A great percentage of highways and roads in California are constructed with Hot Mix Asphalt (HMA) and, as California infrastructure ages, these highways and roads must be maintained and rehabilitated. Reclaimed Asphalt Pavement (RAP) is considered an excellent alternative to virgin (raw, unprocessed) materials because it reduces the use of virgin aggregate and binder. Also, the use of RAP decreases the amount of construction waste placed into landfills. This laboratory study investigated the effect of two different polymer fibers on the mechanical properties of HMA with RAP. Three different HMA with RAP mixes were used in the study. One mix that is commonly utilized on the Central Coast of California contained 15% RAP while the second and third mixes contained 25% and 40% RAP. Three different fiber dosages (0.05%, 0.10% and 0.15% of the total mix) were investigated. Specimens were prepared and tested for rutting and moisture sensitivity using Hamburg Wheel Tracker (HWT). Test results showed that adding fibers improved resistance to rutting for mixes with RAP content higher than 25%. Also, adding fibers improved mixes resistance to moisture damage. In addition, one of the two fibers used in the study outperformed the other. Overall, results indicate that adding polymer fibers to HMA mixes containing RAP has the potential to improve mixes resistance to rutting and moisture damage depending on fiber dosage and type. This study offers information valuable to the maintenance and rehabilitation of roads and highways. Keywords: Polymer Fiber, HMA, Rutting Resistance, High RAP, Stripping
Artemis lunar settlement project has renovated the interest in space gardening, a topic addressed in 2011 by the section Human Spaceflight (HSF) of the European Space Agency (ESA) through the instructional project Greenhouse in Space (GHIS). Targeted to young teens (ages 12 to 14), the GHIS compared the growth of the Arabidopsis thaliana on Earth, at different latitudes and without sunlight, and in space (micro-g environment). The plants were seeded contemporarily in standard miniature greenhouses by around eight-hundred European schoolers, by the crew of the isolation experiment Mars500 (Moscow), and by the Italian astronaut Paolo Nespoli within the MagISStra mission on the International Space Station (ISS). Despite a toxic mold hazard alert on the ISS, the GHIS became a successful STEM experience, enthusiastically accomplished in sundry schools. Its heritage about microgravity plant research was formidable also outside Europe, notably in the US National Aeronautics and Space Administration (NASA). We illustrate how GHIS was implemented in numerous hands-on laboratories via learning-by-doing, and how such massive participation persuaded the space agencies to pursue further educational ventures and partnerships. We focus on the GHIS activity of 25 Italian pupils from the Scientific High School "Giovanni Battista Grassi" in Latina as a case study. The key reference is a talk delivered in the 98th annual congress of the Italian Physical Society at the University of Naples (September 17–21, 2012) together with a poster presentation in the international conference GIREP-EPEC 2011 at the University of Jyväskylä in Finland (August 1–5, 2011). Keywords: GHIS, ISS, ESA, HSF, EPO, NASA, ASI, STEM, HOL, ESD, NDVI, SEND, ICT, TRL, secondary school, educational project, Arabidopsis thaliana, Artemis
Recursion is a difficult mathematical concept. It can lead to infinite loops in computers but on the other hand, if controlled, helps to train our natural neuronal network in our brain. Nevertheless, in the domain of τέχνη, recursion is difficult to handle. For Ancient Greek philosophers, such as Socrates, Plato, and Aristotle, τέχνη was a difficult term, most probably because they did not yet have the notion of mathematical recursion. The difference between γνώση resp. επιστήμη and τέχνη – observational knowledge (Gnosis) respectively science (Episteme), and technology (Techne) is reflected in today’s discussion about explainable AI. This paper attempts to address today’s problems with AI, whether AI becomes trustworthy, or authorities even can certify AI for safety purposes, by recurring to these antique philosophical notions. Combinatory Logic is explained, the graph model introduced, recursiveness discussed, and outlined how to make current AI explainable by defining Controlling Combinators. Keywords: Explainable AI, Theoretical Computer Science, Graph Model of Combinatory Logic, Safe AI, Automated Decision Making, Controlling Combinators, Intelligent Systems.
Generative Artificial Intelligence (AI) tools are rapidly becoming embedded within landscape architecture studios, raising important questions about authorship, accuracy, and their potential to reshape core analytical and representational workflows in the classroom setting. This study evaluates the effectiveness, limitations, and pedagogical implications of AI-based design workflows and applications within an undergraduate landscape architecture curriculum. Senior-level students at the University of Guelph completed the Urban Atlas – Community Case Studies assignment, producing figure-ground maps, analytical diagrams, and public-realm visualizations for selected urban districts. Sixteen groups participated: seven employed AI-dominant workflows incorporating platforms such as MidJourney, DALL-E, Stable Diffusion, Firefly, Runway ML, Aino, Meshy, Gemini, and ChatGPT, while nine groups relied primarily on traditional digital tools including Photoshop, Illustrator, InDesign, ArcGIS, AutoCAD, Rhino, and Blender. All final submissions were evaluated blindly by external reviewers, including principals and senior associates from landscape architecture and urban design firms, using a standardized 25-point scoring rubric. Recent AI research in the environmental design disciplines has shifted from data-driven optimization and environmental modelling toward tools capable of producing images, text, diagrams, three-dimensional forms, code, and analytical diagramming with rapid iterations. Within the studio context, this technological shift raises important questions about representation, authorship, learning outcomes, and the evolving skillsets required of contemporary landscape architects. Emerging scholarship increasingly positions AI as a creative ideation platform, providing new modes of visual experimentation, conceptual development, and rapid visualizations, provided that its use is framed within critical and ethical pedagogical structures. Students using conventional design software and workflows indicate greater control over linework, mapping accuracy, hierarchy, and labeling conventions, though it can result in longer production times and less compelling visual experimentation. Reviewer comments reflected these outcomes. AI submissions were noted for visual richness and the traditional submissions were commended for stronger analytical output. The results indicate that AI tools currently function as augmentative rather than transformative components of the design studio. Their primary strengths lie in the ideation, visualization, and stylistic exploration. The traditional digital skills remain essential for accurate, measurable, scalable, and analytically outputs. The study highlights the need for pedagogical models that integrate AI literacy while preserving foundational competencies in mapping, spatial analysis, and graphic communication. Keywords: visual representation, Artificial Intelligence (AI), landscape architecture, site analysis, design communication
This systematic review and meta-analysis aims to evaluate the application of robotic and drone technology in waste management, specifically focusing on image recognition and deep learning for waste identification and categorisation. Due to urbanisation and the rapid growth of modern cities, ecosystems face challenges such as waste management. Therefore, there is a growing trend to develop innovative, efficient and environmentally sustainable solutions. These solutions include integrating advanced technologies such as robotic and drone technologies. It is coupled with artificial intelligence (AI), particularly image recognition and deep learning algorithms, which have emerged as promising tools to enhance waste management processes. The methodology of this review involves a comprehensive search of databases such as IEEE Xplore for studies published up to June 2024. The inclusion criteria are studies that describe the use of robotics and drones equipped with image recognition and deep learning capabilities in waste management settings. The primary outcomes assessed include the accuracy of waste identification, the efficiency of waste categorisation, and the overall impact on waste management practices. The results of this meta-analysis reveal significant advancements in the accuracy and efficiency of waste management processes facilitated by robotic and drone technologies. Image recognition and deep learning algorithms have shown high efficacy in identifying and categorising various types of waste, thereby optimising the sorting and recycling process. Furthermore, the application of these technologies has demonstrated potential in reducing human exposure to hazardous waste, improving the speed of waste processing, and enhancing the precision of waste data collection for better environmental management. This review discusses the implications of these findings for developing intelligent waste management systems. It highlights the role of robotic and drone technology in advancing sustainable waste management practices, addressing challenges such as waste segregation and recycling rates. Additionally, the review identifies gaps in current research. It suggests directions for future studies, emphasising the need for scalable solutions and integrating these technologies into existing waste management frameworks. Through this analysis, the review contributes to understanding how emerging technologies can be leveraged to improve environmental sustainability and waste management efficiency. Keywords: robotic technologies, drone technologies, waste management, deep learning, image recognition, artificial intelligence, waste categorisation
As the global demand for sustainable and low-carbon energy sources intensifies, the United Nations’ Sustainable Development Goals (SDGs) advocate for renewable, sustainable, and environmentally friendly energy sources. Ocean wave energy, a predictable and stable renewable resource, presents a significant opportunity to diversify the energy mix, enhance energy security, and mitigate climate change. Despite having a moderate energy flux compared to open oceans, the Mediterranean Sea offers a unique opportunity for harnessing wave energy. This is mainly attributed to the stable sea conditions, lower installation risks, and proximity to coastal populations. However, realizing this potential requires overcoming challenges related to technology durability, high capital costs, environmental impact, and fragmented regional policies. Through a qualitative synthesis of recent resource assessments, this paper provides an overview of the Mediterranean Sea’s wave energy capacity, highlighting the performance and deployment of wave energy converters (WECs) in various locations within the basin. This study also explores the role of WECs, their technological advancements, integration with hybrid renewable systems, and emerging pilot projects in Mediterranean countries. The study highlights the need for regional cooperation, innovation in wave-hybrid systems, and climate adaptation strategies to harness wave energy as a viable contributor to the Mediterranean’s sustainable energy transition. Keywords: Mediterranean Sea, wave energy resource, SDGs, WECs, assessment
In recent years, substantial advancements in computer vision have been propelled by the advent of deep learning techniques. The transformative impact of deep learning is evident across various computer vision applications, spanning object detection, image classification, and semantic segmentation. Within the dynamic realm of architectural parametric design, a pivotal force shaping contemporary and future architectural pursuits, designers frequently encounter scenarios necessitating the reshaping, augmentation, or modification of building components and facades. This demand arises from diverse factors, including the integration of human-interactive facades, the implementation of sustainable facade systems, and more. In such instances, the foundational structure must possess malleability, enabling openness to edits. However, the grand scale and intricate details inherent in architectural designs pose a challenge, as a densely detailed mesh comprising millions of elements becomes increasingly unwieldy and challenging to modify for subsequent stages. This challenge underscores the critical need to generate objects capable of seamless evolution in tandem with evolving design requirements. This paper, will focus on categorizing 3D deep learning approaches into Learning-based 3D reconstruction in architecture and delving into their methods. Keywords: Algorithm Development, Computational Design, Learning-based Generative Design, Deep Learning, 3D Reconstruction
The first paper introducing the use of tropical semirings in public key cryptography was published in 2013. Since then, numerous tropical schemes have been proposed, incorporating various idempotent semirings. The most commonly used idempotent semirings for cryptographic purposes are the min-plus and max-plus semirings, with some schemes also utilizing the max-time and min-time semirings. The main mathematical problems that ensure the security of these schemes include the tropical discrete logarithm problem, the tropical semigroup action problem, the tropical semidirect product problem; the problem of solving two-sided linear systems in tropical semirings; tropical polynomial factorization and finding greatest common divisor problems, and the tropical matrix power function problem. Unfortunately, most of the proposed tropical schemes have been successfully attacked, raising concerns about the future of tropical cryptography. In this work, we review existing tropical schemes, discuss the complexity of the underlying problems, examine current attacks on these schemes, and explore future prospects and directions for cryptography based on tropical semirings. Keywords: semirings, tropical semirings, tropical cryptography, tropical problems, crypto attacks
Climate change is a reality, and the entire world is experiencing its effects. Coastal areas are highly affected by climate change. In addition to effects such as heat waves, floods, droughts, and decreased biodiversity, coastal areas are threatened by sea level rise and erosion. Türkiye, one of the most exposed countries, has struggled with an average temperature increase of 1 °C in the last decade. The coastal city of Izmir, Türkiye’s third largest city, has become vulnerable under increasing population pressure, causing massive urbanization of the fringe. This research investigates the feasibility and effectiveness of implementing Nature-Based Solutions (NBS) in the Konak district, which represents one of the city’s most ecologically vulnerable urban areas due to limited green space and high density. Within Konak, Alsancak, being one of its most historical neighborhoods, is used as a pilot site to test spatial interventions. The study aims to enhance coastal resilience and reduce vulnerability to climate change impacts. To achieve this, Geographic Information System (GIS) and InVEST tools were employed to evaluate key ecosystem services including carbon sequestration, urban heat island mitigation, air quality, and habitat quality. The vulnerability of Izmir, particularly Konak, to sea level rise, storm surges, and erosion was analyzed. Potential NBS suitable for adaptation were identified through parametric analysis. The socio-economic and environmental benefits of NBS were compared with traditional engineering-based solutions under two scenarios. Based on the findings, a comprehensive design proposal was developed to integrate NBS into the urban context. The reuse of post-industrial sites, such as the Sümerbank Factory, is proposed to enhance ecosystem connectivity and resilience. This research aims to serve as a roadmap for other coastal cities facing similar climate challenges. Keywords: climate change, nature-based solutions, ecosystem services, coastal resilience, urban heat island effect
Information technology is a major contributor to global energy consumption. This study estimates event-level energy usage through linear regression applied to synthetic signals generated from process event logs. These signals incorporate sine-modulated baselines, randomized noise, and event-specific energy costs. A custom framework supports signal generation, cost attribution, and performance evaluation. The model demonstrates high estimation accuracy for frequent and independent events, though performance declines with tightly sequenced process chains. R-squared values suggest a strong overall model fit, while percentage deviations vary depending on signal characteristics. The method assumes linearity, independence, and homoscedasticity—assumptions are largely satisfied in the synthetic dataset. Real-world applications, however, are limited by potential multicollinearity and require validation using actual event logs and measured energy data. Modular processes with stable event costs offer a promising testbed. Potential applications include process-level energy attribution, optimization, and client-specific billing. Although not yet deployable, this research lays the foundation for event-based energy modelling in complex IT environments. Keywords: process mining, energy efficiency, IT sustainability, data visualization, supply chain
Researching biosensing electrodes for glucose measurement is crucial for human health. These electrodes need to be accurate, reliable, and long-lasting. In this study, diamond-like carbon amorphous coatings were fabricated to contain nano-crystal Cu or Ag (a-C:Cu, a-C:Ag) by the complex deposition of the filtered cathode vacuum arc of graphite and magnetron sputtering of copper or silver. All electrodes showed good conductivity. The a-C:Cu electrodes had sp3C fraction 29~45 %, while a-C:Ag had 26–42 at.%. The morphologies, microstructures, and the carbon hybridization structure, and the resistance of the specimens were characterized using SEM, XRD, Raman, XPS spectroscopy, and the four-point-probe. It is found that the catalytic activity of nano-crystal Cu particles (np-Cu) for glucose oxidation is not affected by a carbon layer on their surface, and this is different from nano-crystal Ag particles (np-Ag), which are sensitive to surface conditions. Thus, a-C:Cu, made by one-step vacuum deposition, is suitable for sensing glucose in an alkaline solution. The anodic peak current at 0.21 V vs. Hg/HgO increased in two log-linear regions with glucose content. However, in the 1M KCl+50 Mm K3Fe(CN)6, a-C:Cu and a-C:Ag cannot be used as glucose-sensing electrodes. The a-C:Ag coatings present challenges for glucose sensing due to difficulties in controlling the surface conditions of np-Ag. Keywords：Transition metal doping; Diamond-like carbon; Electrochemical performance; Glucose; Biosensing.
In Estonia, upper secondary school students are required to take national examinations in Estonian, mathematics, and a foreign language. To support exam readiness, a free 20-week mathematics preparatory course was offered to all interested students. The course concluded with a final test covering key curriculum topics. This study analyzes the test results to identify strengths and weaknesses in students’ mathematical knowledge. Quantitative analysis was used to assess accuracy across topic areas, and qualitative examples illustrate frequent and critical errors. Topics such as algebraic manipulation and basic functions were generally well understood, whereas geometry proofs and word problems presented greater challenges. Common errors included misinterpreting problem statements, calculation mistakes, and incomplete reasoning in open-ended tasks. Results are compared with patterns from previous years’ national exam statistics to evaluate the course’s alignment with exam expectations. While individual exam data are confidential, aggregate comparisons help highlight consistent learning challenges. The study provides practical insights for improving future preparatory programs. It also supports broader educational goals promoted by the Estonian Engineering Academy (INSA), which funded the course and works to increase university applicants and reduce dropout rates by strengthening foundational skills in STEM education. Keywords: mathematics education, national exams, student performance analysis, error patterns, exam preparation
A novel Transformer variation architecture is proposed in the implicit sparse style. Unlike “traditional” Transformers, instead of attention to sequential or batch entities in their entirety of whole dimensionality, in the proposed Batch Transformers, attention to the “important” dimensions (primary components) is implemented. In such a way, the “important” dimensions or feature selection allows for a significant reduction of the bottleneck size in the encoder-decoder ANN architectures. The proposed architecture is tested on the synthetic image generation for the face recognition task in the case of the makeup and occlusion data set, allowing for increased variability of the limited original data set. Keywords: attention, transformer, synthetic image, data augmentation, emotion recognition
The ability to predict the peak impact of slams, which occur during high-speed motion of planing watercraft, could lead to significant opportunities for improving the safety of passengers and equipment aboard. We apply Random Forest and Convolutional Neural Networks to achieve this goal using peak acceleration, freefall duration, and velocity change from high-speed crafts to quantify the amplitude of wave impact. We analyze different characteristics separately in the freefall and impact regions for each wave. We report numerical results of both models, demonstrating the accuracy of these predictions. Keywords: high-speed craft, machine learning, random forest, convolutional, neural networks
Chemical signals transmit a large amount of information about the environment, enabling animals to identify desirable elements (such as food) or dangers to avoid (such as predators) and to locate mating partners. Considering the wide range of different chemicals, it is not surprising that organisms, living in different contexts, use an important repertoire of receptors and signaling pathways to probe their environment. The aim of this work was to describe the major stages in the development of the olfactory organ during the course of evolution. The few cells on the body surface of primitive organisms gave way to an individualised organ, at first rudimentary and then increasingly complex and efficient. Keywords: chemicals, sense organs, olfactory system, vertebrates, invertebrates
The paper considers the adequacy of the current Western Educational System in preparing Sapiens for an impending phase transition, potentially as significant as the cognitive, agricultural, scientific, and industrial revolutions that have shaped human history. Drawing on Yuval Noah Harari's conceptual equation B × C × D = AHH (Biological knowledge × Computational power × Data availability = Ability to Hack Humans), the paper explores the rapid advancements in biotechnology, artificial intelligence, and data science that are challenging traditional educational paradigms. The research critically analyzes the limitations of the scientific method that underpins modern education, highlighting its tendency towards hyper-specialization, oversimplification, and mutually exclusive logic. The author argues that this approach, while historically successful, may be insufficient to address the complex, interconnected challenges of the 21st century and beyond. To address these limitations, a more holistic educational framework is proposed that integrates Eastern philosophical traditions, meditative practices, and systems thinking alongside rigorous scientific inquiry. This approach aims to cultivate a deeper understanding of the subjective "meaning" in human experience, complementing objective scientific knowledge. Keywords: scientific method, STEM, higher education, systems view, quality
What does literacy mean in AI? Generative AI, especially Large Language Models (LLM), use statistical relevance to build responses to prompts. Literacy in education means understanding cause and effect from a text and why one observation follows another. It has to do with the real world and some understanding of how the grounding behaves and works. This kind of learning can be achieved with intelligent systems that combine AI engines with traditional programming, or in terms of the Graph Model of Combinatorial Logic: Observations and Concepts with Lambda Concepts. In conclusion, it is very helpful to listen to other disciplines for making AI intelligent. The respective task list for AI engineers includes, but is not limited to, education and teaching to children and humans. Keywords: artificial intelligence, generative pretrained translators, knowledge acquisition transformation, intelligent systems
Pome and stone fruit trees are heterozygous and cannot be propagated by seed. Various techniques are used for their vegetative propagation worldwide. The experiment's results are noteworthy, especially in the realm of fruit tree propagation. By utilizing a heated callus with a hot water system, combined with the cleft grafting method, successful propagation of both pome and stone fruit species has been accomplished. The experiment's results were intriguing, revealing varying success rates among different fruit species. The apple, pear, and plum fruit species achieved a higher percentage of callus-forming, adapted, and fruit-planting material compared to the sweet cherry. The highest success rate was observed in apples – 83.1%, followed by pears – 67.5% and plums 63.8%. The sweet cherry, a stone fruit species, had the lowest success rate at 20.6%. These findings open up new avenues for further research and experimentation. The use of a hot water system during the winter dormancy of apple, pear, and plum species has proven to be a successful propagation method. Therefore, we confidently recommend this method for these species, as it can greatly enhance fruit tree propagation practices. This recommendation is based on the solid results and advancements achieved in our experiment. Keywords: fruit tree, hot callus, grafting, planting material, cleft technique
Climate changes are more and more evident and their effect is increasingly extensive, and in the current context the environmental taxes may become a key factor in ensuring the sustainable development for the entire society. This article presents a medium-term analysis of the main categories of environmental taxes, their evolution compared to the investments for air and climate protection, as a percentage of GDP, made in Romania. Four main categories of environmental taxes: energy taxes (including transport fuels); transport taxes (excluding transport fuels); pollution taxes and resource taxes are collected in Romania, yearly. The data used in this study provides from the National Institute of Statistics. During 2006-2020, in Romania the highest percentage is represented by energy taxes 88%, in second place are taxes for transports 10% and in the third and fourth places with insignificant percentages (about 1%) are the taxes for resources and for pollution. From the four categories of environmental taxes, it can be seen that resource taxes have a decreasing trend from 51.6 million euros in 2006 to 3.84 million euros in 2020, while energy taxes, transport taxes and pollution taxes have an increasing trend. Keywords: climate changes, environmental taxes, sustainable development, air and climate protection
The escalating generation of chemical waste in healthcare facilities is a pressing concern, particularly during rapidly spreading epidemics. This study proposes a system dynamics model to predict hospital chemical-waste generation rates. The model incorporates various variables, such as patient arrival and departure rates, to provide accurate estimations of waste generation. A case study conducted at a hospital validates and demonstrates the proposed model's practical application. The findings reveal that specific departments, such as Main Operations, Obstetrics, and Catheter, significantly influence healthcare chemical-waste generation rates. Additionally, the Tissue Department plays a substantial role. This research has two important implications. Firstly, it offers valuable insights into the complex factors affecting waste generation in healthcare facilities, identifying the departments that contribute most to the problem. Secondly, it provides waste management departments with a insights for capacity planning, scheduling, and resource allocation. Hospitals can enhance their waste management practices, leading to improved environmental sustainability and better public health outcomes. This study's ability to anticipate chemical waste generation and drive the development of comprehensive and long-term waste management programs is highlighted by its identification of the most significant departments in generating chemical waste. This study blends predictive intelligence with pragmatic insights, emerging as a crucial instrument for tackling the severe difficulties posed by the rising flow of chemical waste. It ultimately protects both individual well-being and the balance of the ecosystem. Keywords: system dynamics, chemical waste, healthcare facilities
