Search Results (474)

The study focuses on the experimental investigation of the impact of using coconut oil (CO) as a phase-change material (PCM) for heat storage on the root-zone temperature within a greenhouse in Adana, Türkiye. The study examines the efficacy of PCM as latent heat-storage material and predicts root-zone temperature using three machine learning algorithms. The dataset used in the analysis consists of 2658 data at hourly resolution with six variables from February to April in 2022. A greenhouse with PCM shows a remarkable increase in both ambient (0.9–4.1 °C) and root-zone temperatures (1.1–1.6 °C) especially during the periods without sunlight compared to a conventional greenhouse. Machine learning algorithms used in this study include Multivariate Adaptive Regression Splines (MARS), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). Hyperparameter tuning was performed for all three models to control model complexity, flexibility, learning rate, and regularization level, thereby preventing overfitting and underfitting. Among these algorithms, R² values for testing data listed from largest to smallest are MARS (0.95), SVR (0.96), and XGBoost (0.97), respectively. The results emphasize the potential of machine learning approaches for applying thermal energy storage systems to agricultural greenhouses. In addition, it provides insight into a net-zero energy greenhouse approach by storing heat in a bio-based PCM, alongside its implementation and operational procedures. Full article

Integrating bioluminescent organisms as passive lighting sources in the built environment is currently a hot topic. However, there are several limitations facing the implementation and up-scaling of these naturally bioluminescent organisms in the built environment on architectural and urban scales, such as the scale, sensitivity, enclosure, and difficulty of maintenance. Moreover, there are complex technicalities and operational aspects of conventional bioreactors that host these bioluminescent agents, especially in terms of managing their recharge and effluent, not to mention their high maintenance cost. The current work offers a sustainable, stand-alone, bioluminescent urban screen system employing Aliivibrio fischeri CECT 524 bioink on 3D-printed customized scaffolds as bioreceptive panel design based on a field-diffusion pattern to host the bioluminescent bacterial bioink. The field-diffusion pattern was employed thanks to its proven efficiency in entrapment of the various microbial cultures. Three different growth media were tested for culturing Aliivibrio fischeri CECT 524, including Luria Bertani Broth (LB), the Tryptone Soy Broth (TSB), and the standard Marine Broth (MB). The results revealed that the Marine Broth (MB) media achieved the highest bioluminescent intensity and duration. The maximum light emission typically in range of ~490 nm of blue–green light captured by a conventional reflex camera (human eye vision) was observed for 10 consecutive days in complete darkness after 3–10 s, at a room temperature of 25 °C. This was visible mainly at the thin curvilinear peaks of the 3D-printed field pattern. P1 achieved the highest performance in terms of visible blue–green light, and a duration of 10 days of active bioluminescence was achieved without the need for refilling, thanks to the high number of peaks and narrow wells at <0.5 cm of its field-diffusion pattern. This study proves the efficiency of this biomimetic pattern in terms of the bioreceptivity of the bioluminescent bacterial bioink. Furthermore, the proposed 3D-printed urban screens proved their economic sustainability in terms of affordability and their minimized production processes, in addition to their easy maintenance and recharge. These results qualify these 3D-printed bioluminescent urban screens for easy and decentralized adoption and application on an architectural and urban scale. Full article

Pollution and freshwater scarcity, coupled with the energy sector’s continued dependence on fossil fuels, constitute a dual challenge to sustainable development. A promising response is biosystems that jointly address wastewater treatment and the production of renewable products. This review centers on a managed consortium of aquatic macrophytes and microalgae, in which the spatial architecture of plant communities, rhizosphere processes, and the photosynthetic activity of microalgae act in concert. This configuration simultaneously expands the spectrum of removable pollutants and yields biomass suitable for biorefinery, thereby linking remediation to the production of energy carriers and bioproducts within a circular bioeconomy. The scientific novelty lies in treating the integrated platform as a coherent technological unit, and in using the biomass “metabolic passport” to align cultivation conditions with optimal valorization trajectories. The work offers a practical framework for designing and scaling such consortia that can reduce the toxicological load on aquatic ecosystems, return macronutrients to circulation, and produce low-carbon energy carriers. Full article

A vision-based autonomous system for emasculating okra enhances agriculture by enabling precise flower bud identification, overcoming the labor-intensive, error-prone challenges of traditional manual methods with improved accuracy and efficiency. This study presents a framework for an adaptive, automated bud identification method to assist the emasculation process, hybridized optical coherence tomography (OCT). Three YOLOv8 variants were evaluated for accuracy, detection speed, and frame rate to identify the most efficient model. To strengthen the findings, YOLO was hybridized with OCT, enabling non-invasive sub-surface verification and precise quantification of the emasculated depth of both sepal and petal layers of the flower bud. To establish a solid benchmark, gold standard color histograms and a digital imaging-based method under optimal lighting conditions with confidence scoring were also employed. The results demonstrated that the proposed method significantly outperformed these conventional frameworks, providing superior accuracy and layer differentiation during emasculation. Hence, the developed YOLOv8 hybridized OCT method for flower bud identification and emasculation offers a powerful tool to significantly improve both the precision and efficiency of crop breeding practices. This framework sets the stage for implementing scalable, artificial intelligence (AI)-driven strategies that can modernize and optimize traditional crop breeding workflows. Full article

Background/Objectives: Programmed cell death-ligand 1 (PD-L1) is one of the key biomarkers for immune checkpoint inhibitors. We are developing a novel PD-L1 CAL10 immunohistochemistry (IHC) assay (Leica Biosystems) on BOND-III staining system and have analyzed its initial performance by comparing it to the PD-L1 SP263 assay (Ventana) assay in a feasibility study. The study objective was to determine the concordance of the Leica Biosystems PD-L1 CAL10 assay with the comparator SP263 assay at the tumor proportion score (TPS) cutoff of ≥50% in non-small cell lung cancer (NSCLC) tissue samples. Additionally, the concordance between the two assays at the TPS cutoff of ≥1% was also evaluated. For informational purposes, we also evaluated the concordance between manual slide reads vs. digital reads (whole slide images generated using the Aperio GT 450) for the CAL10 assay. Methods: Two pathologists read and scored the glass slides. The CAL10 PD-L1 assay concordance with the PD-L1 SP263 assay was evaluated by assessing the agreement rates between the two assays. Results: The lower bound of the 95% confidence interval (CI) of the overall percent agreement (OPA) at ≥50% cutoff was 86.2%, while for ≥1% TPS cutoff, it was 94.0%, which met the predefined target of a minimum OPA lower bound of the 95% CI value of 85%. Conclusions: The Leica Biosystems CAL10 PD-L1 assay has demonstrated comparable performance to the SP263 assay. Additionally, the PD-L1 CAL10 stained glass slides and the corresponding whole side images generated by GT 450 showed comparable concordance rate. Full article

Crop yield prediction (CYP) has become increasingly critical in addressing the adverse effects of abnormal climate and enhancing agricultural productivity. This review investigates the application of advanced Artificial Intelligence (AI) techniques including Machine Learning (ML), Deep Learning (DL), Ensemble Learning, and Explainable AI (XAI) to CYP. It also explores the use of remote sensing and imaging technologies, identifies key environmental factors, and analyzes the primary causes of yield reduction. A wide diversity of input features was observed across studies, largely influenced by data availability and specific research goals. Stepwise feature selection was found to be more effective than increasing feature volume in improving model accuracy. Frequently used algorithms include Random Forest (RF) and Support Vector Machines (SVM) for ML, Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for DL, as well as stacking-based ensemble methods. Although XAI remains in the early stages of adoption, it shows strong potential for interpreting complex, multi-dimensional CYP models. Hyperspectral imaging (HSI) and multispectral imaging (MSI), often collected via drones, were the most commonly used sensing techniques. Major factors contributing to yield reduction included atmospheric and soil-related conditions under abnormal climate, as well as pest outbreaks, declining soil fertility, and economic constraints. Providing a comprehensive overview of AI-driven CYP frameworks, this review offers insights that support the advancement of precision agriculture and the development of data-informed agricultural policies. Full article

Integrating renewable energy with biomass valorization offers a scalable pathway toward circular and climate-resilient campus operations. This study presents a replicable model implemented at Alanya Alaaddin Keykubat University (ALKU, Türkiye), where post-consumer food waste from 30 cafeteria menus is converted into pet food and compost using a 150 L ECOAIR-150 thermal drying and grinding unit powered entirely by a 1.7 MW rooftop photovoltaic (PV) system. The PV infrastructure, established under Türkiye’s first public-sector Energy Performance Contract (EPC), ensures zero-electricity-cost operation. On average, 260 kg of organic waste are processed monthly, yielding 180 kg of pet food and 50 kg of compost, with an energy demand of 1.6 kWh h⁻¹ and a conversion efficiency of 68.4%, resulting in approximately 17.5 t CO₂ emissions avoided annually. Economic analysis indicates a monthly revenue of USD 55–65 and a payback period of ~36 months. Sensitivity analysis highlights the influence of input quality, seasonal waste composition, PV output variability, and operational continuity during academic breaks. Compared with similar initiatives in the literature, this model uniquely integrates EPC financing, renewable energy generation, and waste-to-product transformation within an academic setting, contributing directly to SDGs 7, 12, and 13. Full article

The accumulation of agricultural residues presents an environmental challenge. PBLs have emerged as effective agents for biodegrading such biomass, producing frass rich in HA with low phytotoxicity, positioning it as a potential biofertilizer. However, the influence of PBL bioconversion on HA yields and microbial communities across different substrates remains underexplored. In this research, PBL is fed on two Pleurotus SMSs and oak sawdust. The resulting frass was characterized and showed low phytotoxicity based on seed germination and plant growth. The extracted HA quantity and quality were significantly higher in frass than diet samples. Microbial profiling using 16S rRNA gene high-throughput sequencing revealed the enrichment of potential PGP genera, including Pseudoxanthomonas, Cellulomonas, Flavobacterium, and Mucilaginibacter. In addition, the actino-genera Cellulomonas, Demequina, Xylanimicrobium, Mycolicibacter, Nakamurella, and Glutamicibacter were positively correlated with HA content and quality parameters. This study highlights the potential of PBL systems in waste valorization and biofertilizer production as a novel approach for sustainable agriculture. Full article

Background/Objectives: Cervical cancer (CC), caused mainly by high-risk human papillomavirus (hrHPV), remains a global health challenge despite being preventable. The disease’s incidence and mortality rates significantly vary across regions, highlighting the need for effective screening programs. The World Health Organization prioritizes CC screening to monitor and eliminate the disease. The Screening for Cervical Cancer and Early Treatment (SCCET) project aligns with this goal by adhering to the 2012 National Program for Cervical Cancer Screening and implementing the European Guidelines of Quality Assurance. Methods: The SCCET initiative facilitates access to equitable and high-quality preventive medical services for Romanian women, incorporating the Babeș–Papanicolaou smear (Pap test) and/or hrHPV DNA screening. Focused on the Muntenia Region of South Romania, the project leverages a methodical approach to gather substantial medical data on hrHPV infection rates and cervical lesions, thereby improving health management for women in the screening program. Results: Through public information and educational campaigns about HPV and its link to CC, the SCCET project has significantly enhanced participation in the screening program. In the study conducted between September 2022 and March 2023, 14,385 women aged 30 to 64 years voluntarily participated; of these, 11,996 (83.4%) underwent primary hrHPV DNA screening and were tested using the PowerGene 9600 Plus Real-Time polymerase chain reaction (PCR) system and the commercial Atila BioSystems AmpFire^® HPV Screening 16/18/HR test, version 4.1. This substantial participation indicates a positive shift in public attitudes towards CC prevention and highlights the success of the project’s outreach efforts. The study revealed an overall prevalence of hrHPV infection of 12.24%; of these, the most common genotype was other hrHPV types (9.84%), followed by HPV 16 (2.3%) and HPV 18 (0.71%). Conclusions: The SCCET project’s recent data on primary hrHPV DNA screening showcases its pivotal role in advancing the management and prevention of CC in Romania. By providing accessible, high-quality screening services and fostering public education on HPV, the initiative has made significant strides toward reducing the burden of CC. This effort aligns with global public health goals, and providing updated information on the prevalence of hrHPV types will allow the development of personalized national screening and vaccination programs to eradicate CC. Full article

Medium-chain fatty acid triglycerides (MCTs) possess antibacterial, antiviral, nutritional, and other biological activities and have demonstrated significant application potential in humans and terrestrial animals. In recent years, with the development of the green aquaculture industry, MCTs have been gradually applied to aquaculture animals, which can enhance growth performance, improve flesh quality, regulate lipid metabolism, boost immune activity, and modulate the intestinal flora, thereby improving the production efficiency of aquaculture. This paper elaborates in detail on the biological activities of MCTs and their applications in aquatic animals, providing a theoretical and practical basis for the application of MCTs in aquaculture. Full article

The integration of unmanned aerial vehicle (UAV) remote sensing and deep learning has emerged as a highly effective strategy for inventorying forest resources. However, the spatiotemporal variability of forest environments and the scarcity of annotated data hinder the performance of conventional supervised deep-learning models. To overcome these challenges, this study has developed efficient tree (ET), a semi-supervised tree detector designed for forest scenes. ET employed an enhanced YOLO model (YOLO-Tree) as a base detector and incorporated a teacher–student semi-supervised learning (SSL) framework based on pseudo-labeling, effectively leveraging abundant unlabeled data to bolster model robustness. The results revealed that SSL significantly improved outcomes in scenarios with sparse labeled data, specifically when the annotation proportion was below 50%. Additionally, employing overlapping cropping as a data augmentation strategy mitigated instability during semi-supervised training under conditions of limited sample size. Notably, introducing unlabeled data from external sites enhances the accuracy and cross-site generalization of models trained on diverse datasets, achieving impressive results with F1, mAP50, and mAP50-95 scores of 0.979, 0.992, and 0.871, respectively. In conclusion, this study highlights the potential of combining UAV-based RGB imagery with SSL to advance tree species identification in heterogeneous forests. Full article

Energy Performance Contracts (EPC) are performance-based financing mechanisms designed to improve energy efficiency and support renewable energy adoption in the public sector. This study examines the economic efficiency of a 1710.72 kWp solar power plant (SPP), implemented under an EPC at Alanya Alaaddin Keykubat University, using a regression-based analysis. The model evaluates the effects of solar radiation, investment cost, and electricity sales price on unit production cost, and its predictions were compared with actual production data. Results show the system exceeded the EPC contract target by 16.2%, producing 2,423,472.28 kWh in its first year and preventing 1168.64 tons of CO₂ emissions. The developed multiple linear regression model achieved a predictive error margin of 14.7%, confirming its validity. This study highlights the technical, economic, and environmental benefits of EPC applications in Türkiye’s public institutions and offers a practical decision-support framework for policymakers. The novelty lies in integrating a regression model with operational data and providing a comparative assessment of planned, predicted, and actual outcomes. Full article

Both biosystems and engineering fields demand advanced friction-reducing and lubricating materials. Due to their hydrophilicity and tissue-mimicking properties, hydrogels are ideal candidates for use as lubricants in water-based environments. They are particularly well-suited for applications involving biocompatibility or interactions with intelligent devices such as soft robots. However, external environments, whether within the human body or in engineering applications, often present a wide range of dynamic conditions, including variations in shear stress, temperature, light, pH, and electric fields. Additionally, hydrogels inherently possess low mechanical strength, and their dimensional stability can be compromised by changes during hydration. This review focuses on recent advancements in using environmentally responsive hydrogels as lubricants. It explores strategies involving physical or structural modifications, as well as the incorporation of smart chemical functional groups into hydrogel polymer chains, which enable diverse responsive mechanisms. Drawing on both the existing literature and our own research, we also examine how composite friction materials where hydrogels serve as water-based lubricants offer promising solutions for demanding engineering environments, such as bearing systems in marine vessels. Full article

The search for waste management opportunities is crucial for achieving environmentally friendly waste practices and ensuring the country’s energy security. This research aimed to valorize biomass and waste generated in greenhouses and to analyze the potential for electricity production from this waste. The analyses compared the situations in Turkey and Poland, where greenhouse production of vegetables is developing and constitutes an important link in agricultural activities, despite differences in climatic conditions. The cultivation of vegetables and flowers under cover is rapidly expanding in both countries and, with changing climatic conditions, is expected to shape the future of agriculture. In addition to estimating the energy that can be obtained, the study also evaluated the economic benefits of such a solution and the volume of avoided CO₂ emissions from fossil fuels. The issue of utilizing these wastes is significant because current methods of their management do not lead to energy production, so their considerable energy potential is wasted, as highlighted in this study. Moreover, there is a lack of similar studies in the literature. The plant species chosen as materials in this study were tomatoes, peppers, eggplant, watermelon, and melon in the case of Turkey. For Poland, the analysis was conducted for tomatoes and greenhouse cucumbers. These crops represent the largest cultivated areas under cover in the respective countries. Results indicated that the average yearly amount of vegetable residue is approximately 463 thousand Mg in Turkey, and 77 thousand Mg in Poland. The estimated annual electricity potential is 430 GWh in Turkey and 80 GWh in Poland. Considering the efficiency of power generation in a typical power plant, the real amount of electricity to be obtained is 0.46 MWh per Mg of waste in Turkey and 0.52 MWh in Poland. Full article

Golden mulberry (Morus macroura Miq.) is favored for its rich bioactive components and unique flavor, but fruit quality depends on harvest time. In the present study, golden mulberry fruits were collected at 18 (T1), 21 (T2), 24 (T3), and 27 (T4) days after flowering to investigate the impact of the harvesting stage on its physicochemical properties, antioxidant capacity, and aroma profile. Physicochemical parameters such as total phenols, total soluble solids, titratable acidity, and sensory evaluation revealed that the hardness gradually decreased with fruit maturity, whereas the weight of single fruit, total soluble solids, and solid–acid ratio increased, and soluble sugars, titratable acidity, total polyphenols and sugar–acid ratio initially increased and then decreased. Antioxidant capacity, measured by ABTS, FRAP, and DPPH assays, decreased with ripening, but stabilized at T3. In addition, the aroma components of golden mulberry fruit were analyzed by GC-MS, and it was found that aldehyde, alcohol, and ester were the main aroma components of the golden mulberry fruit. Combining the physicochemical indices, sensory evaluation, and aroma profiles, T3 period considered the optimal harvesting time. These findings offer practical guidance for the optimal harvesting and utilization of golden mulberry fruits. Full article