Abstract: This paper introduces a sophisticated programming language specifically designed for high-performance, dataparallel operations across diverse data types, including streams and data frames. Our framework integrates with advanced compiler infrastructure to facilitate the efficient translation and deployment of data-intensive programs on distributed systems. This innovative language aims to simplify the development of robust, scalable data science applications by abstracting away low-level distributed programming complexities and enabling powerful domain-specific optimizations.

Abstract: Deep Reactive Ion Etching (DRIE), as a key process in silicon micromachining, remains constrained in high-precision applications by sidewall angle deviation and aspect ratio limitations. This study systematically investigates the mapping relationship between process parameters and etching morphology, focusing on: The influence mechanism of C₄F₈ passivation time and bottom RF power on sidewall perpendicularity The effect patterns of etch cycle count, single-step time, and bottom RF power on aspect ratio and top-bottom line width (CD) difference Findings reveal: Dynamic adjustment of bottom RF power significantly influences sidewall angle. Incremental adjustment tends to cause sharp angles (decreased angular precision), while decremental adjustment tends to form obtuse angles. Simply increasing cycle count leads to a bottleneck in etch depth growth. Combining incremental bottom RF power adjustment can overcome depth limitations but induces axial variation in aperture dimensions. Optimizing the pas-sivation-etch time ratio effectively controls etch morphology characteristics. This study achieved an etch depth of 112.2 μm for a 5 μm wide trench with an overall ap-erture size difference of 0.279 μm, providing a theoretical basis and practical guidance for parameter optimization in DRIE processes for high-precision silicon structure fab-rication.

Abstract: This study experimentally investigates the cooling performance of a single-opening wind catcher model under varying orientations and wind speeds. The wind catcher was connected to a horizontal cavity representing an indoor space, with a rear outlet simulating a window opening. Electric resistors were installed at the catcher shaft and in the middle of the cavity length to simulate the building’s heat loads. Experiments were conducted in a wind tunnel, where K-type thermocouples were employed to record temperature variations for both closed and open cavity end. Five wind speeds (4–9 m/s) and five orientations (0°–180°) were examined. Under the closed-cavity configuration, the maximum temperature reduction (cooling) of 4 °C occurred at an orientation of 180°, at which the catcher opening was positioned on the leeward side. This orientation created a low-pressure region at the catcher’s inlet, located within the wake of the model, which combined with a favorable vertical temperature gradient enhanced suction-driven cooling. In the open-cavity configuration, cooling was observed for all orientations and wind speeds. The greatest temperature reduction of 9 °C occurred at the 135° orientation, whereas other orientations produced temperature drops from 2 °C to 6 °C.

Abstract: This study examines how the injection rate affects fracture complexity during hydraulic fracturing using water as the fracturing fluid. Experiments were conducted on concrete, impermeable sandstone, and brittle bituminous coal across a wide range of injection rates. The results showed that both low (< 1.0mL/min) and high (≥5.0mL/min) injection rates produced simple, planar fractures with limited branching. In contrast, an intermediate rate of 2.2mL/min consistently generated the most complex fracture networks. Fracture complexity was evaluated using 3D-scanned surface roughness quantified by the Joint Roughness Coefficient (JRC), which reached its highest values at the optimum injection rate. Although water is sometimes considered less effective than unconventional fluids, the findings demonstrate that it performs well in moderately permeable rocks with sufficient tensile strength. However, water was less effective in brittle coal and highly impermeable sandstone, where more compressible, low-viscosity fluids such as supercritical CO2 or nitrogen may be advantageous. Overall, three fracture behavior regimes were identified: a low-rate regime producing simple fractures, a mid-rate optimal regime producing complex networks, and a high-rate regime where fracture complexity decreased. Thin fractures dominated because controlled injection conditions focused on observing fracture behavior rather than merely inducing fracture, as reflected by the relatively high post-fracture injection pressure.

Abstract: Engineering education remains one of the least examined domains within sexuality and gender research, despite mounting evidence that heteronormative academic cultures push queer students toward concealment, psychological distress, and attrition. The absence of an integrated synthesis of these experiences has hindered both scholarly understanding and the development of inclusive educational practices. This systematic review addresses that critical gap by consolidating and analyzing the fragmented empirical evidence on queer students’ identity negotiation, belonging, and inclusion within U.S. engineering programs. It synthesizes nine empirical studies on queer students in U.S. engineering education to identify how identity negotiation, belonging, and institutional climate shape their experiences and outcomes. By critically integrating these findings, the review aims to clarify recurring patterns and propose evidence-based directions for future inclusion research and practice. Drawing on Foucauldian and queer theoretical frameworks of power, heterotopia, and identity assemblage, it maps how queer students navigate visibility and marginalization across spaces historically structured by masculine and heteronormativity. Nine empirical studies were identified through comprehensive database searches and examined using thematic synthesis. Data extraction emphasized participants’ lived experiences, contextualized within institutional and sociocultural forces shaping inclusion and exclusion. Across studies, queer students engaged in adaptive strategies of covering or selective disclosure to manage stigma which are coping mechanisms that safeguarded social survival but eroded authenticity and well-being. Persistent isolation and a heightened intent to leave the discipline were common. Yet, heterotopic spaces such as peer networks, affirming mentors, and visibly allied faculty, emerged as sites of resistance and belonging that redefined the cultural boundaries of engineering. These findings underscore the pressing need to move inclusion efforts beyond recruitment metrics toward structural and cultural transformation. To fully realize diversity in engineering, queer identities must be recognized not as peripheral but as integral to the discipline’s intellectual and social fabric.

Abstract: Advanced Air Mobility (AAM), having drones (aerial robots) as a core, is becoming an integral additional part of Disaster Management (DM) systems in metropolitan regions, and of future smart urban development and systems. Unmanned Aircraft Systems (UAS) are able to transport efficiently high added value goods and to provide efficient monitoring during the disaster events and their development. This paper provides a foresight on the overall System of Systems (SoS) needed including the UAS application in the studied use cases, as well as the crucial integration of relevant advanced communication systems (ACS), for the safe and sustainable UAS operation. In the evolving AAM, ACS is a crucial enabler and core element of a functioning SoS -- for the purposes of UAS navigation and operations safety, DM data collection and processing. The emphasised SoS enables emergency goods deliveries and the complete and efficient deployment and operation of an entire DM system (meant for monitoring, search and rescue, and decision making support), where UASs are used as logistic tools, and simultaneously for the monitoring of the environment and the disaster events in the affected regions. AAM is being operated mainly in the third dimension (airspace), which enables us to be minimally dependent on any types of ground transport infrastructure. Due to this, its precise navigation and management as well as relevant data streams transfer are crucial for the operational efficiency and safety. This foresight study provides a comprehensive, SUDEM (EU), REGUAS (DE), 5G!Drones (EU), and ETHER (EU) projects' lessons learnt-based path for understanding and efficiently deploying the SUDEM SoS including AAM and ACS for the purpose of the described 2 combined use cases: (i) high-added value goods transport and (ii) live monitoring, and the necessary educational model.

Abstract: Laboratories across educational levels have traditionally required in-person attendance, limiting practical activities to specific times and physical spaces. This paper presents a technological architecture based on a system-on-chip (SoC) and a connectivist model, grounded in Connectivism Learning Theory, for implementing a remote laboratory in digital logic design using FPGA devices. The architecture leverages an Internet of Things (IoT) environment to provide applications and servers that enable remote access, programming, manipulation, and visualization of FPGA-based development boards located in the institution’s laboratory, from anywhere and at any time. The connectivist model allows learners to interact with multiple nodes for attending synchronous classes, performing laboratory exercises, managing the remote laboratory, and accessing educational resources asynchronously. This approach aims to enhance learning, knowledge transfer, and skills development. A four-year evaluation was conducted, including one experimental group using an e-learning approach and three in-person control groups from a Digital Logic Design course. The experimental group achieved an average performance score of 9.777, surpassing the control groups, suggesting improved academic outcomes with the proposed system. Additionally, a Technology Acceptance Model–based survey showed very high acceptance among learners. This paper presents a novel connectivist model, which we have called the Massive Open Online Laboratory.

Abstract: The most important step for the installation of a wind farm is to know the wind regime in the region, since an error in estimating this wind speed causes an error proportional to the cube of power, resulting in financial losses for investors. Therefore, knowing the methods used for predicting wind energy becomes important and the knowledge of how research and studies in this area are going help map the subject and outline strategies for developing research in strategic areas. For this purpose, a *** using the Scopus database considering some keywords, such as ("forecast" OR "prevision") AND "wind" AND ("turbine" OR "power" OR "energy" or "velocity" or "speed"), considering the period since 2020, and analyzing the data of the documents found using the Bibliometrix package. With the results found, it was possible to map researchers, and institutions that are developing work in this area, in addition to the most cited articles, as an indication parameter. Future works could include CFD simulation models most applied in different wind speed analysis reviews.

Abstract: This study compares the publication performance of Open-Access (OA) and subscrip-tion-based (SB) journals in Engineering, using bibliometric indicators from Scopus (2023 view). A total of 3012 active Engineering journals were analysed, of which 757 are OA, and 2255 are SB. Four metrics were examined for the period 2020–2023: CiteScore, total citations, number of published documents, and the percentage of cited articles, stratified by CiteScore quartiles (Q1–Q4) and the top 10% CiteScore group. SB journals concentrate most citations and tend to achieve higher mean CiteScores, larger publication volumes, and higher percentages of cited articles in the upper strata, with statistically significant differences on several indicators. At the same time, OA journals display CiteScore medians that are very similar to those of SB journals and lower var-iance for several indicators, particularly within the top 10% group, indicating more consistent performance among well-established OA titles. OA journals represent around one quarter of Engineering journals in Scopus, but remain underrepresented in the most highly cited segment. These findings suggest a hybrid configuration in which SB journals retain dominance at the top of the impact hierarchy. In contrast, OA jour-nals offer competitive and more homogeneous outlets with implications for publication strategies and open-access policies in Engineering.

Abstract: Accurate diagnosis of crop water demand is a core challenge in alleviating agricultural water scarcity. Traditional diagnostic methods, which rely mainly on soil moisture sensor monitoring or empirical models based on meteorological data, suffer from limitations such as insufficient spatiotemporal representativeness and an inability to reflect crop physiological status in real time, leading to an annual water waste of 10–30%. Therefore, developing technologies that enable real-time, non-destructive, and precise monitoring of crop water status is crucial. In recent years, the rapid advancement of high-throughput phenotyping technology has provided revolutionary tools to address this challenge. By integrating multi-source sensors (e.g., thermal infrared and hyperspectral imaging), multi-dimensional response characteristics of crops under water stress can be rapidly acquired. This paper systematically reviews research progress in using high-throughput phenotyping to obtain water-sensitive phenotypic traits and construct crop water demand diagnosis models. It focuses on: (1) the connotation and acquisition techniques of key water-sensitive phenotypic indicators, such as canopy temperature, spectral indices, and chlorophyll fluorescence; (2) the advantages, limitations, and fusion strategies of multi-platform data acquisition systems, including unmanned aerial vehicles (UAVs), ground mobile platforms, and satellite remote sensing; and (3) the construction methods, performance evaluation, and practical application cases of diagnostic models based on machine learning (e.g., Random Forest, XGBoost), deep learning (e.g., CNN, LSTM), and mechanism-coupled models. The innovation of this review lies in its systematic integration of the entire technological chain—"phenotyping acquisition → model construction → decision-making"—while identifying current research challenges, including field environmental complexity, model generalization capability, data barriers, and interpretability. Future development pathways are proposed, focusing on low-cost sensing, explainable AI, multi-source data fusion, and cloud-edge collaborative decision systems. This review aims to provide a systematic theoretical and practical reference for water management in precision irrigation and smart agriculture.

Abstract: Rapid urbanization in African cities has increased demand for safe and reliable energy infrastructure, with Liquefied Petroleum Gas (LPG) emerging as a leading option for clean cooking. In Nairobi, Kenya, the siting of LPG refill stations is critical to minimizing safety risks and protecting public health. This study applied a GIS-based Multi-Criteria Decision Analysis (MCDA) to identify suitable areas for LPG stations. The analysis integrated land use, elevation, slope, geology and soil data with regulatory and planning constraints. Two approaches were compared: Boolean analysis, which produced a strict exclusion mask identifying 33 restricted zones and weighted overlay (with the exclusion mask applied), which yielded 2439 feasible locations across varying levels of suitability. Peri-urban neighborhoods in Embakasi ward consistently emerged as the most favorable. Validation using high-resolution imagery confirmed the contextual appropriateness and regulatory compliance of selected sites, with weighted overlay achieving 87.1% accuracy and Boolean analysis 85.7%. The findings show that weighted overlay combined with an exclusion mask provides a more flexible and comprehensive framework than rigid Boolean methods balancing safety with regulatory requirements. The study provides evidence-based guidance for expanding LPG infrastructure in rapidly urbanizing cities supporting strategic urban planning while reducing environmental, social and safety risks.

Abstract: This article presents the design, fabrication, and experimental validation of a centimeter-scale autonomous robot that achieves bidirectional locomotion and trajectory control through 3D-printed resonators actuated by piezoelectricity and integrated with miniature legs. Building on previous works that employed piezoelectric bimorphs, the proposed system replaces them with custom-designed 3D-printed resonant plates that exploit the excitation of standing waves (SW) to generate motion. Each resonator is equipped with strategically positioned passive legs that convert vibratory energy into effective thrust, enabling both linear and rotational movement. A differential drive configuration, implemented through two independently actuated resonators, allows precise guidance and the execution of complex trajectories. The robot integrates onboard control electronics consisting of a microcontroller and inertial sensors, which enable closed-loop trajectory correction via a PD controller and allow autonomous navigation. The experimental results demonstrate high-precision motion control, achieving linear displacement speeds of 8.87 mm/s and a maximum angular velocity of 37.88°/s, while maintaining low power consumption and a compact form factor. Furthermore, the evaluation using the mean absolute error (MAE) yielded a value of 0.83° in trajectory tracking. This work advances the field of robotics and automatic control at the insect scale by integrating efficient piezoelectric actuation, additive manufacturing, and embedded sensing into a single autonomous platform capable of agile and programmable locomotion.

Abstract: This study conducts systematic experimental and numerical investigations to address the parameter calibration issue in the discrete element model of seashells, aiming to establish a high-fidelity numerical model that accurately characterizes their macroscopic mechanical behavior, thereby providing a basis for optimizing parameters of seashell crushing equipment. Firstly, intrinsic parameters of seashells were determined through physical experiments: density of 2.2 kg/m³, Poisson's ratio of 0.26, shear modulus of 1.57×10⁸ Pa, and elastic modulus of 6.5×10¹⁰ Pa. Subsequently, contact parameters between seashells and between seashells and 304 stainless steel, including static friction coefficient, rolling friction coefficient, and coefficient of restitution, were obtained via the inclined plane method and impact tests. The reliability of these contact parameters was validated by the angle of repose test, with a relative error of 5.1% between simulation and measured results. Based on this, using ultimate load as the response indicator, the Plackett-Burman experimental design was employed to identify normal stiffness per unit area and tangential stiffness per unit area as the primary influencing parameters. The Bonding model parameters were then precisely calibrated through the steepest ascent test and Box-Behnken design, resulting in an optimal parameter set. The error between simulation results and physical experiments was only 3.8%, demonstrating the high reliability and accuracy of the established model and parameter calibration methodology.

Abstract: Current paradigms in Self-Supervised Learning (SSL) achieve state-of-the-art results through complex, heuristic-driven pretext tasks such as contrastive learning or masked image modeling. This work proposes a departure from these heuristics by reframing SSL through the fundamental principle of Minimum Description Length (MDL). We introduce the MDL-Autoencoder (MDL-AE), a framework that learns visual representations by optimizing a VQ-VAE-based objective to find the most efficient, discrete compression of visual data. We conduct a rigorous series of experiments on CIFAR-10, demonstrating that this compression-driven objective successfully learns a rich vocabulary of local visual concepts. However, our investigation uncovers a critical and non-obvious architectural insight: despite learning a visibly superior and higher-fidelity vocabulary of visual concepts, a more powerful tokenizer fails to improve downstream performance, revealing that the nature of the learned representation dictates the optimal downstream architecture. We show that our MDL-AE learns a vocabulary of holistic object parts rather than generic, composable primitives. Consequently, we find that a sophisticated Vision Transformer (ViT) head, a state-of-the-art tool for understanding token relationships, consistently fails to outperform a simple linear probe on the flattened feature map. This architectural mismatch reveals that the most powerful downstream aggregator is not always the most effective. To validate this, we demonstrate that a dedicated self-supervised alignment task, based on Masked Autoencoding of the discrete tokens, resolves this mismatch and dramatically improves performance, bridging the gap between generative fidelity and discriminative utility. Our work provides a compelling end-to-end case study on the importance of co-designing objectives and their downstream architectures, showing that token-specific pre-training is crucial for unlocking the potential of powerful aggregators.

Abstract: Understanding resonance and frequency behavior is fundamental in engineering acoustics and in technology-supported music-learning environments. This work presents an Educational Acoustics Audio System (EAAS) designed as a sensor-based hardware–software toolkit that enables experiential learning of acoustic resonance through listening, spectrogram visualization, and analytical modeling. The system integrates a bass-reflex loudspeaker with interchangeable vent configurations, a microphone-based sensing module, automated spatial sampling, and a MATLAB interface for generating logarithmic sweeps, recording responses, and computing high-resolution spectrograms. The instructional design is grounded in the Educational Acoustics Conceptual Framework (EACF), originally proposed by the authors, which structures learning through concrete, graphical, and abstract levels. Learners first explore perceptual changes in low-frequency amplification, then interpret time–frequency patterns using spectrograms, and finally compute Helmholtz-based resonance frequencies based on physical parameters. Experimental measurements collected at multiple microphone distances reveal stable resonance peaks at approximately 546 Hz (full vent) and 265 Hz (half vent), alongside consistent amplitude differences between vent configurations. By integrating auditory perception, sensor-based acquisition, and mathematical modeling in a unified and low-cost system, the EAAS provides an effective technological platform for hands-on exploration of resonance and frequency response. This approach strengthens conceptual understanding in engineering acoustics while supporting its application in related educational contexts.

Abstract: The expansion of Artificial Intelligence (AI) research has generated a massive and complex scientific ecosystem that requires systematic characterization, where no comprehensive studies have analyzed applications for engineering. This work conducts one of the most extensive scientometric analyses to date, encompassing 159,139 publications of the specialized literature indexed in the Web of Science (2005–2024). Using data cleaning, citation normalization (NCII), institutional productivity measures and keyword mining algorithms, the study maps the global evolution of AI research. Results reveal the dominance of Engineering and Computer Science disciplines, with China and the United States leading scientific output. High-impact open-access journals, such as IEEE Access, serve as the main dissemination channels. Emerging topics such as ChatGPT, Big Data, Internet of Things (IoT), and Digital Twins define the current research frontiers. The study provides a macroscopic evidence-based framework for understanding the dynamics of AI research for engineering problems and identifies future directions such as sentiment-based analytics, predictive modeling, and the evaluation of Large Language Models (LLMs) in scientific production. Overall, the main findings highlight AI’s growing role as a multidisciplinary driver of innovation across global research ecosystems.

Abstract: The exponential growth of astronomical time-series data from missions such as Kepler, TESS, and LSST has created an urgent need for statistical frameworks capable of providing both scalability and interpretability. Gaussian Processes (GPs) have emerged as a powerful tool for probabilistic modeling due to their ability to capture correlated structures and quantify uncertainty. However, their computational complexity, which scales cubically with dataset size, has limited their applicability to large-scale astronomical datasets. This paper introduces a novel Gaussian Process framework, termed celerite, which achieves exact and efficient inference for one-dimensional time-series data. The proposed method exploits the semiseparable structure of covariance matrices derived from mixtures of exponential kernels, reducing computational complexity from O(N 3 ) to O(N). Unlike traditional sparse or approximate GP methods, celerite maintains full model fidelity while enabling rapid processing of datasets containing millions of observations. Experimental evaluations on both simulated and real-world stellar light curves demonstrate that the proposed model accurately captures quasi-periodic and oscillatory variability with minimal loss of precision. The framework’s physical interpretability, numerical stability, and linear scalability make it highly suitable for modern astronomical pipelines and timedomain analyses. Beyond astrophysics, the principles of the celerite approach hold promise for other domains requiring fast, interpretable, and probabilistic time-series modeling.

Abstract: Ensuring adequate statistical power is paramount in longitudinal clinical trials evaluating pharmaceutical interventions. Underpowered studies can lead to unreliable conclusions regarding drug efficacy. This paper introduces a computational framework, implemented as an R package and a user-friendly web application, to facilitate robust sample size and power calculations specifically for longitudinal data arising in pharmacological research. The methodology encompasses various statistical models commonly employed in analyzing repeated measures in treatment versus control settings. Utilizing illustrative examples relevant to pharmaceutical outcomes, such as disease progression in neurodegenerative conditions and changes in physiological markers under drug administration, we demonstrate the utility of this software in optimizing study design parameters. Furthermore, the application allows researchers to incorporate pilot data, potentially derived from large-scale initiatives like the Alzheimer’s Disease Neuroimaging Initiative (ADNI), to enhance the precision of these crucial computations, thereby improving the rigor and ethical conduct of pharmaceutical trials.

Abstract: Artificial General Intelligence (AGI) has remained largely theoretical due to vague definitions, non-measurable criteria, and architectures that cannot be implemented in practice. Existing interpretations of AGI, from cognitive theories to universal intelligence models, provide valuable insights but do notoffer a concrete pathway for building or evaluating an actual general intelligence system. This paper introduces a new, measurable, and operational definition of AGI that emphasizes autonomous knowledge acquisition, reasoning across diverse and clearly defined domains, cross-domain transfer, adaptive self-improvement, and alignment with human goals. To support this definition, we propose a modular cognitive framework designed specifically for practical implementation. A working prototype is developed to demonstrate the feasibility of this approach. The system is capable of learning new knowledge, storing it in an adaptive memory, applying multi-step reasoning, transferring understanding across unrelated domains, and improving its performance through user feedback. Built using currently available technologies such as the Gemini API and structured memory mechanisms, the prototype shows that AGI can be demonstrated meaningfully even with today’s tools. The paper also presents a standardized evaluation suite that measures generalization, transfer, reasoning accuracy, learning efficiency, memory retention, adaptability, and alignment stability. Together, the definition, architecture, and prototype form a complete foundation for practical AGI research and represent a significant step toward realizing general-purpose intelligence.

Abstract: Efficient access to similar legal cases is a crucial requirement for lawyers, judges, and researchers. Traditional text-based search systems often fail to capture both the semantic similarity and the relational context of legal documents \cite{article}. To address this challenge, we present LEGRA, a novel graph-based dataset of Polish court rulings designed for Retrieval-Augmented Generation (RAG) and legal research support \cite{https://doi.org/10.48550/arxiv.2005.11401}. LEGRA is automatically constructed through an end-to-end pipeline: rulings are collected from public sources, converted and cleaned, chunked into passages, and enriched with TF-IDF vectors and embedding representations. The data is stored in a Neo4j graph database where documents, chunks, embeddings, judges, courts, and cited laws are modeled as nodes connected through explicit relations. This structure enables hybrid retrieval that combines semantic similarity with structural queries, allowing legal professionals to quickly identify not only textually related cases but also those linked through judges, locations, or legal references. We discuss the construction pipeline, the graph schema, and potential applications for legal practitioners. LEGRA demonstrates how graph-based datasets can open new directions for AI-powered legal research.