Application Scenarios Research Articles

A digital twin modeling and application for gear rack drilling rigs lifting system

A comprehensive digital transformation has been undergone by the oil and gas industry, wherein digital twins are leveraged to enable real-time data analysis, providing predictive and diagnostic engineering insights. The potential for developing intelligent oil and gas fields is substantial with the implementation of digital twins. A digital twin framework for gear rack drilling rigs is proposed, built upon an understanding of the digital twin composition and characteristics of the gear rack drilling rig lifting system. The framework encompasses descriptions of digital twin characteristics specific to drilling rigs, the application environment, and behavioral rules. The modeling approach integrates mechanism modeling, real-time performance response, instantaneous data transmission, and data visualization. To illustrate this framework, exemplary case studies involving the transmission unit and support unit of the lifting system are presented. Mechanism models are constructed to analyze dynamic gear performance and support unit response. Real-time data transmission is facilitated through sensor-based monitoring, enhancing the prediction speed and accuracy of dynamic performance through a synergy of mechanism modeling, machine learning, and real-time data analysis. The digital twin of the lifting system is visualized utilizing the Unity3D platform. Furthermore, functionalities on data acquisition, processing, and visualization across diverse application scenarios are encapsulated into modular components, streamlining the creation of high-fidelity digital twins. The frameworks and modeling methodologies presented herein can serve as a foundational and methodological guide for the exploration and implementation of digital twin technology within the oil and gas industry, ultimately fostering its advancement in this sector.

High Sensitivity Bi2O3/Ti3C2Tx Ammonia Sensor Based on Improved Synthetic MXene Method at Room Temperature.

The MXene Ti3C2Tx was synthesized using hydrofluoric acid and an improved multilayer method in this study. Subsequently, a Bi2O3/Ti3C2Tx composite material was produced through hydrothermal synthesis. This composite boasts a unique layered structure, offering a large surface area that provides numerous contact and reaction sites, facilitating the adsorption of ammonia on its surface. The prepared Bi2O3/Ti3C2Tx-based sensor exhibits excellent sensing performance for ammonia gas, including high responsiveness, good repeatability, and rapid response-recovery time. The sensor's response to 100 ppm ammonia gas is 61%, which is 11.3 times and 1.6 times the response values of the Ti3C2Tx gas sensor and Bi2O3 gas sensor, with response/recovery times of 61 s/164 s at room temperature, respectively. Additionally, the gas sensitivity mechanism of the Bi2O3/Ti3C2Tx-based sensor was analyzed, and the gas sensing response mechanism was proposed. This study shows that the sensor can effectively enhance the accuracy and precision of ammonia detection at room temperature and has a wide range of application scenarios.

Modification Strategies and Prospects for Enhancing the Stability of Black Phosphorus.

Black phosphorus is a two-dimensional layer material with promising applications due to its many excellent physicochemical properties, including high carrier mobility, ambipolar field effect and unusual in-plane anisotropy. Currently, BP has been widely used in biomedical engineering, photocatalysis, semiconductor devices, and energy storage electrode materials. However, the unique structure of BP makes it highly chemically active, leading to its easy oxidation and degradation in air, which limits its practical applications. Recently, researchers have proposed a number of initiatives that can address the environmental instability of BP, and the application of these physical and chemical passivation techniques can effectively enhance the environmental stability of BP, including four modification methods: covalent functionalization, non-covalent functionalization, surface coordination, physical encapsulation and edge passivation. This review highlights the mechanisms of the above modification techniques in addressing the severe instability of BP in different application scenarios, as well as the advantages and disadvantages of each method. This review can provide guidance for more researchers in studying the marvellous properties of BP and accelerate the practical application of BP in different fields.

A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics

China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China.

Activating Metal-Organic Cages by Incorporating Functional M(ImPhen)3 Metalloligands: From Structural Design to Applications.

ConspectusThe emulation of ingenious biofunctions has been a research focus for several decades. Metal-organic cages (MOCs), as a type of discrete supramolecular assembly with well-defined shapes and cavities, have aroused great interest in chemists to imitate natural protein cages or enzymes. However, to genuinely achieve tailored functionalities or reactivities of enzymes, the design of cage structures combining both the confined microenvironment and the active site is a prerequisite. Therefore, the integration of functionalized motifs into MOCs is expected to provide a feasible approach to construct biofunctional confined nanospaces, which not only allows the modulation of cage properties for applications such as molecular recognition, transport, and catalysis but also creates unique microenvironments that promote enzymatic effects for special reactivities and selectivities, thereby providing a versatile platform to achieve exceptional biomimetic functions and beyond.In this Account, we specifically focus on our research toward engineering active confined-nanospaces in MOCs via incorporation of M(ImPhen)3 metalloligands, a typical tris-chelate coordination moiety comprising imidazophenanthroline ligands and variable metal ions, as the principle functional units for stepwise assembly of active-MOCs. Starting from their structure design and merits, we describe the versatility of M(ImPhen)3 centers for multifunctionalization of the confined cage-nanospaces. By integrating different metal ions like Ru, Os, Fe, Co, Ni, Zn, the metal ion inherent properties, e.g., redox activity of Fe/Co-centers, chirality, and photoactivity of Ru-centers, and dynamics of Co/Zn-centers, could be integrated and tailored on the cages as isostructural nanosized containers or reactors. Changing the Pd or Pt cage vertices to organic clips could remarkably enhance acid-base stability and endow cages with flexibility and allostery. Utilization of ImPhen organic ligands containing imidazole groups introduces proton transfer capability, which can couple with the high-positive charges on the cage to create amphoteric microenvironments in the porous open-cage solution. Moreover, the nonplanar stereoconfiguration of M(ImPhen)3 confers multiple peripheral pockets on the cage, which render multisite, high-order, and dynamics guest binding for the benefit of applications such as drug delivery, molecular separation, and catalytic turnover.The construction of active-MOCs from tailorable M(ImPhen)3 metalloligands provides us with a new perspective on their structural design and functionalities. Merging the cage confinement with distinct physicochemical properties on a supramolecular level makes it practical to realize synergistic and cooperative effects for functionality enhancement beyond molecular components or the reactivity different from the bulky solution, which could largely expand the potential of MOCs as a multirole platform to wide application scenarios such as artificial photosynthesis, unconventional catalysis, and theranostic nanomedicine.

Significant improving magnetic properties and thermal conductivity of FeBSiPCNbCu nanocrystalline powder cores by designing novel ZnO insulating layer

As the application scenarios of soft magnetic composites (SMCs) tend to be high frequency, reducing their high-frequency core loss (Pcv) and improve the heat-sinking capability to avoiding thermal failure of electronic system are still greatly challenging. In present work, we successfully designed and prepared novel ZnO insulating layers on the surface of FeBSiPCNbCu nanocrystalline powders and discussed their growth processes. In addition, we investigated the effects of the ZnO insulating layers on the magnetic properties and heat-sinking capability of the FeBSiPCNbCu nanocrystalline magnetic powder cores (NMPCs) and explored the related mechanisms. The experimental results show that the hydrolysis of 1.75 wt% zinc acetate dihydrate under the coupling of 1 wt% polyvinylpyrrolidone forms uniform and thin ZnO insulating layers on the powders surface. The FeBSiPCNbCu@ZnO NMPCs annealed at 510 °C for 60 min exhibit excellent magnetic properties and superior heat-sinking capability with a low core loss of 698.4 mW/cm3 (20 mT, 2 MHz), high DC bias permeability of 68.3% (100 Oe) and heat conductivity coefficient of 2.22 W/(m•K) as compared with those of 859.8 mW/cm3, 57.1% and 1.86 W/(m•K), respectively, for the NMPCs without ZnO insulating layer.

Optimization Study on Nozzle Selection Based on the Influence of Nozzle Parameters on Jet Flow Field Structure

Currently, the primary method for controlling red tides in the ocean involves spraying water solutions with special chemicals as solutes. High-pressure spraying results in the formation of typical jet structures. In this study, numerical simulation methods are employed to investigate the velocity variations, turbulent characteristics, and gas content distribution of jet flow fields under different initial jet flow pressures, cone angles, and nozzle diameters. Based on practical application scenarios, cluster analysis is used to explore the similarities and differences in jet equivalent diameters under different parameter conditions. The research findings indicate the following. (1) The difference of jet velocity distribution at the far field exit will be enlarged with the increase in the nozzle cone angle. When the nozzle cone angle is 4 mm, the velocity uniformity at the outlet is the best. (2) The TKE of the flow field has no consistent change law along the central axis. At the jet exit, the TKE shows an obvious multi-peak structure. (3) The gas content demonstrates a typical “double-valley” feature at the jet outlet cross-section. Increasing the initial pressure leads to a decrease in the gas content within the jet due to reduced entrainment, while the entrainment range remains largely constant. (4) Cluster analysis reveals that the similarity of jet flow width when it reaches the water surface is minimal compared to other operating conditions when the initial pressure is 0.36 MPa, the cone angle is 115°, and the nozzle diameter is 2 mm. All conditions can be categorized into two or three groups to ensure jet effectiveness. The study results provide scientific guidance for selecting spray devices for controlling red tides in the ocean.

Joint Approach for Vehicle Routing Problems Based on Genetic Algorithm and Graph Convolutional Network

The logistics demands of industries represented by e-commerce have experienced explosive growth in recent years. Vehicle path-planning plays a crucial role in optimization systems for logistics and distribution. A path-planning scheme suitable for an actual scenario is the key to reducing costs and improving service efficiency in logistics industries. In complex application scenarios, however, it is difficult for conventional heuristic algorithms to ensure the quality of solutions for vehicle routing problems. This study proposes a joint approach based on the genetic algorithm and graph convolutional network for solving the capacitated vehicle routing problem with multiple distribution centers. First, we use the heuristic method to modularize the complex environment and encode each module based on the constraint conditions. Next, the graph convolutional network is adopted for feature embedding for the graph representation of the vehicle routing problem, and multiple decoders are used to increase the diversity of the solution space. Meanwhile, the REINFORCE algorithm with a baseline is employed to train the model, ensuring quick returns of high-quality solutions. Moreover, the fitness function is calculated based on the solution to each module, and the genetic algorithm is employed to seek the optimal solution on a global scale. Finally, the effectiveness of the proposed framework is validated through experiments at different scales and comparisons with other algorithms. The experimental results show that, compared to the single decoder GCN-based solving method, the method proposed in this paper improves the solving success rate to 100% across 15 generated instances. The average path length obtained is only 11% of the optimal solution produced by the GCN-based multi-decoder method.

Dependency-Aware Code Naturalness

Code naturalness, which captures repetitiveness and predictability in programming languages, has proven valuable for various code-related tasks in software engineering. However, precisely measuring code naturalness remains a fundamental challenge. Existing methods measure code naturalness over individual lines of code while ignoring the deep semantic relations among different lines, e.g., program dependency, which may negatively affect the precision of the measure. Despite the intuitive appeal of extending the code naturalness measure to the code dependency domain (as there are some work that have initiated the utilization of code dependency for diverse code-related tasks), this assumption remains unexplored and warrants direct investigation. In this study, we aim to perform the first empirical study to investigate whether incorporating code dependency, instead of analyzing individual lines, can enhance the precision of measuring code naturalness. To achieve that, we first propose a new method named DAN for measuring code naturalness by incorporating the rich dependency information in the code. Specifically, DAN extracts multiple sequences of code lines by traversing the program dependency graph, where different code lines are connected by dependencies in each sequence, and then the code naturalness will be measured by taking each sequence as a whole. In this way, the dependency information can be well captured. Finally, we have conducted an extensive study to evaluate the influence of code dependency for measuring code naturalness with DAN, and compared it with the state-of-the-art methods under three emerging application scenarios of code naturalness. The results demonstrate that DAN can not only better distinguish natural and unnatural code, but also substantially boost two important downstream applications of code naturalness, i.e., distinguishing buggy and non-buggy code lines and data cleansing for training better code models, reflecting the significance of code dependency in measuring code naturalness.

Two-stage optimal PMU placement for distribution systems

The rapid growth of distributed energy resources has highlighted the importance of monitoring the system state of distribution networks. One key technology that enhances observability in distribution systems is the Phasor Measurement Unit (PMU). However, there are still challenges in effectively deploying PMUs in practice. One challenge is the solution multiplicity in optimal PMU placement (OPP). Another challenge arises from the limited deployment space available at distribution nodes, leading to incomplete observability and the need for optimization to ensure node observability. To address the challenge of solution multiplicity, this work proposes a two-stage OPP approach that considers critical node observability. The first stage optimization model determines the minimum number of PMUs required to meet observability requirements. The second stage model identifies the solution with the greatest observability redundancy while satisfying practical constraints. To validate the effectiveness of our approach, this work conducts simulations using both IEEE testing systems and a real-world distribution feeder. These simulations provide empirical evidence of the benefits of the proposed OPP model and demonstrate its applicability in practical scenarios.

Early-Exit Deep Neural Network - A Comprehensive Survey

Deep neural networks (DNNs) typically have a single exit point that makes predictions by running the entire stack of neural layers. Since not all inputs require the same amount of computation to reach a confident prediction, recent research has focused on incorporating multiple ”exits” into the conventional DNN architecture. Early-exit DNNs are multi-exit neural networks that attach many side branches to the conventional DNN, enabling inference to stop early at intermediate points. This approach offers several advantages, including speeding up the inference process, mitigating the vanishing gradients problems, reducing overfitting and overthinking tendencies. It also supports DNN partitioning across devices and is ideal for multi-tier computation platforms such as edge computing. This paper decomposes the early-exit DNN architecture and reviews the recent advances in the field. The study explores its benefits, designs, training strategies, and adaptive inference mechanisms. Various design challenges, application scenarios, and future directions are also extensively discussed.

Leveraging Numerical Simulation Technology to Advance Drug Preparation: A Comprehensive Review of Application Scenarios and Cases.

Numerical simulation plays an important role in pharmaceutical preparation recently. Mechanistic models, as a type of numerical model, are widely used in the study of pharmaceutical preparations. Mechanistic models are based on a priori knowledge, i.e., laws of physics, chemistry, and biology. However, due to interdisciplinary reasons, pharmacy researchers have greater difficulties in using computer models. In this paper, we highlight the application scenarios and examples of mechanistic modelling in pharmacy research and provide a reference for drug researchers to get started. By establishing a suitable model and inputting preparation parameters, researchers can analyze the drug preparation process. Therefore, mechanistic models are effective tools to optimize the preparation parameters and predict potential quality problems of the product. With product quality parameters as the ultimate goal, the experiment design is optimized by mechanistic models. This process emphasizes the concept of quality by design. The use of numerical simulation saves experimental cost and time, and speeds up the experimental process. In pharmacy experiments, part of the physical information and the change processes are difficult to obtain, such as the mechanical phenomena during tablet compression and the airflow details in the nasal cavity. Therefore, it is necessary to predict the information and guide the formulation with the help of mechanistic models.

NOMA Visible Light Communications with Distinct Optical Beam Configurations

Visible light communication (VLC) has been viewed as one promising candidate to mitigate the challenging spectrum crisis and radio frequency interference in future 6G mobile communications and networking. Due to the relatively limited baseband modulation bandwidth of VLC light sources—typically, light-emitting diodes—non-orthogonal multiple access (NOMA) techniques have been proposed and explored to enhance the spectral efficiency (SE) of VLC systems. However, almost all reported NOMA VLC schemes focus on well-discussed applications employing a Lambertian light beam configuration and ignore the potential applications with distinct non-Lambertian optical beam configurations. To address this issue, in this work, the performance of non-Lambertian optical beam configuration-based NOMA VLC is comparatively investigated for future 6G mobile networks. The numerical results demonstrate that, for a fundamental two-user application scenario with the far user located at the corner position, a maximum sum rate gain of about 15.6 Mbps could be provided by the investigated distinct non-Lambertian beam-based NOMA VLC, compared with the maximum sum rate of about 93.3 Mbps for the conventional Lambertian configuration with the same power splitting factor.

Decision Transformer-Based Efficient Data Offloading in LEO-IoT.

Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people's attention due to their broader coverage and shorter transmission delay. They are capable of offloading more IoT computing tasks to mobile edge computing (MEC) servers with lower latency in order to address the issue of scarce computing resources on the ground. Nevertheless, it is highly challenging to share bandwidth and power resources among multiple IoT devices and LEO satellites. In this paper, we explore the efficient data offloading mechanism in the LEO satellite-based IoT (LEO-IoT), where LEO satellites forward data from the terrestrial to the MEC servers. Specifically, by optimally selecting the forwarding LEO satellite for each IoT task and allocating communication resources, we aim to minimize the data offloading latency and energy consumption. Particularly, we employ the state-of-the-art Decision Transformer (DT) to solve this optimization problem. We initially obtain a pre-trained DT through training on a specific task. Subsequently, the pre-trained DT is fine-tuned by acquiring a small quantity of data under the new task, enabling it to converge rapidly, with less training time and superior performance. Numerical simulation results demonstrate that in contrast to the classical reinforcement learning approach (Proximal Policy Optimization), the convergence speed of DT can be increased by up to three times, and the performance can be improved by up to 30%.

Organic probes for three‐photon fluorescence imaging in NIR‐II window: Design, applications, and perspectives

AbstractThree‐photon fluorescence (3PF) imaging is an emerging technology for imaging deep‐tissue submicroscopic structures by nonlinearly redshifting the excitation wavelength to the second near‐infrared (NIR‐II) window; thus, this approach has great advantages, including deep penetration depth, good spatial resolution, low background, and a high signal‐to‐noise ratio. 3PF imaging has been demonstrated to be a powerful tool for noninvasively visualizing all kinds of deep tissues in recent years. Benefiting from excellent biosecurity and structural controllability, the development of organic 3PF probes is highly important for advancing 3PF imaging in vivo. However, there is no summary of the generalizability of the design and recent progress in organic 3PF probes. Herein, this review introduces the fundamental principle of 3PF imaging and highlights the advantages of 3PF bioimaging. The molecular design of these organic 3PF probes is also summarized based on relative optical indices. Furthermore, different 3PF imaging application scenarios are listed in detail. In the end, the main challenges, significance of probe exploitation, and prospective orientation of organic probes for precise 3PF imaging are proposed and discussed for promoting future applications and clinical translation.

Vanadium-dioxide-assisted multifunctional switchable terahertz metamaterial devices

A multifunctional, switchable terahertz (THz) metamaterial (MM) device with wideband absorption and polarization conversion capabilities has been developed, based on the insulator-metal phase transition of vanadium dioxide (VO2). In its metallic state, the device operates as a wideband absorber within the range of 2.56–6.74 THz, achieving a bandwidth of 4.18 THz and an absorption rate of ≥90%. The wideband absorption is insensitive to both oblique incident angles and polarization. When VO2 is in its insulating state, the device switches to a polarization converter, facilitating linear-to-cross polarization (LTX) conversion between 1.04 and 4 THz, and linear-to-circular polarization (LTC) conversion between 1 and 1.04 THz, with a conversion efficiency exceeding 90%. Additionally, the effects of incident angle and polarization angle on the device’s performance were analyzed. This THz device offers advantages of wide angle, wide bandwidth, and high efficiency, making it a valuable reference for research into new multifunctional THz devices. It has great potential applications in short-range wireless THz communication, ultrafast optical switches, high-temperature resistant switches, transient spectroscopy, and optical polarization control devices. In specific application scenarios, particularly in fields requiring efficient detection, transmission, and analysis—such as security and non-destructive testing, secure communication systems, imaging and sensing, multidimensional spectral analysis, pollutant detection, smart stealth coatings, dynamic optical control devices, and integrated optical systems—these devices offer multifunctional capabilities. They enhance system performance and flexibility, meeting diverse application needs.

Organophosphorus Hydrolase-like Nanozyme with an Activity-Quenched Aggregation-Induced Emission Effect: A Self-Reporting and Specific Assay of Nerve Agents.

Given the promising prospect of aggregation-induced emission luminogens (AIEgens) in fluorescence assays, it is interesting and significant to endow AIEgens with molecular recognition capability (such as enzyme-like activity). Here, an AIE nanomaterial with intrinsic enzyme-like activity (named as "AIEzyme") is designed and synthesized via a facile coordination polymerization of Zr4+ and AIE ligands. AIEzyme possesses enhanced and stable fluorescence in different solvents because of the AIE effect of ligands in the rigid structure of a coordination polymer. On the other hand, the organophosphorus hydrolase (OPH)-mimicking activity of AIEzyme exhibits excellent affinity and specific activity. Interestingly, the OPH-like activity can quench the inherent fluorescence of AIEzyme by the hydrolysate of a typical organophosphorus nerve agent (OPNA), diethyl-4-nitrophenylphosphate. Due to the sensitive activity-induced quenching effect for AIE, the self-reporting fluorescence assay method based on AIEzyme was established, which shows ultrahigh sensitivity, high selectivity, good storage stability, and acceptable reliability for a real sample assay. Moreover, the simultaneous colorimetric method broadens the detection range and the application scenarios. The proposed assay method avoided the interference of O2 during detection because the OPH-like activity does not derive from the generation of ROS. As a bonus, AIEzyme can also be used for the degradation of OPNAs by OPH-like activity, and the process can be self-monitored by AIE quenching. This work would provide a new opportunity for expanding the application of AIEgens and artificial enzymes by endowing AIEgens with enzyme-like activity.

An Ontology-Based Vehicle Behavior Prediction Method Incorporating Vehicle Light Signal Detection.

Although deep learning techniques have potential in vehicle behavior prediction, it is difficult to integrate traffic rules and environmental information. Moreover, its black-box nature leads to an opaque and difficult-to-interpret prediction process, limiting its acceptance in practical applications. In contrast, ontology reasoning, which can utilize human domain knowledge and mimic human reasoning, can provide reliable explanations for the speculative results. To address the limitations of the above deep learning methods in the field of vehicle behavior prediction, this paper proposes a front vehicle behavior prediction method that combines deep learning techniques with ontology reasoning. Specifically, YOLOv5s is first selected as the base model for recognizing the brake light status of vehicles. In order to further enhance the performance of the model in complex scenes and small target recognition, the Convolutional Block Attention Module (CBAM) is introduced. In addition, so as to balance the feature information of different scales more efficiently, a weighted bi-directional feature pyramid network (BIFPN) is introduced to replace the original PANet structure in YOLOv5s. Next, using a four-lane intersection as an application scenario, multiple factors affecting vehicle behavior are analyzed. Based on these factors, an ontology model for predicting front vehicle behavior is constructed. Finally, for the purpose of validating the effectiveness of the proposed method, we make our own brake light detection dataset. The accuracy and mAP@0.5 of the improved model on the self-made dataset are 3.9% and 2.5% higher than that of the original model, respectively. Afterwards, representative validation scenarios were selected for inference experiments. The ontology model created in this paper accurately reasoned out the behavior that the target vehicle would slow down until stopping and turning left. The reasonableness and practicality of the front vehicle behavior prediction method constructed in this paper are verified.

A Robust Core-Shell Nanofabric with Personal Protection, Health Monitoring and Physical Comfort for Smart Sportswear.

Smart textiles with a high level of personal protection, health monitoring, physical comfort, and wearing durability are highly demanded in clothing for harsh application scenarios, such as modern sportswear. However, seamlessly integrating such a smart clothing system has been a long-sought but challenging goal. Herein, based on coaxial electrospinning techniques, a smart non-woven textile (Smart-NT) integrated with high impact resistance is developed, multisensory functions, and radiative cooling effects. This Smart-NT is comprised of core-shell nanofibers with an ionic conductive polymer sheath and an impact-stiffening polymer core. The soft smart textile, with a thickness of only 800µm, can attenuate over 60% of impact force, sense pressure stimulus with sensitivity up to 201.5 kPa-1, achieve temperature sensing resolution of 0.1°C, and reduce skin temperature by ≈17°C under a solar intensity of 1kWm-2. In addition, the stretchable Smart-NT is highly durable and robust, retaining its multifunction features over 10000 bending and multiple washing cycles. Finally, application scenarios are demonstrated for real-time health monitoring, body protection, and physical comfort of smart sportswear based on Smart-NT for outdoor sports. The strategy opens a new avenue for seamless integration of smart clothing systems.

Applying Low-Impact Development Techniques for Improved Water Management in Urban Areas

Worldwide, the increase in impervious surfaces due to urbanization has led to significant water cycle issues such as groundwater depletion, urban heat islands, and flooding. To address these challenges, Low-Impact Development (LID) techniques are increasingly being applied in stormwater management. This study focuses on Ulsan, designated as a water cycle model city in South Korea, with a particular emphasis on the highly urbanized Okgyo drainage watershed. Using the Stormwater Management Model (SWMM) version 5.1, long-term runoff simulations were conducted to evaluate the effects of LID implementation on water cycle change rates and recovery rates. The model incorporates detailed hydrological and hydraulic parameters, including inflow, runoff, infiltration, and evapotranspiration for six subcatchments within the watershed. The SWMM was calibrated and validated using 30 years of historical rainfall data (1987–2016) from the Ulsan weather station. Calibration and validation processes used the NRCS-CN (Curve Number) method to ensure accuracy in simulating runoff patterns and water balance. The study specifically evaluated the effectiveness of two LID techniques: bioretention and permeable pavements. Three scenarios were modeled: bioretention applied to 5% of the area, permeable pavements applied to 5% of the area, and a combined application of both techniques. The results showed that the combined scenario provided the best outcome, with a 7.80% reduction in surface runoff and a 14.56% improvement in water cycle health. The LID application scenario confirmed the potential to achieve the water cycle management target of handling 25.5 mm of rainfall. These findings demonstrate that the introduction of LID techniques in public spaces can significantly enhance water management. This research provides insights into effective water cycle management methods tailored to specific urban land uses, laying a foundation for future urban planning and sustainable development.