Search Results (14,771)

The traditional mantis search algorithm (MSA) suffers from limitations such as slow convergence and a high likelihood of converging to local optima in complex optimization scenarios. This paper proposes an improved mantis search algorithm (IMSA) to overcome these issues. An adaptive probability conversion factor is designed, which adaptively controls the proportion of individuals entering the search phase and the attack phase so that the algorithm can smoothly transition from large-scale global exploration to local fine search. In the search phase, a probability update strategy based on both subspace and full space is designed, significantly improving the adaptability of the algorithm to complex problems by dynamically adjusting the search range. The elite population screening mechanism, based on Euclidean distance and fitness double criteria, is introduced to provide dual guidance for the evolution direction of the algorithm. In the attack stage, the base vector adaptive probability selection mechanism is designed, and the algorithm’s pertinence in different optimization stages is enhanced by dynamically adjusting the base vector selection strategy. Finally, in the stage of sexual cannibalism, the directed random disturbance update method of inferior individuals is adopted, and the population is directly introduced through the non-greedy replacement strategy, which effectively overcomes the loss of population diversity. The experimental results of 29 test functions on the CEC2017 test set demonstrate that the IMSA exhibits significant advantages in convergence speed, calculation accuracy, and stability compared to the original MSA and the five best meta-heuristic algorithms. Full article

The smart grid concept is based on the full integration of different types of energy sources and intelligent devices. Due to the short- and long-term volatility of these sources, new flexibility measures are necessary to ensure the smart grid operates stably and reliably. One option is to convert renewable energy into hydrogen, especially during periods of generation overcapacity, in order that the hydrogen that is produced can be stored effectively and used “just in time” to stabilize the power system by undergoing a reverse conversion process in gas turbines or fuel cells which then supply power to the network. On the other hand, in order to achieve a sustainable general energy system (GES), it is necessary to replace other forms of fossil energy use, such as that used for heating and other industrial processes. Research indicates that a comprehensive hydrogen supply infrastructure is required. This infrastructure would include electrolyzers, conversion stations, pipelines, storage facilities, and hydrogen gas turbines and/or fuel cell power stations. Some studies in Germany suggest that the existing gas infrastructure could be used for this purpose. Further, nuclear and coal power plants are not considered reserve power plants (as in the German case), and an additional 20–30 GW of generation capacity in H₂-operated gas turbines and strong H₂ transportation infrastructure will be required over the next 10 years. The novelty of the approach presented in this article lies in the development of a unified modeling framework that enables the simultaneous and coherent representation of both economic and technical aspects of hydrogen production systems which will be used for planning and pre-decision making. From the technical perspective, the model, based on the black box approach, captures the key operational characteristics of hydrogen production, including energy consumption, system efficiency, and operational constraints. In parallel, the economic layer incorporates capital expenditures (CAPEX), operational expenditures (OPEX), and cost-related performance indicators, allowing for a direct linkage between technical operation and economic outcomes. This paper describes the systematic transformation from today’s power system to one that includes a hydrogen economy, with a particular focus on practical experiences and developments, especially in the German energy system. It discusses the components of this new system in depth, focusing on current challenges and applications. Some scaled current applications demonstrate the state of the art in this area, including not only technical requirements (reliability, risks) and possibilities, but also economic aspects (cost, business models, impact factors). Full article

Antibiotic resistance gene (ARG) monitoring in environmental systems increasingly relies on DNA-based molecular approaches; however, the extent to which DNA extraction strategies bias downstream resistome interpretation remains insufficiently understood. This study systematically evaluated the effects of single versus successive DNA extraction on DNA recovery, microbial community composition, and the abundance and diversity of 385 genes related to antibiotic resistance including ARGs and mobile genetic elements (MGEs) across three contrasting matrices: water, sediment, and fish intestinal tissue. Successive extraction markedly increased DNA yield and detection of functional genes in water and sediment, particularly for low-abundance and particle-associated taxa. Enhanced recovery resulted in higher richness and abundance of ARGs and MGEs and strengthened correlations between intI1, ARGs, and bacterial taxa, indicating that single-cycle extraction may underestimate resistome magnitude and potential host associations in complex matrices. Conversely, fish intestinal tissue, used here as a representative biological matrix, showed limited benefit or even reduced gene abundance with repeated extraction, likely due to rapid depletion of extractable nucleic acids and DNA degradation. While successive extraction improves recovery efficiency, the potential inclusion of extracellular or relic DNA suggests caution in interpreting inflated ARG abundance. Overall, our findings demonstrate that DNA extraction is a matrix-dependent methodological driver that can reshape both quantitative outcomes and ecological inference. Matrix-specific optimization and careful protocol selection are therefore essential for improving data comparability and minimizing methodological underestimation in environmental resistome assessments. Full article

The escalating global demand for large-scale, cost-effective, and sustainable high-quality protein has positioned single-cell protein (SCP) production from one-carbon (C1) gases as a highly promising solution. In this study, eight chemolithoautotrophic hydrogen-oxidizing bacteria (HOB) were isolated from mangrove sediments. Based on the 16S rRNA gene sequence analysis, they belonged to genera Sulfurimonas, Sulfurovum, Thiomicrolovo, and Marinobacterium. Among these, Thiomicrolovo sp. ZZH C-3 was identified as the most promising candidate for SCP production based on the highest biomass and protein content, and was selected for further characterization. Strain ZZH C-3 is a Gram-negative, short rod-shaped bacterium with multiple flagella. It can grow chemolithoautotrophically by using molecular hydrogen as an energy source and molecular oxygen as an electron acceptor. Genomic analysis further confirmed that ZZH C-3 harbors a complete reverse tricarboxylic acid (rTCA) cycle gene set for carbon fixation, and diverse hydrogenases (Group I, II, IV) for hydrogen oxidation. Subsequently, its cultivation conditions and medium composition for SCP production were systematically optimized using single-factor experiments and response surface methodology (RSM). Results showed that the optimal growth conditions were 28 °C, pH 7.0, and with 1 g/L (NH₄)₂SO₄ as the nitrogen source, 5–10% oxygen concentration, 9.70 mg/L FeSO₄·7H₂O, 0.17 g/L CaCl₂·2H₂O, and 1.90 mg/L MnSO₄·H₂O. Under the optimized conditions, strain ZZH C-3 achieved a maximum specific growth rate of 0.46 h⁻¹. After 28 h of cultivation, the optical density at 600 nm (OD₆₀₀) reached 0.94, corresponding to a biomass concentration of 0.60 g/L, and the protein content ranked at 73.56%. The biomass yield on hydrogen (Y_H2) was approximately 3.01 g/g H₂, with an average H₂-to-CO₂ consumption molar ratio of about 3.78. Compared to the model HOB Cupriavidus necator, strain ZZH C-3 exhibited a lower H₂/CO₂ consumption ratio, superior substrate conversion efficiency, and high protein content. Overall, this study not only validated the potential of mangrove HOB for SCP production but also offers new insights for future metabolic engineering strategies designed to enhance CO₂-to-biomass conversion efficiency. Full article

This study explores a circular economy strategy for vineyard residue management through the conversion of pruning biomass into carbonaceous materials by hydrothermal carbonization (HTC) and pyrolysis (PYR), with and without iron (Fe) addition. A preliminary pot-based vegetation experiment was conducted as a screening assay to assess initial plant tolerance and exclude evident phytotoxic effects. Chlorophyll index values in grapevine leaves remained within physiological ranges across treatments and sampling dates, although no consistent treatment-related trends could be established. Overall, the results provide a physicochemical characterization of the carbonaceous materials derived from vineyard residues and demonstrate their initial compatibility with grapevine cultivation under controlled conditions. This work lays the groundwork at the material level for future, more comprehensive studies that integrate long-term soil, plant, and field assessments. Full article

The efficient conversion of CO₂ into valuable chemicals using highly efficient, environmentally friendly, and renewable heterogeneous catalysts is paramount for the progression of a carbon circular economy. In pursuit of this goal, this study introduces a metal-free, scalable melamine-based organic polymer catalyst designed to integrate CO₂ adsorption with customizable functional properties. Employing both solid-state thermal synthesis (SST) and hydrothermal methods, we synthesized three amine-based hydrogen bond donor catalysts, thereby balancing environmentally conscious practices with scalable synthesis: MCA, a high-nitrogen-content polymer derived from trichlorocyanuric acid; MCA-SST; and MTAB, a triazine-trichlorocyanuric acid polymer. Under mild conditions (100 °C, 0.1 MPa, 24 h), MCA demonstrated superior catalytic performance in the CO₂ cycloaddition of epichlorohydrin, achieving a 99% conversion rate, significantly surpassing MCA-SST (60%) and MTAB (78%). MCA’s high specific surface area and structural integrity facilitate efficient catalysis under mild conditions, and it retains 79% of its initial activity after five cycles, indicating exceptional stability. These results suggest that while the incorporation of secondary amines and increased nitrogen content generally promote the reaction, densely packed adjacent secondary amine linkages can induce repulsion between nitrogen atoms, thereby weakening active sites and reducing catalytic activity. Consequently, this study not only presents MCA as a novel metal-free catalyst exhibiting remarkable performance in catalyzing CO₂ cycloaddition under ambient pressure and mild conditions, but also elucidates the structure–activity relationship between secondary amine density and catalytic activity. This work provides a deeper mechanistic understanding and offers a theoretical foundation for future rational catalyst design. Full article

University dormitories, as crucial living spaces for students, significantly influence their physical and mental health based on the quality of spatial design. However, whether the use of an upper bunk (UB) or lower bunk (LB) induces differential physiological and psychological effects remains unclear. This study aimed to measure participants’ physiological and psychological responses in UB and LB environments to explore the differential impact of bunk bed positions on student comfort. A crossover experiment was conducted with 28 participants (14 male, 14 female). Dormitory scenes were recreated using point cloud scanning and virtual reality technology, and a crossover experimental design was implemented. Physiological and psychological responses during the use of UB and LB spaces were measured via heart rate variability (HRV), electroencephalography (EEG), and the Profile of Mood States (POMS). Key findings indicated that the UB space promoted a state of deeper relaxation, evidenced by significantly higher Delta activity (p = 0.039) and lower heart rate (p = 0.042) compared to the LB. Psychologically, participants reported significantly higher vitality (Vigor, p = 0.032) and lower total mood disturbance (TMD, p = 0.038) in the UB. Conversely, the LB environment tended to trigger neural alertness, with significantly elevated High Beta waves (p = 0.009). Furthermore, gender significantly moderated emotional responses, particularly for Vigor (p = 0.045). Overall, from the perspective of promoting physical and mental health, the UB space provided greater comfort than the LB. These findings offer empirical evidence to inform the optimization of dormitory spatial design. Full article

Second-generation bioethanol from food industry lignocellulosic residues offers a promising route toward low-carbon, circular bioenergy systems. However, the reported environmental impacts differ markedly across studies, challenging efforts to assess the true sustainability of these waste-derived bioethanol routes. This review synthesizes current knowledge on the production of bioethanol from key agro-industrial wastes including oil palm empty fruit bunches, sugarcane bagasse, brewers’ spent grain, spent coffee grounds, tea waste, citrus residues, and potato peel waste. We outline feedstock characteristics, availability, and prevailing management practices, and map the principal biochemical conversion routes to identify process steps that drive environmental performance. A systematic comparison of life cycle assessments reveals substantial methodological heterogeneity across functional units, system boundaries, allocation procedures, and impact assessment methods. Nonetheless, consistent hotspots emerge, particularly associated with pretreatment severity, enzyme production, thermal energy demand, and co-product handling. The review highlights robust cross-study trends, pinpoints methodological gaps, and proposes recommendations for harmonized LCA practice. By integrating technological and methodological perspectives, this work aims to support the development and policy uptake of sustainable, waste-based bioethanol within circular bioeconomies. Full article

The conversion of biowaste into biofertilizer offers a sustainable alternative to synthetic fertilizers by supporting nutrient recycling and agricultural productivity. However, industrial pelletization can compromise the viability of microorganisms essential for biofertilizer function. In this study, a 40/60 (dry wt%) blend of biochar and commercial potting mix (biowaste blend) was used to produce a biochar biofertilizer (BCBF) through pelletization. Microbial population dynamics were then assessed at different stages of the BCBF pelletization process and under variations in key pelleting parameters—moisture content (15–35%), die surface temperature (70–180 °C), and feed rate (75–150 lb/h). The results showed that fungal and protozoan populations increased during the composting stage of BCBF, but declined to undetectable levels following drying and coating of the BCBF pellets. Bacterial populations increased after composting, but decreased substantially after pelleting and subsequent storage of the BCBF, while actinobacteria remained low throughout the pelletization process. Elevated temperatures and moisture loss were identified as major contributors to microbial inactivation during pelletization. These findings demonstrate that careful control of pelletization parameters is essential for maintaining microbial viability, thereby supporting the development of higher-quality, microbially active biochar-based biofertilizers. Full article

Background: Chemodynamic therapy (CDT) and photothermal therapy (PTT) based on nanomaterials have garnered widespread attention in cancer treatment. However, most single-modal nanotherapeutics suffer from limited therapeutic efficacy. Methods: Herein, a magnetic mesoporous composite nanoparticle, Fe₃O₄@MSN@PDA-HA-FA, is successfully fabricated, with Fe₃O₄ nanoparticles as the magnetic core; mesoporous silica nanoparticles (MSNs) as the mesoporous shell; and dopamine hydrochloride (DA·HCl), hyaluronic acid (HA), and folic acid (FA) as the functional ligands. Results: Notably, this composite serves as both an efficient photothermal converter and a chemodynamic promoter, enhancing hydroxyl radical (·OH) generation and improving PTT efficacy. Under near-infrared (NIR) light irradiation, Fe₃O₄@MSN@PDA-HA-FA exhibits high photothermal conversion and heat transfer efficiencies. The Fe²⁺ ions in Fe₃O₄ enable a Fenton reaction-mediated conversion of endogenous hydrogen peroxide (H₂O₂) into ·OH for CDT. Additionally, the MSNs provide a substantial drug-loading capacity, while the HA and FA provide additional surface functionalities that can modulate the nano-bio interactions and improve colloidal stability. Conclusions: In vitro experiments validate the synergistic therapeutic efficacy of PTT, CDT, and chemotherapy. This study demonstrates that Fe₃O₄@MSN@PDA-HA-FA exhibits antitumor efficacy, laying a promising foundation for its potential clinical translation in cancer treatment. Full article

Underground coal gasification (UCG) is an emerging energy technology that involves the in situ conversion of coal into syngas through controlled combustion within a subsurface excavation. The geomechanical processes associated with UCG can lead to significant overburden caving and surface subsidence, posing risks to surface infrastructure and groundwater systems. To accurately predict the size of overburden caving zones and associated surface subsidence, a prediction model was developed based on simulation results using discrete element method (DEM) numerical models. The main purpose of developing such a model is to establish a systematic and computationally efficient method for the rapid prediction of the height of overburden caving and its associated surface subsidence induced by underground excavation. The model is broadly applicable to different types of underground excavations, and UCG is used in this study as a representative application scenario to demonstrate the relevance and performance of the model. Sensitivity analysis indicates that excavation span, tensile strength, and burial depth are the primary controls on the height of the caving zone within the ranges of parameters investigated. Rock density is retained as a secondary background parameter to represent gravitational loading and its contribution to the in situ stress level. The derived model was validated using published numerical, experimental, and field measurement data, showing good agreement within practical ranges. To further demonstrate the application of the model developed, the predicted caving geometries were incorporated into finite element method (FEM) models to simulate surface subsidence under different geological conditions. The results highlight that the arch structure formed by overburden caving can help redistribute stresses and thereby reduce surface deformation. The proposed model provides a practical, parameter-driven tool to assist in underground excavation design, environmental risk evaluation, and ground stability management. Full article

Greenhouse gases (GHG), accumulated in the atmosphere, are the main cause of climate change. In 2017, the increase in average temperature was about 1 °C (between 0.8 °C–1.2 °C) above pre-industrial levels. Global warming refers to the increase in air surface, sea surface, and soil surface temperature and according to IPCC (Intergovernmental Panel Climate Change), since the industrial revolution, C emissions are due to land use changes like deforestation, biomass burning, conversion of natural lands, drainage of wetlands, soil cultivation, and tillage. As the world population has increased, world food production has risen too with a subsequent increase in GHG emissions and agricultural production, which is worsened by climate change. Negative consequences are well known such as the loss in water availability and in soil fertility, and pest infestations which are climate change’s effects on agriculture activity. Climate change’s main aftermath is the frequency of extreme weather events influencing crop yields. As climate change exacerbates degradation processes, land management can mitigate its impact and aid adaptation strategies for climate change. About 21–37% of GHGs have been caused by the agriculture activity, so the application of Nature-based Solutions (NbS) like sustainable agriculture could be a way to reduce GHGs worldwide. The aim of this article is to review how NbS may mitigate GHG emissions from soil, with solutions defined as an integrated approach to tackle climate change and to sustainably restore and manage ecosystems, delivering multiple benefits. NbS is a low-cost tool working within and with nature, which holds many benefits for people and the environment. Full article

This study aims to identify an effective neural network architecture for the task of semantic segmentation of the surface of beer wort at the stage of primary fermentation, using deep learning methodologies. Four contemporary architectures were evaluated and contrasted. The following networks are presented in both baseline and optimized forms: U-Net, DeepLabV3+, Fast-SCNN, and SegNet. The models were trained on a dataset of images depicting real beer surfaces at the primary fermentation stage. This was followed by the validation of the models using key metrics, including pixel classification accuracy, Mean Intersection over Union (mIoU), Dice Coefficient, inference time per image, and Graphics Processing Unit (GPU) resource utilization. Results indicate that the optimized U-Net achieved the optimal balance between performance and efficiency, attaining a validation accuracy of 88.85%, mIoU of 76.72%, and a Dice score of 86.71%. With an inference time of 49.5 milliseconds per image, coupled with minimal GPU utilization (18%), the model proves suitable for real-time deployment in production environments. Conversely, complex architectures, such as DeepLabV3+, did not yield the anticipated benefits, thereby underscoring the viability of utilizing compact models for highly specialized industrial tasks. This study establishes a novel quantitative metric for the assessment of fermentation. This is based on the characteristics of the foam surface and thus offers an objective alternative to traditional subjective inspections. The findings emphasize the potential of adapting optimized deep learning architectures to quality control tasks within the food industry, particularly in the brewing sector, and they pave the way for further integration into automated computer vision systems. Full article

To overcome the limitations of ground-based observations, this study aims to identify optimal color indices for detecting tree phenological events using time-series Unmanned Aerial Vehicle(UAV) orthomosaics. We monitored 37 woody taxa at the Korea National Arboretum from April to November 2025. By extracting Red, Green, and Blue (RGB) values from canopy polygons, we calculated four indices: Brightness, Green Chromatic Coordinate (GCC), Red Chromatic Coordinate (RCC), and Green-Red Vegetation Index (GRVI). We then evaluated signal detectability using Z-score standardization. The analysis confirmed that 74.6% of phenological events were detectable. Specifically, flowering and autumn coloration showed high detection rates (88.9% and 100%, respectively), identifying Brightness, RCC, and GRVI as key indicators for capturing these distinct visual changes. Conversely, gradual transitions like leaf-out showed lower detectability. These findings demonstrate that selecting specific color indices based on the visual characteristics of each event enables effective quantitative monitoring. This study provides a methodological basis for utilizing UAV-based indices as a complementary tool in long-term ecological monitoring. Full article

This paper presents an efficient optimization framework for high-frequency control of active downhole Flow Control Valves (FCVs) under geological uncertainty. Traditional proactive optimization methods for FCVs, while capable of maximizing life-of-field objectives such as Net Present Value (NPV), are computationally prohibitive when frequent updates are required. Conversely, reactive approaches are efficient but often neglect long-term recovery objectives. To address these challenges, we integrate two complementary strategies within a reservoir simulator: a reactive nonlinear programming method to maximize instantaneous cash flow, and a proactive streamline-based Time-of-Flight (TOF) equalization approach to improve sweep efficiency by balancing flood front arrival times. The framework is demonstrated on synthetic and realistic reservoir models, including the Olympus and Almakman references. Results show that, compared to conventional annual control strategies, the proposed approach increases NPV by 15–25% while reducing water handling costs and deferring breakthrough by up to four years. Furthermore, hybrid optimization effectively neutralizes fracture uncertainty, improving both mean recovery and the certainty of outcomes. Three field-scale case studies highlight the practical benefits of FCVs in improving lift performance, maximizing recovery from bypassed hydrocarbons, and reducing the number of wells required to meet production targets. By combining reactive and proactive control within a computationally tractable workflow, this study advances the practical deployment of intelligent completions for closed-loop reservoir management. Full article