation in MA (ΔmHKA vs. ΔGTDA: ρ = −0.479, p = 0.006) and a weak correlation in KA (ΔaHKA vs. ΔGTDA: ρ = −0.360, p = 0.043). Hip parameters remained unchanged in both groups. Conclusions: Mechanical alignment induces larger tibial-driven coronal corrections, increases joint line obliquity, and produces measurable valgus shift at the ankle. In contrast, kinematic alignment preserves native alignment and joint-line obliquity while minimising distal ankle compensatory changes. Full article

This study presents an adaptive learning framework that integrates fuzzy logic and learning analytics to support personalized education and multi-factor student assessment. The proposed system combines cognitive and behavioral indicators to provide an interpretable representation of the learner’s state within a dynamic digital learning environment. The architecture is based on adaptive learner modeling and classroom-level monitoring mechanisms, enabling personalized guidance, adaptive content sequencing, and continuous performance monitoring at both individual and classroom levels. A core contribution of the approach is a fuzzy logic-based evaluation mechanism that aggregates multiple signals, including quiz performance, time spent on theory, help-seeking behavior, and system interaction patterns. These inputs are transformed into fuzzy sets and combined through inference rules to produce interpretable learning level estimates aligned with Bloom’s taxonomy. The approach is grounded in Vygotsky’s Zone of Proximal Development, supporting adaptive scaffolding and targeted instructional interventions. The evaluation results demonstrate a strong correlation between the model outputs and conventional exam performance (r ≈ 0.91), while exhibiting reduced variability (SD ≈ 0.15 compared to SD ≈ 0.20), indicating a more stable representation of learner performance. Furthermore, statistical analysis confirms that the differences between traditional and model-based scores are significant (p < 0.01), suggesting that the proposed approach captures additional dimensions of learner behavior beyond conventional grading metrics. Overall, the findings indicate that integrating fuzzy reasoning with behavioral analytics enables a more interpretable, stable, and pedagogically grounded approach to learner assessment, supporting adaptive and interpretable personalized learning. Full article

Generative artificial intelligence (AI) is increasingly present in medical education, yet its indiscriminate use risks impairing the acquisition of foundational clinical competencies, including clinical reasoning, hypothesis generation, and patient-centered communication, through processes of never-skilling, mis-skilling, and deskilling. This paper presents M3RGE-AI (Responsible, Reliable, and Reflexive use of Generative AI in Medical Education), a conceptual framework for the purposeful integration of AI as a cognitive scaffold in medical training. Drawing on established learning theories, zone of proximal development, deliberate practice, and peer learning, the framework assigns progressively expanding AI functions across training stages, prioritizes Socratic over directive interactions, requires transparent and verifiable sourcing of AI-generated content, and incorporates peer moderation and AI-off assessment checkpoints to mitigate over-reliance. The framework is operationalized through alternating AI-on and AI-off cycles, governance processes, and educator training protocols. Applied within these constraints, AI can shorten feedback loops and broaden clinical exposure while preserving independent reasoning and authentic patient communication. M3RGE-AI offers a theoretically grounded and institutionally implementable model for integrating generative AI into medical curricula without sacrificing the essential human competencies that underpin safe clinical practice. Full article

Parkia platycephala pod (PP) is a native feed resource with good nutritional value that can be included in sheep diets as a potential alternative ingredient without impairing the meat quality. Thus, the aim of this study was to evaluate the effect of two feeding strategies differing in forage source and corn processing form, including the use of Parkia platycephala pod and whole corn grain, on the physicochemical characteristics, proximate composition, and mineral profile of sheep meat. For this, the Semimembranosus muscle from the hind legs of twelve castrated male Dorper × Santa Inês sheep was evaluated. The animals were fed two diets: diet 1—without Parkia platycephala pod (30% Tifton hay + 20% ground corn)—and diet 2—with Parkia platycephala pod (30% Parkia platycephala pod + 0% ground corn). A completely randomized design with two treatments and six replicates was used. There was no effect of diet on the physicochemical characteristics, proximate composition and mineral profile of the Semimembranosus muscle (p > 0.05). Therefore, the inclusion of Parkia platycephala pod in sheep diets is recommended, as it does not alter meat quality. Full article

Urban traffic congestion remains a persistent global challenge, contributing to significant economic inefficiencies, elevated greenhouse gas emissions, and diminished quality of life. This paper presents a real-world video-based traffic monitoring study combined with a proposed adaptive signal control framework. In the monitoring component, YOLOv11 object detection was applied directly to footage recorded from an overhead bridge position on a 40 km/h road. The model successfully detected and tracked multiple road-user categories, including cars, trucks, buses, motorcycles, cyclists, and pedestrians, yielding 1041 vehicle detections across 25 unique tracked objects. Vehicle speeds were estimated from inter-frame centroid displacement, and a Region of Interest (ROI) occupancy model was used to classify congestion states as High, Medium, or Free Flow using thresholds grounded in Highway Capacity Manual (HCM) level-of-service criteria. The system detected 11 high-congestion frames (3.8%), 184 medium-congestion frames (63.9%), and 93 free-flow frames (32.3%), consistent with moderate congestion observed during the recording period. In the proposed control component, a Proximal Policy Optimisation (PPO)-based reinforcement learning signal controller is designed around the YOLOv11 detection outputs as its state representation. Based on comparable adaptive traffic signal control studies in the literature, the proposed framework is projected to achieve approximately 25% higher peak-hour throughput, 35% shorter queue lengths, and 32% lower average waiting times relative to a fixed-time signal baseline. The detection accuracy (mAP@0.5 = 93.2%) and inference speed (32 FPS) cited are published YOLOv11 benchmarks used as indicative performance references. This work bridges real-world perception and proposed intelligent control, providing a transparent and reproducible methodology for next-generation smart city traffic management. Full article

Despite a century of learning research, why human beings desire to learn remains theoretically unresolved. Behaviorist, cognitivist, and constructivist paradigms explain the mechanisms of learning but presuppose rather than account for its motivational genesis—a gap this paper terms motivational minimalism. Drawing on René Girard’s mimetic anthropology, this paper develops Mimetic Learning Theory (MLT), grounded in philosophical anthropology (Plessner, Gehlen), hermeneutics (Rosa, Gadamer), and normative theory (Biesta, Honneth, Arendt). MLT reconceives learning as the reflective transformation of mediated desire. Humans do not merely copy actions but appropriate the desires of models who render knowledge, identity, and recognition worth striving for. Eight dominant learning paradigms are reread as partial articulations of this mimetic dynamic. Two novel constructs are introduced: mimetic load (the affective–cognitive tension from competing models of desire, complementing cognitive load theory) and zones of desire (a reformulation of Vygotsky’s zone of proximal development). MLT does not displace existing frameworks but re-grounds them in a shared anthropological logic—that learning begins not in the mind, but in the field of mediated desire. Full article

Background/Objectives: Establishing laparoscopic access remains a critical and complication-prone step in minimally invasive surgery. Previous work has shown that proximal vibroacoustic sensing can identify peritoneal puncture events in porcine cadavers. The present pilot study evaluated whether these findings translate to human anatomy under controlled, ex vivo conditions. Methods: A vibroacoustic sensing prototype was proximally attached to a standard Veress needle during 14 insertions into a fresh human body donor (within 48 h post-mortem). An endoscope was introduced laterally to provide visual ground truth of peritoneal entry. Vibroacoustic signals were recorded at the proximal end of the instrument. Time–frequency analyses, transient excitation detection, and statistical comparisons were performed to assess whether (1) peritoneal puncture can be identified in the vibroacoustic signal, (2) signal phases and dynamics correspond to those previously observed in porcine cadavers, and (3) peritoneal punctures can be statistically differentiated from non-peritoneal events. Results: All 14 peritoneal punctures were identifiable in the vibroacoustic signal under the experimental conditions. Characteristic signal phases previously described in porcine tissue, including transient excitation associated with cavity entry, were consistently reproduced with comparable temporal and spectral profiles. Statistical analyses demonstrated group-level differences between peritoneal and non-peritoneal events, and the peritoneal puncture was the highest-energy event of its insertion in 13 of 14 cases (92.9%). Conclusions: Under the controlled ex vivo conditions of this single-donor pilot study, vibroacoustic sensing was feasible for identifying peritoneal puncture in human tissue and reproduced signal dynamics observed in porcine models. To our knowledge, this is the first demonstration of the proximal vibroacoustic sensing concept on a human body donor and the first cross-species replication of the previously reported puncture phase structure, establishing an important translational stepping stone between animal cadaver studies and in vivo investigations. The study demonstrates feasibility rather than clinical reliability: the single-donor design and the retrospective annotation framework limit generalizability. Prospective validation in living patients, across multiple subjects and operators, is required before clinical deployment. Full article

Air transport is a key driver of economic development, tourism, and regional connectivity, yet its growth generates increasing environmental costs. Grounded in the catalytic effects framework and the sustainability trade-off perspective, this exploratory study examines the economic and sustainability dimensions of air traffic recovery and growth at Belgrade Nikola Tesla Airport during 2019–2024, a period encompassing a pandemic shock and record post-pandemic expansion. Descriptive statistical analysis and Pearson correlation analysis were applied to six annual data points, supplemented by an approximate CO₂ emission estimation. Passenger traffic increased from 6.16 to 8.37 million (+35.9%), and the destination network expanded from 99 to 135 routes. A positive co-movement was observed between passenger traffic and foreign tourist arrivals (r = 0.970; p = 0.001). No detectable association was found between passenger traffic and annual GDP growth rate (r = 0.143; p = 0.79). Estimated CO₂ emissions grew proportionally from 0.831 to 1.130 million tonnes, consistent with the proportional growth pattern generated by the fixed-factor estimation framework applied. The passengers-per-movement ratio improved from 87.5 to 97.2, indicating a proximate improvement in operational efficiency. These preliminary findings provide exploratory evidence relevant to Sustainable Development Goals 8 and 9 and may inform future research and policy discussions on the sustainability dimensions of airport development. Full article

In this study, I propose the need for a model of healthcare in Africa grounded in the healing mission of the Catholic Church understood as a “field hospital,” a pastoral image proposed by Pope Francis to describe the Church’s duty to accompany a wounded humanity. In the African context, where many communities face persistent challenges such as poverty and social inequalities, the Church’s healthcare and pastoral ministries play a critical role in promoting integral healing. Drawing on the model of Church-as-field-hospital, African communitarian values, and the lived experiences of grassroots communities, I argue that authentic healthcare needs to address not only physical illness but also the spiritual, psychological, and sociocultural dimensions of human well-being. The “field hospital” metaphor provides a theological and pastoral framework that prioritizes compassion, proximity to the suffering, and practical responses to human vulnerability in the realm of healthcare. By engaging the Church’s longstanding commitment to healthcare in Africa, this research demonstrates how Catholic institutions can contribute to a more integrated approach to healthcare, showing how ecclesiology and healthcare ethics can work together to produce positive outcomes for grassroots communities. Ultimately, the study envisions a model of healthcare that reflects the Church’s mission of mercy, promotes human dignity, and fosters communal solidarity in the pursuit of holistic healing. I propose that such a holistic healthcare mission of the Church must begin from below, that is, from the perspective of marginal grassroots communities. Since discussing the healthcare situation across the entire African continent is complex and practically impossible, I will focus on Kenya as a representative case study. Full article

As educators play a pivotal role in advancing sustainable futures, understanding their readiness to translate sustainability-related knowledge and values into teaching practice has become an important concern in teacher education and professional development research. This study aims to develop and validate a measurement instrument assessing student teachers’ intention to implement education for sustainable development (ESD). By operationalizing intention as a proximal indicator of future-oriented professional readiness for ESD, the study addresses the need for empirically grounded tools that capture educators’ preparedness to engage in sustainability-oriented teaching. The Intention to Implement ESD Scale (IESDS) was developed through a theory-informed item construction process grounded in ESD literature, teacher agency, and competence-oriented approaches to sustainable education. The instrument was validated on a sample of 706 student teachers enrolled in the final years of teacher education programs. The findings indicate that a bifactor model provided the best fit to the data, with a dominant general factor supporting the interpretation of the IESDS as a primarily unidimensional measure of intention to implement ESD. In addition, intention to implement ESD was positively associated with teacher self-efficacy for ESD, providing evidence of convergent validity and reinforcing the role of perceived capability in sustainability-oriented professional action. The IESDS can support teacher education institutions and professional development providers in monitoring and strengthening educators’ readiness for sustainability-oriented teaching. It may be used to evaluate the effectiveness of courses, modules, and professional learning interventions aimed at promoting teaching for sustainable futures. Full article

This study focuses on the construction sequence of three complex and atypical Scythian kurgans at the Vodoslavka burial ground in the Northern Sivash region, which incorporate several unique structural and ritual elements. One of the most striking features is the layer of mud applied to the ground surface prior to mound construction, which, in several cases, formed anthropomorphic outlines. Funerary feasting, which took place both before and during the burial ceremony, was just one of the other features. So too was the deliberate shaping of soil removed from the central grave into a spherical segment, and the ritual activity associated with this prepared spoil heap. Although the mounds’ preserved height is relatively modest (originally about 3–5 m), their internal organisation and the composition of the grave goods suggest that they were used for burying individuals of high status within Scythian society. The cemetery’s proximity to major salt lakes suggests that the local elite’s affluence may have been linked to their control over this vital resource. The architectural and depositional features of the kurgans can be interpreted as elements of a ritual system designed to ensure the deceased’s proper transition to the afterlife. The design of the burial chambers and the richness of the grave goods reflect a concern for the conditions of existence in the afterlife, while the associated manipulations of the sub-mound space and mound deposits, prepared surfaces, deliberately shaped spoil heaps, and related ritual practices can be understood as material markers and procedures intended to secure the successful passage of the deceased to the afterlife. Full article

This paper advances Divine Compositionalism (DC) as a comprehensive theistic ontology capable of integrating divine sovereignty with genuine human libertarian freedom. Here we extend DC’s commitment to physical causation in natural mechanisms, to human agency, arguing that DC can accommodate a novel, integrated free will position without collapsing into classical occasionalism or reductive determinism. In a six-category ontology, God remains the continuous, existence-conferring cause of all physical processes, including the neurological and biological substrates of decision and action, while human agents possess genuine causal power to generate originate thoughts, intentions, and choices. In a precise mapping to the neuroscience of proximal intention formation, a model is given proposing that God acts differentially within the structures of human neurophysiology in an occasionalist manner and in a concurrentist manner with human agent free will. This preserves agent moral responsibility and divine non-culpability for sin and evil, while affirming God’s providential governance. By reconceptualizing the relationship between divine causality and human freedom, DC offers a synthetic position that transcends the traditional libertarian–compatibilist dichotomy. Freedom is neither illusory within a deterministic system nor an autonomous power severed from divine causality, but a contingent participation in God’s ongoing creative act. The result is a biblically grounded, metaphysically coherent framework that seeks to offer a theistic ontology in which God’s continuous, existence-conferring action in nature harmonizes with the genuine moral freedom of human persons. Full article