Search Results (11)

Aluminum foil sealing is widely employed in industrial packaging, and the quality of sealing plays a crucial role in ensuring product integrity and safety. Thermal infrared images frequently exhibit non-uniform heat distribution and indistinct boundaries within the sealing region. Additionally, variations in thermal response and local structural characteristics are observed across different defect types. Thus, traditional detection methods exhibit limitations regarding their stability and adaptability. In this paper, a novel thermal image recognition algorithm called EAC-Net is proposed for the classification and detection of sealing defects in thermal infrared images. In the proposed method, EfficientNet-B0 is utilized as the backbone network to improve its adaptability for industrial deployment. Furthermore, the Atrous Spatial Pyramid Pooling module is incorporated to enhance the multi-scale perception of defect regions, while the Channel–Spatial Attention Mixing with Channel Shuffle module is adopted to strengthen the focus on critical thermal features. Significant improvements in recognition performance were verified in experiments, while both computational complexity and inference latency were effectively kept at low levels. In the experiments, EAC-Net demonstrated an accuracy of 99.06% and a precision of 99.07%, indicating its high robustness and application potential. Full article

We propose a wind spectrum–response surface dual-domain coupling method to study the reliability optimization of tall structures under the action of unsteady wind fields. Unlike traditional research, the dual-domain coupling analysis can quickly and accurately capture structural response defects and optimize size. The first parametric modeling technology establishes a high-precision finite element model of the movable boom structure by establishing a dual-domain coupling framework of pulsating wind and structural optimization. Using MATLAB (instructional R2024b version), the pulsating wind is simulated with uncertainty. The pulsating wind speeds at different heights are converted into wind loads acting on the net-sealed movable boom structure. Secondly, the boom structure’s dynamic response analysis and response surface optimization design were carried out. The final results show that the maximum value of displacement of the optimized net sealer boom is reduced by about 8.06%, the maximum value of stress is reduced by about 11.04%, and the stiffness is improved through the stress monitoring of the existing structure. It showed that the wind-resistant capability of the boom studied by this method is enhanced, improving the study’s efficiency. Full article

Carbon geological storage (CGS) is one of the key processes in carbon capture and storage (CCS) technologies, which are used to reduce CO₂ emissions and achieve carbon-neutrality and net-zero emissions in developing countries. In Thailand, the Mae Moh basin is a potential site for implementing CGS due to the presence of a structural trap that can seal the CO₂ storage formation. However, the cost of CGS projects needs to be subsidized by selling carbon credits in order to reach the project breakeven. Therefore, this paper estimates the economic components of a CGS project in the Mae Moh basin by designing the well completion and operating parameters for CO₂ injection. The capital costs and operating costs of the process components were calculated, and the minimum carbon credit cost required to cover the total costs of the CGS project was determined. The results indicate that the designed system proposes an operating gas injection rate of 1.454 MMscf/day, which is equivalent to 29,530 tCO₂e per year per well. Additionally, the minimum carbon credit cost was estimated to be USD 70.77 per tCO₂e in order to achieve breakeven for the best case CGS project, which was found to be much higher than the current market price of carbon credit in Thailand, at around USD 3.5 per tCO₂e. To enhance the economic prospects of this area, it is imperative to promote a policy of improving the cost of carbon credit for CGS projects in Thailand. Full article

The Ross Sea region Marine Protected Area (RSR MPA) is one of the most productive regions in the Southern Ocean. Mesozooplankton intermediates the primary product to the higher predators, such as penguins and seals, in this ecosystem. In this study, the mesozooplankton community structure and spatial pattern in the RSR MPA in January were investigated by using 505 μm-mesh-size bongo net samples. As a result, 37 mesozooplankton taxa with a total mean abundance of 35.26 ind./m³, ranging from 2.94 to 139.17 ind./m³, were confirmed. Of the 37 taxa, 7 occupied almost 84% of the total abundance, with copepods being the main dominant taxa. As shown by our hierarchical analysis, the mesozooplankton community was divided into four groups, each associated with a specific geographical distribution. Group A was composed of stations around Terra Nova Bay and showed relatively low abundance. Group B included stations around the continental slope region. Group D was composed of the Ross Sea continental shelf stations, while group C consisted of stations geographically located between those of groups B and D. These four groups were influenced by various environmental factors, such as water temperature, salinity, and nutrients. In summary, the mesozooplankton community can be separated according to geographical pattern. This pattern is related to several environmental factors. Full article

Sealing net structures are frequently used as protective devices for newly constructed transmission lines that intersect existing lines. They aim to ensure safety during the construction and operation of the intersecting lines. To explore the wind deflection response characteristics of sealing net structures under wind loads, a model of the sealing net structure was created using ANSYS/APDL software for finite element analysis. This model represents an overhead sealing net structure spanning an existing line. The results obtained from finite element simulations aligned well with the theoretical calculations, indicating the validity of the established finite element model for accurately analyzing the wind-induced response of the sealing net structure. Expanding on this foundation, the study analyzed the impact of various factors on the wind deflection of the sealing net. These factors encompass the span, wind speed, pretension of the catenary cable, wind direction angle, arrangement angle, and method of additional stay cables. The research investigated the influence of these factors on the wind deflection of the sealing net structure. Additionally, a proposal was presented for arranging the closure structure to mitigate wind deflection. This proposal provides valuable guidance for the design and safety considerations of transmission lines and sealing net structures. Full article

Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas. Using road segmentation, the bot itself and physical facilities may be protected and the sweeping efficiency of the SDSB promoted. However, roads in the real world are generally exposed to intricate noise conditions as a result of changing weather and climate effects; these include sunshine spots, shadowing caused by trees or physical facilities, traffic obstacles and signs, and cracks or sealing signs resulting from long-term road usage, as well as different types of road materials, such as cement or asphalt; all of these factors greatly influence the effectiveness of road segmentation. In this work, we investigate the extension of Primordial U-Net by the proposed EnRDeA U-Net, which uses an input channel applying a Residual U-Net block as an encoder and an attention gate in the output channel as a decoder, to validate a dataset of intricate road noises. In addition, we carry out a detailed analysis of the nets’ features and segmentation performance to validate the intricate noises dataset on three U-Net extensions, i.e., the Primordial U-Net, Residual U-Net, and EnRDeA U-Net. Finally, the nets’ structures, parameters, training losses, performance indexes, etc., are presented and discussed in the experimental results. Full article

Interactions between fisheries and marine mammals have been well documented in almost all existing fishing gears around the world, often associated with detrimental consequences. Interactions of the endangered Mediterranean monk seal (Monachus monachus) with small-scale fisheries have been previously documented in the Mediterranean; this is a problem that seems to be growing in recent years. The present study aims to understand for the first time the nature and extent of interactions between the Mediterranean monk seal and small-scale fisheries in the Republic of Cyprus. The data were collected by conducting in-person semi-structured interviews, between November and December 2020, with 90 fishermen operating from nine different ports, extending throughout the entire coastline of the Republic of Cyprus. The results revealed minimal interactions between the Mediterranean monk seals and small-scale fisheries. The findings indicate that interactions are more likely to occur at depths of less than 10 m, closer to the coast, with the use of trammel nets and gillnets, and during the spring and summer months. The encounter, depredation and incidental capture rates were calculated at 0.01 (0.95%), 0.005 (0.51%) and 0.0004 (0.04%) per fisher, respectively. Spatiotemporal closed areas are proposed as a potential solution to mitigate these interactions. Full article

The recycling or burial of carbon dioxide in depleted petroleum reservoirs and re-imagining exploration strategies that focus on hydrogen reservoirs (with any associated hydrocarbon gas as the upside potential) are a necessity in today’s environmental and geopolitical climate. Given that geologic hydrogen and hydrocarbon gases may occur in the same or different reservoirs, there will be gains in efficiency when searching for both resources together since they share some commonalities, but there is no geophysical workflow available yet for this purpose. Three-dimensional (3D) marine controlled-source electromagnetic (CSEM) and magnetotelluric (MT) methods provide valuable information on rock-and-fluid variations in the subsurface and can be used to investigate hydrogen and hydrocarbon reservoirs, source rocks, and the migration pathways of contrasting resistivity relative to the host rock. In this paper, a process-oriented CSEM-MT workflow is proposed for the efficient combined investigation of reservoir hydrocarbon and hydrogen within a play-based exploration and production framework that emphasizes carbon footprint reduction. It has the following challenging elements: finding the right basin (and block), selecting the right prospect, drilling the right well, and exploiting the opportunities for sustainability and CO₂ recycling or burial in the appropriate reservoirs. Recent methodological developments that integrate 3D CSEM-MT imaging into the appropriate structural constraints to derive the geologically robust models necessary for resolving these challenges and their extension to reservoir monitoring are described. Instructive case studies are revisited, showing how 3D CSEM-MT models facilitate the interpretation of resistivity information in terms of the key elements of geological prospect evaluation (presence of source rocks, migration and charge, reservoir rock, and trap and seal) and understanding how deep geological processes control the distribution and charging of potential hydrocarbon, geothermal, and hydrogen reservoirs. In particular, evidence is provided that deep crustal resistivity imaging can map serpentinized ultramafic rocks (possible source rocks for hydrogen) in offshore northwest Borneo and can be combined with seismic reflection data to map vertical fluid migration pathways and their barrier (or seal), as exemplified by the subhorizontal detachment zones in Eocene shale in the Mexican Ridges fold belt of the southwest of the Gulf of Mexico, raising the possibility of using integrated geophysical methods to map hydrogen kitchens in different terrains. The methodological advancements and new combined investigative workflow provide a way for improved resource mapping and monitoring and, hence, a technology that could play a critical role in helping the world reach net-zero emissions by 2050. Full article

This study investigates direct powder forging (DPF) as a new approach for near-net-shape processing of titanium alloys using a coarse particle size distribution (PSD) between 90 and 250 μm. This route was utilised to takes advantage of DPF’s enclosed nature to make near-net-shape components with conventional forging equipment, making it attractive and viable even for reactive powder such as titanium. In this study, the uncompacted Ti-6Al-4V ELI powder was sealed under vacuum in a stainless-steel canister and hot forged in air to produce a fully dense titanium femoral stem. After the final forging stage, the excess material in the flash region was cut, which efficiently released the canister, revealing the forged part with minimal surface contamination. The as-forged microstructure comprises coarse β grains with a martensitic structure. The subsequent annealing was able to generate a fine and homogenous lamellar microstructure with mechanical properties that respects the surgical implant standard, showing that DPF offers significant potential for forged titanium parts. Therefore, the DPF process provides a suitable alternative to produce titanium components using basic equipment, making it more available to the industry. Full article

Plant extracts represent a rich repository of metabolites with antioxidant and antimicrobial properties. Neem (Azadirachta indica) is a medicinal plant considered the tree of the 21st century. In this study, we investigated the antioxidant and antimicrobial effects of propyl disulfide (PD), a major volatile compound in neem seed, against the pericarp browning (BI), microbial decay incidence (DI), and water loss of longan fruit. Fresh longan cv. Shixia samples were packaged in oriented polypropylene (OPP) and polyethene (PE) packages of different thicknesses (20, 40, and 60 µm). Sterile gauze was fixed inside the packages and 500 uL of PD was placed on them to avoid the direct contact of PD with fruit samples. Packages were sealed immediately to minimize vaporization and stored at 12 ± 1 °C for 18 days. Fruit samples packaged in open net packages served as controls. The results showed that fruit treated with PD in OPP and PE packages significantly prevented losses of water, DI, and BI compared to control treatment. PD also maintained the color, TSS values, TA values, pH values, high peel firmness, high TPC content, and high TFC content, and reduced the activity levels of PPO and POD. Scanning electron microscope (SEM) analysis indicated that the exocarp, mesocarp, and endocarp of longan peel were smooth, uniform, and compact with no free space compared to control, where crakes, a damaged and loose structure, and a lot of fungal mycelia were found. The shortest shelf life of 9 days was observed in control as compared to 18 days in OPP-20 and OPP-40; 15 days in OPP-60, PE-20, and PE-40; and 12 days in PE-60 packaging films. Therefore, PD as a natural antioxidant and antimicrobial agent, in combination with OPP-20 and OPP-40 polymeric films, could successfully be applied commercially to extend the postharvest shelf life of longan. Full article

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios. Full article