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Abstract Hydrogen, a high-density and clean energy, has been widely used in various critical applications. However, the safety risk caused by hydrogen leakage during storage and transportation is still a non-negligible issue. Therefore, it is necessary to offer hydrogen sensors with fast response and high repeatability, and it will be perfect for achieving in situ monitoring over the lifecycle of hydrogen production and utilization. Here, we propose a compact optical fiber sensor with a short section of the tilted Bragg fiber grating (TFBG) inscribed in the fiber core and a palladium and tungsten trioxide (Pd/WO3) combined film of 40 nm thickness over the fiber surface. The TFBG excites tens of narrow cladding resonances, part of which possess refractive indexes matching that of the Pd/WO3 coating and providing the high sensitivity to the surrounding hydrogen concentration change. The sensor offers improved sensing characteristics, including the fast response time (less than 10 s), high repeatability (over tens of measurement), and excellent linear response (higher than 99.6%) over the 0% to 3% concentration range.

Abstract Background The β-1,3-glucanase gene is widely involved in plant development and stress defense. However, an identification and expression analysis of the grape β-1,3-glucanase gene (VviBG) family had not been conducted prior to this study. Results Here, 42 VviBGs were identified in grapevine, all of which contain a GH-17 domain and a variable C-terminal domain. VviBGs were divided into three clades α, β and γ, and six subgroups A–F, with relatively conserved motifs/domains and intron/exon structures within each subgroup. The VviBG gene family contained four tandem repeat gene clusters. There were intra-species synteny relationships between two pairs of VviBGs and inter-species synteny relationships between 20 pairs of VviBGs and AtBGs. The VviBG promoter contained many cis-acting elements related to stress and hormone responses. Tissue-specific analysis showed that VviBGs exhibited distinct spatial and temporal expression patterns. Transcriptome analysis indicated that many VviBGs were induced by wounds, UV, downy mildew, cold, salt and drought, especially eight VviBGs in subgroup A of the γ clade. RT-qPCR analysis showed that these eight VviBGs were induced under abiotic stress (except for VviBG41 under cold stress), and most of them were induced at higher expression levels by PEG6000 and NaCl than under cold treatment. Conclusions The chromosome localization, synteny and phylogenetic analysis of the VviBG members were first conducted. The cis-acting elements, transcriptome data and RT-qPCR analysis showed that VviBG genes play a crucial role in grape growth and stress (hormone, biotic and abiotic) responses. Our study laid a foundation for understanding their functions in grape resistance to different stresses.

Abstract Objective Stereo‐electroencephalography‐guided three‐dimensional radiofrequency thermocoagulation (SEEG‐3D RFTC) is a minimally invasive treatment for mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE‐HS). This study aimed to investigate the long‐term prognosis after SEEG‐3D RFTC treatment in patients with MTLE‐HS. Methods This single‐center retrospective study included 28 patients with MTLE‐HS treated with SEEG‐3D RFTC from January 2016 to May 2018. Postoperative curative effects were evaluated using the Engel classification, and the patients were followed up for 5 years. Results The proportions of patients categorized as Engel I between 1 and 5 years after surgery were 72.41% (12 months after surgery), 67.86% (18 months after surgery), 62.07% (24 months after surgery), 50.00% (36 months after surgery), 42.86% (48 months after surgery), and 42.86% (60 months after surgery), respectively. Regarding long‐term efficacy, based on the Engel classification, SEEG‐3D RFTC showed room for improvement. Significance This was the first study to evaluate the efficacy of SEEG‐3D RFTC for MTLE‐HS with long‐term follow‐up. SEEG‐3D RFTC is a promising alternative for patients with MTLE‐HS. Plain Language Summary This study explored the potential of stereoelectroencephalography‐guided three‐dimensional radiofrequency thermocoagulation, a minimally invasive approach, for treating medial temporal lobe epilepsy with hippocampal sclerosis. Involving 28 patients, the research tracked the treatment’s success over five years using the Engel classification. Initial results were promising, with 72.41% of patients achieving the most favorable outcome (Engel I) at one year. While there was a gradual decrease in this proportion over time, 42.86% of patients maintained this positive outcome at five years, highlighting the treatment's potential for long‐term efficacy.

After normal pollination and fertilization of pseudoparthenocarpic seedless grapes, their embryos often stop developing due to certain developmental factors, resulting in embryo abortion. Hybrid breeding using seedless grapes as the maternal parent requires embryo rescue breeding technology. This technology plays a crucial role in seedless grape breeding. Although previous studies have extensively explored this technology, knowledge regarding its impact on embryo abortion and the effectiveness of rescue techniques remains limited. This study aimed to investigate the correlation between embryo rescue and hormonal changes in seedless grapes. Four Eurasian seedless grape cultivars, “Thompson Seedless,” “Flame Seedless,” “Heshi Seedless,” and “Ruby Seedless,” were selected for examination. We investigated endogenous hormone levels, including indole-3-acetic acid (IAA), gibberellic acid (GA3), and abscisic acid (ABA), in both berries and in vitro ovules during the most suitable embryo rescue time for these cultivars. Based on the observed fluctuations in endogenous hormone levels and previous research findings, appropriate doses of exogenous hormones, such as IAA, GA3, and ABA, were applied during seedless grape embryo rescue. The results indicated significant differences in endogenous hormone levels between berries with varying ovule counts of the same cultivar and ovules cultured in vitro, suggesting a hormonal influence on ovule abortion and embryo development in seedless grapes. Further research has identified effective ratios of exogenous hormones: 30 mg·L–1 IAA + 30 mg·L–1 ABA for berry ovule development, 1.0 mg·L–1 IAA + 2.0 mg·L–1 6-benzylaminopurine (6-BA) + 1.0 mg·L–1 GA3 + 1.0 mg·L-1 ABA for in vitro ovule development, and 1.0 mg·L–1 IAA + 2.0 mg·L–1 6-BA + 1.0 mg·L–1 GA3 for embryo germination and seedling formation. In summary, hormonal changes significantly influence ovule and embryo development and are closely related to seedless grape embryo rescue breeding. This study deepened our understanding of the correlation between seedless grape embryo rescue and hormonal changes. It also resulted in the successful production of a batch of embryo rescue seedlings, further improving embryo rescue breeding technology and providing new germplasm materials for developing new seedless grape cultivars.

Abstract As the population ages, the worldwide prevalence of Alzheimer's disease (AD) as the most common dementia in the elderly is increasing dramatically. However, a long‐term challenge is to achieve rapid and accurate early diagnosis of AD by detecting hallmarks such as amyloid beta (Aβ42). Here, a multi‐channel microfluidic‐based plasmonic fiber‐optic biosensing platform is established for simultaneous detection and differentiation of multiple AD biomarkers. The platform is based on a gold‐coated, highly‐tilted fiber Bragg grating (TFBG) and a custom‐developed microfluidics. TFBG excites a high‐density, narrow‐cladding‐mode spectral comb that overlaps with the broad absorption of surface plasmons for high‐precision interrogation, enabling ultrasensitive monitoring of analytes. In situ detection and in‐parallel discrimination of different forms of Aβ42 in cerebrospinal fluid (CSF) are successfully demonstrated with a detection of limit in the range of ≈30–170 pg mL−1, which is one order of magnitude below the clinical cut‐off level in AD onset, providing high detection sensitivity for early diagnosis of AD. The integration of the TFBG sensor with multi‐channel microfluidics enables simultaneous detection of multiple biomarkers using sub‐µL sample volumes, as well as combining initial binding rate and real‐time response time to differentiate between multiple biomarkers in terms of binding kinetics. With the advantages of multi‐parameter, low consumption, and highly sensitive detection, the sensor represents an urgently needed potentials for large‐scale diagnosis of diseases at early stage.

Abstract The development of soft electronics and soft fiber devices has significantly advanced flexible and wearable technology. However, they still face the risk of damage when exposed to sharp objects in real‐life applications. Taking inspiration from nature, self‐healable materials that can restore their physical properties after external damage offer a solution to this problem. Nevertheless, large‐scale production of self‐healable fibers is currently constrained. To address this limitation, this study leverages the thermal drawing technique to create elastic and stretchable self‐healable thermoplastic polyurethane (STPU) fibers, enabling cost‐effective mass production of such functional fibers. Furthermore, despite substantial research into the mechanisms of self‐healable materials, quantifying their healing speed and time poses a persistent challenge. Thus, transmission spectra are employed as a monitoring tool to observe the real‐time self‐healing process, facilitating an in‐depth investigation into the healing kinetics and efficiency. The versatility of the fabricated self‐healable fiber extends to its ability to be doped with a wide range of functional materials, including dye molecules and magnetic microparticles, which enables modular assembly to develop distributed strain sensors and soft actuators. These achievements highlight the potential applications of self‐healable fibers that seamlessly integrate with daily lives and open up new possibilities in various industries.

Abstract Lithium (Li) dendrite growth significantly deteriorates the performance and shortens the operation life of lithium metal batteries. Capturing the intricate dynamics of surface localized and rapid mass transport at the electrolyte–electrode interface of lithium metal is essential for the understanding of the dendrite growth process, and the evaluation of the solutions mitigating the dendrite growth issue. Here we demonstrate an approach based on an ultrasensitive tilted fiber Bragg grating (TFBG) sensor which is inserted close to the electrode surface in a working lithium metal battery, without disturbing its operation. Thanks to the superfine optical resonances of the TFBG, in situ and rapid monitoring of mass transport kinetics and lithium dendrite growth at the nanoscale interface of lithium anodes have been achieved. Reliable correlations between the performance of different natural/artificial solid electrolyte interphases (SEIs) and the time-resolved optical responses have been observed and quantified, enabling us to link the nanoscale ion and SEI behavior with the macroscopic battery performance. This new operando tool will provide additional capabilities for parametrization of the batteries’ electrochemistry and help identify the optimal interphases of lithium metal batteries to enhance battery performance and its safety.

This study was envisaged to investigate the physiological reasons affecting the embryo development and abortion of seedless grapes on the basis of the previous embryo rescue breeding techniques of seedless grapes. Specifically, the relationship between the embryo rescue breeding of seedless grapes and the change of polyamine content was evaluated, in order to provide hybrid germplasm in the breeding of new seedless grape cultivars. Four ovules of 4 naturally pollinated Eurasian seedless grape cultivars, including ‘Thompson Seedless’ grape (hereinafter referred to as ‘Seedless White’ grape), ‘Flame Seedless’ grape, ‘Heshi Seedless’ grape and ‘Ruby Seedless’ grape were employed for the study. Changes in the endogenous polyamine content, exogenous polyamine content, and the suitable combination of exogenous polyamines in the seedless grape berries and isolated ovules were determined during the best embryo rescue period. Furthermore, the effect of different exogenous polyamine contents on the germination and seedling rate of different seedless grape embryos was analyzed. In the best embryo rescue period, the number of ovules had different effects on the content of polyamines. For seedless grape cultivars with 4 ovules, a high content of polyamines was found to be more beneficial in the embryonic development. The existence of embryos had different effects on the development of embryos. In the ovules with embryo, an increase in the content of polyamine was beneficial to the growth and development of the ovule. Different ratios of exogenous polyamines had varying effects on the embryonic development. Putrescine (Put) exhibited the greatest effect on the embryonic development. Further, correlation analysis showed that different combinations of exogenous polyamines had varying effects on the embryonic development. A maximal ovule development was observed in the combination of exogenous polyamines of putrescine2+spermidine2+spermine1. For maximal embryo germination and seeding formation, the optimal combination was putrescine2+spermidine2+spermine2. Irrespective to the number of ovules or the existence of embryos, the results indicated that a high content of endogenous polyamines promoted the growth and development of embryos. The embryo rescue efficiency of different exogenous polyamines was different, and the appropriate combination of exogenous polyamines was beneficial to the growth and development of ovules, with a high development rate of the ovule and seedling.

Abstract Owing to the complexity and variability of global climate, the study of extreme events to ensure food security is particularly critical. The standardized precipitation requirement index (SPRI) and chilling injury index (ICi) were introduced using data from agrometeorological stations on the Songliao Plain between 1981 and 2020 to identify the spatial and temporal variability of drought, waterlogging, and low-temperature cold damage during various maize growth periods. Compound drought and low-temperature cold damage events (CDLEs) and compound waterlogging and low-temperature cold damage events (CWLEs) were then identified. To measure the intensity of compound events, the compound drought and low-temperature cold damage magnitude index (CDLMI), and compound waterlogging and low-temperature cold damage magnitude index (CWLMI) were constructed by fitting marginal distributions. Finally, the effects of extreme events of various intensities on maize output were examined. The findings demonstrate that: (1) There were significant differences in the temporal trends of the SPRI and ICi during different maize growth periods. Drought predominated in the middle growth period (MP), waterlogging predominated in the early growth period (EP) and late growth period (LP), and both drought and waterlogging tended to increase in intensity and frequency. The frequency of low-temperature cold damage showed a decreasing trend in all periods. (2) The CDLMI and CWLMI can effectively determine the intensity of CDLEs and CWLEs in the study area; these CDLEs and CWLEs had higher intensity and frequency in the late growth period. (3) Compared to single events, maize relative meteorological yield had a more significant negative correlation with the CDLMI and CWLMI.

Abstract Background Lipoprotein lipase (LPL) is the key enzyme responsible for the hydrolysis of triglycerides. Loss-of-function variants in the LPL gene are associated with hypertriglyceridemia (HTG) and HTG-related diseases. Unlike nonsense, frameshift and canonical GT-AG splice site variants, a pathogenic role for clinically identified LPL missense variants should generally be confirmed by functional analysis. Herein, we describe the clinical and functional analysis of a rare LPL missense variant. Methods Chinese patients with HTG-associated acute pancreatitis (HTG-AP) were screened for rare nonsense, frameshift, missense or canonical GT-AG splice site variants in LPL and four other lipid metabolism-related genes (APOC2, APOA5, GPIHBP1 and LMF1) by Sanger sequencing. The functional consequences of the LPL missense variant of interest were characterized by in vitro expression in HEK-293T and COS-7 cells followed by Western blot and LPL activity assays. Results Five unrelated HTG-AP patients were found to be heterozygous for a rare East Asian-specific LPL missense variant, c.862G > A (p.Ala288Thr). All five patients were adult males, and all were overweight and had a long history of alcohol consumption. Transfection of LPL wild-type and c.862G > A expression vectors into two cell lines followed by Western blot analysis served to exclude the possibility that the p.Ala288Thr missense variant either impaired protein synthesis or increased protein degradation. Contrary to a previous functional study that claimed that p.Ala288Thr had a severe impact on LPL function (reportedly having 36% normal activity), our experiments consistently demonstrated that the variant had a comparatively mild effect on LPL functional activity, which was mediated through its impact upon LPL protein secretion (~ 20% reduced secretion compared to wild-type). Conclusions In this study, we identified the East Asian-specific LPL c.862G > A (p.Ala288Thr) missense variant in five unrelated HTG-AP patients. We demonstrated that this variant exerted only a relatively mild effect on LPL function in two cell lines. Heterozygosity for this LPL variant may have combined with alcohol consumption to trigger HTG-AP in these patients.

Optical fiber technology has changed the world by enabling extraordinary growth in world-wide communications and sensing. The rapid development and wide deployment of optical fiber sensors are driven by their excellent sensing performance with outstanding flexibility, functionality, and versatility. Notably, the research on specialty optical fibers is playing a critical role in enabling and proliferating the optical fiber sensing applications. This paper overviews recent developments in specialty optical fibers and their sensing applications. The specialty optical fibers are reviewed based on their innovations in special structures, special materials, and technologies to realize lab in/on a fiber. An overview of sensing applications in various fields is presented. The prospects and emerging research areas of specialty optical fibers are also discussed.

Hazard assessment is the basic work in the field of disaster risk. For the agricultural field which is seriously affected by extreme events, it is necessary to carry out hazard assessment of compound extreme events (CEEs) to increase the accuracy of risk assessment. First, in this study, the SMDI and TCI were used to characterize the spatial and temporal distribution patterns of agricultural drought and heat, respectively, and the drought and heat compound extreme events (DHCEEs) were identified by characterizing the drought and heat events based on runs theory. Then, the compound event magnitude index (CEMI) was constructed by fitting the non-transcendent probabilities. next, the optimal copula function of severity and duration of DHCEEs was screened, and the fitted probabilities were used in the hazard assessment. The hazard assessment model of DHCEEs is constructed by considering the occurrence probability, severity, and duration. We evaluated the hazard of all DHCEEs identified in the Songliao Plain from 1990 to 2020. The results show that the whole study area is affected by agricultural drought, and heat mainly affects the western region. CEMI can effectively determine the severity of DHCEEs. In the study area, 46.51% of the DHCEEs have a hazard less than 0.1, and 2.71% have a hazard greater than 0.5. The event that had the biggest hazard occurred in 2001, and the proportion of high hazard compound events gradually increased after 2000. Similar compound extreme events are gaining attention in important food-producing regions around the world, and the hazard assessment model constructed in this study can be used to assess crop damage from compound extreme events in real-time, increasing the relevance and accuracy of risk assessment.

Abstract Background Infected pancreatic necrosis (IPN) is a life-threatening complication of acute pancreatitis (AP). Timely diagnosis of IPN could facilitate appropriate treatment, but there is a lack of reliable non-invasive screening tests. In this study, we aimed to evaluate the diagnostic value of plasma metagenomic next-generation sequencing (mNGS) based on circulating microbial cell-free DNA in patients with suspected IPN. Methods From October 2020 to October 2021, 44 suspected IPN patients who underwent plasma mNGS were reviewed. Confirmatory diagnosis of IPN within two weeks after the index blood sampling was considered the reference standard. The confirmation of IPN relied on the microbiological results of drains obtained from the necrotic collections. The distribution of the pathogens identified by plasma mNGS was analyzed. Positive percent agreement (PPA) and negative percent agreement (NPA) were evaluated based on the conformity between the overall mNGS results and culture results of IPN drains. In addition, the clinical outcomes were compared between mNGS positive and negative patients. Results Across all the study samples, thirteen species of bacteria and five species of fungi were detected by mNGS. The positivity rate of plasma mNGS was 54.55% (24/44). Of the 24 mNGS positive cases, twenty (83.33%, 95% CI, 68.42–98.24%) were consistent with the culture results of IPN drains. The PPA and NPA of plasma mNGS for IPN were 80.0% (20/25; 95% CI, 64.32–95.68%) and 89.47% (17/19; 95% CI, 75.67–100%), respectively. Compared with the mNGS negative group, patients in the positive group had more new-onset septic shock [12 (50.0%) vs. 4 (20.0%), p = 0.039]. Conclusion IPN relevant pathogens can be identified by plasma mNGS, potentially facilitating appropriate treatment. The clinical application of mNGS in this cohort appears feasible.

Abstract The climate changes influence the growing suitability of peanut, an important oil crop. Climatic suitability evaluation in the Huang-Huai-Hai region, the main peanut producing region of China, which can optimize peanut planting structure and provide basis for increasing output. In this study, the temperature, precipitation, sunshine and comprehensive suitability models were established by using the climatic suitability function in different growth periods of peanut. In this study, the climate suitability function of peanut in different growth periods was used to establish the temperature, precipitation, sunshine and comprehensive suitability model. Combined with the meteorological data after Anusplin interpolation, the spatial distribution and chronological change of peanut climate suitability were analyzed. The results show that with climate change, the overall climate becomes warmer and drier and the temperature and precipitation suitability increase, but the sunshine suitability decreases. Based on the comprehensive suitability model, the suitability evaluation results are divided into four levels: the most suitable, suitable, sub-suitable and unsuitable. Among them, the most suitable peanut planting areas in the Huang-Huai-Hai region are concentrated in the west of the Haihe River Basin and the Huaihe River Basin. The data from the next 30 years show that both the most suitable and suitable areas have been expanded. Through the verification of yield correlation analysis and spatial distribution of disaster frequency, it can be seen that the evaluation results have high accuracy, which can be used to guide and optimize peanut production practices.

Operando monitoring of highly localized and rapid temperature changes during photo-involved catalysis reactions at liquid-solid interfaces via narrow bandwidth fiber-optic spectral combs.

Operando tracking the ion dynamics/states of battery is critical to understanding of electrolyte-electrode interactions. Here the authors propose to use the surface plasmon waves to rapidly screen localized electrochemical events on a sub-μm-scale thickness adjacent to the electrode interface, without perturbing battery operation.

An optical fiber biosensor displaying superfine plasmonic spectral combs and enhanced by conjugate-induced amplification enables the detection of environmental estrogens down to pg/mL estradiol equivalent concentration level.

Alzheimer's disease (AD) is one of the most common neurodegenerative illnesses displaying the highest death rate in the elderly. However, the existing AD diagnostic system remains elusive due to lack of a technology that may ensure enough sensitivity and reproducibility, detection accuracy, and specificity. Herein, a straightforward approach is reported to realize lab‐on‐fiber (LoF) technology for AD biomarker detection based on a D‐shaped single‐mode fiber combined with nanometer‐scale metal‐oxide film. The proposed sensing system, which permits the generation of lossy‐mode resonance (LMR), remarkably increases the evanescent field of light guided through the fiber, and hence the fiber‐surrounding medium interaction. Moreover, such optical sensors are highly repeatable in results and can safely be embedded into a compact and stable microfluidic system. Herein, the specific detection of Tau protein (as one of the classical AD biomarkers that is highly correlated with AD progression) in a complex biofluid with a detection limit of 10−12 m and over a wide concentration range (10−3–10 μg mL−1) is successfully demonstrated. The proposed LoF biosensor is an appealing solution for rapid, sub‐microliter dose and highly sensitive detection of analytes at low concentrations, hereby having the potential toward early screening and personalized medicine in AD.

Due to the improvement of living standards and the adjustment of dietary structure, the cereal industry shows excellent growth prospects. Clarifying millet drought risk distribution and determining its dynamic evolution characteristics are therefore of great importance for guiding millet production, preventing drought risks, and ensuring food security. In this study, SPRI was used to identify millet drought, and drought intensity-yield loss rate curves were constructed to characterize millet drought sensitivity. The potential exposure of millet was explored by considering millet climatic suitability and farmland proportion. In light of this, a comprehensive assessment of millet drought risk in different growth periods was made using natural disaster risk theory. The results showed that: (1) The sensitivity model for each growth period had passed the F-test of α = 0.05 (R2 were 0.544, 0.408 and 0.347 respectively), reaching a significant level, which proved that the model can be used to assess millet drought sensitivity. (2) More than 70 % of Liaoning Province had a suitable climatic environment for millet production. Combined with the farmland proportion, the area with high potential exposure was distributed in the central region of Liaoning. (3) The millet drought risk in different growth periods showed a characteristic distribution of high in the northwest and low in the southeast. Among them, Chaoyang had the largest high and extremely high drought risk area in the three growth periods, accounting for 92.11 %, 99.34 % and 98.86 % respectively. At the same time, the center-of-gravity model shows that the barycenter of millet drought risk has a trend of spreading from the west of Liaoning to the northeast and southwest. To sum up, the millet drought risk area in Liaoning may increase in the future, measures should be taken to prevent and mitigate millet drought from three aspects: before, during and after the disaster.

Abstract As the fundamental building block of optical fiber communication technology, thermally drawn optical fibers have fueled the development and prosperity of modern information society. However, the conventional step‐index configured silica optical fibers have scarcely altered since their invention. In recent years, thermally drawn multifunctional fibers have emerged as a new yet promising route to enable unprecedented development in information technology. By adopting the well‐developed preform‐to‐fiber manufacturing technique, a broad range of functional materials can be seamlessly integrated into a single fiber on a kilometer length scale to deliver sophisticated functions. Functions such as photodetection, imaging, acoustoelectric detection, chemical sensing, tactile sensing, biological probing, energy harvesting and storage, data storage, program operation, and information processing on fiber devices. In addition to the original light‐guiding function, these flexible fibers can be woven into fabrics to achieve large‐scale personal health monitoring and interpersonal communication. Thermally drawn multifunctional fibers have opened up a new stage for the next generation of information technology. This review article summarizes an overview of the basic concepts, fabrication processes, and developments of multifunctional fibers. It also highlights the significant progress and future development in information applications.

Accurate measurement of the junction temperature Tj is crucial for ensuring safe operation and evaluating the performance of silicon carbide (SiC) metal–oxide–semiconductor field-effect transistors (MOSFETs). In this paper, the junction temperature of a half-bridge is monitored by combining transient voltage and current peaks across the lower device. Both theoretical analysis and switch tests validate that the peak induced voltage and peak reversed current during the commutation transient exhibit a linear relationship with the temperature of the corresponding devices. The impact of load current and bus voltage on the measurement results is also investigated. Moreover, the effectiveness of the proposed method is confirmed by carrying out tests at different temperatures, and the feasibility of online implementation is discussed. The experimental results show that the proposed method has high linearity and sensitivity and can simultaneously provide temperature information for both devices of a half-bridge.

Early retention or initial threshold voltage shift (IVS) is one of the key reliability challenges in charge trapping memory (CTM) based 3D NAND flash. Re-program scheme was introduced in quad-level-cell (QLC) NAND (Shibata et al., 2007, Lee et al., 2018, Shibata et al., 2019, and Khakifirooz et al., 2021), and the IVS improvement by re-program scheme was reported. In this work, it is found that re-program can suppress ~81% of IVS in 3D NAND, which is much more significant than that of 2D NAND ~50% (Chen et al., 2010). The mechanisms of IVS improvement by re-program scheme in 3D NAND are investigated. Both vertical de-trapping in the BE-tunneling oxide and charge lateral migration (LM) in the charge-trap layer are suppressed in re-program. Re-program is effective in vertical de-trapping suppression both in checker-board pattern (C/P) and solid-board pattern (S/P) cases, and is effective in LM suppression only in C/P case. Furthermore, the LM improvement by re-program scheme is more pronounced with gate length (Lg) and inter-gate space (Ls) scaling down, showing potential in the reliability improvement of advanced 3D NAND technologies.

As the 3D NAND technology developing toward more and more stack layers, it is essential to shrink the gate length (Lg) and inter-gate space (Ls). However, one of key concerns of scaling Lg/Ls 3D NAND flash is post-cycling data retention characteristics. The impact of cycling induced intercell trapped charge on two primary charge loss mechanisms (vertical and lateral charge loss) was studied in this work. According to experimental analysis and TCAD simulation, it is found that, in vertically scaled 3D NAND, the vertical charge loss is deteriorated not only by the cycling induced tunnel oxide degradation (introducing interface/oxide traps), but also by the cycling induced intercell trapped charge (enhancing word-lines edge electric field), on account of the enhanced Poole-Frenkel effect and tunneling effect. On the other hand, the cycling induced intercell trapped charge can also suppress lateral charge migration. Therefore, the vertical charge loss, rather than the lateral charge migration, still can be the dominant factor for post-cycling retention characteristics in scaling Lg/Ls 3D NAND flash memory.

As the climate warms, the impact of drought on plants has increased. We aimed to construct a comprehensive drought index (CDI), coupling soil-vegetation-atmosphere drought and heat conditions based on multi-source information, and to combine it with static and dynamic drought hazard evaluation models to analyze the spatial and temporal distribution characteristics of agricultural drought disasters and hazards during the growing season (May to September) in Northeast China (NEC). The results demonstrated that the CDI could combine the benefits of meteorology (standardized precipitation evapotranspiration index, SPEI), vegetation (vegetation health index, VHI), and soil (standardized soil moisture condition index, SMCI) indices. This was performed using a relative weighting method based on the remote sensing data of solar-induced chlorophyll fluorescence (SIF) to determine the weights of SPEI, VHI, and SMCI. The CDI for drought monitoring has the advantages of broad spatial range, long time range, and high accuracy, and can effectively reflect agricultural drought; the growing season in NEC showed a trend of becoming drier during 1982–2020. However, the trends of the drought index, the impact range of drought events, and the hazard of agricultural drought all turned around 2000. The drought hazard was highly significant (p < 0.001) and decreased from 2000 to 2020. The frequency of drought disasters was the highest, and the hazard was the greatest in May. The best level of climatic yield anomalies in maize were explained by drought hazard in August (R2 = 0.28). In the center and western portions of the study area, farmland and grassland areas were where higher levels of hazard were most commonly seen. The dynamic hazard index is significantly correlated with climatic yield anomalies and can reflect the actual impact of drought on crop yield. The study results serve as a scientific foundation for drought risk assessment and management, agricultural planning, and the formulation of drought adaptation policies, as well as for ensuring food security in China.

Guangxi is the primary producer of sugarcane in China and provides a highly suitable habitat for sugarcane growth. However, its distribution range has changed significantly in recent years due to climate change as well as human factors. Without extensive knowledge of the changing trends in suitable sugarcane planting areas, efforts to improve its productivity in Guangxi may be insufficient. In this study, the interdecadal change in sugarcane distribution in Guangxi in response to climate change from 1960 to 2019 was estimated using the MaxEnt model and the landscape pattern of land use in the suitable sugarcane area was analyzed. In addition, we discuss the effects of global warming on sugarcane production in the sustainable development of the sugar industry in Guangxi. Our results indicate: (1) from 1960 to 2019, approximately 65% of Guangxi Province could grow sugarcane. Chongzuo City, Nanning City and Parts of Baise City, are highly suitable areas, and unsuitable areas are mainly concentrated in the north. In general, sugarcane climate suitability extended further in low-altitude areas, and then extended to high- altitude areas. However, from the 2000s to the 2010s, climate suitability showed a decreasing trend, decreasing from 16.036 × 106 ha to 15.4985 × 106 ha (2) The order of land use area in the suitable sugarcane climate range was as follows: woodland > cropland > grassland > construction land > water. With the increase in climate suitability, the distribution of cultivated land expanded. From 1980 to 2005, cropland in suitable areas showed a fragmentation trend. By 2010, the cropland patches disappeared after fragmentation. (3) Due to landscape constraints, infertile soil, and labor costs, the sugar industry faces various challenges. The evaluation of climate suitability could provide a theoretical reference for a planting layout of sugarcane, and landscape pattern analysis of suitable sugarcane climate areas is conducive to the integration of small pieces of land into large ones, making mechanization possible. Overall, strict layout and management measures are required in sugarcane planting areas.

Agricultural drought is a major problem facing China’s agricultural production. In this study, the cash crop ‘peanut’ was used as an example to explore vulnerability. Through the atmosphere–plant–soil continuum system, a single index that could represent different types of droughts affecting peanuts was selected and weighted using the CRITIC weighting method to construct a multi-source data fusion drought index (MFDI). Then, Pearson correlation analysis between the comprehensive drought index and relative meteorological yield and the Mann–Kendall trend test for different growth periods of peanuts were used to verify MFDI and analyze the variation over time. A three-dimensional vulnerability assessment method of drought intensity–drought duration–yield reduction rate was established based on the run theory and trend surface analysis. The results show that the constructed multi-source data fusion drought index (MFDI) can more accurately characterize the actual drought conditions of peanuts in Shandong Province. The MFDI results showed that the drought severity in the coastal areas of the study area decreased with the growth and development of peanuts, while the drought became more severe in the western and northern parts during the late growth period of peanuts. The vulnerability surface of the drought intensity–drought duration–yield reduction rate showed that when the drought intensity was < 0.8 and the duration was < 3.5 months, the vulnerability of peanut crops was low, and then with the increase in drought intensity or duration, the vulnerability increased. The impact of drought duration cannot be ignored. In contrast to traditional vulnerability assessment methods, this study established a three-dimensional vulnerability surface, which provides a new approach for agricultural drought vulnerability assessment. The research results are helpful for a deeper understanding of the relationship between drought and crop vulnerability and provide scientific support for local governments in formulating disaster prevention and mitigation policies.

Using unmanned aerial vehicle (UAV) hyperspectral images to accurately estimate the chlorophyll content of summer maize is of great significance for crop growth monitoring, fertilizer management, and the development of precision agriculture. Hyperspectral imaging data, analytical spectral devices (ASD) data, and SPAD values of summer maize in different key growth periods were obtained under the conditions of a micro-spray strip drip irrigation water supply. The hyperspectral data were preprocessed by spectral transformation methods. Then, several algorithms including Findpeaks (FD), competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and CARS_SPA were used to extract the sensitive characteristic bands related to SPAD values from the hyperspectral image data obtained by UAV. Subsequently, four machine learning regression models including partial least squares regression (PLSR), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN) were used to establish SPAD value estimation models. The results showed that the correlation coefficient between the ASD and UAV hyperspectral data was greater than 0.96 indicating that UAV hyperspectral image data could be used to estimate maize growth information. The characteristic bands selected by different algorithms were slightly different. The CARS_SPA algorithm could effectively extract sensitive hyperspectral characteristics. This algorithm not only greatly reduced the number of hyperspectral characteristics but also improved the multiple collinearity problem. The low frequency information of SSR in spectral transformation could significantly improve the spectral estimation ability for SPAD values of summer maize. In the accuracy verification of PLSR, RF, XGBoost, and the DNN inversion model based on SSR and CARS_SPA, the determination coefficients (R2) were 0.81, 0.42, 0.65, and 0.82, respectively. The inversion accuracy based on the DNN model was better than the other models. Compared with high-frequency information, low-frequency information (DNN model based on SSR and CARS_SPA) had a strong estimating ability for SPAD values in summer maize canopy. This study provides a reference and technical support for the rapid non-destructive testing of summer maize.

As real-world graphs are often evolving over time, interest in analyzing the temporal behavior of graphs has grown. Herein, we propose Auxo, a novel temporal graph management system to support temporal graph analysis. It supports both efficient global and local queries with low space overhead. Auxo organizes temporal graph data in spatio-temporal chunks. A chunk spans a particular time interval and covers a set of vertices in a graph. We propose chunk layout and chunk splitting designs to achieve the desired efficiency and the abovementioned goals. First, by carefully choosing the time split policy, Auxo achieves linear complexity in both space usage and query time. Second, graph splitting further improves the worst-case query time, and reduces the performance variance introduced by splitting operations. Third, Auxo optimizes the data layout inside chunks, thereby significantly improving the performance of traverse-based graph queries. Experimental evaluation showed that Auxo achieved 2.9× to 12.1× improvement for global queries, and 1.7× to 2.7× improvement for local queries, as compared with state-of-the-art open-source solutions.

Herein, a broadband ultrasensitive acoustic sensor based on an optical nanofiber coupler (ONC) attached to a diaphragm is designed and experimentally demonstrated. The ONC is sensitive to axial strain and works as the core transducing element to monitor the deformation of the diaphragm driven by acoustic waves. We first theoretically studied the sensing property of the ONC to axial strain and the deformation of the diaphragm. The results reveal that ONC working at the dispersion turning point (DTP) shows improved ultra-sensitivity towards axial strain, and the largest deformation of the circular diaphragm occurs at the center. Guided by the theoretical results, we fabricated an ONC with a DPT at 1550 nm, and we fixed one end of the ONC to the center of the diaphragm and the other end to the edge to construct the acoustic sensor. Finally, the experimental results show that the sensor can achieve accurate measurement in the broadband acoustic wave range of 30~20,000 Hz with good linearity. Specifically, when the input acoustic wave frequency is 120 Hz, the sensitivity reaches 1923 mV/Pa, the signal-to-noise ratio is 42.45 dB, and the minimum detectable sound pressure is 330 μPa/Hz1/2. The sensor has the merits of simple structure, low cost, and high performance, and it provides a new method for acoustic wave detection.

Thermally drawn multimaterial fibers have experienced rapid development in the past two decades owing to the high scalability, uniformity, and material and structure compatibility of the thermal drawing technique. This article reviews various multimaterial fibers based on different functional structures and their applications in disparate fields. We start from the functional structures achieved in optical fibers developed in the early stage of thermally drawn fibers. Subsequently, we introduce both typical functional structures and unique structures created in multimaterial fibers for varying applications. Next, we present the early attempts in breaking the axial symmetric structures of thermally drawn fibers for extended functionalities. Additionally, we summarize the current progress on creating surface structures on thermally drawn fibers. Finally, we provide an outlook for this trending topic towards wearable devices and smart textiles.

Drought is one of the key limiting factors for potato yield in the northern single cropping area (NSCA) in China. To analyze the impact of drought on potato yield in the NSCA, this study first analyzed the variation of dry/wet conditions in the plantable areas on a seasonal scale using the standardized precipitation evapotranspiration index (SPEI). Secondly, the changes in yield structure in the last 36 years were systematically analyzed and divided the total yield change into planting area contribution and climate yield contribution. Finally, a regression model of the seasonal drought index and contributing factors of total yield change in different administrative regions was constructed. The results showed that the main factors affecting the total potato yield of the NSCA began to change from yield to planting area in the 1990s, while the barycenter of the output structure and population moved to the southwest, with grassland being the main source; dry/wet conditions (year i) had varying degrees of effect on contributing factors (year i, year i + 1) of total yield change in different administrative regions that were not limited to the growing season; the non-overlap of high-yield area, high-adaptability area and planting area was the urgent problem to be solved for the NSCA. The results of this study can provide a scientific basis for NSCA crop management and communication with farmers, providing new ideas for sustainable production in other agricultural regions in the world.

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions.

Under the background of global warming, the frequent occurrence and long-term persistence of drought events have substantial negative effects on agricultural production. As the main maize production area in midwestern Jilin Province, frequent drought and a shortage of irrigation water pose substantial threats to the production of maize. We analyzed the balance of water supply and demand in each growth period and the degree of maize yield affected by drought. The results indicate that the FIO-ESM climate model can effectively simulate the changes in temperature and precipitation, and was highly applicable to the study area. From 1980 to 2020, the drought risk indices for the sowing to jointing, jointing to tasseling, tasseling to milk-ripe, and milk-ripe to maturity stages were 0.62, 0.52, 0.48, and 0.60, respectively. In the future, the chances of a RCP8.5 scenario drought risk and an enhanced RCP4.5 scenario have eased. Spatially, the high-risk areas shift in a “west−central−southwest” pattern. Effective precipitation will decrease in the future, while the increasing water requirement of maize increases the dependence on irrigation water. The irrigation requirement index is more than 70% for all periods, particularly in the milk-ripe to maturity stage. The relative meteorological yields were positively correlated with the CWDI of the whole growth period, with the rate of reduction in maize yield and the yield reduction coefficient of variation at a high level of risk between 1980 and 2020. In the future, the negative impact of drought risk on the yield of maize lessened with no obvious trend in production. In particular, the rate of reduction and reduction coefficient of variation for the RCP8.5 scenario were 1.24 and 1.09, respectively.

In this study, Yulin city and Yan’an city in northern Shaanxi Province were taken as the study area. Based on the diurnal dry–wet events abrupt alternation index DWAAI, the joint probability distribution of two characteristic variables of “urgency” and “alternation” of dry–wet events abrupt alternation was established by using copula function, and the characteristics of dry–wet events abrupt alternation were analyzed. DWAAI was calculated from daily precipitation data and the applicability of the index was verified. On this basis, the two characteristic variables of “urgency” and “alternation” were separated, and the appropriate marginal distribution function was selected to fit them, and the correlation between the two variables was evaluated. Finally, the appropriate copula function was selected to fit the bivariate of each station, and the joint cumulative probability and recurrence period of the two variables were calculated. The results show that the DWAAI index is suitable for the identification of dry–wet events abrupt alternation in the study area. Light and moderate dry–wet events abrupt alternation occurs more frequently, while severe events rarely occur in the study area. The frequency of severe dry–wet events abrupt alternation in Jingbian station and its northern area is greater than that in the southern area, and the risk of dry–wet events abrupt alternation of disasters in the northern area is higher. The greater the degree of “urgency” and “alternation”, the greater the joint cumulative probability and the greater the return period. The return period of severe dry–wet events abrupt alternation was more than five years, while the return period of light and moderate dry–wet events abrupt alternation was less than five years.

Tea trees are the main economic crop in Zhejiang Province. However, spring cold is a frequent occurrence there, causing frost damage to the valuable tea buds. To address this, a regional frost-hazard early-warning system is needed. In this study, frost damage area was estimated based on topography and meteorology, as well as longitude and latitude. Based on support vector machine (SVM) and artificial neural networks (ANNs), a multi-class classification model was proposed to estimate occurrence of regional frost disasters using tea frost cases from 2017. Results of the two models were compared, and optimal parameters were adjusted through multiple iterations. The highest accuracies of the two models were 83.8% and 75%, average accuracies were 79.3% and 71.3%, and Kappa coefficients were 79.1% and 67.37%. The SVM model was selected to establish spatial distribution of spring frost damage to tea trees in Zhejiang Province in 2016. Pearson’s correlation coefficient between prediction results and meteorological yield was 0.79 (p < 0.01), indicating consistency. Finally, the importance of model factors was assessed using sensitivity analysis. Results show that relative humidity and wind speed are key factors influencing accuracy of predictions. This study supports decision-making for hazard prediction and defense for tea trees facing frost.

Precipitation is low and drought occurs frequently in Northern Shaanxi. To study the characteristics and occurrence and development of drought events in Northern Shaanxi is beneficial to the prevention and control of drought disasters. Based on the monthly rainfall data of 10 meteorological stations in Northern Shaanxi from 1960 to 2019, the characteristic variables of drought events at each meteorological station in Northern Shaanxi were extracted by using run theory and copula function. The joint probability distribution and recurrence period were obtained by combining the duration and intensity of drought, and the relationship between drought characteristics and crop drought affected area was studied. The results show that (1) from 1960 to 2019, drought events mainly occurred in Northern Shaanxi with long duration and low severity, short duration and high severity, or short duration and low severity, among which the frequency of drought events that occurred in Yuyang and Baota districts was higher. The frequency of light drought and extreme drought was more in the south and less in the north, while the frequency of moderate drought and severe drought was more in the north and less in the south. (2) The optimal edge distribution of drought intensity and drought duration in Northern Shaanxi is generalized Pareto distribution, and the optimal fitting function is Frank copula function. The greater the duration and intensity of drought, the greater the cumulative probability and return period. (3) The actual recurrence interval and the theoretical recurrence interval of drought events in Northern Shaanxi were close, and the error was only 0.1–0.3a. The results of the joint return period can accurately reflect the actual situation, and this study can provide effective guidance for the prevention and management of agricultural dryland in Northern Shaanxi.

Plant phenology depends largely on temperature, but temperature alone cannot explain the Northern Hemisphere shifts in the start of the growing season (SOS). The spatio–temporal distribution of SOS sensitivity to climate variability has also changed in recent years. We applied the partial least squares regression (PLSR) method to construct a standardized SOS sensitivity evaluation index and analyzed the combined effects of air temperature (Tem), water balance (Wbi), radiation (Srad), and previous year’s phenology on SOS. The spatial and temporal distributions of SOS sensitivity to Northern Hemisphere climate change from 1982 to 2014 were analyzed using time windows of 33 and 15 years; the dominant biological and environmental drivers were also assessed. The results showed that the combined sensitivity of SOS to climate change (SCom) is most influenced by preseason temperature sensitivity. However, because of the asymmetric response of SOS to daytime/night temperature (Tmax/Tmin) and non-negligible moderating of Wbi and Srad on SOS, SCom was more effective in expressing the effect of climate change on SOS than any single climatic factor. Vegetation cover (or type) was the dominant factor influencing the spatial pattern of SOS sensitivity, followed by spring temperature (Tmin > Tmax), and the weakest was water balance. Forests had the highest SCom absolute values. A significant decrease in the sensitivity of some vegetation (22.2%) led to a decreasing trend in sensitivity in the Northern Hemisphere. Although temperature remains the main climatic factor driving temporal changes in SCom, the temperature effects were asymmetric between spring and winter (Tems/Temw). More moisture might mitigate the asymmetric response of SCom to spring/winter warming. Vegetation adaptation has a greater influence on the temporal variability of SOS sensitivity relative to each climatic factor (Tems, Temw, Wbi, Srad). More moisture might mitigate the asymmetric response of SCom to spring/winter warming. This study provides a basis for vegetation phenology sensitivity assessment and prediction.

Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aims to explore the performance of Bayesian optimization (BO) in the random forest (RF) and gradient boosting decision tree (GBDT) model for LSM and applied in Shuicheng County, China. Multiple data sources are used to obtain 17 conditioning factors of landslides, Borderline-SMOTE and Randomundersample methods are combined to solve the imbalanced sample problem. RF and GBDT models before and after BO are adopted to calculate the susceptibility value of landslides and produce LSMs and these models were compared and evaluated using multiple validation approach. The results demonstrated that the models we proposed all have high enough model accuracy to be applied to produce LSM, the performance of the RF is better than the GBDT model without BO, while after adopting the Bayesian optimized hyperparameters, the prediction accuracy of the RF and GBDT models is improved by 1% and 7%, respectively and the Bayesian optimized GBDT model is the best for LSM in this four models. In summary, the Bayesian optimized RF and GBDT models, especially the GBDT model we proposed for landslide susceptibility assessment and LSM construction has a very good application performance and development prospects.

Landslides are among the most frequent natural hazards in the world. Rainfall is an important triggering factor for landslides and is responsible for topples, slides, and debris flows—three of the most important types of landslides. However, several previous relevant research studies covered general landslides and neglected the rainfall–topples–slides–debris flows disaster chain. Since landslide hazard mapping (LHM) is a critical tool for disaster prevention and mitigation, this study aimed to build a GeoDetector and Bayesian network (BN) model framework for LHM in Shuicheng County, China, to address these geohazards. The GeoDetector model will be used to screen factors, eliminate redundant information, and discuss the interaction between elements, while the BN model will be used for constructing a causality disaster chain network to determine the probability and risk level of the three types of landslides. The practicability of the BN model was confirmed by error rate and scoring rules validation. The prediction accuracy results were tested using overall accuracy, Matthews correlation coefficient, relative operating characteristics curve, and seed cell area index. The proposed framework is demonstrated to be sufficiently accurate to construct the complex LHM. In summary, the combination of the GeoDetector and BN model is very promising for spatial prediction of landslides.

With the increasing demand of achieving comprehensive perception in every aspect of life, optical fibers have shown great potential in various applications due to their highly-sensitive, highly-integrated, flexible and real-time sensing capabilities. Among various sensing mechanisms, plasmonics based fiber-optic sensors provide remarkable sensitivity benefiting from their outstanding plasmon–matter interaction. Therefore, surface plasmon resonance (SPR) and localized SPR (LSPR)-based hybrid fiber-optic sensors have captured intensive research attention. Conventionally, SPR- or LSPR-based hybrid fiber-optic sensors rely on the resonant electron oscillations of thin metallic films or metallic nanoparticles functionalized on fiber surfaces. Coupled with the new advances in functional nanomaterials as well as fiber structure design and fabrication in recent years, new solutions continue to emerge to further improve the fiber-optic plasmonic sensors’ performances in terms of sensitivity, specificity and biocompatibility. For instance, 2D materials like graphene can enhance the surface plasmon intensity at the metallic film surface due to the plasmon–matter interaction. Two-dimensional (2D) morphology of transition metal oxides can be doped with abundant free electrons to facilitate intrinsic plasmonics in visible or near-infrared frequencies, realizing exceptional field confinement and high sensitivity detection of analyte molecules. Gold nanoparticles capped with macrocyclic supramolecules show excellent selectivity to target biomolecules and ultralow limits of detection. Moreover, specially designed microstructured optical fibers are able to achieve high birefringence that can suppress the output inaccuracy induced by polarization crosstalk and meanwhile deliver promising sensitivity. This review aims to reveal and explore the frontiers of such hybrid plasmonic fiber-optic platforms in various sensing applications.

Wavelength tracking methods are widely employed in fiber-optic interferometers, but they suffer from the problem of fringe order ambiguity, which limits the dynamic range within half of the free spectral range. Here, we propose a new sensing strategy utilizing the unique property of the dispersion turning point in an optical microfiber coupler mode interferometer. Numerical calculations show that the position of the dispersion turning point is sensitive to the ambient refractive index, and its position can be approximated by the dual peaks/dips that lay symmetrically on both sides. In this study, we demonstrate the potential of this sensing strategy, achieving high sensitivities of larger than 5327.3 nm/RIU (refractive index unit) in the whole refractive index (RI) range of 1.333−1.4186. This sensor also shows good performance in narrow RI ranges with high resolution and high linearity. The resolution can be improved by increasing the length of the coupler.

Based on the 1965−2017 climate data of 18 meteorological stations in the Songliao Plain maize belt, the Coupled Model Intercomparision Project (CMIP5) data, and the 1998−2017 maize yield data, the drought change characteristics in the study area were analyzed by using the standardized precipitation evapotranspiration index (SPEI) and the Mann−Kendall mutation test; furthermore, the relationship between meteorological factors, drought index, and maize climate yield was determined. Finally, the maize climate yields under 1.5 °C and 2.0 °C global warming scenarios were predicted. The results revealed that: (1) from 1965 to 2017, the study area experienced increasing temperature, decreasing precipitation, and intensifying drought trends; (2) the yield of the study area showed a downward trend from 1998 to 2017. Furthermore, the climate yield was negatively correlated with temperature, positively correlated with precipitation, and positively correlated with SPEI-1 and SPEI-3; and (3) under the 1.5 °C and the 2.0 °C global warming scenarios, the temperature and the precipitation increased in the maize growing season. Furthermore, under the studied global warming scenarios, the yield changes predicted by multiple regression were −7.7% and −15.9%, respectively, and the yield changes predicted by one-variable regression were −12.2% and −21.8%, respectively.

Examining the effects of climate change on spring maize, and its suitability under dynamic cultivation patterns, will aid strategic decision-making for future agricultural adaptation. This paper investigates the climate suitability of spring maize, based on daily data from 50 meteorological stations, and statistics on maize yield and area at the county level in Jilin Province, China, between 1986 and 2015. Based on a significant correlation between the cultivation patterns indicator ≥10 °C accumulated temperature (AAT10) and the average yield (R2 = 0.503), the yield data are used to determine suitable thresholds for meteorological factors under the dynamic cultivation pattern, and a fuzzy fitness approach is used to evaluate the climate suitability. The results showed a good agreement between suitability estimates and scaled observed yields (average d = 0.705). Moreover, good consistency between cultivation patterns, climate suitability and yield show that the late-maturing varieties of maize have gradually moved northward and eastward, and the areas of high suitability and high yield have gradually expanded eastward. In addition, drought and chilling hazard factors limit the suitability of climate resources, especially in the eastern and western regions.

Integration of functional nanomaterials with optical micro/nanofibers (OMNFs) can bring about novel optical properties and provide a versatile platform for various sensing applications. OMNFs as the key element, however, have seldom been investigated. Here, we focus on the optimization of fiber diameter by taking micro/nanofiber-based localized surface plasmon resonance sensors as a model. We systematically study the dependence of fiber diameter on the sensing performance of such sensors. Both theoretical and experimental results show that, by reducing fiber diameter, the refractive index sensitivity can be significantly increased. Then, we demonstrate the biosensing capability of the optimized sensor for streptavidin detection and achieve a detection limit of 1 pg/mL. Furthermore, the proposed theoretical model is applicable to other nanomaterials and OMNF-based sensing schemes for performance optimization.