In the rapid process of urbanization, urban green spaces are critical for mitigating the urban heat island effect, enhancing carbon sink capacity, and improving overall environmental quality. This study employed multi-source high-resolution remote sensing data, fused with LiDAR observations and validated by field sampling, to accurately extract key parameters including green space types, coverage, and biomass. A systematic evaluation of ecological service functions and their dynamic changes was conducted. The results indicate that: (1) ecological functions differ significantly across green space types, with intensity closely linked to spatial configuration, highlighting the importance of balancing both “quality” and “quantity”; (2) the integration of remote sensing and LiDAR greatly improves the accuracy of ecological assessments; (3) dense construction and fragmented green patterns weaken ecological benefits, while higher vegetation diversity helps mitigate these negative impacts; (4) differentiated strategies tailored to functional areas can maximize ecological effectiveness. Overall, the study advances technical applications of remote sensing in urban ecology, deepens the scientific understanding of the “pattern–function” mechanism, and provides practical guidance for urban ecological planning and green space management under dual-carbon goals.

This study thoroughly examines the current application and technological breakthroughs of Building Information Modeling (BIM) technology in prefabricated construction. Driven by strong national policy support, BIM technology has been widely adopted throughout the entire lifecycle of prefabricated buildings—from design and production to construction and operation—significantly enhancing design efficiency, construction precision, and management effectiveness. Through case studies, this research reveals significant progress in areas such as intelligent design optimization and refined construction management. For instance, in a construction project in Ziyang District, Yiyang City, BIM visualization not only substantially improved communication and coordination efficiency among participating entities but also enhanced the implementability of design solutions. However, current BIM implementation in prefabricated construction still faces numerous challenges, including inconsistent technical standards, poor software compatibility, low industry chain coordination, and a shortage of multidisciplinary talent. Looking ahead, breakthroughs in cutting-edge technologies such as blockchain, IoT, and big data are expected to empower BIM to enable more efficient data exchange and sharing, establish unified data standards, and drive prefabricated construction toward intelligent and digital development.

Road bikes, as eco-friendly transportation and exercise equipment, gain growing popularity under global carbon neutrality initiatives, yet their narrow tires (weak grip on slippery surfaces) and rigid frames (poor stability on bumpy roads) cause cornering stability issues, contributing to accidents like 1,084 U.S. cyclist deaths in 2022 (IIHS). This study explores factors influencing their cornering stability via theoretical analysis and literature review, focusing on force dynamics: friction (classified into sliding, F=μN, with μ=0.7-1.0 on dry ground and 0.3-0.6 on wet ground, and rolling friction, 1/40-1/60 of sliding friction, where lateral static friction provides centripetal force for cornering), air resistance (F=0.5CρSV², indirectly affecting stability by altering speed and thus centripetal force F=mv²/R), and centripetal force. It derives critical skidding conditions (μmg=mv²/R) and a cornering radius formula (R=v²/(g tanθ)), defining a safe tilt angle range (θ≤v²/(gR)). Conclusions show lateral static/sliding friction directly determines stability, while air/rolling friction act indirectly by adjusting speed; limitations lie in reliance on theoretical static analysis, and future research should integrate experiments and dynamic models for higher accuracy.

The development of lithium-ion batteries (LIBs) is essential for electric vehicles and renewable energy. It makes them a key technology to achieve global “dual carbon” goals. Among all components, the electrolyte plays a central role—not only in ensuring safety but also in determining wide-temperature performance and fast power delivery. Today’s commercial electrolytes, usually based on organic carbonates like EC or DMC with LiPF6salt, have supported large-scale applications. However, they still face major problems: they are volatile and flammable, creating safety risks; their performance drops sharply at both low and high temperatures due to viscosity or decomposition issues; and their ionic conductivity is not enough for ultra-fast charging. On top of that, PFAS and toxic byproducts raise environmental and health concerns. This work takes a close look at these fundamental challenges and highlights emerging solutions, such as solid-state electrolytes for safer operation, new liquid systems like localized high-concentration electrolytes, ionic liquids, and fluorinated solvents for better stability and ion transport, as well as synergistic strategies including advanced salts (LiFSI, LiTFSI), functional additives, and solvation structure design. Collectively, these advances aim to address the safety, temperature resilience, and fast charging requirements of next-generation energy storage systems.

The Proportional-Integral-Derivative (PID) controller is widely adopted in motor control due to its simple structure, high robustness, and ease of implementation. However, in the face of increasingly complex industrial environments and higher precision control requirements, traditional PID control encounters major challenge in terms of response speed of nonlinear systems, anti-interference ability and parameter adaptive adjustment. The paper reviews the integration of intelligent optimization algorithms, like reinforcement learning, genetic algorithms, and particle swarm optimization, with PID controllers, and examines their effects on DC motors, brushless DC motors (BLDCMs), and linear induction motors (LIMs). The results show that intelligent algorithms can effectively enhance dynamic response, trajectory tracking accuracy, and disturbance rejection via online PID parameter optimization.

Quantum dots (QDs) are colloidal semiconductors with quantum confinement, allowing bright, narrowband, size-tunable emission under broad excitation. They operate robustly in protein-rich fluids and support precise bioconjugation. This review links fluorescence detection to imaging-guided intervention, using FRET, inner-filter, photo-induced electron transfer, and ratiometric schemes to convert molecular recognition into quantitative optical signals. Heavy-metal-reduced I–III–VI and III–V cores and carbon/graphene dots provide NIR-I/II readouts with high signal-to-background, enabling repeated irradiation. Integrating shared optical backbones across detection and treatment will increase information density per microliter, shorten decision cycles, and enable closed-loop theranostics in oncology and infection.

This paper is targeted at FPGA and proposes an engineering optimization scheme for a Newton-Raphson (NR) iterative divider based on Verilog. Starting from the baseline implementation, it uses MSB normalization and small LUT to provide initial values, combined with phased pipelining driven by fixed iteration and counter; it also combines the optimization and reuse of multipliers, and introduces the fast path of power of two and dual/four-channel interleaving to stabilize throughput. The design achieves a more balanced performance in terms of timing convergence, resource controllability, and stable throughput. Through synthesis and simulation, it evaluates LUT/FF/DSP, Fmax, end-to-end delay, and error quantile indicators to verify the deployability of this scheme in actual signal chains.

With the rapid urbanization of Zhengzhou City, the continuous expansion of construction land and population has led to extensive occupation of arable land and forested areas, resulting in a decline in biodiversity, fragmentation of green infrastructure (GI), and weakened connectivity, thereby threatening ecological security. This study focuses on the Yellow River Basin within Zhengzhou, employing landscape ecology principles, and utilizing Morphological Spatial Pattern Analysis (MSPA) alongside landscape pattern indices to quantitatively assess the spatial configuration of GI across three temporal points: 2015, 2020, and 2025. The results indicate a consistent reduction in total GI area, significant contraction of core zones, increased landscape fragmentation, and decreased patch connectivity. Although the areas of edge zones and ring roads have expanded, the proportion of isolated patches has risen, and overall connectivity remains insufficient. The primary driving factors include rapid urban expansion, land resource pressures from demographic and economic growth, ecological degradation coupled with climate change, industrial restructuring, and the reconfiguration of green spaces. The findings suggest that Zhengzhou must optimize the spatial arrangement of GI, enhance the protection of ecological source areas and corridor connectivity, and implement systematic management strategies to effectively mitigate ecological degradation and support sustainable urban development.

With the in-depth popularization of the Internet of Things technology, short-range wireless communication has become a key link connecting the physical world and digital services. Benefiting from its "touch-and-interact" feature and low-power consumption, Near Field Communication (NFC) technology has developed rapidly in consumer electronics and public services. This paper explains the working principles of NFC technology, outlines its applications in daily life and commercial sectors, and analyzes the challenges it faces at the current stage. The findings show that relying on its convenience, NFC has achieved large-scale use in scenarios such as mobile payments and public transportation ticketing. However, its limitations are evident: mobile payments are efficient but risk unauthorized transactions, and although NFC improves logistics efficiency, its tags are significantly more costly than Radio Frequency Identification tags. In addition, technical shortcomings and market competition from alternative technologies further constrain its promotion. The conclusion points out that NFC remains in the early stage of application. To overcome current bottlenecks, it is necessary to advance core technology iteration, promote cross-technology integration, and simplify operation processes. With the synergy of Internet technology, NFC is expected to expand from fixed application scenarios to broader popularization.

With the rapid advancement of industrial automation, the demand for efficient and precise control systems has become increasingly critical. Proportional–integral–derivative (PID) control remains one of the most widely adopted strategies in industrial process control due to its structural simplicity, reliability, and ease of implementation. However, conventional PID tuning methods often rely heavily on manual experience and trial-and-error, which are inefficient and inadequate for complex and dynamic industrial environments. This paper explores the application and optimization of PID control in modern industrial systems. It begins by introducing the fundamental principles of PID control and then analyzes the influence of its parameters on system performance. Subsequently, existing tuning techniques are reviewed, and a novel PID parameter optimization method based on intelligent algorithms—such as genetic algorithm and particle swarm optimization—is proposed. Simulation results demonstrate that the proposed method enhances control performance by automatically searching for global optimal parameters. Finally, the paper summarizes the findings and suggests future research directions aimed at improving computational efficiency and adaptability in real-world applications.