Search Results (282)

In this study, we present a novel high-thermal-conductivity-organosilicon potting adhesive developed for use in power modules. The adhesive is designed to enhance power modules’ thermal properties and mechanical strength, addressing the need for more efficient and reliable encapsulation materials in electronic applications. By optimizing the resin formulation, the adhesive exhibits improved tensile strength and elongation at break properties, making it particularly suitable for applications requiring high durability and resilience under thermal and mechanical stress. Herein, we propose a high-thermal-conductivity organosilicon electronic potting adhesive designed for power modules. The adhesive consists of two components: Component A and Component B. Component A is composed of a base polymer (0.5–10 parts), silicone resin (0.15–10 parts), plasticizer (0.5–5 parts), color paste (0.01–0.2 parts), thermally conductive filler (70–120 parts), filler treatment agent (2–8 parts), and a catalyst (0.1–2 parts). Component B includes a base polymer (0.5–10 parts), silicone resin (0.15–10 parts), plasticizer (0.5–5 parts), thermally conductive filler (70–120 parts), crosslinking agent (0.1–10 parts), chain extender (0.1–10 parts), and crosslinking inhibitor (0.01–1 part). The adhesive is designed to improve the tensile strength and elongation at break. These materials were engineered to facilitate easy repair and disassembly, ensuring cost-effective maintenance and reuse in power module systems. This work demonstrates the potential of the adhesive in advancing the performance and longevity of power electronics, providing valuable insights into its practical application for high-performance electronic devices. Full article

Deep-well radar telemetry over ultra-long cables suffers from strong frequency-selective attenuation and impedance drift under high temperature and pressure. We have proposed a channel-adaptive “communication + acquisition” architecture for a 7500 m borehole radar system. The scheme integrates spread-spectrum time domain reflectometry (SSTDR; m-sequence with BPSK) to monitor the cable in situ, identify termination/cable impedance, and adaptively match the load, thereby reducing reflection-induced loss. On the receiving side, we combine time domain adaptive equalization—implemented as an LMS-driven FIR filter—with frequency domain OFDM equalization based on least-squares (LS) channel estimation, enabling constellation recovery and robust demodulation over the distorted channel. The full processing chain is realized in real time on a Xilinx Artix-7 (XC7A100T) FPGA with module-level reuse and pre-stored training sequences for efficient hardware scheduling. In a field deployment in the Shunbei area at 7500 m depth, radar results show high agreement with third-party geological logs: the GR-curve correlation reaches 0.92, the casing reflector at ~7250 m is clearly reproduced, and the key bottom depth error is 0.013%. These results verify that the proposed system maintains stable communication and accurate imaging in harsh deep-well environments while remaining compact and implementable on cost-effective hardware. Full article

In the task of integrated circuit micrograph acquisition, image super-resolution reconstruction technology can significantly enhance acquisition efficiency. With the advancement of deep learning techniques, the performance of image super-resolution reconstruction networks has improved markedly, but their demand for inference device memory has also increased substantially, greatly limiting their practical application in engineering and deployment on resource-constrained devices. Against this backdrop, we designed image super-resolution reconstruction networks based on feature reuse and structural reparameterization techniques, ensuring that the networks maintain reconstruction performance while being more suitable for deployment in resource-limited environments. Traditional image super-resolution reconstruction networks often redundantly compute similar features through standard convolution operations, leading to significant computational resource wastage. By employing low-cost operations, we replaced some redundant features with those generated from the inherent characteristics of the image and designed a reparameterization layer using structural reparameterization techniques. Building upon local feature fusion and local residual learning, we developed two efficient deep feature extraction modules, and forming the image super-resolution reconstruction networks. Compared to performance-oriented image super-resolution reconstruction networks (e.g., DRCT), our network reduces algorithm parameters by 84.5% and shortens inference time by 49.8%. In comparison with lightweight image reconstruction algorithms, our method improves the mean structural similarity index by 3.24%. Experimental results demonstrate that the image super-resolution reconstruction network based on feature reuse and structural reparameterization achieves an excellent balance between network performance and complexity. Full article

Unmanned aerial vehicle (UAV) image object detection has become an increasingly important research area in computer vision. However, the variable target shapes and complex environments make it difficult for the model to fully exploit its features. In order to solve this problem, we propose a UAV image object detection method based on a backbone feature reuse detection network, named BFRDNet. First, we design a backbone feature reuse pyramid network (BFRPN), which takes the model characteristics as the starting point and more fully utilizes the multi-scale features of backbone network to improve the model’s performance in complex environments. Second, we propose a feature extraction module based on multiple kernels convolution (MKConv), to deeply mine features under different receptive fields, helping the model accurately recognize targets of different sizes and shapes. Finally, we design a detection head preprocessing module (PDetect) to enhance the feature representation fed to the detection head and effectively suppress the interference of background information. In this study, we validate the performance of BFRDNet primarily on the VisDrone dataset. The experimental results demonstrate that BFRDNet achieves a significant improvement in detection performance, with the mAP increasing by 7.5%. To additionally evaluate the model’s generalization capacity, we extend the experiments to the UAVDT and COCO datasets. Full article

Wellbore washing technology is a basic operation in wellbore maintenance. Problems such as low automation levels, long processing times, the fact that it is easy to cause downhole falling, and cleaning blind areas greatly affect the use and maintenance of traditional cleaning equipment. These problems usually come from design defects such as a complicated installation process, a lack of an anti-impact structure, and a fixed jet direction. To address the aforementioned issues, this paper proposes an efficient and integrated rapid-disassembly and -assembly automatic filtration rotary jet cleaning device. The device is divided into two main units and further subdivided into four modules. The quick-assembly unit comprises an elastic connection module and a downstroke quick-assembly module, which can automatically compensate for deviations in equipment position during the installation process, ensuring the reliability of the installation process and the sealing of the equipment and facilitating the rapid connection and separation of the tool string. The wellbore cleaning unit includes a hydraulic rotary washing module and a rotary filtration storage module. The wellbore is jet-flushed by hydraulic drive, and the solid particles are separated and filtered during the cleaning fluid circulation process to realize the purification and reuse of the cleaning fluid. The device reduces the installation operation time and labor cost, improves the reliability of equipment in the well, improves the flushing coverage area and the cleaning efficiency, realizes the reuse of the cleaning liquid in the wellbore, reduces the energy consumption of the flowback treatment, and comprehensively improves the cleaning efficiency and the energy utilization efficiency. Full article

High levels of phosphorus cause eutrophication, leading to water blooms and making the water undesirable in aquatic environments. Surface water pollution by phosphorus (P) is caused by both point and diffuse sources. Despite the recent technological advancements in wastewater phosphorus removal, this element persists in aquatic ecosystems, particularly in sediments, often in non-bioavailable forms (in the case of precipitation by aluminum salts) or within biomass associated with high concentrations of heavy metals, rendering it unsuitable for reuse. In this paper, we review the measures and methods commonly used for reducing or removing bioavailable phosphorus, with a focus on the strategies and methods for direct in situ phosphorus removal or reuse, including the use of microbial biofilms and aquatic macrophytes, natural and constructed wetlands, and biotised (biologically enhanced) solid-phase sorbents or woodchip bioreactors. This paper also highlights the significance of bioavailable phosphorus from both the hydrochemical perspectives, examining phosphorus speciation, solubility, and the geochemical interactions influencing mobility in water and sediments, and the biological perspectives, which consider phosphorus uptake, bioaccumulation in aquatic organisms, and the role of microbial and plant communities in modulating phosphorus cycling. This overview presents sustainable phosphorus management approaches that are key to reducing eutrophication and supporting ecosystem health. Full article

Photovoltaic (PV) energy conversion is expected to contribute to the creation of a clean energy society. For realizing such a vision, various developments such as high-efficiency, low-cost and highly reliable materials, solar cells, modules and systems are necessary. Cooperation with storage batteries is also very important for regulation and self-consumption. The creation of new applications such as building integrated PV, vehicle integrated PV, agriculture PV and floating PV is also very important for further installation of PV and reducing CO₂ emission. The sustainability of material consumption, along with reducing, reusing and recycling are also key issues for widespread deployment of PV. This paper provides an overview of the current status of photovoltaics and discusses future directions for photovoltaics from the view-points of high-efficiency, low-cost, reliability, and importance of integrated photovoltaics and sustainability. Full article

Quantifying the environmental benefits of designing buildings for disassembly and flexibility (DfD/DfF) remains challenging within current life cycle assessment (LCA) frameworks. This study assesses the climate impact of a two-story Swedish timber multifamily building (377 m²) designed for future transformation and reuse. An LCA covering modules A–D was performed for one linear scenario (S0: demolition without reuse) and three circular scenarios (S1: layout change, S2: relocation, S3: vertical extension), applying three allocation methods: 100:0, 50:50, and system expansion. All circular scenarios reduced climate impact compared to the linear reference, though to varying degrees. Reductions ranged from 8–50% within the system boundary (A–C), depending on scenario and allocation method. While the 50:50 approach attributed significant reductions within A–C, the 100:0 method emphasized benefits primarily in module D. The 50:50 method yielded the lowest impacts within the system boundary, whereas system expansion showed the largest overall reductions but relied on uncertain assumptions. The study concludes that including future scenarios in LCA is more effective in promoting circularity than the specific choice of allocation method. It emphasizes the need for standardized frameworks that account for multiple use cycles and support fair comparisons in policy and procurement. Full article

Non-cooperative spatial target detection plays a vital role in enabling autonomous on-orbit servicing and maintaining space situational awareness (SSA). However, due to the limited computational resources of onboard embedded systems and the complexity of spaceborne imaging environments, where spacecraft images often contain small targets that are easily obscured by background noise and characterized by low local information entropy, many existing object detection frameworks struggle to achieve high accuracy with low computational cost. To address this challenge, we propose YOLO-GRBI, an enhanced detection network designed to balance accuracy and efficiency. A reparameterized ELAN backbone is adopted to improve feature reuse and facilitate gradient propagation. The BiFormer and C2f-iAFF modules are introduced to enhance attention to salient targets, reducing false positives and false negatives. GSConv and VoV-GSCSP modules are integrated into the neck to reduce convolution operations and computational redundancy while preserving information entropy. YOLO-GRBI employs the focal loss for classification and confidence prediction to address class imbalance. Experiments on a self-constructed spacecraft dataset show that YOLO-GRBI outperforms the baseline YOLOv8n, achieving a 4.9% increase in mAP@0.5 and a 6.0% boost in mAP@0.5:0.95, while further reducing model complexity and inference latency. Full article

The biological production of hydrogen offers a renewable and potentially sustainable alternative for clean energy generation. In Northeast Brazil, depleted oil reservoirs (DORs) present a unique opportunity to integrate biotechnology with existing fossil fuel infrastructure. These subsurface formations, rich in residual hydrocarbons (RH) and native H₂ producing microbiota, can be repurposed as bioreactors for hydrogen production. This process, often referred to as “Gold Hydrogen”, involves the in situ microbial conversion of RH into H₂, typically via dark fermentation, and is distinct from green, blue, or grey hydrogen due to its reliance on indigenous subsurface biota and RH. Strategies include nutrient modulation and chemical additives to stimulate native hydrogenogenic genera (Clostridium, Petrotoga, Thermotoga) or the injection of improved inocula. While this approach has potential environmental benefits, such as integrated CO₂ sequestration and minimized surface disturbance, it also presents risks, namely the production of CO₂ and H₂S, and fracturing, which require strict monitoring and mitigation. Although infrastructure reuse reduces capital expenditures, achieving economic viability depends on overcoming significant technical, operational, and biotechnological challenges. If widely applied, this model could help decarbonize the energy sector, repurpose legacy infrastructure, and support the global transition toward low-carbon technologies. Full article

At present, environmental pollution is becoming more and more serious, and the energy problem is becoming more prominent. Energy-braking recovery can collect the mechanical energy lost in the traditional braking process and convert it into electricity or other forms of energy for vehicle reuse, thus reducing carbon emissions, achieving energy saving and emission reduction, and promoting green development. Based on this, this paper studies the energy-braking recovery method. The study focuses specifically on the recovery of energy during vehicle braking triggered by brake-signal activation, without addressing alternative deceleration strategies under braking conditions. The proposed energy-braking recovery scheme is evaluated primarily through simulation, with the analysis grounded in practical application scenarios and leveraging existing technologies. Firstly, the principle of energy-braking recovery is introduced, and the method of estimating the State on Charge (SOC) of the battery and controlling the motor speed is determined. Then, the simulation model of the energy brake recovery system is built with MATLAB R2023b (MathWorks, Natick, MA, USA), and the design ideas and specific structures of the three modules of the simulation model are introduced in detail. Finally, the results of the simulated motor speed and SOC value of the battery are analysed, and it is confirmed that they meet the requirements of the system and achieve close to the ideal effect. Full article

An advanced gasification module (AGM) for green hydrogen production involves a small-scale biomass gasification process owing to the low energy density of biomass. Therefore, significant heat loss and the endothermic nature of gasification system require additional fossil fuel heat, increasing CO₂ emissions. This study focuses on bioenergy conversion with carbon capture and utilization (BECCU), where carbon-neutral CO₂ from biomass gasification is captured and reused as a gasifying agent to reduce the greenhouse gas intensity of green hydrogen. BECCU is expected to achieve negative emissions and enhance gasification efficiency by promoting conversion of char and tar through CO₂ gasification. To evaluate the effectiveness of BECCU in the AGM, we conducted a sensitivity analysis of the reformer temperature and S/C ratio using process simulation combined with life cycle assessment. In both sensitivity analyses, the GWP for CO₂ capture was lower compared with conventional conditions, considering recovered CO₂ from purification and the additional steam generated through heat recovery. This suggests improved hydrogen yields from enhanced char and tar conversion. Consequently, the GWP was reduced by more than 50%, demonstrating BECCU’s effectiveness in the AGM. This represents a step toward operating biomass gasification systems with lower environmental impact and contributing to sustainable energy production. Full article

Ultrafiltration strips water of bacteria. The common misconception is that the filtrate is thus free of bacteria. This only applies, however, in the case that the filtrate compartment is sterile. In real-world applications, the filtrate is rapidly re-colonized, followed by regrowth. In extreme cases of low water usage, the cell numbers in the filtrate can even exceed those in the feed water, probably due to a combination of the microbial enrichment of the bulk water from surfaces, regrowth in the water body itself, and nutrient enrichment on the filter membrane. Regrowth is made possible because dissolved nutrients can freely pass through the membranes. This explains why the initial decrease in cell numbers in drinking water installation systems with ultrafiltration is often followed by an increase in the periphery of the plumbing system. The extent of actual regrowth hereby depends mostly on water usage behaviours. A shorter frequency of membrane wash cycles is beneficial for reducing cell numbers. Neither frequent wash cycles nor cleaning in place (CIP) in filtration units, however, seem to modulate the maximal regrowth potential. Although the effect of ultrafiltration on cell numbers is not sustainable, it causes profound changes in the bacterial communities, with highly distinct populations in the feed water and the filtrate. The microbiological “reset” is demonstrated using examples both from the fields of drinking water and water reuse. Overall, our results suggest that ultrafiltration has a profound impact on the microbiome, but the cell numbers in filtrates depend mostly on the water usage and operational conditions. Full article

Introduction: Efficient communication and collaboration among local health departments (LHDs), healthcare organizations, governmental entities, and other community stakeholders are critical for public health preparedness and response. This study evaluates (1) the impact of informatics on LHDs’ frequency and collaboration in creating consistent COVID-19 messaging; (2) the influence of informatics on targeted messaging for vulnerable populations; and (3) LHD characteristics linked to their consistent and/or targeted messaging engagement. Methods: This study analyzed the 2020 National Association of County and City Health Officials (NACCHO) Forces of Change (FOC) survey, the COVID-19 Edition. Of the 2390 LHDs invited to complete the core questionnaire, 905 were asked to fill out the module questionnaire as well. The response rate for the core was 24% with 587 responses, while the module received 237 responses, achieving a 26% response rate. Descriptive analyses and six logistic regression models were utilized. Results: Over 80% (183) of LHDs collaborated regularly with public health partners, and 95% (222) used information management applications for COVID-19. Most interacted with local and state agencies, but only half with federal ones. LHDs that exchanged data with local non-health agencies, engaged with local non-health agencies, and communicated weekly to daily with the public about long-term/assisted care had higher odds of creating consistent messages for the public, and about the use and reuse of masks had lower odds of collaborating with public health partners to develop consistent messages for the public. Conclusion: Our findings underscore the centrality of informatics infrastructure and collaboration in ensuring equitable public health messaging. Strengthening public health agencies and investing in targeted training are crucial for effective communication across the communities served by these agencies. Full article

Lane detection is a key technology in automatic driving environment perception, and its accuracy directly affects vehicle positioning, path planning, and driving safety. In this study, an enhanced real-time model for lane detection based on an improved DeepLabV3+ architecture is proposed to address the challenges posed by complex dynamic backgrounds and blurred road boundaries in suburban road scenarios. To address the lack of feature correlation in the traditional Atrous Spatial Pyramid Pooling (ASPP) module of the DeepLabV3+ model, we propose an improved LC-DenseASPP module. First, inspired by DenseASPP, the number of dilated convolution layers is reduced from six to three by adopting a dense connection to enhance feature reuse, significantly reducing computational complexity. Second, the convolutional block attention module (CBAM) attention mechanism is embedded after the LC-DenseASPP dilated convolution operation. This effectively improves the model’s ability to focus on key features through the adaptive refinement of channel and spatial attention features. Finally, an image-pooling operation is introduced in the last layer of the LC-DenseASPP to further enhance the ability to capture global context information. DySample is introduced to replace bilinear upsampling in the decoder, ensuring model performance while reducing computational resource consumption. The experimental results show that the model achieves a good balance between segmentation accuracy and computational efficiency, with a mean intersection over union (mIoU) of 95.48% and an inference speed of 128 frames per second (FPS). Additionally, a new lane-detection dataset, SubLane, is constructed to fill the gap in the research field of lane detection in suburban road scenarios. Full article