Search Results (15,613)

The vibration of a ship’s propulsion shaft system directly affects the ship’s lifespan, and many studies have designed vibration absorbers only for one of the natural frequencies of a ship’s propulsion shaft system without considering the influence of multiple low-order resonance frequencies. In this paper, a vibration absorber combined with a magnetorheological elastomer vibration absorber and a rubber vibration absorber in series is designed, and it can cover two torsional natural frequency band ranges to achieve better vibration reduction performances in multiple different torsional natural frequencies. The torsional natural frequency of the propulsion shafting of a 45 m fishing vessel is determined based on a multiple-degrees-of-freedom equivalent discretization model. Two natural frequencies, 22.4 Hz and 131.4 Hz, of a ship propulsion shaft system are selected as the design goal parameters of the combined vibration absorber. The magnetic field is simulated to ensure that the magnetic field generated by an energized coil can meet requirements. Then, a dynamic simulation of the ship propulsion shaft system with a combined vibration absorber is conducted via co-simulation. Afterward, the device is installed on the intermediate shaft of the ship propulsion shaft system for simulation, and the vibration reduction effect of the device is analyzed at different frequencies by controlling the current. When the device is controlled to operate at the optimal frequency point, the results show that the angular acceleration vibration amplitude reduction around the first and third torsional natural frequencies of the propulsion shaft system reaches 90% and 18%, respectively. This study provides new ideas for the intelligent and controllable vibration damping of ship propulsion shaft systems, especially for the development trend of intelligent ship equipment under complex working conditions. Full article

To address the COVID-19 pandemic, governments worldwide implemented mandatory restrictions. As an unintended consequence of these responses, significant air pollution reductions have been recorded across the world. We provide cross-national evidence on the causal impact of pandemic-induced lockdowns on air quality. Using daily air pollution data between 1 January and 31 December 2020, covering 596 major cities in 77 countries, we analyzed the data with a generalized difference-in-differences approach. The results show that lockdown restrictions reduced global concentrations of NO₂ by 21~35%, PM₁₀ by 14~26%, PM_2.5 by 9~18%, CO by 6~16%, and SO₂ by 5~16%, while the O₃ concentrations increased by 15~29% under eight specific lockdown measures. Furthermore, a simultaneous equations model suggests that reductions in public and private mobility, measured by changes in public transportation and car ridership, partly explain the observed decreases in air pollution. These findings have significant implications for ongoing global efforts to mitigate air pollution and underscore the pivotal role of public transit in achieving this goal. Full article

Global warming is a significant threat to the future of humankind. It is caused by greenhouse gases that accumulate in the atmosphere. CO₂ emissions are one of the main drivers of global warming, and the energy sector is one of the main contributors to CO₂ emissions. Recent technological advances in artificial intelligence (AI) have accelerated the adoption of AI in numerous applications to solve many problems. This study carries out a scoping review to understand the use of AI solutions to reduce CO₂ emissions in the energy sector. This paper follows the PRISMA-ScR guidelines in reporting the findings. The academic search engine Google Scholar was utilized to find papers that met the review criteria. Our research question was “How is artificial intelligence used in the energy sector to reduce CO2 emissions?” Search phrases and inclusion criteria were decided based on this research question. In total, 186 papers from the search results were screened, and 16 papers fitting our criteria were summarized in this study. The findings indicate that AI is already used in the energy sector to reduce CO₂ emissions. Three main areas of application for AI techniques were identified. Firstly, AI models are employed to directly optimize energy generation processes by modeling these processes and determining their optimal parameters. Secondly, AI techniques are utilized for forecasting, which aids in optimizing decision-making, energy transmission, and production planning. Lastly, AI is applied to enhance energy efficiency, particularly in optimizing building performance. The use of AI shows significant promise of reducing CO₂ emissions in the energy sector. Full article

Cogeneration with energy cane, a highly productive variety compared to conventional sugarcane, significantly increases ash generation, presenting waste management challenges for the sugar and ethanol industries. This study evaluates the potential of energy cane ash as a sustainable alternative material for partial cement replacement in construction, contributing to circular economy practices. A productivity analysis was conducted for planted areas, and the different parts of sugarcane and energy cane were dried and examined using scanning electron microscopy. These parts were calcined at 450 °C and 600 °C and analyzed using scanning electron microscopy, X-ray fluorescence, particle size distribution, and thermal analysis. The reactivity of the ashes was tested in cement mortars with 5%, 10%, and 20% cement replacement using washed ash. The results revealed that energy cane produces approximately four times more ash per hectare than sugarcane, with leaf ash containing up to 60% silica and stalk ash being rich in potassium. The highest compressive strength was observed in a mortar with 10% cement replacement using washed energy cane ash, achieving 102.43% of the reference value after 28 days of curing, indicating excellent pozzolanic reactivity. These findings highlight the potential of energy cane ash to enhance sustainability in cementitious systems by reducing Portland cement use and promoting waste valorization. Furthermore, the reuse of ash can mitigate waste accumulation and support the development of more sustainable construction materials, contributing to a circular economy and a low-carbon society. Full article

Introduction: This study aimed to capture the early metabolic changes before osteoporosis occurs and identify metabolomic biomarkers at the osteopenia stage for the early prevention of osteoporosis. Materials and Methods: Metabolomic data were generated from normal, osteopenia, and osteoporosis groups with 320 participants recruited from the Nicheng community in Shanghai. We conducted individual edge network analysis (iENA) combined with a random forest to detect metabolomic biomarkers for the early warning of osteoporosis. Weighted Gene Co-Expression Network Analysis (WGCNA) and mediation analysis were used to explore the clinical impacts of metabolomic biomarkers. Results: Visual separations of the metabolic profiles were observed between three bone mineral density (BMD) groups in both genders. According to the iENA approach, several metabolites had significant abundance and association changes in osteopenia participants, confirming that osteopenia is a critical stage in the development of osteoporosis. Metabolites were further selected to identify osteopenia (nine metabolites in females; eight metabolites in males), and their ability to discriminate osteopenia was improved significantly compared to traditional bone turnover markers (BTMs) (female AUC = 0.717, 95% CI 0.547–0.882, versus BTMs: p = 0.036; male AUC = 0.801, 95% CI 0.636–0.966, versus BTMs: p = 0.007). The roles of the identified key metabolites were involved in the association between total fat-free mass (TFFM) and osteopenia in females. Conclusion: Osteopenia was identified as a tipping point during the development of osteoporosis with metabolomic characteristics. A few metabolites were identified as candidate early-warning biomarkers by machine learning analysis, which could indicate bone loss and provide new prevention guidance for osteoporosis. Full article

The ability to freely manipulate the wavefronts of surface plasmon polaritons (SPPs) or surface waves (SWs), particularly with multifunctional integration, is of great importance in near-field photonics. However, conventional SPP control devices typically suffer from low efficiency and single-function limitations. Although recent works have proposed metasurfaces that achieve bifunctional SPP manipulation, their implementation relies on the excitations of circularly polarized (CP) light with different helicities. Here, we propose a generic approach to designing bifunctional SPP meta-devices under single-helicity circularly polarized incidence. Constructed using carefully selected and arranged meta-atoms that possess both structural resonance and a geometric phase, this kind of meta-device can exhibit two distinct SPP manipulation functionalities in both co- and cross-polarized output channels under one CP incidence. As proof of this concept, we designed a bifunctional meta-device in the microwave regime and numerically demonstrated that it can convert a normally incident left circularly polarized (LCP) beam into SWs, exhibiting both a focused wavefront in the co-polarized output channel and a deflected wavefront in the cross-polarized output channel. Our findings substantially enrich the capabilities of metasurfaces to manipulate near-field electromagnetic waves, which can find many applications in practice. Full article

With the consequences of climate change becoming more urgent, there has never been a more pressing need for technologies that can help to reduce the carbon dioxide (CO₂) emissions of the most polluting sectors, such as power generation, steel, cement, and the chemical industry. This review summarizes the state-of-the-art technologies for carbon capture, for instance, post-combustion, pre-combustion, oxy-fuel combustion, chemical looping, and direct air capture. Moreover, already established carbon capture technologies, such as absorption, adsorption, and membrane-based separation, and emerging technologies like calcium looping or cryogenic separation are presented. Beyond carbon capture technologies, this review also discusses how captured CO₂ can be securely stored (CCS) physically in deep saline aquifers or depleted gas and oil reservoirs, stored chemically via mineralization, or used in enhanced oil recovery. The concept of utilizing the captured CO₂ (CCU) for producing value-added products, including formic acid, methanol, urea, or methane, towards a circular carbon economy will also be shortly discussed. Real-life applications, e.g., already pilot-scale continuous methane (CH₄) production from flue gas CO₂, are shown. Actual deployment of the most crucial technologies for the future will be explored in real-life applications. This review aims to provide a compact view of the most crucial technologies that should be considered when choosing to capture, store, or convert CO₂, informing future researchers with efforts aimed at mitigating CO₂ emissions and tackling the climate crisis. Full article

In this study, a novel Z-scheme heterojunction photocatalyst was developed by integrating g-C₃N₄ nanoplates into ZnIn₂S₄ microspheres. X-ray photoelectron spectroscopy analysis revealed a directional electron transfer from g-C₃N₄ to ZnIn₂S₄ upon heterojunction formation. Under irradiation, electrochemical tests and electron paramagnetic resonance spectroscopy demonstrated significantly enhanced charge generation and separation efficiencies in the ZnIn₂S₄/g-C₃N₄ composite, accompanied by reduced charge transfer resistance. In photocatalytic CO₂ reduction, the ZnIn₂S₄/g-C₃N₄ composite achieved the highest CO yield, 1.92 and 5.83 times higher than those of pristine g-C₃N₄ and ZnIn₂S₄, respectively, with a notable CO selectivity of 91.3% compared to H₂ (8.7%). The Z-scheme heterojunction mechanism, confirmed in this work, effectively preserved the strong redox capabilities of the photoinduced charge carriers, leading to superior photocatalytic performance and excellent long-term stability. This study offers valuable insights into the design and development of g-C₃N₄-based heterojunctions for efficient solar-driven CO₂ reduction. Full article

A concise, transition metal-free four-step synthetic pathway has been developed for the synthesis of tetracyclic heterosteroidal compounds, 14-aza-12-oxasteroids, starting from readily available 2-naphthol analogues. After conversion of 2-naphthols to 2-naphthylamines by the Bucherer reaction, subsequent selective C-acetylation was achieved via the Sugasawa reaction and reduction of the acetyl group using borohydride, which resulted into the corresponding amino-alcohols. The naphthalene-based amino-alcohols underwent double dehydrations and double intramolecular cyclization with oxo-acids leading to one-pot formation of a C-N bond, a C-O bond and an amide bond in tandem, to generate two additional rings completing the steroidal framework. A series of 14-aza-12-oxasteroids were synthesized using our developed synthetic strategy in moderate yields, and the structure of one of the final products, 12a-Methyl-11-phenyl-11,12a-dihydro-1H-naphtho[2,1-d]pyrrolo[2,1-b][1,3]oxazin-3(2H)-one, was further confirmed by single crystal X-ray crystallography. Full article

Damage to human health caused by As-contaminated water can be prevented using proper As-removal techniques, such as employing excellent arsenic adsorbents. In this study, the combined addition of Mg- and Ca-based adsorbents was investigated for the efficient removal of As from contaminated water. Following a previous study on As(V), As-removal tests targeting As(III) and several additional tests, including X-ray diffraction analysis, were conducted to clarify the mechanism of the improved performance of the combined-addition As removal. Similarly as for As(V), the combined additions of both MgCO₃ + CaO and MgCO₃ + Ca(OH)₂ improved As(III)-removal performance while inhibiting the leaching of base material components; however, they did not remove As(III) as effectively as As(V). The differences in the removal ratios of As(V) and As(III) in these combined additions were concluded to be primarily due to the different As-removal mechanisms. Mg(OH)₂ and CaCO₃ were generated, and As(III) was incorporated into the generated precipitate of Mg(OH)₂ but not into that of CaCO₃. Conversely, As(V) was incorporated into both Mg(OH)₂ and CaCO₃. Additionally, MgCO₃ + Ca(OH)₂ was evaluated as a more efficient combined-addition method because MgCO₃ + Ca(OH)₂ exhibited a higher As-removal ratio value than MgO + CaO. Proposals have been made to remove As(III) using activated carbon modified with heavy metals or transition elements, or concrete waste grafted with polymers, but these methods are complicated to prepare, costly, and involve the risk of leaching of harmful components. Adsorbents that use general Mg and Ca components as their base material do not contain such harmful components. The Mg- and Ca-based adsorbents are readily available and low-cost, and, best of all, there is no concern that they will leach harmful components. Therefore, widespread use of Mg- and Ca-based adsorbents as a measure against arsenic contamination could greatly contribute to a sustainable society. Full article

Early detection of plant diseases is crucial for agro-holdings, farmers, and smallholders. Various neural network architectures and training methods have been employed to identify optimal solutions for plant disease classification. However, research applying one-shot or few-shot learning approaches, based on similarity determination, to the plantdisease classification domain remains limited. This study evaluates different loss functions used in similarity learning, including Contrastive, Triplet, Quadruplet, SphereFace, CosFace, and ArcFace, alongside various backbone networks, such as MobileNet, EfficientNet, ConvNeXt, and ResNeXt. Custom datasets of real-life images, comprising over 4000 samples across 68 classes of plant diseases, pests, and their effects, were utilized. The experiments evaluate standard transfer learning approaches alongside similarity learning methods based on two classes of loss function. Results demonstrate the superiority of cosine-based methods over Siamese networks in embedding extraction for disease classification. Effective approaches for model organization and training are determined. Additionally, the impact of data normalization is tested, and the generalization ability of the models is assessed using a special dataset consisting of 400 images of difficult-to-identify plant disease cases. Full article

With the continuous adjustment of energy structure and the improvement of environmental protection requirements, combined heat and power microgrids (CHP-MG) have received widespread attention as an efficient and economical way of utilizing energy. The complexity of energy supply relationships and energy coupling within the microgrid system necessitates optimizing the power output of each equipment unit. In this paper, an optimization strategy for a multi-energy microgrid system is proposed based on the efficient energy supply of cogeneration microgrids: decoupling the thermoelectric connection by using the energy storage equipment on the supply side, utilizing the flexibility of the electrical loads and the diversity of the system’s heating methods, and reducing the electrical loads and changing the selection of the heating methods on the demand side. The optimization model in the paper is mainly based on mixed-integer linear programming and demand-side management theory, which simulates the system operation under different scenarios so as to find the optimal equipment output and load management strategies. Simulation results show that the optimized CHP-MG system can ensure a reliable power supply while effectively reducing operating costs, improving energy utilization and promoting sustainable operation of the energy system. The optimized microgrid system offers significant advantages in terms of economic efficiency and energy management when compared to conventional CHP systems. These findings provide actionable insights for policymakers, system operators, and researchers aimed at driving the development of efficient and sustainable energy management solutions. Full article

Many developed and developing countries are concerned about climate change and greenhouse gas (GHG) reduction, with landfills being a major contributor due to the presence of important GHGs such as carbon dioxide (CO₂) and methane (CH₄) in landfill emissions. The appropriate technology or suitable innovation could result in the extraction of significant amounts of energy from CH₄ in landfills. This work used landfill gas emissions (LandGEM) software modeling to analyze the distribution patterns of the gas emissions of two urban landfills in Chonburi and Phuket Provinces, Thailand, from 2013 to 2023. The methane emissions from the Chonburi landfill were 1.063 × 10⁴ Mg/year, and they were 1.077 × 10³ Mg/year for the Phuket landfill, in 2023. According to estimates, the Chonburi landfill emitted 2.916 × 10⁴ Mg/year of CO₂ in 2023, while Phuket emitted 2.955 × 10³ Mg/year. The Chonburi landfill generated 8.67 MWh/year and 195.74 MWh/year of electrical energy potential from CH₄ in 2014 and 2023. In 2014 and 2023, the electrical energy potential from CH₄ was 1.00 MWh/year and 19.83 MWh/year for the Phuket landfill. This study’s results show that landfills can produce CH₄ and that it is possible to collect this gas and stop GHG emissions from entering the atmosphere. This would be beneficial for local authorities considering the potential of landfill gas. Full article

Knowing the energy balance in agricultural systems is essential for a holistic understanding of sustainability, productivity and economic return. The aim of this study was to estimate the cumulative energy demand (CED), greenhouse gas (GHG) emissions and carbon footprint in industrial potato and tomato production systems in the Southeast region of Brazil, identifying mitigation strategies in different scenarios. The Life Cycle Analysis methodology was used, and two functional units were defined: one hectare of cultivation and one kilogram of vegetable produced. The CEDs for tomato and potato production were 59,553.56 MJ ha^–1 (or 0.54 MJ kg^–1) and 57,992.02 MJ ha^–1 (or 1.45 MJ kg^–1), respectively. The GHG emissions were 5425.13 kg CO₂ eq ha^–1 for potato production and 5270.9 kg CO₂ eq ha^–1 for tomato production, resulting in carbon footprints of 0.135 and 0.042 kg CO₂ eq kg^–1, respectively. Fertilizers, diesel and pesticides were the main contributors to CED and GHG emissions. Thus, in order to achieve greater sustainability in the production of these vegetables and mitigate the impacts on the environment generated by the high demand for energy and GHG emissions, it is necessary to replace synthetic fertilizers with organic sources, chemical pesticides with biological pesticides, diesel with biodiesel or the use of electric vehicles and tractors, resulting in reductions of up to 39 and 52% in the GHG emissions for potatoes and tomatoes, respectively. Full article

In modern agriculture today, the cultivation of agricultural products cannot be imagined without greenhouses. This paper presents an energy optimization of a solar greenhouse with a photovoltaic system (PV) and a ground-source heat pump (GSHP). The PV system generates electricity, while the GSHP is used for heating and cooling. A greenhouse is designed with an Open Studio plug-in in the Google SketchUp environment, the EnergyPlus software (8.7.1 version) was used for energy simulation, and the GenOpt software (2.0.0 version) was used for optimization of the azimuth angle and PV cell efficiency. Results for different solar greenhouse orientations and different photovoltaic module efficiency are presented in the paper. The obtained optimal azimuth angle of the solar greenhouse was −8°. With the installation of a PV array with higher module efficiency (20–24%), it is possible to achieve annual energy savings of 6.87–101.77%. Also, with the PV module efficiency of 23.94%, a concept of zero-net-energy solar greenhouses (ZNEG) is achieved at optimal azimuth and slope angle. Through the environmental analysis of different greenhouses, CO₂ emissions of PV and GSHP are calculated and compared with electricity usage. Saved CO₂ emission for a zero-net-energy greenhouse is 6626 kg CO₂/year. An economic analysis of installed renewable energy systems was carried out: with the total investment of 19,326 € for ZNEG, the payback period is 8.63 years. Full article