Vertical-Cavity Surface-Emitting Lasers (VCSELs) based on Aluminum Gallium Nitride (AlGaN) structures for Deep Ultraviolet (DUV) emission have great potential for use in sterilizers, photolithography, and solar-blind detectors. Unfortunately, the creation of high performance Ultraviolet C (UVC) VCSELs has been hampered by both physical and structural problems that make them difficult to manufacture. In this paper, I examine the design status of DUV VCSELs based upon AlGaN and identify the factors affecting their performance; specifically, they have reduced internal quantum efficiency in Quantum Wells, poor mirror reflectivity, as well as loss of light due to absorption and scattering, occurring within the microcavity. Device design factors that will either limit or benefit VCSEL designs include: AlGaN Multiple Quantum Wells, Distributed Bragg Reflectors and the Microcavity Structure. Other factors relating to minimizing optical losses with fabrication processes will also be discussed in this paper, including substrate removal, Chemical Mechanical Polishing and Surface Smoothing. A review of the most recent UVC (~275-278 nm) and Ultraviolet B (UVB) (~310 nm) device demonstrations will be used to illustrate existing performance deficiencies as well as the disparities between optically pumped and electrically driven devices. Finally, future areas of research are proposed which include: polarization engineering, control of cavity detuning, development of low loss dielectric mirrors compatible with current injection devices and improvements in P-type conductivity using tunneling injection techniques.

Anaerobic fermentation of food waste for methane production can help realize waste resource utilization and the "dual carbon" targets. We examine biochemical process and technology development of this method in full detail, covering how reaction happens at each stage and how equipment has changed over time. Also, we look at how key parameters affect methane production. It is worth noting that carbon-nitrogen ratio and temperature, together with external additives, can change efficiency in different ways. The results show that co-fermentation substrate adjustment and carbon-based material addition can avoid system acidification, and directional control of microbial community is expected to improve gas production stability. Current studies focus on single factor control and verification in laboratory scale, which makes it hard to apply in practice. Future work should examine multi-factor coupling mechanisms. Also, we should push results from lab to actual engineering applications. This review tries to build systematic knowledge framework for this field. In fact, results can be used as reference for large-scale application and process optimization of food waste anaerobic fermentation technology.

The development of the digital industry and the growing embrace of green development have driven the advancement of high-performance, low-cost green batteries. As an electrode material, biochar boasts diverse raw sources, excellent stability and low cost, endowing it with significant application value in both electric double-layer and pseudocapacitive energy storage systems. Oxygen-containing functional groups in biochar serve as a key factor in tuning the chemical properties, safety and cycling stability of capacitors. Their content, type and distribution directly determine the performance of the devices. This paper systematically reviews the origin, regulation strategies and structure-activity relationships of oxygen-containing functional groups in biochar, with a focus on the effects of inherent feedstock components, pyrolysis conditions and oxidative modification on such groups. It clarifies the dual regulation and critical points of oxygen-containing functional groups in capacitors. By comparing the impacts of various oxygen-containing functional groups (OFGs), the mechanism and contradictory effects governing capacitor performance are revealed. The conflict between capacitance enhancement and compromised cycling stability is addressed through a B/N/P ternary co-doping strategy, aiming to explore future development pathways and achieve a balanced progress toward high capacity, high efficiency and long cycle life.This work systematically summarizes the origin, regulatory mechanisms and dual-regulation behavior of oxygen-containing functional groups, and outlines future development directions consistent with the green development philosophy. It holds important research significance for realizing low-cost, high-performance energy storage.

NK cells (natural killer cells) represent a critical component of innate immune effectors, capable of directly eliminating malignant cells without requiring prior sensitization. Extracellular vesicles (EVs), particularly exosomes, have emerged as essential mediators of intercellular signaling and have also gained considerable attention as cell-free therapeutic agents in immunotherapy. Nevertheless, the mechanisms underlying the anti-tumor efficacy of NK cell-derived exosomes in the background of lung cancer remain insufficiently characterized. First of all, NK-92 cells were cultured and then their exosomes were collected from culture supernatants through successive ultracentrifugation. As for size distribution, it was found that most particles had an average diameter of around 104.76 nanometers, while the zeta potential was about minus twenty-three millivolts. By means of western blotting analysis, it was shown that some NK-specific molecules, such as CD56 and NKG2D, were present in the NK-Exo group. Some functional tests showed that labeled exosomes attached to A549 cells very well, and part of them eventually entered A549 cells. What is more, treatment with the NK-92-derived exosomes reduced A549 cell viability quite efficiently and induced tumor-cell death in vitro compared with control groups. Therefore, it is reasonable to conclude that NK-cell-derived exosomes may develop into a cell-free therapy platform which would have great clinical application value in future lung cancer treatment.

This study aimed to explain how key root-zone microorganisms affect mango growth and fruit quality in different regions. It provided theoretical support for targeted regional microbial control in mango cultivation. We chose three main mango-growing areas: Hainan, Yunnan and Southeast Asia. We used field test data from local mango orchards and high-throughput sequencing technology to analyze root-zone microbial community features. These features are shaped by soil and climate factors such as pH, altitude and humidity. The results showed a clear "core-stable + region-specific" distribution pattern in mango root-zone microbes. Stable core microbes meet mangoes' basic growth and metabolic needs, while area-specific microbes help the crop adapt better to local environmental stresses. Using too many chemical fertilizers and pesticides, together with extreme weather, breaks the balance of microbial communities. It causes soil quality to drop, reduces helpful microbial types, and then lowers mango yield and fruit quality. Targeted microbial control methods are effective. These methods include using custom microbial inoculants, breeding weather-resistant microbial strains and using integrated disease control. They restore soil microbial balance, improve soil health, and greatly increase mango yield, fruit quality and environmental stress resistance. This builds a solid scientific base for sustainable and region-specific mango growing.

Constraint satisfaction problems usually exhibit a distinct satisfiability transition phenomenon under stochastic models, and an exact phase transition threshold has been strictly proven to exist especially in the RB model. Aiming at the difficulty in solving hard instances of the random binary model with growing domains (RB) near the phase transition region, this paper proposes a guided decimation algorithm based on asynchronous belief propagation with gradual perturbation (P-BP). On the basis of the asynchronous BP-guided decimation framework, the algorithm introduces a linear annealing perturbation mechanism, which enables the variable-to-constraint messages to transit smoothly from deterministic update to the style of Gibbs sampling. Meanwhile, in the late stage of decimation, the variable fixing strategy is changed from greedy selection to direct sampling from the marginal distribution, supplemented by an automatic restart strategy with a maximum of 3 restarts. These improvements retain the asynchronous update, damping factor and A/B/C edge processing rules of the original algorithm, while significantly enhancing the stochastic exploration capability and effectively alleviating the problems of message oscillation, local convergence and error propagation of fixed variables in the phase transition region. Research shows that the combination of gradual perturbation with late-stage probabilistic sampling and a restart mechanism provides an effective way to enhance the robustness of belief propagation-based algorithms for stochastic constraint satisfaction problems.

Deep-strata oil and gas have become the important strategic successor area for global energy exploration, but classical petroleum geology theories face challenges in explaining their accumulation mechanisms. This study systematically reviews the research progress on the deep-strata oil and gas accumulation mechanism driven by the multi-sphere theory. Starting from the evolution of petroleum geology theory, we delve into the multi-sphere interaction framework within the perspective of Earth system science, analyzes the core controlling role of deep-strata fluid activity during the petroleum accumulation, summarizes the orderly symbiotic relationship between coexisting conventional and unconventional resources. Besides, we explore the unique characteristics of accumulation of ultra-deep marine resources. This study also shows that multi-sphere theory breaks through the limitations of the traditional basin perspective, unifying deep dynamic processes with shallow resource effects, and providing theoretical support for deepening the understanding of deep-strata hydrocarbon accumulation mechanisms and expanding new areas of associated resources.

Enzymes are chemical catalysts that are essential in all the biochemical changes that occur in the biological world. Their catalytic activities are closely connected with the three-dimensional (3D) organization, which terms active sites, substrate-binding pockets, and other mechanically relevant conformational states. Protein sequences have been sequenced affordably at an unprecedented rate using high-throughput methods; but experimentally measured structures and high-quality functional annotation are relative rarities, resulting in an ongoing gap in the so-called sequence to structure to functional gap. In recent years (in the last five years), the deep-learning branch of artificial intelligence (AI) brought disruptive improvements that created a significant undercut in this gap. The structure predictors of deep learning structure predictors such as AlphaFold2 have demonstrated high-confidence structural models of a large number of proteins, including enzymes, leading to lower time and resource costs associated with structure determination. Simultaneously, prediction of enzyme functions has moved away both homology-driven transfer prediction and manually hand-crafted features, to representation learning with sequences and structures, such as protein language models, graph neural networks, and contrastive learning models, but also integrative (multimodal) prediction with the combination of complementary signals. The review presents the latest developments in AI-based enzyme structure prediction and AI-based enzyme function prediction and describes its scientific and practical importance introduced by such developments. We emphasize such representative uses as enzyme engineering, synthetic biology, and drug discovery and development. We also specify outstanding constraints like the prediction of multimeric assemblies, conformational heterogeneity and dynamics, and realistic enzyme-ligand interfaces, and the outlook of future directions through additional training information, combining physical constraints, and close integration of AI prediction with experimental justification.

Research on the spatio-temporal patterns of landscapes is crucial for understanding the complexity of human-land relationships. With the in-depth application of Geographic Information Systems (GIS) technology, the research paradigm in this field has undergone significant evolution, progressing from static pattern identification to dynamic process simulation and expanding towards multidimensional frontiers. This paper systematically reviews the recent applications of GIS in landscape studies in China. At the level of pattern identification, methods of spatial visualization and quantification have revealed the spatial differentiation rules of cultural landscapes, ecological elements, and urban-rural settlements. At the level of process simulation, dynamic models and multi-source historical data have enabled the mechanistic analysis and process reconstruction of urban expansion, landscape evolution, and historical spatial reconstruction. At the level of frontier expansion, the integration of 3D GIS and interdisciplinary methods has deepened research from two-dimensional planes to three-dimensional spaces and from single analyses to integrated simulations. Despite significant progress, current research still faces challenges in integrating historical data, improving the accuracy of 3D models, and intelligently fusing multi-source data. In the future, deepening the application of 3D technology, promoting AI-driven intelligent analysis, developing interactive simulation platforms, and strengthening interdisciplinary methodological innovation will be key directions for advancing research on landscape spatio-temporal patterns to a deeper level.

Articular cartilage degeneration and focal defects contribute to chronic pain and functional limitation, yet durable regeneration remains challenging because cartilage is avascular and has low cellularity. Injectable hydrogels adaptively fill defects, which can provide an extracellular matrix microenvironment and deliver therapeutic signals. This review surveys recent injectable hydrogel technologies for articular cartilage regeneration, emphasizing how network formation mechanisms govern retention and early function at the joint. Three representative strategies are discussed, including in situ gelling systems where precursor is injected and solidified on demand, shear thinning and self-healing hydrogels that flow under shear via reversible interactions and recover after injection, and injectable hydrogel microparticles where building blocks assemble into porous scaffolds. The results reveal that successful injectable hydrogels are a balance among operability, spatiotemporal control of network formation, early structural integrity, and tissue interaction.