Search Results (4,160)

The forest bioeconomy is an emerging global sector that uses forest material to make value-added bioproducts that range from pharmaceuticals to biofuels. Notwithstanding their capacity to advance various United Nations Sustainable Development Goals, forest bioproducts face considerable sustainability challenges in global supply chains associated with harvesting, processing, and transportation. Using a systematic literature review focused on challenges and solutions to sustainability in forest bioeconomy supply chains, we analyze 81 peer-reviewed studies to identify the primary sustainability challenges and their attendant solutions. We find that economic barriers to scaling the forest bioeconomy are the most commonly studied challenge, while social and environmental challenges are often marginalized. Increasing stakeholder engagement is the most commonly mentioned solution, but the limitations of stakeholder engagement are largely absent from scholarly discourse. Lastly, we identify significant gaps in the literature related to coverage of non-European countries and analysis of key sectors like mass timber construction. The results gesture to the need for more research on under-represented regions and sectors, greater attention to social and environmental supply chain challenges, and deeper engagement with adjacent literatures on the intersection of public policy with sustainable supply chain governance. Full article

Feijoa (Acca sellowiana Berg) is an emerging Mediterranean crop valued for its nutraceutical potential but still underexplored with respect to cultivar and harvest stage. This study investigated two cultivars, ‘Mammoth’ and ‘Apollo’, harvested one week apart (4 and 11 November), to assess morphological traits, phenolic composition, antioxidant activity, vitamin C, and iodine. Fruit morphology, firmness, and basic quality indices (TSS, TA, pH, TSS/TA) were determined, while phenolic compounds were profiled by UHPLC–Q-Orbitrap HRMS. Antioxidant activity was measured by ABTS, DPPH, and FRAP assays; vitamin C by DCPIP titration; and iodine by iodometric analysis. ‘Apollo’ produced larger and firmer fruits, especially at the first harvest (105.6 g), while ‘Mammoth’ showed smaller and softer fruits. TSS remained stable (11 °Brix), whereas TA decreased and pH increased over time, raising the TSS/TA ratio and suggesting improved flavor balance at later harvests. Peel consistently contained higher bioactive levels than pulp, with catechin as the dominant phenolic compounds (up to 345 µg g⁻¹ dw in ‘Apollo’ peel). Antioxidant activity was markedly higher in peel, with ‘Mammoth’ showing stronger early FRAP values and ‘Apollo’ increasing at the later harvest. Vitamin C and iodine were about threefold higher in peel than pulp and increased over time, reaching maxima in late-harvest peel samples. Overall, cultivar and harvest stage significantly influenced fruit quality and nutraceutical value. Peel, particularly that of late-harvested ‘Apollo’, represents a promising resource for functional foods and the valorization of processing by-products. Full article

Background: Centralization and certification mark constant processes in everyday clinical routine. Despite the continuously rising number of certified pancreatic cancer (PAC) centers in recent years, fewer than 40% of PAC resections are still performed in certified institutions nationwide. The main objective of the certification is the enhancement of patient survival. Furthermore, certification is intended to improve structural quality, multidisciplinary cooperation, and the transparency of treatment pathways. In addition, it should have a positive effect on patient satisfaction. However, it requires the substantial effort of all partners involved. We aim to illustrate both advantages and limitations of the certification process. Methods: We analyzed perioperative outcomes of patients undergoing pancreatic resection for PAC (ICD C25) before and after our center’s first certification, and benchmarked these results against national data from the German Cancer Society. In addition, we analyzed quality of life (QoL) longitudinally using the validated QLQ-C30 questionnaire administered preoperatively and at 1, 4, and 18 months postoperatively. Results: The study cohort included 47 patients treated in the three years prior to certification and 130 patients during the subsequent seven years as a certified center. The mean annual number of PAC resections increased from 15 (ranged 14–18) to 19 (ranged 10–26). In-hospital mortality, length of stay, and rate of exploration-only procedures remained unchanged. Indicators of procedural quality, such as the number of harvested lymph nodes (p = 0.1485) and the precision of histopathological assessment, improved slightly but not significantly. QoL scores generally improved after discharge in both groups; however, functional scales and symptom measures demonstrated unexpectedly inferior values following certification, possibly reflecting higher case complexity. Conclusion: Achieving and maintaining certification requires substantial and continuous effort from all disciplines involved. While major improvements in morbidity, mortality, and long-term QoL were not observed, certification ensured clearer delegation of responsibilities, standardized documentation, and structured quality control. We therefore consider the certification process valuable for promoting multidisciplinary collaboration, maintaining high treatment volumes, and ensuring transparent oncological care pathways. Full article

The header platform of a combine harvester is subjected to severe abrasive and corrosive wear from rice stalks and environmental factors, which significantly limits its service life and operational efficiency. Accurately predicting the complex distribution of this wear over time and across the platform’s surface, however, remains a significant challenge. This paper, for the first time, systematically establishes a quantitative mapping relationship from “material motion trajectory” to “component wear profile” and introduces a novel method for time-sequence wear validation based on corrosion color gradients, providing a complete research paradigm to address this challenge. To this end, an analytical model based on rigid-body dynamics was first developed to predict the motion trajectory of a single rice stalk. Subsequently, a full-scale Discrete Element (DEM) model of the header platform–flexible rice stalk system was constructed. This model simulated the complex flow process of the rice population with high fidelity and was used to analyze the influence of key operating parameters (spiral auger rotational speed, cutting width) on wear distribution. Finally, real-world wear data were obtained through in situ mapping of a header platform after long-term service (1300 h) and multi-period (0–1600 h) image analysis. Through a three-way quantitative comparison among the theoretical trajectory, simulated trajectory, and the actual wear profile, the results indicate that the simulated and theoretical trajectories are in good agreement in terms of their macroscopic trends (Mean Squared Error, MSE, ranging from 0.4 to 6.2); the simulated and actual wear profiles exhibit an extremely high degree of geometric similarity, with the simulated wear area showing a 95.1% match to the actual measured area (Edit Distance: 0.14; Hamming Distance: 1). This research not only confirms that the flow trajectory of rice is the determining factor for the wear distribution on the header platform but, more importantly, the developed analytical and numerical methods offer a robust theoretical basis and effective predictive tools for optimizing the wear resistance and predicting the service life of the header platform, thereby demonstrating significant engineering value. Full article

As the world’s population ages, the incidence of chronic disorders is on the rise. Active Implantable Medical Devices are, therefore, evolving to meet the challenge. As the size of these devices decreases to facilitate implantation, the challenge of providing stable, continuous power becomes significant. Lithium batteries provide reliable, stable power to implants; however, their miniaturization leads to a reduction in the stored energy capacity, total lifespan, and overall capability. Consequently, there is a need for on-body energy harvesting alternatives. This study utilizes literature data on abiotic glucose fuel cells to feed into a finite element model incorporating both diffusion and reaction aspects to investigate how the 3D macro-architecture of the fuel cell device can be used to optimize the energy output. Accordingly, optimal 3D architectures are determined to enable power outputs ranging from several tens of microwatts to one hundred microwatts from an implantable package. This will help with the 3D architecture design of future similar abiotic fuel cell units and speed up the process of figuring out the best settings for key parameters (like shape, size, and separation). Full article

Agri-food supply chains, particularly in the rice sector, face persistent challenges in transparency, quality control, and sustainability due to their complexity and fragmentation. Blockchain technology provides a promising solution by ensuring secure, immutable, and verifiable records of production and supply chain activities, supporting both consumer trust and compliance with the EU Common Agricultural Policy (CAP). This study reports on the TRACE-RICE Mediterranean pilot project, which developed a blockchain-enabled traceability system for rice production in Portugal. A Rice Field Data Recording App, built with ArcGIS Survey123, digitized agronomic and compliance records from Integrated Production systems and linked them to blockchain-verified QR codes on consumer packaging. The pilot conducted during the 2023 harvest demonstrated the potential to enhance data consistency and streamline field recording processes, thereby improving transparency in farming practices. A total of 174 QR code interactions, primarily from Lisbon, revealed consumer engagement patterns valuable for future business analysis. The scaling phase during the 2024 harvest confirmed the system’s adaptability to different varieties and production contexts, positioning blockchain as a replicable model for sustainable and competitive rice supply chains. Full article

Cells of Serratia liquefaciens were grown on trypticase soy agar (TSA) for 28 d under Martian conditions of 7 mbar, 0 °C, and CO₂-enriched anoxic atmospheres (called Mars low-PTA conditions). Earth controls were maintained for 24 h at 1013 mbar, 30 °C, and a standard pN₂/pO₂ gas composition. Cells were harvested at either 24 h or 28 d from TSA surfaces and processed for SEM and TEM imaging. Cells of S. liquefaciens grown under Earth conditions were uniform in shape and size, averaging approximately 1.25 µm in length and 0.5 µm in width. Fimbriae were observed on 10–20% of cells grown under Earth conditions. Key features of low-PTA grown cultures were (1) cells exhibited swollen blunt ends at sites of cell division tapering to unusually constricted points on the distal ends of progeny cells, (2) cell division appeared disrupted with division planes occurring at odd angles often forming right-angle oriented daughter cells, (3) some cells failed to form divisional planes resulting in long spiral and oddly shaped cells measuring up to 6–8 µm in length, and (4) fimbriae were lacking. Cell walls were found to be approx. 17% thinner when cells were grown in low-PTA environments compared to lab-standard conditions. Full article

Artificial Intelligence (AI) is a key technological enabler for the transition of agricultural production and management from experience-driven to data-driven, continuously advancing modern agriculture toward smart agriculture. This evolution ultimately aims to achieve a precise agricultural production model characterized by low resource consumption, high safety, high quality, high yield, and stable, sustainable development. Although machine learning, deep learning, computer vision, Internet of Things, and other AI technologies have made significant progress in numerous agricultural production applications, most studies focus on singular agricultural scenarios or specific AI algorithm research, such as object detection, navigation, agricultural machinery maintenance, and food safety, resulting in relatively limited coverage. To comprehensively elucidate the applications of AI in agriculture and provide a valuable reference for practitioners and policymakers, this paper reviews relevant research by investigating the entire agricultural production process—including planting, management, and harvesting—covering application scenarios such as seed selection during the cultivation phase, pest and disease identification and intelligent management during the growth phase, and agricultural product grading during the harvest phase, as well as agricultural machinery and devices like fault diagnosis and predictive maintenance of agricultural equipment, agricultural robots, and the agricultural Internet of Things. It first analyzes the fundamental principles and potential advantages of typical AI technologies, followed by a systematic and in-depth review of the latest progress in applying these core technologies to smart agriculture. The challenges faced by existing technologies are also explored, such as the inherent limitations of AI models—including poor generalization capability, low interpretability, and insufficient real-time performance—as well as the complex agricultural operating environments that result in multi-source, heterogeneous, and low-quality, unevenly annotated data. Furthermore, future research directions are discussed, such as lightweight network models, transfer learning, embodied intelligent agricultural robots, multimodal perception technologies, and large language models for agriculture. The aim is to provide meaningful insights for both theoretical research and practical applications of AI technologies in agriculture. Full article

Insufficient harvesting of visible photons, limited adsorption, and fast recombination of photogenerated electron-hole pairs restrict the application of graphitic carbon nitride (g-C₃N₄). Here, we propose a straightforward solid-phase synthesis method for fabricating 2D/2D graphitic carbon nitride/reduced graphene oxide (SCN/GR) hybrid photocatalysts. The synthesis process involves the thermal condensation of three precursors: dicyandiamide (as the g-C₃N₄ source), NH₄Cl (as a pore-forming agent), and graphene oxide (GO, which is in situ reduced to rGO during thermal treatment). The incorporation of reduced graphene oxide (rGO) into the g-C₃N₄ matrix not only narrows the bandgap of the material but also expedites the separation of photogenerated carriers. The photocatalytic activity of the SCN/GR hybrid was systematically evaluated by degrading ciprofloxacin in aqueous solution under different light conditions. The results demonstrated remarkable degradation efficiency: 72% removal within 1 h under full-spectrum light, 81% under UV light, and 52% under visible light. Notably, the introduction of rGO significantly improved the visible light absorption capacity of g-C₃N₄. Additionally, SCN/GR exhibits exceptional cyclic stability, maintaining its structural integrity and photocatalytic properties unchanged across five successive degradation cycles. This study offers a simple yet effective pathway to synthesize 2D/2D composite photocatalysts, which hold significant promise for practical applications in water treatment processes. Full article

This paper explores the optimal control issue for a linear–quadratic–Gaussian (LQG) system under the conditions of imperfect feedback and constraints related to energy harvesting. The system is equipped with various energy options, which allow it to gather energy for information transmission while also receiving imperfect feedback from an auxiliary filter that estimates packet loss. The primary goal of this study is to jointly design the energy selector and the controller to achieve an optimal balance between transmission costs and control performance. Initially, we separate the controller’s synthesis task from the energy selection task. The subproblem of optimal controller synthesis is characterized by a Riccati equation that takes continuous packet loss into account. Simultaneously, the energy selection task, influenced by imperfect feedback and constraints on energy costs, is reformulated as a Markov decision process (MDP) that operates with perfect acknowledgments through iterative updates of state information. Ultimately, the optimal energy selection policy that guarantees filtering performance is derived by solving a Bellman equation. The effectiveness of the proposed approach is confirmed through simulation results. Full article

Silver selenide (Ag₂Se) is a promising n-type thermoelectric material for near-room-temperature energy harvesting due to its high electrical conductivity and low lattice thermal conductivity. In this study, Ag₂Se-based composites were synthesized using a cold sintering process (CSP), enabling densification at low temperature under applied pressure. Reduced graphene oxide (RGO) was incorporated into the Ag₂Se matrix in small amounts (0.25–1.0 wt.%) to enhance thermoelectric performance. Structural analysis confirmed phase-pure β-Ag₂Se, while SEM and TEM revealed homogeneous RGO dispersion and strong interfacial adhesion. RGO addition led to a reduced carrier concentration due to carrier trapping by oxygen-bearing functional groups, resulting in decreased electrical conductivity. However, the absolute Seebeck coefficient increased with RGO content, maintaining a balanced power factor. Simultaneously, RGO suppressed thermal conductivity to below 0.75 W m⁻¹ K⁻¹ at room temperature. The optimal composition, 0.75 wt.% RGO, exhibited the highest average zT of 0.98 across the temperature range from room temperature to 383 K. These results demonstrate that combining the CSP with RGO incorporation offers a scalable and cost-effective strategy for enhancing the thermoelectric performance of Ag₂Se-based materials. Full article

Aerobic anoxygenic phototrophs (AAPs) are intrinsically paradoxical; these species use a pathway commonly found in oxygen-deprived environments called anoxygenic photosynthesis, as a supplementary energy source to their obligately aerobic respiration. At the surface, such a combination seems odd, but AAPs thrive in a plethora of environments and are phylogenetically broad, suggesting that this feature is advantageous and ecologically valuable. The range of habitats and taxonomy have been reviewed, yet the main element which unites the group, their anoxygenic photosynthesis, which is diverse in its components, has not received the deserved attention. The intricate light-capturing photosynthetic complex forms the site of photon-induced energy transfer and therefore, the core basis of the process. It has two parts: the reaction center and light harvesting complex(es). The variability in composition and overall usage of the apparatus is also reflected in the genome, specifically the photosynthetic gene cluster. In this review, what is known about the differences in structure, light wavelength absorption range, activity, and related genomic content and the insights into potential AAP evolution from anaerobic anoxygenic phototrophs will be discussed. The work provides an elegant summation of knowledge accumulated about the photosynthetic apparatus and prospects that can fill yet remaining gaps. Full article

Improving the accuracy and real-time performance of strawberry recognition and localization algorithms remains a major challenge in intelligent harvesting. To address this, this study presents an integrated approach for strawberry maturity detection and 3D localization that combines a lightweight deep learning model with an RGB-D camera. Built upon the YOLOv11 framework, an enhanced RAFS-YOLO model is developed, incorporating three core modules to strengthen multi-scale feature fusion and spatial modeling capabilities. Specifically, the CRA module enhances spatial relationship perception through cross-layer attention, the HSFPN module performs hierarchical semantic filtering to suppress redundant features, and the DySample module dynamically optimizes the upsampling process to improve computational efficiency. By integrating the trained model with RGB-D depth data, the method achieves precise 3D localization of strawberries through coordinate mapping based on detection box centers. Experimental results indicate that RAFS-YOLO surpasses YOLOv11n, improving precision, recall, and mAP@50 by 4.2%, 3.8%, and 2.0%, respectively, while reducing parameters by 36.8% and computational cost by 23.8%. The 3D localization attains millimeter-level precision, with average RMSE values ranging from 0.21 to 0.31 cm across all axes. Overall, the proposed approach achieves a balance between detection accuracy, model efficiency, and localization precision, providing a reliable perception framework for intelligent strawberry-picking robots. Full article

The increasing demand for monoclonal antibody (mAb) therapeutics has intensified the need for more efficient and consistent biomanufacturing processes. We present a data-driven, machine-learning (ML) approach to exploring and predicting upstream yield behavior. Drawing on industrial-scale batch records for a single mAb product from a contract development and manufacturing organization, we applied regression models to identify key process parameters and estimate production outcomes. Random forest regression, gradient boosting machine, and support vector regression (SVR) were evaluated to predict three yield indicators: bioreactor final weight (BFW), harvest titer (HT), and packed cell volume (PCV). SVR outperformed other models for BFW prediction (R² = 0.978), while HT and PCV were difficult to model accurately with the available data. Exploratory analysis using sequential least-squares programming suggested parameter combinations associated with improved yield estimates relative to historical data. Sensitivity analysis highlighted the most influential process parameters. While the findings demonstrate the potential of ML for predictive, data-driven yield improvement, the results should be interpreted as an exploratory proof of concept rather than a fully validated optimization framework. This study highlights the need to incorporate process constraints and control logic, along with interpretable or hybrid modeling frameworks, to enable practical deployment in regulated biomanufacturing environments. Full article