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Insect protein has received considerable attention as an alternative to conventional animal proteins with its high nutritional contents and eco-friendly credentials. Exploring commercially available insect-protein-enhanced foods, this study aims to profile and compare such products in the ultra-processed category with products protein-enhanced with dairy (e.g., milk and whey) and plants (e.g., pea and rice). A global product audit was conducted drawing from English-language online retail portals to determine the product formats and statistically compare their nutritional contents with products fortified with non-insect proteins. The results show that four categories—flour/powder, pasta/noodle, starch-based snacks (e.g., chips, crackers, and cookies), and energy bars—are involved with food enhanced with insect protein. Flour/powder and pasta/noodles with insects demonstrated comparable protein contents to non-insect equivalents, highlighting insects’ potential as effective protein sources. However, insect protein’s performances in snacks and energy bars were less favourable, with significantly lower protein contents compared to products enhanced with non-insect sources. This may be attributed to the high fat content of insects, which may also contribute to undesirable flavours in complex foods, limiting their usage. The study highlights the need for industry innovation and scientific collaboration to overcome the challenges to widely applying insects as food ingredients, offering benefits for both the industry and consumers.

How different fertilization practices modify soil organic carbon (SOC) sequestration is still unclear. Our study aimed to evaluate the changes in SOC stocks and their physical fractions after 10 years of organic and inorganic fertilization. Five treatments were established under a wheat-maize system in Northern China: control (CK), chemical fertilizer (F), straw plus chemical fertilizer (SF), manure plus chemical fertilizer (MF), and straw and manure plus chemical fertilizer (SMF). The results showed that the SOC sequestration rate at 0–20 cm depth decreased in the following order: SMF (1.36 Mg C/ha/yr) > MF (1.13 Mg C/ha/yr) > SF (0.72 C/ha/yr) > F (0.15 Mg C/ha/yr) > CK (−0.25 Mg C/ha/yr). The values indicated that straw returning and manure application were important measures to achieve the “4 per 1000” target, and the application of manure was a more effective strategy. The high input of chemical fertilizer only maintained the initial SOC level and was not a powerful C-farming practice. A minimum input of 4.93 Mg C/ha/yr was required to keep the initial SOC storage. The SOC associated with small macroaggregate (0.25–2 mm) was the most sensitive indicator for the changes of bulk SOC. In addition, the accumulation of SOC under SMF, MF, and SF treatments mainly occurred in the occluded particulate organic C (oPOC) in small macroaggregates, indicating that the physical protection of macroaggregates played a predominant role in SOC sequestration. The SMF, MF, and SF treatments also displayed higher mineral organic C (mSOC) in soil aggregates than the CK and F treatments. A transformation of oPOC towards the mSOC fraction indicated that exogenous C further shifted into stable C pools under the physical protection of soil aggregates. In conclusion, these findings confirmed the important role of straw returning and manure application in SOC accumulation and stabilization, highlighting that a combination strategy of straw + manure + chemical fertilizer had the best effect.

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator’s behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB.

Background: The intricate interplay between the platelet-coagulation system and the progression of malignant tumors has profound therapeutic implications. However, a thorough examination of platelet and coagulation markers specific to colorectal cancer (CRC) is conspicuously absent in the current literature. Consequently, there is an urgent need for further exploration into the mechanistic underpinnings of these markers and their potential clinical applications. Methods: By integrating RNA-seq data and clinicopathological information from patients with CRC in the cancer genome atlas, we identified genes related to the platelet-coagulation system using weighted gene co-expression networks and univariate Cox analysis. We established a prognostic risk model based on platelet- and coagulation-related genes using Lasso Cox regression analysis and validated the model in two independent CRC cohorts. We explored potential biological functional disparities between high-risk and low-risk groups through comprehensive bioinformatics analysis. Results: Our findings indicate that colorectal cancer patients classified as high-risk generally exhibit poorer prognoses. Moreover, the model’s risk scores were associated with the differential composition of the immune tumor microenvironment, suggesting its applicability to infer immunotherapy responsiveness. Cellular functional experiments and animal experiments indicated that CYP19A1 expression in CRC influences malignant phenotype and platelet activation. Conclusions: In summary, we present a novel platelet- and coagulation-related risk model for prognostic assessment of patients with CRC and confirm the important role of CYP19A1 in promoting malignant progression of CRC.

In this paper, the process of solid-state metalized reduction and magnetic separation was investigated for preparation of nickel–iron concentrate from a low-grade laterite nickel ore. The effects of reduction temperature, reduction time, amount of dosages, and magnetic field strength on grades and recoveries of nickel and iron were studied. The results showed that nickel–iron concentrate with a nickel grade of 7.32%, nickel recovery of 81.84%, iron grade of 78.74%, and iron recovery of 69.78% were obtained under the conditions of a reduction temperature of 1200 °C, reduction time of 120 min, calcium fluoride addition of 12%, ferric oxide addition of 10%, coal addition of 12%, and magnetic field strength of 170 kA/m.

The impact of degradation on plastics is a critical factor influencing their properties and behavior, particularly evident in polyethylene (PE) and polypropylene (PP) and their blends. However, the effect of photoaging and thermal degradation, specifically within recycled polyethylene (rPE) and recycled polypropylene (rPP), on the thermo-mechanical and thermostability aspects of these blends remains unexplored. To address this gap, a range of materials, including virgin polyethylene (vPE), recycled polyethylene (rPE), virgin polypropylene (vPP), recycled polypropylene (rPP), and their blends with different ratios, were comprehensively investigated. Through a systematic assessment encompassing variables such as melting flow index (MFI), functional groups, mechanical traits, crystallization behavior, microscopic morphology, and thermostability, it was found that thermo-oxidative degradation generated hydroxyl and carboxyl functional groups in rPE and rPP. Optimal mechanical properties were achieved with a 6:4 mass ratio of rPE to rPP, as validated by FTIR spectroscopy and microscopic morphology. By establishing the chemical model, the changes in the system with an rPE–rPP ratio of 6:4 and 8:2 were monitored by the molecular simulation method. When the rPE–rPP ratio was 6:4, the system’s energy was lower, and the number of hydrogen bonds was higher, which also confirmed the above experimental results. Differential scanning calorimetry revealed an increased crystallization temperature in rPE, a reduced crystallization peak area in rPP, and a diminished crystallization capacity in rPE/rPP blends, with rPP exerting a pronounced influence. This study plays a pivotal role in enhancing recycling efficiency and reducing production costs for waste plastics, especially rPE and rPP—the primary components of plastic waste. By uncovering insights into the degradation effects and material behaviors, our research offers practical pathways for more sustainable waste management. This approach facilitates the optimal utilization of the respective performance characteristics of rPE and rPP, enabling the development of highly cost-effective rPE/rPP blend materials and promoting the efficient reuse of waste materials.

Optical Wireless Communication (OWC) technology has gained significant attention in recent years due to its potential for providing high-data-rate wireless connections through the large license-free bandwidth available. A key challenge in OWC systems, similar to high-frequency Radiofrequency (RF) systems, is the presence of dead zones caused by obstacles like buildings, trees, and moving individuals, which can degrade signal quality or disrupt data transmission. Traditionally, relays have been used to mitigate these issues. Intelligent Reflecting Surfaces (IRSs) have recently emerged as a promising solution, enhancing system performance and flexibility by providing reconfigurable communication channels. This paper presents an overview of the application of IRSs in OWC systems. Specifically, we categorize IRSs into two main types: mirror array-based IRSs and metasurface-based IRSs. Furthermore, we delve into modeling approaches of mirror array-based IRSs in OWC and analyze recent advances in IRS control, which are classified into system power or gain optimization-oriented, system link reliability optimization-oriented, system data rate optimization-oriented, system security optimization-oriented, and system energy optimization-oriented approaches. Moreover, we present the principles of metasurface-based IRSs from a physical mechanism perspective, highlighting their application in OWC systems through the distinct roles of light signal refraction and reflection. Finally, we discuss the key challenges and potential future directions for integrating IRS with OWC systems, providing insights for further research in this promising field.

Currently, key factors hindering application of steer-by-wire systems are their inadequate safety and reliability, which are significant criteria for evaluating automotive active safety. Based on the steer-by-wire platform, a dual-redundant steering motor control system is proposed, featuring dual three-phase permanent magnet synchronous motors as execution motors, achieving redundancy from hardware. A torque vector-space-decoupling control method is introduced for these motors to ensure balanced and stable torque output. Upon a fault, fault-tolerant measures are taken by disconnecting power supply to the affected motor, which, despite reducing system functionality, allows for normal steering control. This research starts with modeling the dual three-phase motors to construct a simulation model. It then proceeds with hardware-in-the-loop testing integrated with the dual-redundancy steer-by-wire control system, conducting tests under dual-lane-change trajectory conditions. Finally, a steering system fault is simulated to assess fault handling and functional degradation. These experiments confirmed that the proposed method enabled balanced torque output from the dual three-phase motors in the redundant steering control and facilitated fault-tolerant processing post fault, ensuring the vehicle’s steering functions were maintained.

The low Earth orbit (LEO) satellite internet network (LEO-SIN) has become a heated issue for the next generation of mobile communications, serving as a crucial means to achieve global wide-area broadband coverage and, especially, mobile phone directly to satellite cell (MPDTSC) communication. The ultra-high-speed movement of LEO satellites relative to the Earth results in serious Doppler effects, leading to signal de-synchronization at the user end (UE), and relative high-speed motion leading to frequent satellite handovers. Satellite ephemeris, which indicates the satellite’s position, has the potential to determine the position of the transmit (Tx) within the LEO-SIN, thereby enhancing the reliability and efficiency of satellite communication. The adoption of ephemeris in the LEO-SIN has met some new challenges: (1) how UEs can acquire ephemerides before signal synchronization is complete, (2) how to minimize the frequency of ephemeris broadcasting, and (3) how to decrease the overhead of ephemeris broadcasting. To address the above challenges, this paper proposes a method for extrapolating the LEO-SIN orbit based on multi-satellite ephemeris coordination (MSEC) and the multi-stream fractional autoregressive integrated moving average (MS-FARIMA). First, a multi-factor global error analysis model for ephemeris-extrapolation error is established, which decomposes it into three types; namely, random error (RE), trending error (TE), and periodic error (PE), with a focus on increasing the extrapolation accuracy by improving RE and TE. Second, RE is eliminated by utilizing the ephemerides from multiple satellites received at the same UE at the same time, as well as multiple ephemerides from the same satellite at different times. Subsequently, we propose a new FARIMA algorithm with the innovation of a multi-stream data time-series forecast (TSF), which effectively improves ephemeris extrapolation errors. Finally, the simulation results show that the proposed method reduces ephemeris extrapolation errors by 33.5% compared to existing methods, which also contributes to a performance enhancement in the Doppler frequency offset (DFO) estimation of MPDTSC.

The ANGPT1 gene plays a crucial role in the regulation of angiogenesis and muscle growth, with previous studies identifying copy number variations (CNVs) within this gene among Leizhou black goats. In this study, we investigated three ANGPT1 CNVs in 417 individuals of LZBG using quantitative PCR (qPCR), examining the impact of different CNV types on the ANGPT1 gene expression and their associations with growth and meat quality traits. Notably, the ANGPT1 CNV-1 (ARS1_chr14:24950001-24953600) overlaps with protein-coding regions and conserved domains; its gain-of-copies genotype (copies ≥ 3) was significantly correlated with ANGPT1 mRNA expression in muscle tissue (p < 0.01). Furthermore, the gain-of-copies genotype of CNV-1 demonstrated significant correlations with various phenotypic traits, including carcass weight, body weight, shear stress, chest circumference, and cross-sectional area of longissimus dorsi muscle. These findings indicate that the CNV-1 gain-of-copies genotype in the ANGPT1 gene may serve as a valuable marker for selecting Leizhou black goats exhibiting enhanced growth and muscular development characteristics, thereby holding potential applications in targeted breeding programs aimed at improving meat quality.

With the increase in human activities and the acceleration of urbanization, over-exploitation of natural resources has led to a decline in ecosystem services (ESs), subsequently affecting the achievement of sustainable development goals (SDGs). As the key ecological zone of Qinghai-Tibet Plateau, the stability and enhancement of ESs in the Hehuang Valley are crucial for achieving SDGs and biodiversity conservation. This study quantifies nine SDGs for the Hehuang Valley in the last twenty years. Four ecological models were utilized to compute key ESs: net primary productivity (NPP), water yield, soil retention, and sand fixation. Panel data were analyzed using a coupling coordination model to quantify the relationship between ESs and sustainable development level (SDL) in each county. Additionally, the Geographically and Temporally Weighted Regression (GTWR) model was employed to examine the correlation between ESs and SDL. The results indicate the following: (1) During the period, NPP and water yield first increased and then decreased. The capacity for soil retention and sand fixation showed an overall increase, highlighting substantial variability among counties in their ability to deliver these ESs. (2) The SDL of counties in the Hehuang Valley increased, with Xining City showing slightly higher SDL than other counties. (3) The overall coupling coordination degree among NPP, water yield, soil retention, sand fixation, and SDL in the Hehuang Valley exhibited an upward trend in the last twenty years. SDL demonstrated the highest coordination degree with NPP, followed by soil retention, water yield, and sand fixation. (4) Most counties in the Hehuang Valley exhibited a lag in SDL relative to NPP, water yield, and soil retention in the last twenty years. In the early stage, sand fixation and SDL were primarily lagging in SDL, while in the late stages, sand fixation lagged behind SDL. (5) During the period, there was an increasing negative correlation observed between the four ESs and SDL. The positive contribution of NPP and sand fixation in some counties gradually shifted to a negative effect, and the negative effect of water yield and soil retention on SDL intensified. The impact of human activities on ecosystem function hindered local SDL. This study offers scientific theoretical backing and practical recommendations for promoting SDL and biodiversity conservation in the Hehuang Valley.

The inefficiency of grain refinement processes has traditionally been attributed to the limited utilization of heterogeneous nucleation particles within master alloy systems, resulting in the formation of abundant inactive particles. This study aims to investigate the alternative influences of particles by incorporating external micrometer-sized TiB2 particles into the grain refinement process. Through a series of experiments, the refinement efficiency, grain refinement mechanism, and resultant microstructure of TiB2 particle-induced grain refinement specimens are comprehensively examined using various microscopy and analytical techniques, including polarization microscopy (OM), scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and transmission electron microscopy (TEM). Our findings demonstrate a direct correlation between increased levels of TiB2 particles and enhanced grain refinement efficiency. Moreover, the microstructure analysis reveals the distribution of TiB2 particles along grain boundaries, forming a coating due to self-assembly phenomena, while regions with a lower particle content may exhibit irregular grain structures. DSC analysis further confirms reduced undercooling, indicating the occurrence of heterogeneous nucleation events. However, TEM observations suggest that heterogeneous nucleation is not significantly influenced by the growth restriction factor attributed to TiAl3 2DC compounds. The grain refinement mechanism involving TiB2 particles is elucidated to entail both heterogeneous nucleation and physical growth restriction effects. Specifically, a reduction in average grain size is attributed not only to heterogeneous nucleation but also to the physical growth restriction effect facilitated by the TiB2 particle coating. This study offers insights into leveraging particles that do not participate in heterogeneous nucleation within master alloy-based grain refinement systems.

Wear-resistant steel/carbon steel composite plates not only have the double performance advantages of high strength and wear resistance but can also reduce energy consumption and production costs. Based on a 50% reduction rate, the wear resistance of the BTW1/Q345 composite was studied at different annealing temperatures, and the dry friction and wear tests of the BTW1/Q345 composite at different annealing temperatures were carried out using RETC MFT-5000. By using the white-light interference three-dimensional surface profiler, scanning electron microscope (SEM), and backscattered electron diffraction (EBSD) technology, we carried out a detailed analysis of the macroscopic and microscopic morphology and wear mechanism of wear traces at different annealing temperatures. The effects of the annealing process on the thickness and composition of the wear layer were studied, and the causes of wear failure were analyzed based on the results of scanning electron microscopy. It was found that as the annealing temperature gradually increased, the particle size near the scratch of BTW1 in the wear-resistant layer of the composite plate became smaller. On this basis, the effects of different annealing temperatures on the friction and wear characteristics of the composite plate were further studied. At the annealing temperature of 860 ° C, the wear resistance of the material was the best.

Cucumber (Cucumis sativus L.) is a vital economic vegetable crop, and the TONNEAU1 Recruiting Motif (TRM) gene plays a key role in cucumber organ growth. However, the pan-genomic characteristics of the TRM gene family and their expression patterns under different stresses have not been reported in cucumber. In this study, we identified 29 CsTRMs from the pan-genomes of 13 cucumber accessions, with CsTRM29 existing only in PI183967. Most CsTRM proteins exhibited differences in sequence length, except five CsTRMs having consistent protein sequence lengths among the 13 accessions. All CsTRM proteins showed amino acid variations. An analysis of CsTRM gene expression patterns revealed that six CsTRM genes strongly changed in short-fruited lines compared with long-fruited lines. And four CsTRM genes strongly responded to salt and heat stress, while CsTRM14 showed responses to salt stress, powdery mildew, gray mold, and downy mildew. Some CsTRM genes were induced or suppressed at different treatment timepoints, suggesting that cucumber TRM genes may play different roles in responses to different stresses, with expression patterns varying with stress changes. Remarkably, the expression of CsTRM21 showed considerable change between long and short fruits and in responses to abiotic stresses (salt stress and heat stress), as well as biotic stresses (powdery mildew and gray mold), suggesting a dual role of CsTRM21 in both fruit shape determination and stress resistance. Collectively, this study provided a base for the further functional identification of CsTRM genes in cucumber plant growth and stress resistance.

Because of the substantial progress in quantum computing technology, the safety of traditional cryptologic schemes is facing serious challenges. In this study, we explore the quantum safety of the lightweight cipher MIBS and propose quantum key-recovery attacks on the MIBS cipher by utilizing Grover’s algorithm and Bernstein–Vazirani algorithm. We first construct linear-structure functions based on the 5-round MIBS cipher according to the characteristics of the linear transformations, and then we obtain a quantum distinguisher of the 5-round MIBS cipher by applying Bernstein–Vazirani algorithm to the constructed functions. Finally, utilizing this distinguisher and Grover’s algorithm, we realize a 7-round key-recovery attack on the MIBS cipher, and then we expand the attack to more rounds of MIBS based on a similar idea. The quantum attack on the 7-round MIBS requires 156 qubits and has a time complexity of 210.5. An 8-round attack requires 179 qubits and has a time complexity of 222. Compared with existing quantum attacks, our attacks have better time complexity when attacking the same number of rounds.

In the high-precision optoelectronic tracking system (OTS) based on a charge-coupled device (CCD), the boresight error extracted from the tracking image contains an undeniable delay, which directly limits the control bandwidth of visual tracking. High bandwidth means high response speed and tracking accuracy. Generally, a model-based delay compensation control method called the Smith predictor is utilized to separate time delay from the closed loop to promote the control bandwidth. However, due to the existence of errors between the established model and the real object, the improvement in the bandwidth is still limited to ensure system stability, resulting in insufficient tracking performance. In this paper, to solve the problem, a Smith predictor modified with pseudo feedforward control for the OTS is proposed. The experimental results demonstrate that the proposed method achieves significant improvements in tracking performance, reducing the maximum residual error at 1 Hz from 365 arcseconds (using the classic Smith predictor) to 283 arcseconds, a 22.5% improvement. Across the main frequency band (0.2 Hz to 2 Hz), the residual errors were consistently lower using the proposed method.

State of health (SOH) estimation plays a vital role in ensuring the safe and stable operation of lithium-ion battery management systems (BMSs). Data-driven methods are widely used to estimate SOH; however, existing methods often suffer from fixed or excessively high feature dimensions, impacting the model’s adjustability and applicability. This study first proposed a layered knee point strategy based on the charging voltage curve, which reduced the complexity of feature extraction. Then, a new hybrid framework called the adaptive gated sequence network (AGSN) model was proposed. This model integrated independently recurrent neural network (IndRNN) layers, active state tracking long short-term memory (AST-LSTM) layers, and adaptive gating mechanism (AGM) layers. By integrating a multi-layered structure and an adaptive gating mechanism, the SOH prediction performance was significantly improved. Finally, batteries under different operating conditions were tested using the NASA battery dataset. The results show that the AGSN model demonstrated higher accuracy and robustness in battery SOH estimation, with estimation errors consistently within 1%.

In fermented foods, acetic acid bacteria (AAB), kinds of bacteria with a long history of utilization, contribute to safety, nutritional, and sensory properties primarily through acetic acid fermentation. AAB are commonly found in various fermented foods such as vinegar, sour beer, fermented cocoa and coffee beans, kefir beverages, kombucha, and sourdough. They interact and cooperate with a variety of microorganisms, resulting in the formation of diverse metabolites and the production of fermented foods with distinct flavors. Understanding the interactions between AAB and other microbes is crucial for effectively controlling and utilizing AAB in fermentation processes. However, these microbial interactions are influenced by factors such as strain type, nutritional conditions, ecological niches, and fermentation duration. In this review, we examine the relationships and research methodologies of microbial interactions and interaction studies between AAB and yeasts, lactic acid bacteria (LAB), and bacilli in different food fermentation processes involving these microorganisms. The objective of this review is to identify key interaction models involving AAB and other microorganisms. The insights gained will provide scientific guidance for the effective utilization of AAB as functional microorganisms in food fermentation processes.

Person re-identification (ReID) refers to the task of retrieving target persons from image libraries captured by various distinct cameras. Over the years, person ReID has yielded favorable recognition outcomes under typical visible light conditions, yet there remains considerable scope for enhancement in challenging conditions. The challenges and research gaps include the following: multi-modal data fusion, semi-supervised and unsupervised learning, domain adaptation, ReID in 3D space, fast ReID, decentralized learning, and end-to-end systems. The main problems to be solved, which are the occlusion problem, viewpoint problem, illumination problem, background problem, resolution problem, openness problem, etc., remain challenges. For the first time, this paper uses person ReID in special scenarios as a basis for classification to categorize and analyze the related research in recent years. Starting from the perspectives of person ReID methods and research directions, we explore the current research status in special scenarios. In addition, this work conducts a detailed experimental comparison of person ReID methods employing deep learning, encompassing both system development and comparative methodologies. In addition, we offer a prospective analysis of forthcoming research approaches in person ReID and address unresolved concerns within the field.

Due to the superiority of quantum computing, traditional cryptography is facing a severe threat. This makes the security evaluation of cryptographic systems in quantum attack models both significant and urgent. For symmetric ciphers, the security analysis heavily relies on cryptanalysis tools. Thus, exploring the use of quantum algorithms in traditional cryptanalysis tools has garnered considerable attention. In this study, we utilize quantum algorithms to improve impossible differential attacks and design two quantum automated tools to search for impossible differentials. The proposed quantum algorithms exploit the idea of miss-in-the-middle and the properties of truncated differentials. We rigorously prove their validity and calculate the quantum resources required for their implementation. Compared to the existing classical automated cryptanalysis, the proposed quantum tools have the advantage of accurately characterizing S-boxes while only requiring polynomial complexity, and can take into consideration the impact of the key schedules in a single-key model.

Two-dimensional (2D) materials have drawn extensive attention due to their exceptional characteristics and potential uses in electronics and energy storage. This investigation employs simulations using molecular dynamics to examine the mechanical and thermal transport attributes of the 2D silicene–germanene (Si-Ge) lateral heterostructure. The pre-existing cracks of the Si-Ge lateral heterostructure are addressed with external strain. Then, the effect of vacancy defects and temperature on the mechanical attributes is also investigated. By manipulating temperature and incorporating vacancy defects and pre-fabricated cracks, the mechanical behaviors of the Si-Ge heterostructure can be significantly modulated. In order to investigate the heat transport performance of the Si-Ge lateral heterostructure, a non-equilibrium molecular dynamics approach is employed. The efficient phonon average free path is obtained as 136.09 nm and 194.34 nm, respectively, in the Si-Ge heterostructure with a zigzag and armchair interface. Our results present the design and application of thermal management devices based on the Si-Ge lateral heterostructure.

Due to the ability of optical modulators to achieve rapid modulation of optical signals, meeting the demands of high-speed data transmission, modulators based on different novel nanomaterials have become one of the research hotspots over the past dacade. Recently, TiN/Ti3C2 heterojunction exhibits highly efficient thermo-optic performance and extremely strong stability. Therefore, we have demonstrated an all-optical modulator based on the principle of Michelson interference and the thermo-optic effect in this paper. The modulator employs a TiN/Ti3C2 heterojunction-coated microfiber (THM) and further demonstrates its ability to generate phase shifts through an ASE light source. The modulator, with a phase shift slope of 0.025π/mW, can also convert the phase shifts of signal light into amplitude modulation through Michelson interference. The fixed signal light wavelength is 1552.09 nm, and the modulation depth is stable at about 26.4 dB within a wavelength detuning range of −10 to 6 nm; The waveforms of signal light at modulation rates of 500 Hz, 1000 Hz, 2000 Hz, and 3000 Hz were tested, and a 3 dB modulation bandwidth of 2 kHz was measured. The all-optical modulator based on THM has the advantages of high efficiency and stability and has broad application prospects in the fields of all-optical signal processing and high-speed optical communication.

Per- and polyfluoroalkyl substances (PFASs) are a class of anthropogenic organic compounds widely present in the natural and human living environments. These emerging persistent pollutants can enter the human body through multiple channels, posing risks to human health. In particular, exposure to PFASs in women may cause a series of reproductive health hazards and infertility. Based on a review of the existing literature, this study preliminarily summarizes the effects of PFAS exposure on the occurrence and development of female reproductive endocrine diseases, such as polycystic ovary syndrome (PCOS), endometriosis, primary ovarian insufficiency (POI), and diminished ovarian reserve (DOR). Furthermore, we outline the relevant mechanisms through which PFASs interfere with the physiological function of the female ovary and finally highlight the role played by nutrients in reducing the reproductive health hazards caused by PFASs. It is worth noting that the physiological mechanisms of PFASs in the above diseases are still unclear. Therefore, it is necessary to further study the molecular mechanisms of PFASs in female reproductive diseases and the role of nutrients in this process.

The Chinese government has implemented numerous ecological policies in Qinghai Province aimed at protecting and restoring the natural ecosystem. Yet, amid global climate change, the precise effects of these policies on ecological improvement remain ambiguous. There is an urgent need for a comprehensive evaluation of the effects of these policies at a regional scale and an analysis of the changes in policy implementation stages to optimize the strategic direction of regional ecological policies. In this study, using mathematical statistics and spatial analysis, we analysed the spatial and temporal characteristics of the Normalized Difference Vegetation Index (NDVI) in Qinghai Province from 2000 to 2023. Further, by systematically reviewing ten major ecological policies currently or previously implemented in the region, we explored the response of vegetation restoration to these policies through both horizontal and vertical evaluations by reasonably delineating the policy study sub-zones. The study identified distinct stages of policy implementation—regreening, stabilizing, and natural recovery—and correlated these stages with the efficacy of policy impacts. Our findings indicate significant vegetation coverage improvements across Qinghai Province over the past two decades, with all ecological policies positively influencing the environment. The main contribution of this study is that it comprehensively evaluates the impact of multiple ecological policies on vegetation restoration at the regional scale, providing a reference for the formulation and adjustment of subsequent ecological policies.

With its unique properties and potential applications, nanoparticle-based delivery platforms for messenger RNA (mRNA) vaccines have gained significant attention in recent years. Nanoparticles have the advantages of enhancing immunogenicity, targeting delivery, and improving stability, providing a new solution for drug and vaccine delivery. In some clinical studies, a variety of nanoparticle delivery platforms have been gradually applied to a wide range of vaccine applications. Current research priorities are exploring various types of nanoparticles as vaccine delivery systems to enhance vaccine stability and immunogenicity. Lipid nanoparticles (LNPs) have shown promising potential in preclinical and clinical studies on the efficient delivery of antigens to immune cells. Moreover, lipid nanoparticles and other nanoparticles for nucleic acids, especially for mRNA delivery systems, have shown vast potential for vaccine development. In this review, we present various vaccine platforms with an emphasis on nanoparticles as mRNA vaccine delivery vehicles. We describe several novel nanoparticle delivery platforms for mRNA vaccines, such as lipid-, polymer-, and protein-based nanoparticles. In addition, we provide an overview of the anti-tumor immunity of nanovaccines against different tumors in cancer immunotherapy. Finally, we outline future perspectives and remaining challenges for this promising technology of nanoparticle-based delivery platforms for vaccines.

China is currently experiencing rapid expansion in its transportation land. To promote sustainable land use, accurately estimating transportation land demand is crucial. This study aims to develop a comprehensive framework for urban transportation land forecasting within the Yangtze River Economic Belt (YREB), providing support for optimizing regional land allocation. Employing methods such as meta-analysis, statistical analysis, and BP neural network analysis, this study forecasts the transportation land demand of 127 cities in the YREB. The study findings indicate that cities with high transportation land demand are mainly distributed in the middle and upper reaches of the Yangtze River. Moreover, the growth rate of transportation land in the upper reaches significantly outstrips that in the middle and lower reaches, suggesting a focus shift in transportation infrastructure construction toward the upper regions. Additionally, some cities within the YREB face a mismatch between the supply and demand of transportation land, necessitating proactive adjustments to their land supply plans to achieve a balance between supply and demand. The main contribution of this study is the development of a comprehensive and adaptable framework that guides the development of future strategies for optimal land allocation by forecasting transportation land demand at a regional level.

Accurate long-term water demand forecasting is beneficial to the sustainable development and management of cities. However, the randomness and nonlinear nature of water demand bring great challenges to accurate long-term water demand forecasting. For accurate long-term water demand forecasting, the models currently in use demand the input of extensive datasets, leading to increased costs for data gathering and higher barriers to entry for predictive projects. This situation underscores the pressing need for an effective forecasting method that can operate with a smaller dataset, making long-term water demand predictions more feasible and economically sensible. This study proposes a framework to delineate and analyze long-term water demand patterns. A forecasting model based on generative adversarial networks and multivariate feature fusion (the water demand forecast-mixer, WDF-mixer) is designed to generate synthetic data, and a gradient constraint is introduced to overcome the problem of overfitting. A multi-feature fusion method based on temporal and channel features is then derived, where a multi-layer perceptron is used to capture temporal dependencies and non-negative matrix decomposition is applied to obtain channel dependencies. After that, an attention layer receives all those features associated with the water demand forecasting, guiding the model to focus on important features and representing correlations across them. Finally, a fully connected network is constructed to improve the modeling efficiency and output the forecasting results. This approach was applied to real-world datasets. Our experimental results on four water demand datasets show that the proposed WDF-mixer model can achieve high forecasting accuracy and robustness. In comparison to the suboptimal models, the method introduced in this study demonstrated a notable enhancement, with a 62.61% reduction in the MSE, a 46.85% decrease in the MAE, and a 69.15% improve in the R2 score. This research could support decision makers in reducing uncertainty and increasing the quality of water resource planning and management.

Acrylamide (AA), an odorless and colorless organic small-molecule compound found generally in thermally processed foods, possesses potential carcinogenic, neurotoxic, reproductive, and developmental toxicity. Compared with conventional methods for AA detection, bio/chemical sensors have attracted much interest in recent years owing to their reliability, sensitivity, selectivity, convenience, and low cost. This paper provides a comprehensive review of bio/chemical sensors utilized for the detection of AA over the past decade. Specifically, the content is concluded and systematically organized from the perspective of the sensing mechanism, state of selectivity, linear range, detection limits, and robustness. Subsequently, an analysis of the strengths and limitations of diverse analytical technologies ensues, contributing to a thorough discussion about the potential developments in point-of-care (POC) for AA detection in thermally processed foods at the conclusion of this review.

Marine shales from the Niutitang Formation and marine–continental transitional shales from the Longtan Formation are two sets of extremely important hydrocarbon source rocks in South China. In order to quantitatively compare the pore complexity characteristics between marine and marine–continental transitional shales, the shale and kerogen of the Niutitang Formation and the Longtan Formation are taken as our research subjects. Based on organic petrology, geochemistry, and low-temperature gas adsorption analyses, the fractal dimension of their pores is calculated by the Frenkel–Halsey–Hill (FHH) and Sierpinski models, and the influences of total organic carbon (TOC), vitrinite reflectance (Ro), and mineral composition on the pore fractals of the shale and kerogen are discussed. Our results show the following: (1) Marine shale predominantly has wedge-shaped and slit pores, while marine–continental transitional shale has inkpot-shaped and slit pores. (2) Cylindrical pores are common in organic matter of both shale types, with marine shale having a greater gas storage space (CRV) from organic matter pores, while marine–continental transitional shale relies more on inorganic pores, especially interlayer clay mineral pores, for gas storage due to their large specific surface area and high adsorption capacity (CRA). (3) The fractal characteristics of marine and marine–continental transitional shale pores are influenced differently. In marine shale, TOC positively correlates with fractal dimensions, while in marine–continental shale, Ro and clay minerals have a stronger influence. Ro is the primary factor affecting organic matter pore complexity. (4) Our two pore fractal models show that the complexity of the shale in the Longtan Formation surpasses that of the shale in the Niutitang Formation, and type I kerogen has more complex organic matter pores than type III, aiding in evaluating pore connectivity and flow effectiveness in shale reservoirs.

High-precision landcover classification is a fundamental prerequisite for resource and environmental monitoring and land-use status surveys. Imbued with intricate spatial information and texture features, very high spatial resolution remote sensing images accentuate the divergence between features within the same category, thereby amplifying the complexity of landcover classification. Consequently, semantic segmentation models leveraging deep backbone networks have emerged as stalwarts in landcover classification tasks owing to their adeptness in feature representation. However, the classification efficacy of a solitary backbone network model fluctuates across diverse scenarios and datasets, posing a persistent challenge in the construction or selection of an appropriate backbone network for distinct classification tasks. To elevate the classification performance and bolster the generalization of semantic segmentation models, we propose a novel semantic segmentation network architecture, named the aggregated backbone network (ABNet), for the meticulous landcover classification. ABNet aggregates three prevailing backbone networks (ResNet, HRNet, and VoVNet), distinguished by significant structural disparities, using a same-stage fusion approach. Subsequently, it amalgamates these networks with the Deeplabv3+ head after integrating the convolutional block attention mechanism (CBAM). Notably, this amalgamation harmonizes distinct scale features extracted by the three backbone networks, thus enriching the model’s spatial contextual comprehension and expanding its receptive field, thereby facilitating more effective semantic feature extraction across different stages. The convolutional block attention mechanism primarily orchestrates channel adjustments and curtails redundant information within the aggregated feature layers. Ablation experiments demonstrate an enhancement of no less than 3% in the mean intersection over union (mIoU) of ABNet on both the LoveDA and GID15 datasets when compared with a single backbone network model. Furthermore, in contrast to seven classical or state-of-the-art models (UNet, FPN, PSPNet, DANet, CBNet, CCNet, and UPerNet), ABNet evinces excellent segmentation performance across the aforementioned datasets, underscoring the efficiency and robust generalization capabilities of the proposed approach.

Anthocyanins are precious industrial raw materials. Purple corn is rich in anthocyanins, with large variation in their content between organs. It is imperative to find a rapid and non-destructive method to determine the anthocyanin content in purple corn. To this end, a field experiment with ten purple corn hybrids was conducted, collecting plant images using a digital camera and determining the anthocyanin content of different organ types. The average values of red (R), green (G) and blue (B) in the images were extracted. The color indices derived from RGB arithmetic operations were applied in establishing a model for estimation of the anthocyanin content. The results showed that the specific color index varied with the organ type in purple corn, i.e., ACCR for the grains, BRT for the cobs, ACCB for the husks, R for the stems, ACCB for the sheaths and BRT for the laminae, respectively. Linear models of the relationship between the color indices and anthocyanin content for different organs were established with R2 falling in the range of 0.64–0.94. The predictive accuracy of the linear models, assessed according to the NRMSE, was validated using a sample size of 2:1. The average NRMSE value was 11.68% in the grains, 13.66% in the cobs, 8.90% in the husks, 27.20% in the stems, 7.90% in the sheaths and 15.83% in the laminae, respectively, all less than 30%, indicating that the accuracy and stability of the model was trustworthy and reliable. In conclusion, this study provided a new method for rapid, non-destructive prediction of anthocyanin-rich organs in purple corn.

The purpose of this study was to investigate how students’ disabilities impacted their learning at the university and to measure their perceptions of universal design for learning (UDL). An online survey was administered at a large public research university in the south-central United States; 160 students with disabilities completed the survey. These students with disabilities described how their disabilities influenced their learning in the following aspects: attention issues, slow processing, absence, accessibility issues, reading and writing challenges, mental health challenges, and social interaction challenges. The results also revealed that some UDL teaching practices were not being fully utilized by instructors based on the perceptions of students with disabilities. We discussed those UDL practices which were not being used by instructors and highlighted the impact of using the practices on students’ learning. These practices include effective teaching methods such as helping students organize and summarize learning content, using technology to increase accessibility, providing flexibility in assessments and assignments, providing meaningful feedback, and recruiting students’ attention and engagement.

The skin, as the largest organ, serves as a protective barrier against external stimuli. However, when the skin is injured, wound healing becomes a complex process influenced by physiological conditions, bacterial infections, and inflammation. To improve the process of wound healing, a variety of wound dressings with antibacterial qualities have been created. Electrospun nanofibers have gained significant attention in wound dressing research due to their large specific surface area and unique structure. One interesting method for creating Janus-structured nanofibers is side-by-side electrospinning. This work used side-by-side electrospinning to make cellulose acetate/gelatin Janus nanofibers. Curcumin and zinc oxide nanoparticles were added to these nanofibers. We studied Janus nanofibers’ physicochemical characteristics and abilities to regulate small-molecule medication release. Janus nanofibers coated with zinc oxide nanoparticles and curcumin were also tested for antibacterial activity. The Janus nanofibers with specified physicochemical characteristics were successfully fabricated. Nanofibers released small-molecule medicines in a controlled manner. Additionally, the Janus nanofibers loaded with curcumin exhibited excellent antibacterial capabilities. This research contributes to the development of advanced wound dressings for promoting wound healing and combating bacterial infections.

Facility agriculture is an important initiative to adopt an all-encompassing approach to food and build a diversified food supply system. Understanding the evolution of facility agricultural land and the factors that drive it can contribute to the development of scientifically strategic agricultural planning and agricultural modernization. Therefore, this paper constructs a “situation-structure-behavior-value” theoretical framework; quantifies the relevant driving factors (physical, proximal, and socioeconomic) and their impacts on the development and layout of facility agriculture land by using a multivariate logistic regression model; and provides a strategy for optimizing land use. The results showed that the area of facility agriculture in Huzhou is rapidly expanding. Regarding drivers, facility agricultural land tends to be located in areas with higher slopes according to plot selection. Facility agriculture is more likely to develop in plots with convenient transportation and closer proximity to markets. At the economic level, economic efficiency, agricultural resource superiority, and policies significantly impact facility agriculture expansion. Finally, we propose three land use policy options to facilitate the sustainable development of facility agriculture. This study elucidates the underlying factors driving different types of facility agricultural land and offers methodological guidance for policy support, planning, control, and optimization strategies for facility agriculture.

Leizhou goats are famous for their delicious meat but have inferior growth performance. There is little information on rumen-protected fat (RPF) from the Leizhou goat. Hence, we observed the effects of RPF on growth, fecal short-chain fatty acids, and bacteria community with respect to Leizhou goats. Twelve goats (13.34 ± 0.024 kg) were selected and assigned randomly to one of two treatments: (1) a control diet (CON) and (2) 2.4% RPF with a control diet (RPF). The final body weight and average daily gain (ADG) were greater (p < 0.05), and the dry matter intake (DMI): ADG was lower (p < 0.05) in the RPF group than in the CON group. There were no differences in DMI between the CON and RPF groups. The concentrations of total short-chain fatty acids, acetate, propionate, and butyrate were lower (p < 0.05) in the RPF group than in the CON group. The relative abundances of Ruminococcus, Rikenellaceae_RC9_gut_group, Treponema, norank_f__norank_o__RF39, Eubacterium_siraeum_group, and Ruminococcus_torques_group were lower (p < 0.05) in the RPF group than in the CON group. The relative abundances of Bacteroides, norank_f__norank_o__Clostridia_UCG-014, norank_f__Eubacterium_coprostanoligenes_group, Eubacterium_ruminantium_group, norank_f__Oscillospirale-UCG-010, Oscillospiraceae_UCG-002, and Family_XIII_AD3011_group were greater (p < 0.05) in the RPF group than in the CON group. It was concluded that RPF could improve the goats’ growth performance by regulating their fecal bacteria communities.

Exoplanet imaging with high-contrast imaging adaptive optics systems, though challenging, is a promising path toward the characterization of terrestrial planets. We analyzed the fundamental limitations associated with the direct imaging of terrestrial exoplanets around low-mass stars with Extremely Large Telescopes using laser tomographic adaptive optics (LTAO) and derived the post-coronagraph image shape in the focal plane from LTAO systems. Additionally, the fundamental limitation of direct imaging was found to come from unseen spatial frequencies during tomographic reconstruction. Through the provision of optimization strategies for laser guide star (LGS) asterisms, based on the post-coronagraph image contrast, we aimed to assist in the design of LTAO systems for Extremely Large Telescopes, resulting in a six-fold improvement in the LTAO post-coronagraph image plane at 0.1 arcseconds.

The growing need for the multiband photodetection of a single scene has promoted the development of both multispectral coupling and broadband detection technologies. Photodetectors operating across the infrared (IR) to terahertz (THz) regions have many applications such as in optical communications, sensing imaging, material identification, and biomedical detection. In this review, we present a comprehensive overview of the latest advances in broadband photodetectors operating in the infrared to terahertz range, highlighting their classification, operating principles, and performance characteristics. We discuss the challenges faced in achieving broadband detection and summarize various strategies employed to extend the spectral response of photodetectors. Lastly, we conclude by outlining future research directions in the field of broadband photodetection, including the utilization of novel materials, artificial microstructure, and integration schemes to overcome current limitations. These innovative methodologies have the potential to achieve high-performance, ultra-broadband photodetectors.

An efficient and reliable spacecraft payload thermal management method is one of the key problems to build China Space Station (CSS). Aiming at the outstanding characteristics and difficulties of the payloads rack in the experimental lab module of CSS, in the aspects of thermal control interface, boundary constraint, scale quantity, space layout, diversity of temperature control, operation mode and working life, etc., a two-stage thermal management scheme based on single-phase fluid loop is proposed and constructed. A fluid drive unit is designed to drive the fluid circulation and health management of the module thermal control bus (TCB). In view of the different characteristics of the payload racks, three different types of second-stage thermal control systems are proposed, and different thermal control terminals are designed, in order to solve the problem of thermal control resource allocation and health management in payload racks. A method based on “pump bypass” and “valve resistance ratio” is used to control the flow rate of the thermal management system. Based on the overall scheme of the thermal management system and the developed flight products, on-orbit verification and data analysis are carried out. Flight verification shows that the key parameters of the fluid loop in the thermal management system on orbit are basically consistent with the ground data; the maximum deviation is 1.84%. The pressure control accuracy of the bypass system reaches ±2%, and the active heat transfer efficiency of the integrated thermal management system is over 83%, which can effectively meet the thermal control requirements of the two-stage payloads in the module and rack. After on-orbit flight verification, the designed thermal control system works well on orbit and exhibits good stability.

Catalytic pyrolysis of low-rank coal is currently an effective method for producing high-quality tar and gas. In this study, catalytic upgrading of volatiles from Naomaohu (NMH) coal pyrolysis has been conducted in a two-stage fixed-bed reactor using combined CaO and Ni/olivine (Ni-loaded olivine) catalysts. The effect of catalyst distribution modes and catalytic temperature on the tar and gas quality has been investigated. Simulated distillation and GC-MS analysis have been used to investigate the distribution of tar components. The results indicated that the light oil fraction in tar dramatically increased due to the combination of CaO and Ni/olivine. The CaO-Ni/olivine mode is especially better compared to the layouts of the Ni/olivine-CaO mode and the mixed mode. The CaO-Ni/olivine mode ensures a higher light fraction in tar at 69.3% and a light oil fraction at 29.8% at a catalytic temperature of 450 °C, while the heavy tar fraction decreased to 30.7%. Meanwhile, the contents of benzene (heteroatomic substituents) in tar significantly increased from 2.55% to 6.45% compared with the blank test. In this scenario, CaO breaks down macromolecular compounds in tar and cleaves long-chain esters to produce aliphatic hydrocarbons. These hydrocarbons are then dehydrogenated to produce lighter aromatic hydrocarbons over the CaO surface. Subsequently, the volatiles pass through the Ni/olivine catalysis, where ether compounds are produced by means of dehydration reactions. In addition, the CaO absorbs the CO2 in the pyrolysis gas, leading to an elevation of CH4 and H2 concentration. Particularly, the concentration of H2 significantly increased from 16.2% to 30.37%, while the concentration of CO2 significantly decreased from 37.9% to 10.57%. These findings suggest that the usage of combined CaO and Ni/olivine catalysts is beneficial for improving both the tar and gas quality.

The traditional process of producing Zhenjiang aromatic vinegar faces challenges such as high water usage, wastewater generation, raw material losses, and limitations in mechanization and workshop conditions. This study introduces and evaluates a novel dry gelatinization process, focusing on fermentation efficiency and the vinegar flavor profile. The new process shows a 39.1% increase in alcohol conversion efficiency and a 14% higher yield than the traditional process. Vinegar produced through the dry gelatinization process has a stronger umami taste and a higher lactic acid concentration. Both processes detected 33 volatile substances, with the dry gelatinization process showing a notably higher concentration of 2-methylbutanal, which imparts a distinct fruity and chocolate aroma. These findings suggest that the dry gelatinization process outperforms the traditional process in several aspects.

Currently, fiber-reinforced polymer composites (FRPs) used for demanding structural applications predominantly utilize carbon, glass, and aramid fibers embedded in epoxy resin, albeit occasionally polyester and vinyl ester resins are also used. This study investigates the feasibility of employing recyclable and sustainable materials to formulate a composite suitable for load-bearing structural applications, particularly in scenarios involving low-velocity and high-velocity impacts (LVIs and HVIs, respectively). The paper presents a comparative analysis of the performance of basalt–Elium, a fully recyclable, sustainable, and environmentally friendly composite, with an epoxy-based counterpart. Moreover, an accurate and reliable numerical model has been developed and introduced through which the response of these composites can be examined efficiently and accurately under various loading states. The results of this investigation demonstrate the viability of the basalt–elium composite as a fully recyclable and sustainable material for crafting efficient and lightweight composites. Additionally, the accurately developed finite element model presented here can be used to assess the influence of several parameters on the composite, thereby optimizing it for a given situation.

Sensory attributes strongly influence consumers’ preferences for products. The inoculation of the Klebsiella variicola H8 strain in a reconstituted tobacco leaf concentrate (RTLC) solution increased neutral aroma-enhancing compound (NAEC) production by 45%, decreased the nicotine level by 25%, decreased the water-soluble total sugar content by ~36%, and improved the sensory quality by 5.71%. The production of NAECs such as dihydrokiwi lactone (DHKL: 192.86%), 1,2,3,4-tetrahydro-1,1,6-trimethylnaphthalene (THTMN: 177.77%), 2,4-di-tert-butylphenol (DTBP: 25%), 4-oxoisofolkone (OIFK: 116.66%,) 1,9-heptadecadiene-4,6-diyn-3-ol (HDD: 116.67%), β-damastrone (BDS: 116.67), and megastigmatrienone A (MSTA: 116.67%) was increased. A metagenomics analysis of the microbial community in the fermented RTLC (FRTLC) was performed to elucidate the mechanism by which NAECs were produced. As a result, 24 groups of functional genes were identified, and among them, five families of carbohydrate-active enzymes, (i) glycoside hydrolase (GH), (ii) glycosyltransferase (GT), (iii) polysaccharide lyase (PL), (iv) carbohydrate esterase (CE), and (v) auxiliary active enzyme (AA), were found to be positively correlated with the production of NAECs. However, among the GHs, the GHs annotated from the H8 strain chromosome displayed the highest relative abundance and a positive correlation with the production of NAECs. Specifically, the GH13-14, GH13-20, GH13-38, GH13-25, GH13-10, GH42, and GH28 genes of the H8 strain were relatively more abundant and were key contributors to the production of NAECs. The correlation analyses revealed that the H8 strain plays a leading role among all the microorganisms in FRTLC in the production of NAECs. Our findings support the application of Klebsiella variicola in NAEC production and a reduction in nicotine content in tobacco products.

Deterministic polishing based on jet electrochemical machining (Jet-ECM) is a stress-free machining method for low-rigidity and ultra-precision workpieces. The nozzle is equivalent to a special tool in deterministic polishing, and the workpiece material is removed using the mechanism of electrochemical dissolution at the position where the nozzle passes. By precisely regulating the nozzle’s movement speed and dwell time, the quantity of material removed from the workpiece at a designated position can be finely adjusted. With this mechanism, the improvement of the workpiece shape accuracy can be achieved by planning the nozzle trajectory and nozzle movement speed. However, due to the positioning errors of the polishing device, the actual position of the nozzle may deviate from the theoretical position, resulting in errors in material removal amount, which affects the accuracy and stability of the polishing process. This study established a mathematical model to analyze the influence of nozzle positioning errors in deterministic polishing based on Jet-ECM. This model has been used to design a specific deterministic polishing device based on Jet-ECM. With the proposed deterministic polishing device, the surface shape of the workpiece is converged. The surface peak-to-valley (PV) value of the φ 50 mm workpiece (valid dimensions = 90% of the central region) indicated that the shape error of the surface was reduced from 2.67 μm to 1.24 μm in 34 min. The power spectral density (PSD) method was used to evaluate the height distribution and height characteristics of the workpiece surface. The results show that the low frequency spatial error is reduced significantly after processing. This study improves the accuracy of the stress-free deterministic polishing methods and further expands the use of deterministic polishing in industry.

The formation and development of tubers, the primary edible and economic organ of potatoes, directly affect their yield and quality. The regulatory network and mechanism of tuberization have been preliminarily revealed in recent years, but plenty of relevant genes remain to be discovered. A few candidate genes were provided due to the simplicity of sampling and result analysis of previous transcriptomes related to tuberization. We sequenced and thoroughly analyzed the transcriptomes of thirteen tissues from potato plants at the tuber proliferation phase to provide more reference information and gene resources. Among them, eight tissues were stolons and tubers at different developmental stages, which we focused on. Five critical periods of tuberization were selected to perform an analysis of differentially expressed genes (DEGs), according to the results of the tissue correlation. Compared with the unswollen stolons (Sto), 2751, 4897, 6635, and 9700 DEGs were detected in the slightly swollen stolons (Sto1), swollen stolons (Sto2), tubers of proliferation stage 1 (Tu1), and tubers of proliferation stage 4 (Tu4). A total of 854 transcription factors and 164 hormone pathway genes were identified in the DEGs. Furthermore, three co-expression networks associated with Sto–Sto1, Sto2–Tu1, and tubers of proliferation stages two to five (Tu2–Tu5) were built using the weighted gene co-expression network analysis (WGCNA). Thirty hub genes (HGs) and 30 hub transcription factors (HTFs) were screened and focalized in these networks. We found that five HGs were reported to regulate tuberization, and most of the remaining HGs and HTFs co-expressed with them. The orthologs of these HGs and HTFs were reported to regulate processes (e.g., flowering, cell division, hormone synthesis, metabolism and signal transduction, sucrose transport, and starch synthesis) that were also required for tuberization. Such results further support their potential to control tuberization. Our study provides insights and countless candidate genes of the regulatory network of tuberization, laying the foundation for further elucidating the genetic basis of tuber development.

To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensively utilize historical information and overcome the limitations caused by small sample sizes, garnering significant attention from scholars. However, the traditional ADT Bayesian evaluation method has inherent shortcomings and limitations. Due to the constraints of small samples and an incomplete understanding of degradation mechanisms or accelerated mechanisms, the selected evaluation model may be inaccurate, leading to potentially inaccurate evaluation results. Therefore, describing and quantifying the impact of model uncertainty on evaluation results is a challenging issue that urgently needs resolution in the theoretical research of ADT Bayesian methods. This article addresses the issue of model uncertainty in the ADT Bayesian evaluation process. It analyzes the modeling process of ADT Bayesian and proposes a new model averaging evaluation method for ADT Bayesian based on relative entropy, which, to a certain extent, can resolve the issue of evaluation inaccuracy caused by model selection uncertainty. This study holds certain theoretical and engineering application value for conducting ADT Bayesian evaluation under model uncertainty.

Mare basalts on the lunar surface are tangible expressions of the complex thermal evolution and geological processes that have occurred within the lunar interior. These basaltic manifestations are highly important because they provide invaluable insights into lunar geological evolution. Notably, the Oceanus Procellarum region, which is renowned for its extensive and long-lasting basaltic volcanism, is a premier location to investigate late-stage lunar thermal evolution. The primary aim of this research is to elucidate the geomorphological, compositional, and temporal attributes that define the mare basalts within the Oceanus Procellarum region. To achieve this aim, we comprehensively analyzed the geomorphological features present within the region, leveraging Kaguya/SELENE TC images and digital elevation models. Specifically, these geomorphological features encompass impact craters, wrinkle ridges, sinuous rilles, and volcanic domes. Subsequently, we thoroughly examined the mineralogical attributes of basalts in the Oceanus Procellarum region, leveraging Kaguya/SELENE MI data and compositional map products. To more accurately reflect the actual ages of the mare basalts in the Oceanus Procellarum region, we carefully delineated the geological units within the area and employed the latest crater size-frequency distribution (CSFD) technique to precisely determine their ages. This refined approach allowed for a more comprehensive and accurate understanding of the basaltic rocks in the study area. Overall, our comprehensive study included an in-depth analysis of the volcanic activity and evolution of the Oceanus Procellarum region, along with an examination of the correlation between the mineralogical composition and ages of mare basalts. The findings from this exhaustive investigation reveal a definitive age range for basalt units within the Oceanus Procellarum region from approximately 3.69 Ga to 1.17 Ga. Moreover, the latest mare basalts that formed were pinpointed north of the Aristarchus crater. Significantly, the region has experienced at least five distinct volcanic events, occurring approximately 3.40 Ga, 2.92 Ga, 2.39 Ga, 2.07 Ga, and 1.43 Ga, leading to the formation of multiple basalt units characterized by their unique mineral compositions and elemental abundances. Through the application of remote sensing mineralogical analysis, three primary basalt types were identified: low-titanium, very-low-titanium, and intermediate-titanium basalt. Notably, the younger basalt units exhibit an elevated titanium proportion, indicative of progressive olivine enrichment. Consequently, these younger basalt units exhibit more intricate and complex mineral compositions, offering valuable insights into the dynamic geological processes shaping the lunar surface.

Severe combined immunodeficient (SCID) mice serve as a critical model for human xenotransplantation studies, yet they often suffer from low engraftment rates and susceptibility to graft-versus-host disease (GVHD). Moreover, certain SCID strains demonstrate ‘immune leakage’, underscoring the need for novel model development. Here, we introduce an SCID mouse model with a targeted disruption of the dclre1c gene, encoding Artemis, which is essential for V(D)J recombination and DNA repair during T cell receptor (TCR) and B cell receptor (BCR) assembly. Artemis deficiency precipitates a profound immunodeficiency syndrome, marked by radiosensitivity and compromised T and B lymphocyte functionality. Utilizing CRISPR/Cas9-mediated gene editing, we generated dclre1c-deficient mice with an NOD genetic background. These mice exhibited a radiosensitive SCID phenotype, with pronounced DNA damage and defective thymic, splenic and lymph node development, culminating in reduced T and B lymphocyte populations. Notably, both cell lines and patient-derived tumor xenografts were successfully engrafted into these mice. Furthermore, the human immune system was effectively rebuilt following peripheral blood mononuclear cells (PBMCs) transplantation. The dclre1c-knockout NOD mice described herein represent a promising addition to the armamentarium of models for xenotransplantation, offering a valuable platform for advancing human immunobiological research.

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), remains a global health crisis with substantial morbidity and mortality rates. Type II alveolar epithelial cells (AEC-II) play a critical role in the pulmonary immune response against Mtb infection by secreting effector molecules such as antimicrobial peptides (AMPs). Here, human β-defensin 1 (hBD1), an important AMP produced by AEC-II, has been demonstrated to exert potent anti-tuberculosis activity. HBD1 overexpression effectively inhibited Mtb proliferation in AEC-II, while mice lacking hBD1 exhibited susceptibility to Mtb and increased lung tissue inflammation. Mechanistically, in A549 cells infected with Mtb, STAT1 negatively regulated hBD1 transcription, while CEBPB was the primary transcription factor upregulating hBD1 expression. Furthermore, we revealed that the ERK1/2 signaling pathway activated by Mtb infection led to CEBPB phosphorylation and nuclear translocation, which subsequently promoted hBD1 expression. Our findings suggest that the ERK1/2-CEBPB-hBD1 regulatory axis can be a potential therapeutic target for anti-tuberculosis therapy aimed at enhancing the immune response of AEC-II cells.

The fractured rock mass in the western cold region is affected by freezing and thawing disasters and is prone to local damage and fracture along the fissures’ ends. The fatigue damage induced by repeated frost heave and traffic loads seriously endangers the stability of cold region roadbed. This paper selects sandstone as the research object. Firstly, 20 freeze–thaw cycles were performed on fractured sandstone samples with different inclination angles of 30°, 45°, 60°, and 90°. Subsequently, triaxial compression and triaxial fatigue loading tests were conducted to explore the mechanical properties and fracture morphology evolution mechanism during the compression process of freeze–thaw fractured sandstone. Nuclear magnetic resonance technology (NMR) was used to measure the H-containing fluid inside rock pores. The microscopic damage characteristics inside the rock were analyzed from the NMR T2 relaxation spectrum signal and pore size distribution characteristics. Based on the internal variable theory of continuum mechanics, a fatigue model of freeze–thaw fractured sandstone with different inclination angles was established. The results show that sandstone strength was negatively correlated with the fracture dip angle, and the axial deformation and shear failure angle were positively correlated with the fracture dip angle. The mechanical properties of the sandstone were deteriorated by fatigue loading. When the crack angle was 90°, the fatigue failure strength of the rock sample was the lowest. The T2 spectrum distribution of the fractured sandstone mainly had three peaks and the pore size was mainly medium and small pores. There was a small leftward shift after freeze–thaw cycles and fatigue loading. The T2 spectrum area was significantly affected by fatigue loading, with the highest rate of change at a crack angle of 30°. Through the fine correspondence between the axial residual deformation and the deformation modulus, a fatigue model with different crack inclination angles was established using the axial residual deformation as the internal variable, and the rationality of the model was verified by fatigue loading tests.

The accelerated degradation testing (ADT) Bayesian evaluation method comprehensively utilizes product degradation data under accelerated stress levels collected over a short period of time and multiple sources of prior information, such as historical information, similar product information, simulation information, etc., to conduct life and reliability evaluation. Through the prior distribution, prior information affects the ADT Bayesian evaluation results ultimately. However, different evaluators may obtain different prior distributions based on the same prior information due to varying experiences or rules, which may lead to differences in the ADT Bayesian evaluation results. Therefore, it is necessary to analyze and study the impact of prior distribution uncertainty on the ADT Bayesian evaluation results while also finding criteria to judge the quality of prior distributions. This paper focuses on the ADT Bayesian evaluation method based on the Wiener process and the Arrhenius relation, studying the influence of different prior distributions on the robustness of ADT Bayesian evaluation results. Additionally, based on the deviance information criterion (DIC), a criterion for selecting prior distributions in the ADT Bayesian evaluation method is proposed. Through carrying out uncertainty analysis of prior distribution in the ADT Bayesian evaluation method, a theoretical system and framework for analyzing prior uncertainty in ADT Bayesian evaluation based on DIC are established, providing a better foundation for the practical application of the ADT Bayesian evaluation method in engineering.