Search Results (14,737)

This research investigates the feasibility of producing eco-friendly self-compacting concrete (SCC) by partially replacing both fine and coarse natural aggregates with waste marble sludge (WMS), a byproduct of the marble industry. The objective is to evaluate whether this substitution enhances or compromises the concrete’s performance while contributing to sustainability. A comprehensive experimental program was conducted to assess fresh and hardened properties of SCC with varying WMS content. Fresh-state tests—including slump flow, T₅₀ time, and V-funnel flow time—were used to evaluate workability, flowability, and viscosity. Hardened properties were measured through compressive, flexural, and Brazilian tensile strengths, along with water absorption after 28 days of curing. The mix with 10% replacement of both sand and coarse aggregate showed the most balanced performance, achieving a slump flow of 690 mm and a V-funnel time of 6 s, alongside enhanced mechanical properties—compressive strength 48.6 MPa, tensile strength 3.9 MPa, and flexural strength 4.5 MPa—and reduced water absorption (4.9%). A complementary cost model quantified direct material cost per cubic meter and a performance-normalized efficiency metric (compressive strength per cost). The cost decreased monotonically from 99.1 $/m³ for the base mix to $90.7 $/m³ at 20% + 20% WMS (−8.4% overall), while the strength-per-cost peaked at the 10% + 10% mix (0.51 MPa/USD; +12% vs. base). Results demonstrate that WMS can simultaneously improve rheology and mechanical performance and reduce material cost, offering a practical pathway for resource conservation and circular economy concrete production. Full article

The efficient resource utilization of industrial solid wastes, such as ground granulated blast-furnace slag (GGBS) and coal gangue (CG), is essential for sustainable development. However, their activation commonly depends on expensive and corrosive chemical alkalis. This study proposes a solution by developing a fully waste-based cementitious material using calcium carbide slag (CS), another industrial residue, as an eco-friendly alkaline activator for the GGBS-CG system. The influence of CS dosage (0–20 wt%) on hydration evolution and mechanical properties was examined using uniaxial compression testing, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). The results indicated that a CS dosage of 10 wt% yielded the highest compressive strength, reaching 10.13 MPa—a 16.5% improvement compared to the 20 wt% group. This enhancement is ascribed to the formation of hydrotalcite (HT) and calcium silicate hydrate (C-(A)-S-H) gel, which densify the microstructure. In contrast, higher CS contents led to a passivation effect that restrained further reaction. This work offers a practical and theoretical basis for the development of low-carbon, multi-waste cementitious materials and presents a promising strategy for large-scale valorization of industrial solid wastes. Full article

Agricultural trade not only facilitates the exchange of final products but also leads to the indirect transfer of arable land resources involved in their production processes across countries. These indirect flows are commonly referred to in the literature as virtual land flows or virtual land trade. An in-depth understanding of the factors influencing virtual land flows is crucial for both the management of these flows and the sustainable and efficient allocation of limited arable land resources on a global scale. The objective of this study is to identify the key determinants that influence virtual land flows in Türkiye’s trade of plant-based agricultural products. To achieve this, the virtual land trade balance for Türkiye was computed by estimating the import and export volumes of virtual land from 1986 to 2019, based on crop, year, and country-specific yield values. Subsequently, the relationship between Türkiye’s virtual land trade balance and macroeconomic and environmental variables—such as Gross Domestic Product (GDP), the real effective exchange rate, annual total precipitation, per capita arable land, and fertilizer usage—was investigated using the ARDL bounds testing approach. The findings of this study indicate that the most significant factors influencing Türkiye’s virtual land flows are per capita arable land endowment and fertilizer usage. This result highlights the strong relationship between virtual land flows and variables related to productivity and natural resource endowment, while also emphasizing the importance of integrating sustainability considerations and environmental impacts into contemporary agricultural policy frameworks. Elucidating the dynamics of virtual land trade is a pivotal step toward ensuring the long-term sustainability of international agricultural trade, as well as the equitable and efficient allocation of arable land resources. Furthermore, it represents a fundamental strategy for global agricultural production, offering critical insights for shaping future agricultural policy and practice at the global level. Full article

Water resource management is critical for sustainable agriculture, especially in regions like Kazakhstan that face significant water scarcity challenges. This paper presents the development of a real-time water-level monitoring system designed to optimize water use in agriculture. The system integrates IoT sensors and cloud technologies, and analyzes data on water levels, temperature, humidity, and other environmental parameters. The architecture comprises a data collection layer with solar-powered sensors, a network layer for data transmission, a storage and integration layer for data management, a data processing layer for analysis and forecasting, and a user interface for visualization and interaction. The system was tested at the Left Bypass Canal in Taraz, Kazakhstan, demonstrating its effectiveness in providing real-time data for informed decision-making. The results indicate that the system significantly improves water use efficiency, reduces non-productive losses, and supports sustainable agricultural practices. Full article

To address the key issues in cooperative collision avoidance of multiple aircraft, such as unknown dynamics, external disturbances, and limited communication resources, this paper proposes a reference correction method based on the Artificial Potential Field-Control Barrier Function (APF-CBF) and combines it with a dynamic event-triggered mechanism to achieve efficient cooperative control. This paper adopts a Fuzzy Wavelet Neural Network (FWNN) to design a finite-time disturbance observer. By leveraging the advantages of FWNN, which integrates fuzzy logic reasoning and the time-frequency locality of wavelet basis functions, this observer can synchronously estimate system states and unknown disturbances, to ensure the finite-time uniformly ultimate boundedness of errors and break through the limitation of insufficient robustness in traditional observers. Meanwhile, the APF is embedded in the CBF framework. On the one hand, APF is utilized to intuitively describe spatial interaction relationships, thereby reducing reliance on prior knowledge of obstacles; on the other hand, CBF is used to strictly construct safety constraints to overcome the local minimum problem existing in APF. Additionally, the reference correction mechanism is combined to optimize trajectory tracking performance. In addition, this paper introduces a dynamic event-triggered mechanism, which adjusts the triggering threshold by real-time adaptation to error trends and mission phases, realizing “communication on demand”. This mechanism can reduce communication resource consumption by 49.8% to 69.8% while avoiding Zeno behavior. Theoretical analysis and simulation experiments show that the proposed method can ensure the uniformly ultimate boundedness of system states and effectively achieve safe collision avoidance and efficient formation tracking of multiple aircraft. Full article

Resource utilization of water treatment residuals (WTRs) has emerged as a significant focus in environmental engineering research. In this study, waste iron sludge from a groundwater de-ironing plant was used as the raw material. Ferric salts were recovered via sulfuric acid leaching and subsequently polymerized into polyferric sulfate (PFS) with varying basicity (B = 0.1–0.4) using the alkalization–aging method. The optimal leaching conditions were determined as a liquid–solid ratio of 10:1, a sulfuric acid concentration of 3 mol·L⁻¹, a reaction temperature of 70 °C, and a reaction time of 30 min, yielding a ferric leaching amount of 0.45 g Fe/g dry sludge. Characterization results revealed that the synthesized PFS exhibited similar ferric polymer species, functional group structures, and polymeric crystal structures to those of commercial PFS (CPFS). Coagulation performance tests demonstrated that at a dosage of 30 mg Fe/L, the prepared PFS achieved turbidity and UV₂₅₄ removal efficiencies of 96.88% and 81.87%, respectively, outperforming CPFS. In domestic wastewater treatment, combining the synthesized PFS with magnetic nanoparticles Fe₃O₄@C yielded a magnetic coagulant that further enhanced the removal of turbidity, chemical oxygen demand (COD), and total phosphorus (TP) to maximum efficiencies of 94.66%, 81.97%, and 98.08%, respectively. This study confirms the technical feasibility and environmental–economic benefits of preparing magnetic PFS coagulants from waste iron sludge for wastewater treatment. Full article

This research proposes a novel multi-objective optimization framework for virtual power plants (VPPs) operating in day-ahead electricity markets. The VPP integrates diverse distributed energy resources (DERs) such as wind turbines, solar photovoltaics (PV), fuel cells (FCs), combined heat and power (CHP) systems, and microturbines (MTs), along with demand response (DR) programs and energy storage systems (ESSs). The trading model is designed to optimize the VPP’s participation in the day-ahead market by aggregating these resources to function as a single entity, thereby improving market efficiency and resource utilization. The optimization framework simultaneously minimizes operational costs, maximizes system flexibility, and enhances reliability, addressing challenges posed by renewable energy integration and market uncertainties. A new flexibility index is introduced, incorporating both the technical and economic factors of individual units within the VPP, offering a comprehensive measure of system adaptability. The model is validated on IEEE 24-bus and 118-bus systems using evolutionary algorithms, achieving significant improvements in flexibility (20% increase), cost reduction (15%), and reliability (a 30% reduction in unsupplied energy). This study advances the development of efficient and resilient power systems amid growing renewable energy penetration. Full article

Traditional dairy products remain an essential yet underutilized component of Lebanon’s food system. Amid economic instability, supply chain fragility, and heavy reliance on imported dairy inputs (≈80% of demand), these products offer resilient, low-input alternatives rooted in centuries-old practices. This review analyzes key traditional Lebanese dairy products, including Labneh, Labneh–Anbaris, Akkawi, Shanklish, Halloumi, Karishi, Pressed–Brined Karishi (Lebanese Double-Cream), Qishta, and Kishk, using Codex Alimentarius and Tetra Pak classification frameworks. It examines their compositional attributes, milk-to-product conversion efficiency, preservation methods, and economic characteristics. The findings reveal a continuum from high-yield fresh cheeses to lower-yield preserved forms with extended shelf life, demonstrating diversified strategies for food security and resilience. Unlike prior studies focused mainly on composition or culinary aspects, this review integrates classification systems with cultural geography to map Lebanon’s traditional dairy landscape. It highlights strategies grounded in rural milk availability and artisanal know-how, revealing overlooked food system functions. These practices exemplify circular models that valorize whey, minimize waste, and preserve quality without refrigeration, aligning with sustainability goal SDG-12.3. This review calls for integrating these products into national food strategies, regulatory frameworks, and innovation systems, recognizing traditional Lebanese dairy as both cultural heritage and a strategic resource for a more self-sufficient and resilient food sector. Full article

Addressing the carbon footprint in pig production is a fundamental technical basis for achieving carbon neutrality and peak carbon emissions. Only by systematically studying the carbon footprint can the goals of carbon neutrality and peak carbon emissions be effectively realized. This study aims to reduce the carbon footprint through optimized feeding strategies based on minimizing carbon emissions. To this end, this study conducted a full-lifecycle monitoring of the carbon footprint during pig growth from December 2024 to May 2025, optimizing feeding strategies using a real-time pig weight estimation model driven by deep learning to reduce resource consumption and the carbon footprint. We introduce EcoSegLite, a lightweight deep learning model designed for non-contact real-time pig weight estimation. By incorporating ShuffleNetV2, Linear Deformable Convolution (LDConv), and ACmix modules, it achieves high precision in resource-constrained environments with only 1.6 M parameters, attaining a 96.7% mAP50. Based on full-lifecycle weight monitoring of 63 pigs at the Pianguan farm from December 2024 to May 2025, the EcoSegLite model was integrated with a life cycle assessment (LCA) framework to optimize feeding management. This approach achieved a 7.8% reduction in feed intake, an 11.9% reduction in manure output, and a 5.1% reduction in carbon footprint. The resulting growth curves further validated the effectiveness of the optimized feeding strategy, while the reduction in feed and manure also potentially reduced water consumption and nitrogen runoff. This study offers a data-driven solution that enhances resource efficiency and reduces environmental impact, paving new pathways for precision agriculture and sustainable livestock production. Full article

A learning management system (LMS) plays a crucial role in supporting students’ educational activities by centralized platforms for course delivery, communication, and student support. Recently, many universities have integrated chatbots into their LMS to assist students with various inquiries and tasks. However, existing chatbots often necessitate human interventions to manually respond to complex queries, resulting in limited scalability and efficiency. In this paper, we present a memory-augmented large language model (LLM) framework that enhances the reasoning and contextual continuity of LMS-based chatbots. The proposed framework first embeds user queries and retrieves semantically relevant entries from various LMS resources, including instructional documents and academic frequently asked questions. Retrieved entries are then filtered through a two-stage confidence filtering process that combines similarity thresholds and LLM-based semantic validation. Validated information, along with user queries, is processed by LLM for response generation. To maintain coherence in multi-turn interactions, the chatbot incorporates short-term, long-term, and temporal event memories, which track conversational flow and personalize responses based on user-specific information, such as recent activity history and individual preferences. To evaluate response quality, we employed a multi-layered evaluation strategy combining BERTScore-based quantitative measurement, an LLM-as-a-Judge approach for automated semantic assessment, and a user study under multi-turn scenarios. The evaluation results consistently confirm that the proposed framework improves the consistency, clarity, and usefulness of the responses. These findings highlight the potential of memory-augmented LLMs for scalable and intelligent learning support within university environments. Full article

This study aimed to evaluate the effects of jump rope training on cardiovascular capacity, assessed with the Ruffier test, and muscular strength, measured using isometric dynamometry (BioFET Mustec, Almere, The Netherlands), within a university physical education program. A total of 52 undergraduate students from non-specialist faculties at the University of Oradea were randomly assigned to either an experimental group (EG) or a control group (CG). Over eight weeks, the EG performed a ten-minute jump rope training session once per week in combination with cardiovascular exercises, while the CG participated only in cardiovascular exercises. Statistical analyses revealed a significant group effect on cardiovascular response (Ruffier Index: p = 0.019; Cohen’s d = −0.271) and a substantial increase in right lower limb strength (p = 0.003; d = 1.026) in the EG compared to the CG. Furthermore, improvements were observed in upper limb strength (left arm: p = 0.010; d = 0.922) and left lower limb strength (p = 0.027; d = 0.779). These findings suggest that incorporating jump rope training into university physical education classes may represent an effective and low-cost strategy to enhance both cardiovascular efficiency and muscular strength in young adults. Given its simplicity and affordability, jump rope training appears feasible for implementation in schools and universities, even where resources are limited. Future research should investigate its long-term effects across different populations. Full article

This study investigates the feasibility of incorporating chemically recycled polyol (glycolysate), derived from semi-rigid polyurethane waste from the building industry, into rigid PUF formulations intended for thermal insulation applications. Glycolysis was performed using a diethylene glycol–glycerol mixture (4:1) at 185 °C in the presence of a dibutyltin dilaurate (DBTDL) catalyst. The resulting glycolysate was characterized by a hydroxyl number of 590 mg KOH/g. Foams containing 5–50% recycled polyol were prepared and described in terms of foaming kinetics, cellular structure, thermal conductivity, apparent density, mechanical performance, dimensional stability, flammability, and volatile organic compound (VOC) emissions. The incorporation of glycolysate accelerated the foaming process, with the gel time reduced from 44 s to 16 s in the sample containing 40% recycled polyol, enabling a reduction in catalyst content. The substitution of up to 40% virgin polyol with recycled polyol maintained a high closed-cell content (up to 87.7%), low thermal conductivity (λ₁₀ = 26.3 mW/(m·K)), and dimensional stability below 1%. Additionally, compressive strength improvements of up to 30% were observed compared to the reference foam (294 kPa versus 208 kPa for the reference sample). Flammability testing confirmed compliance with the B2 classification (DIN 4102), while preliminary qualitative VOC screening indicated no formation of additional harmful volatile compounds in glycolysate-containing samples compared to the reference. The results demonstrate that glycolysate can be effectively utilized in high-performance insulation materials, contributing to improved resource efficiency and a reduced carbon footprint. Full article

Camelina sativa is an oilseed crop that has shown strong promise as a biofuel feedstock. The profile of fatty acids greatly influences the oil quality; however, genetic mechanisms that determine the natural variation of fatty acid composition in camelina are not fully understood. A genome wide association study (GWAS) was performed to uncover genetic loci that may contribute to the contents of major fatty acids such as oleic and linolenic acids in camelina seed. Two approaches were taken to improve the GWAS efficiency. First, growing a diversity panel of 212 accessions in four locations and two nitrogen fertilization conditions revealed great variation in fatty acid contents in seeds. Second, using an improved reference genome, abundant markers, including 203,320 single nucleotide polymorphisms (SNPs) and 99,067 insertions/deletions (indels), were developed, which refined the population structure of the diversity panel. GWAS resulted in 118 genetic markers across 31 trait/treatment conditions. Closely linked markers were determined based on linkage decay and by comparing secondarily associated markers when highly associated ones were removed. Candidate genes were examined by comparing the pangenomes of 12 high-quality reference genomes. This study provides new resources to understand seed lipid metabolism and improve camelina oils through molecular breeding. Full article

Underutilized leafy greens are considered as functional plant species primarily due to their resilience to abiotic stress factors, low nutrient requirements, and high nutritional value. Over the past 30 years, many experiments have been conducted to identify nutrient-efficient species, cultivars, landraces, and ecotypes, but few have successfully entered mainstream agriculture. The integration of these species into advanced horticultural systems, such as hydroponics, has the potential to further strengthen their impact on sustainable agriculture by minimizing use of resources, enabling year-round cultivation, and improving the nutritional profile of the harvested produce. As leafy vegetables, a primary food safety concern is the accumulation of nitrates in the leaves. In hydroponics, this issue is usually addressed by balancing the NH₄-N/total-N ratio (N_r) in the nutrient solution. Provided that the plant responses to high ammonia supply are species-dependent, three wild leafy greens, iceplant, corn salad, and common purslane, were grown in a soilless culture, with perlite as the substrate, under low (0.04) and high (0.12) N_r on a molar basis. Additionally, the potential of protein hydrolysates (PH) and seaweed extracts (SW) to alleviate plant tolerance to excess ammonia supply was also investigated. In terms of yield, high N_r led to significant yield restrictions in iceplant that reached 28%, while on corn salad, it had a positive impact, with yield increasing by 18%. Both biostimulant applications enhanced iceplant productivity only under optimal N_r conditions (0.04). Apart from yield responses, biofertilizers had no substantial impact on the plant nutrient profile. In contrast, high N_r suppressed nitrate accumulation in fresh leaves, while enhancing micronutrient uptake in all three plant species. In conclusion, this study highlights the pivotal role of biostimulants as plant stress protectors and growth regulators and identifies the optimal N_r ratio for maximizing the yield and quality performance of corn salad, iceplant, and common purslane in soilless cultivation systems. Full article

The design phase of buildings represents a dynamic and complex process, constantly evolving with modifications and feedback. It involves numerous professionals from various specialties, resulting in a fragmented and iterative trial-and-error process. Analyzing waste is the first step towards increasing the efficiency of the design process for high-rise buildings using Lean methodology. Initially, the design phase was characterized, and processes were classified into productive, contributory, and non-contributory work. Typical waste in building design was identified, analyzed, and ranked based on frequency and impact to facilitate understanding and elimination. Three traditional design stages were identified: Schematic Design (SD), Design Development (DD), and Construction Documentation (CD). A total of 33 typical wastes were classified into the eight Lean categories. Key waste ranked by the Frequency-Adjusted Importance Index (FAII) for cost, schedule, and quality metrics were late-stage design changes, waiting for resources and information, rework, and late-stage clarification of requirements. Full article