Search Results (686)

While the Ankom RF system facilitates efficient high-throughput in vitro fermentation studies, its high cost and limited flexibility constrain its broader applicability. To address these limitations, we developed and validated a low-cost, modular gas monitoring system (FerME), assembled from commercially available components. To evaluate its performance and reproducibility relative to the Ankom RF system (Ankom Technology, Macedon, NY, USA), in vitro rumen fermentation experiments were conducted under strictly controlled and identical conditions. Whole rumen contents were collected approximately 2 h post-feeding from individual mid- or late-lactation dairy cows and immediately transported to the laboratory. Each fermenter received 50 mL of processed rumen fluid, 100 mL of anaerobically prepared artificial saliva buffer, and 1.2 g of the donor cow’s diet. Bottles were sealed with the respective system’s pressure sensors, flushed with CO₂, and incubated in a 50 L water bath maintained at 39 °C. FerME (New Taipei City, Taiwan) and Ankom RF fermenters were placed side-by-side to ensure uniform thermal conditions. To assess the effect of filter bag use, an additional trial employed Ankom F57 filter bags (Ankom Technology, Macedon, NY, USA; 25 μm pore size). Trial 1 revealed no significant differences in cumulative gas production, volatile fatty acids (VFAs), NH₃-N, or pH between systems (p > 0.05). However, the use of filter bags reduced gas output and increased propionate concentrations (p < 0.05). Trial 2, which employed filter bags in both systems, confirmed comparable results, with the FerME system demonstrating improved precision (CV: 4.8% vs. 13.2%). Gas composition (CH₄ + CO₂: 76–82%) and fermentation parameters remained consistent across systems (p > 0.05). Importantly, with 12 pressure sensors, the total cost of FerME was about half that of the Ankom RF system. Collectively, these findings demonstrate that FerME is a reliable, low-cost alternative for real-time rumen fermentation monitoring and could be suitable for studies in animal nutrition, methane mitigation, and related applications. Full article

The global transition to clean energy necessitates integrated solutions that ensure both environmental sustainability and energy security. This paper proposes a scenario-based modeling framework for urban hybrid energy systems combining small modular reactors (SMRs), photovoltaic (PV) generation, and battery storage within a smart grid architecture. SMRs offer compact, low-carbon, and reliable baseload power suitable for urban environments, while PV and storage enhance system flexibility and renewable integration. Six energy mix scenarios are evaluated using a lifecycle-based cost model that incorporates both capital expenditures (CAPEX) and cumulative carbon costs over a 25-year horizon. The modeling results demonstrate that hybrid SMR–renewable systems—particularly those with high nuclear shares—can reduce lifecycle CO₂ emissions by over 90%, while maintaining long-term economic viability under carbon pricing assumptions. Scenario C, which combines 50% SMR, 40% PV, and 10% battery, emerges as a balanced configuration offering deep decarbonization with moderate investment levels. The proposed framework highlights key trade-offs between emissions and capital cost and seeking resilient and scalable pathways to support the global clean energy transition and net-zero commitments. Full article

The impact of five different nets—conventional black, grey, white, and photoselective red and yellow—on the performance of “Gala Redlum” apples was evaluated over a five-year period (2020–2024) and compared to an uncovered control. The cumulative production over this period, ranked from highest to lowest, was as follows: white net (182.4 t/ha), grey net (178.5 t/ha), yellow net (175.8 t/ha), black net (175.5 t/ha), red net (169.5 t/ha), and uncovered control (138.8 t/ha). Vegetative growth results were inconsistent among the studied years. The cumulative photosynthetic rate (An) was slightly higher under the white net (57.9 µmol m⁻² s⁻¹). Fv/Fm values remained closest to optimal levels under the black and grey nets. Netting effectively protected fruits from elevated temperatures, particularly under the grey net, and reduced sunburn damage, with the grey, black, and yellow nets performing best in this regard. Overall profitability was increased by netting: the black net provided the highest cumulative income per hectare over a five-year period (EUR 72,315) alongside the second-lowest sunburn loss (0.69%), while the yellow net also showed strong economic performance (€64,742) with a moderate sunburn loss (1.26%) compared to the red net. Fruit dry matter and soluble solids content (SSC) were generally higher in the uncovered control, whereas °Hue values tended to be higher under the red and yellow nets. In summary, the black and yellow nets provided more balanced microclimatic conditions that enhanced tree performance, particularly under heat stress, leading to improved yield and profitability. However, the economic feasibility of each net type should be evaluated in relation to its installation and maintenance costs. Full article

The development of slow-release fertilizers (SRFs) based on production residues is a promising strategy to improve nutrient use efficiency and promote circular economy practices in agriculture. In this study, a series of experimental formulations were designed and tested using pumice scraps, liquid and dried blood, and bone meal, aiming at producing sustainable and low-cost N-P-K SRFs. These were processed through mixing and granulation, both in the laboratory and on a semi-industrial scale. The formulations were evaluated through release tests in 2% citric acid solution simulating the acidic conditions of the rhizosphere, and in acetic acid to assess potential nutrient leaching under acid rain conditions. The results showed a progressive cumulative release of macronutrients (NPKs), ranging from approximately 8% at 24 h to 73% after 90 days for the most effective formulation (WBF6). Agronomic trials on lettuce confirmed the effectiveness of WBF6, resulting in significant biomass increases compared with both the untreated control and a conventional fertilizer. The use of livestock waste and minerals facilitated the development of a scalable product aligned with the principles of sustainable agriculture. The observed release behavior, combined with the simplicity of production, positions these formulations as a promising alternative to conventional slow-release fertilizers. Full article

Driven by carbon neutrality and peak carbon policies, hydrogen energy, due to its zero-emission and renewable properties, is increasingly being used in hydrogen fuel cell vehicles (H-FCVs). However, the high cost and limited durability of H-FCVs hinder large-scale deployment. Hydrogen internal combustion engine hybrid electric vehicles (H-HEVs) are emerging as a viable alternative. Research on the techno-economics of H-HEVs remains limited, particularly in systematic comparisons with H-FCVs. This paper provides a comprehensive comparison of H-FCVs and H-HEVs in terms of total cost of ownership (TCO) and hydrogen consumption while proposing a multi-objective powertrain parameter optimization model. First, a quantitative model evaluates TCO from vehicle purchase to disposal. Second, a global dynamic programming method optimizes hydrogen consumption by incorporating cumulative energy costs into the TCO model. Finally, a genetic algorithm co-optimizes key design parameters to minimize TCO. Results show that with a battery capacity of 20.5 Ah and an H-FC peak power of 55 kW, H-FCV can achieve optimal fuel economy and hydrogen consumption. However, even with advanced technology, their TCO remains higher than that of H-HEVs. H-FCVs can only become cost-competitive if the unit power price of the fuel cell system is less than 4.6 times that of the hydrogen engine system, assuming negligible fuel cell degradation. In the short term, H-HEVs should be prioritized. Their adoption can also support the long-term development of H-FCVs through a complementary relationship. Full article

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article

Melissa officinalis L. (Lamiaceae) is a perennial plant species widely used in the pharmaceutical and food industries, particularly valued for its sedative properties. This study investigates the impact of synthetic mulch film and planting density as two experimental factors on agronomic performance, raw material quality, and economic efficiency in lemon balm production. The experiment was conducted at three locations in Serbia (L1: Bačko Novo Selo, L2: Bavanište, L3: Vilandrica) from 2022 to 2024, using two planting densities on synthetic mulch film (F1: 8.3 plants m⁻²; F2: 11.4 plants m⁻²) and a control treatment without mulch (C). The synthetic mulch film used was a synthetic black polypropylene film (Agritela Black, 90 g/m²), uniformly applied in strips across the cultivation area, covering approximately 78% of the soil surface. The results showed consistent increases in morphological parameters and yield across the years. Plant height in F1 and F2 treatments ranged from 65 to 75 cm, while in the control it reached up to 50 cm (2022–2024). Fresh biomass yield varied from 13.4 g per plant (C) to 378.08 g per plant (F2), and dry biomass yield from 60.3 g (C) to 125.4 g (F2). The highest essential oil content was observed in F2 (1.2% in 2022), while the control remained at 0.8%. The F2 treatment achieved complete weed suppression throughout the experiment without the use of herbicides, demonstrating both agronomic and ecological advantages. Economic evaluation revealed that F2 generated the highest cumulative profit (€142,164.5) compared to the control (€65,555.3). Despite higher initial investment, F2 had the most favorable cost–benefit ratio in the long term. This study highlights the crucial influence of mulching and planting density on optimizing lemon balm production across diverse climatic and soil conditions, while also underscoring the importance of sustainable, non-chemical weed management strategies in lemon balm cultivation. Full article

Blasting is one of the most widely used and cost-effective techniques for rock excavation and fragmentation in open-pit mining, particularly for large-scale operations. However, repeated or poorly controlled blasting can generate excessive ground vibrations that threaten slope stability by causing structural damage, fracturing of the rock mass, and potential failure. Evaluating the effects of blast-induced vibrations is essential to ensure safe and sustainable mining operations. This study investigates the impact of blasting-induced vibrations on slope stability at the Saindak Copper-Gold Open-Pit Mine in Pakistan. A comprehensive dataset was compiled, including field-monitored ground vibration measurements—specifically peak particle velocity (PPV) and key blast design parameters such as spacing (S), burden (B), stemming length (SL), maximum charge per delay (MCPD), and distance from the blast point (D). Geomechanical properties of slope-forming rock units were validated through laboratory testing. Slope stability was analyzed using pseudo-static limit equilibrium methods (LEMs) based on the Mohr–Coulomb failure criterion, employing four approaches: Fellenius, Janbu, Bishop, and Spencer. Pearson and Spearman correlation analyses quantified the influence of blasting parameters on slope behavior, and sensitivity analysis determined the cumulative distribution of slope failure and dynamic response under increasing seismic loads. FoS values were calculated for both east and west pit slopes under static and dynamic conditions. Among all methods, Spencer consistently yielded the highest FoS values. Under static conditions, FoS was 1.502 for the east slope and 1.254 for the west. Under dynamic loading, FoS declined to 1.308 and 1.102, reductions of 12.9% and 11.3%, respectively, as calculated using the Spencer method. The east slope exhibited greater stability due to its gentler angle. Correlation analysis revealed that burden had a significant negative impact (r = −0.81) on stability. Sensitivity analysis showed that stability deteriorates notably when PPV exceeds 10.9 mm/s. Although daily blasting did not critically compromise stability, the west slope showed greater vulnerability, underscoring the need for stricter control of blasting energy to mitigate vibration-induced instability and promote long-term operational sustainability. Full article

The growing imperative to mitigate climate change and accelerate the shift toward energy sustainability has called for a critical evaluation of heat and electricity generation methods. This article presents a comparative life cycle assessment (LCA) of solar and biogas energy systems on a common basis of 1 kWh of useful energy using SimaPro, the ReCiPe 2016 methodology (both midpoint and endpoint indicators), and cumulative energy demand (CED) analysis. This study is the first to evaluate co-located solar PV, solar thermal compound parabolic concentrator (CPC) and biogas combined heat and power (CHP) systems with in situ data collected under identical climatic and operational conditions. The project costs yield levelized costs of electricity (LCOE) of INR 2.4/kWh for PV, 3.3/kWh for the solar thermal dish and 4.1/kWh for biogas. However, the collaborated findings indicate that neither solar-based systems nor biogas technology uniformly outperform the others; rather, their effectiveness hinges on contextual factors, including resource availability and local policy incentives. These insights will prove critical for policymakers, industry stakeholders, and local communities seeking to develop effective, context-sensitive strategies for sustainable energy deployment, emissions reduction, and robust resource management. Full article

Moroccan oil shale ash (MOSA) represents an underutilized industrial by-product, particularly in the Rif region, where its high mineral content has often led to its neglect in value-added applications. This study highlights the successful conversion of MOSA into amorphous mesoporous silica (AS-Si) using a sol–gel process assisted by polyethylene glycol (PEG-6000) as a soft template. The resulting AS-Si material was extensively characterized to confirm its potential for environmental remediation. FTIR analysis revealed characteristic vibrational bands corresponding to Si–OH and Si–O–Si bonds, while XRD confirmed its amorphous nature with a broad diffraction peak at 2θ ≈ 22.5°. SEM imaging revealed a highly porous, sponge-like morphology composed of aggregated nanoscale particles, consistent with the nitrogen adsorption–desorption isotherm. The material exhibited a specific surface area of 68 m²/g, a maximum in the pore size distribution at a pore diameter of 2.4 nm, and a cumulative pore volume of 0.11 cm³/g for pores up to 78 nm. DLS analysis indicated an average hydrodynamic diameter of 779 nm with moderate polydispersity (PDI = 0.48), while a zeta potential of –34.10 mV confirmed good colloidal stability. Furthermore, thermogravimetric analysis (TGA) and DSC suggested the thermal stability of our amorphous silica. The adsorption performance of AS-Si was evaluated using methylene blue (MB) and ciprofloxacin (Cipro) as model pollutants. Kinetic data were best fitted by the pseudo-second-order model, while isotherm studies favored the Langmuir model, suggesting monolayer adsorption. AS-Si could be used four times for the removal of MB and Cipro. These results collectively demonstrate that AS-Si is a promising, low-cost, and sustainable adsorbent derived from Moroccan oil shale ash for the effective removal of organic contaminants from aqueous media. Full article

The sustainable development of eco-tourism is significantly influenced by multiple conditions within spatiotemporally continuous geographic scenarios. However, existing evaluations of the development value of eco-tourism resources (Eco-TRDVs) are non-spatial and do not sensitively represent their complex relationships. This study proposed a GIS approach for evaluating regional Eco-TRDVs by mapping the complex interconnections with spatial distances. Inherent and external conditions for evaluating Eco-TRDVs were classified under three indicators and digitized using GIS and remote sensing technologies. Then, the analytic hierarchy process and GIS cost distance analysis were introduced to define the initial values and cumulate Eco-TRDVs with distances. Taking the Taihang Honggu National Forest Park, China, as the case area, the Eco-TRDVs over the entire area in 2017 and 2020 were mapped. The results present a continuous spatial variability of Eco-TRDVs and comprehensively reflect the complex interconnections of constraint elements with spatial distances. The evaluation is sensitive to the intrinsic value of poles, as evidenced by the high development values and high-density distribution of their contours. Source additions improve the evaluation considerably, with transportation networks having a greater impact than economic development zones and urban elements. Furthermore, aggravated fragmentation of the price flow field increases spatial heterogeneity. The development value shows a negative linear correlation with distance. The proposed approach handles the spatially oriented relationships of the multi-conditions, and supports future planning and monitoring of spatial-temporal changes in eco-tourism development. Full article

The rapid increase in the deployment of Internet of Things (IoT) sensor networks has led to an exponential growth in data generation and an unprecedented demand for efficient resource management infrastructure. Ensuring end-to-end communication across multiple heterogeneous network domains is crucial to maintaining Quality of Service (QoS) requirements, such as low latency and high computational capacity, for IoT applications. However, limited computing resources at multi-access edge computing (MEC), coupled with increasing IoT network requests during task offloading, often lead to network congestion, service latency, and inefficient resource utilization, degrading overall system performance. This paper proposes an intelligent task offloading and resource orchestration framework to address these challenges, thereby optimizing energy consumption, computational cost, network congestion, and service latency in dynamic IoT-MEC environments. The framework introduces task offloading and a dynamic resource orchestration strategy, where task offloading to the MEC server ensures an efficient distribution of computation workloads. The dynamic resource orchestration process, Service Function Chaining (SFC) for Virtual Network Functions (VNFs) placement, and routing path determination optimize service execution across the network. To achieve adaptive and intelligent decision-making, the proposed approach leverages Deep Reinforcement Learning (DRL) to dynamically allocate resources and offload task execution, thereby improving overall system efficiency and addressing the optimal policy in edge computing. Deep Q-network (DQN), which is leveraged to learn an optimal network resource adjustment policy and task offloading, ensures flexible adaptation in SFC deployment evaluations. The simulation result demonstrates that the DRL-based scheme significantly outperforms the reference scheme in terms of cumulative reward, reduced service latency, lowered energy consumption, and improved delivery and throughput. Full article

Background: Radioligand therapy with [¹⁷⁷Lu]Lu-PSMA-617 represents an emerging treatment for metastatic castration-resistant prostate cancer (mCRPC). Its clinical positioning relative to standard therapies remains under discussion. Objective: To compare overall survival (OS), radiographic progression-free survival (rPFS), PSA response, and treatment burden across randomised trials evaluating [¹⁷⁷Lu]Lu-PSMA-617 versus androgen receptor pathway inhibitors (ARTA), Cabazitaxel, or standard of care (SOC). Evidence Acquisition: We conducted a meta-analysis of five randomised controlled trials, including 2073 patients with PSMA-positive metastatic castration-resistant prostate cancer (mCRPC). We assessed survival endpoints, baseline comparability, and treatment intensity. Evidence Synthesis: [¹⁷⁷Lu]Lu-PSMA-617 significantly improved rPFS and PSA response. While modest overall, the OS benefit was more pronounced in taxane-naïve populations. Compared with Cabazitaxel, [¹⁷⁷Lu]Lu-PSMA-617 was associated with similar or better survival despite shorter treatment duration and potentially lower cumulative toxicity and cost. Economic modelling suggests it could offer a more sustainable therapeutic option under typical willingness-to-pay thresholds. Conclusions: [¹⁷⁷Lu]Lu-PSMA-617 shows clinical effectiveness and economic value in mCRPC, with potential advantages over Cabazitaxel and ARTA. Its use could be prioritised in early treatment lines. Patient Summary: This study suggests that PSMA-targeted radioligand therapy is at least as effective as other treatments for advanced prostate cancer, with potential benefits in terms of toxicity, duration, and overall cost. Full article

This study investigates the influence of agricultural mechanization on maize productivity in Tanzania’s Ruvuma region, a major maize-producing area vital to national food security. It addresses gaps in understanding the cumulative effects of mechanization across the maize production cycle and identifies region-specific barriers to adoption among smallholder farmers. Focusing on five key stages—land preparation, planting, plant protection, harvesting, and drying—this research evaluated mechanization uptake at each stage and its relationship with yield disparities. Statistical analyses using Python libraries included regression modeling, ANOVA, and hypothesis testing to quantify mechanization–yield relationships, controlling for farm size and socioeconomic factors, revealing a strong positive correlation between mechanization and maize yields (r = 0.86; p < 0.01). Mechanized land preparation, planting, and plant protection significantly boosted productivity (β = 0.75–0.35; p < 0.001). However, harvesting and drying mechanization showed negligible impacts (p > 0.05), likely due to limited adoption by smallholders combined with statistical constraints arising from the small sample size of large-scale farms (n = 20). Large-scale farms achieved 45% higher yields than smallholders (2.9 vs. 2.0 tons/acre; p < 0.001), reflecting systemic inequities in access. These inequities are underscored by the barriers faced by smallholders, who constitute 70% of farmers yet encounter challenges, including high equipment costs, limited credit access, and insufficient technical knowledge. This study advances innovation diffusion theory by demonstrating how inequitable resource access perpetuates low mechanization uptake in smallholder systems. It underscores the need for context-specific, equity-focused interventions. These include cooperative mechanization models for high-impact stages (land preparation and planting); farmer training programs; and policy measures such as targeted subsidies for harvesting equipment and expanded rural credit systems. Public–private partnerships could democratize mechanization access, bridging yield gaps and enhancing food security. These findings advocate for strategies prioritizing smallholder inclusion to sustainably improve Tanzania’s maize productivity. Full article

Based on the current research status of fault prediction in rail transit reliability, this paper proposes a cumulative failure rate prediction method for key components of subway vehicles based on the RF-CNN-LSTM model. The article describes the prediction method based on cumulative failure rate data and takes the subway EDCU as an example of cumulative failure rate prediction. Three models, ARIMA, MLP, and LSTM, are introduced and compared with the RF–CNN–LSTM model by R² and adjusted R² index. The results show that the RF–CNN–LSTM model can predict the failure rate of the underground door controller well, with accuracy rates of 99.78% and 97.88%. Based on the prediction results, the cumulative failure rate of the EDCU peaks in about 10 years at 4.5% and 10.6%, respectively; the maintenance strategies can be adjusted through the actual situation of the EDCU to reduce maintenance costs and optimize maintenance plans. Full article