Measurement Time Research Articles

20 µm resolution multipixel ghost imaging with high-energy x-rays

Hard x-ray imaging is indispensable across diverse fields owing to its high penetrability. However, the resolution of traditional x-ray imaging modalities, such as computed tomography (CT) systems, is constrained by factors including beam properties, the limitations of optical components, and detection resolution. As a result, the typical resolution in commercial imaging systems that provide full-field imaging is limited to a few hundred microns, and scanning CT systems are too slow for many applications. This study advances high-photon-energy imaging by extending the concept of computational ghost imaging to multipixel ghost imaging with x-rays. We demonstrate a remarkable resolution of approximately 20 µm for an image spanning 0.9 by 1 cm2, comprised of 400,000 pixels and involving only 1000 realizations. Furthermore, we present a high-resolution CT reconstruction using our method, revealing enhanced visibility and resolution. Our achievement is facilitated by an innovative x-ray lithography technique and the computed tiling of images captured by each detector pixel. Importantly, this method maintains reasonable timeframes and can be scaled up for larger images without sacrificing the short measurement time, thereby opening intriguing possibilities for noninvasive high-resolution imaging of small features that are invisible with the present modalities.

Pemanfaatan Limbah Ban Bekas untuk Sintesis Nanokomposit MnO2/C dengan Metode Hidrotermal sebagai Material Superkapasitor

Activated carbon from waste tires is used as MnO2 metal oxide doping in making MnO2/C-based nanocomposites into high-density and environmentally friendly supercapacitor electrodes. The MnO2/C nanocomposite synthesis process was carried out using the hydrothermal method by varying the mass of activated carbon by 1.25 g, 2.5 g and 3.75 g to determine the optimum results. Based on the results of research that has been carried out, it shows that MnO2/C can be used as a high density supercapacitor electrode. This is in accordance with the XRD test results which show that the MnO2 nanocomposite with the addition of C was successfully synthesized and has an orthorhombic crystalline phase. The SEM test results show that the material has almost the same morphology, namely many protrusions which make each particle have high roughness. The most optimal results were obtained from the MnO2/C-50 variation because it has the highest C element content, namely 39.93%, so it has the highest capacitance value of 5.791 f/g during the CV test. The GCD test shows that electrodes with a carbon variation of 2.5 g have a much longer and constant charge-discharge measurement time. In the EIS test, this variation shows a resistance value that is not too high and not too small, materials that have good storage capacity or capacity have moderate resistance.

Homonuclear Super-Resolution NMR Spectroscopy.

Homonuclear 1H NMR (nuclear magnetic resonance) spectra such as the [1H,1H]-NOESY (Nuclear Overhauser Enhancement spectroscopy) are essential for biomolecular structural biology. However, the spectrum contains hundreds to thousands of cross peaks meaning that within approximately 100 ppm2 there is significant signal overlap. Spectral resolution is thus a limiting factor for data interpretation for dynamics and structure elucidation. Acquiring the spectra at higher magnetic fields such as at a 1.2 GHz 1H frequency helps to reduce spectral crowding, since resolution scales proportionally to the magnetic field strength. Here, we show that the linewidths' of cross peaks in [1H,1H]-NOESY and [1H,1H]-TOCSY spectra can be further reduced by a factor of 2-3 in each dimension by super-resolution spectroscopy. In the indirect dimension a composite exponential-cosine weighted number of scans along the time increments are recorded and digitally smoothened by a window function. Furthermore, measurement time saving by reduced-acquisition super-resolution (RASR) is introduced. Application to the 20 kDa protein KRAS shows that highly resolved NMR spectra suitable for automated analysis can be acquired within less than 3 hours. The method opens an avenue towards automated chemical shift assignment, dynamics and structure determination of unlabeled small and medium size proteins within 24 hours.

Nondestructive diagnosis of mineral deficiencies in wheat leaves by one- or two-shot saturation pulse measurement of chlorophyll fluorescence and P700+ absorbance with machine learning

We hydroponically cultivated wheat and subjected them to mineral deficiency by a week-long exposure to Hoagland's solution minus one essential mineral. Chlorophyll fluorescence and P700+ absorbance were measured under a single 300 ms saturating light pulse (one-shot) or the conventional two pulses (two-shot). A machine learning-based multiclass classification model was developed to assess mineral deficiency diagnosis. The results indicated that partial least squares discriminant analysis (PLS-DA) was ineffective. Conversely, linear discriminant analysis (LDA) without principal components analysis (PCA) successfully identified 13 or 11 (with a class combining the control with Mo- and B-deficiencies; and ten other distinct classes for respective mineral deficiencies) classes of mineral deficiencies, achieving over 81 % diagnostic performance across all five macro metrics of precision, recall, specificity, accuracy, and F1 measure. The one-shot method exhibited only a slight decrease in performance compared to the two-shot method, highlighting the advantage of substantially reduced measurement time. Applying the developed LDA models to outdoor soil-grown leaf data revealed their practicality and limitations. In multiple deficiency scenarios, the model predicted one predominant deficiency or incorrectly diagnosed a different deficiency. This study is the first to quantitatively demonstrate the efficacy of ultra-fast nutritional diagnostic technology in identifying root zone mineral deficiencies using chlorophyll fluorescence and P700+ absorbance measurements. It delineates the requirements for constructing multilabel or quantitative calibration models for field deployment.

FCS videos: Fluorescence correlation spectroscopy in space and time

Fluorescence Correlation Spectroscopy (FCS), invented more than 50 years ago is a widely used tool providing information on molecular processes in a variety of samples from materials to life sciences. In the last two decades FCS was multiplexed and ultimately made into an imaging technique that provided maps of molecular parameters over whole sample cross-section. However, it was still limited by a measurement time on the order of minutes. With the improvement of FCS time resolution to seconds using deep learning, we extend here FCS to so-called FCS videos that can provide information how the molecular parameters determined by Imaging FCS change in space and time. This opens up new possibilities for the investigation of molecular processes. Here, we demonstrate the feasibility of the approach and show FCS video applications to lipid bilayers and cell membranes.

Prospective, multicenter study of the outcome of complex regional pain syndrome after 12months

Complex regional pain syndrome (CRPS) can lead to severe pain and limited functionality in the long term. Guidelines should help to optimize treatment procedures. It should be investigated which outcome is achieved after 1year with guideline-based therapy. In aprospective multicenter study, 40patients with newly diagnosed CRPS were examined to determine how their pain and functional limitations changed within 1year. In addition, it was investigated whether the time of diagnosis and invasive measures influence these outcome parameters. All patients received physiotherapy and/or ergotherapy, treatment with glucocorticoids and/or bisphosphonates 29(72.5%); various invasive measures were carried out in 13(32.5%). After 1year, both pain and functionality were significantly improved; two-thirds reported atolerable average pain intensity. Severe functional impairment according to von Korff disability points was found after 1year in 9(22.5%), and amoderate or severe impairment according to medical evaluation in 6 (15%) and 3(7.5%) patients, respectively. Earlier diagnosis and corresponding earlier start of treatment correlated with better outcome in terms of pain and functionality according to von Korff, but not according to medical evaluation. The influence of invasive procedures on the outcome parameters tended to be low. Guideline-based treatment led to agood outcome in terms of pain and functionality in the majority of patients. Early diagnosis correlated with better outcome, so suspected cases should be referred quickly to amedical facility with appropriate expertise.

Development of hazard prediction test and interventions for two-wheeled electric vehicle riders in China

Objective This paper aimed to develop a hazard prediction test and enhance two-wheeled electric vehicle (TWEV) riders’ hazard perception and prediction capabilities via interventions by executing two distinct studies. Study 1 aimed to develop and validate a hazard prediction test. Study 2 evaluated the efficacy of two interventions, self-commentary and what happens next (WHN), integrating expert commentary. Method For Study 1, a video-based hazard prediction test was developed through video recording and clipping, with participants categorized into high and low prediction ability groups for experimentation. Data analysis employed the receiver operating characteristic (ROC) curve. Study 2 categorized participants into four groups: self-commentary with licenses (SCL), self-commentary without licenses (SCNL), WHN with licenses (WHNL), and WHN without licenses (WHNNL), for conducting a one-week intervention experiment. Data collected from participants’ pre-intervention, post-intervention, and aftereffect tests were subjected to repeated measures analysis of variance (ANOVA). Results Analysis of the ROC curve indicated the test can distinguish the riders with different hazard prediction levels. ANOVA results demonstrated that the measurement time had a significant positive effect on scores (p < 0.001). Both interventions significantly improved hazard prediction ability (p < 0.05), and the effect persisted one week after administration. The effect of the WHN intervention was significantly greater than the self-commentary method across all time points. Conclusion The hazard prediction test developed in this study could assess riders’ hazard prediction ability, with the identified interventions demonstrating effectiveness in enhancing this ability. These findings suggested potential application in future qualification tests for TWEV riders, contributing to enhanced traffic safety awareness among TWEV riders in China, thus advancing overall traffic safety.

An efficient multi‐probe enabled midfield over‐the‐air test method for 5G base station antenna pattern reconstruction

AbstractWith the accelerated deployment of 5G massive multi‐input multi‐output (MIMO)‐structured base stations (BSs), accurate and efficient testing of massive MIMO arrays is essential to ensure that the 5G massive MIMO antenna array can perform as expected. The increasing trend of both antenna element and array physical size makes massive MIMO antenna pattern measurement in the direct‐far‐field OTA environment unrealistic due to the signal attenuation, long measurement time, and ultra‐high test system construction cost. Therefore, the necessity of a compact, efficient, low‐complex, yet accurate OTA test method is evident, especially for 5G massive MIMO BS arrays. This paper proposes a novel multi‐probe‐enabled midfield (MF) OTA test method for 5G massive MIMO devices in compact measurement settings where antenna pattern measurement can be efficiently performed. The detailed theoretical analysis for the 5G massive MIMO array MF antenna pattern reconstruction method is presented, and the simulated validation results based on the typical 5G BS antenna arrays are provided and analysed, which demonstrate the effectiveness of the proposed MF OTA test method and can provide insights into the radio frequency parametric measurement for 5G massive MIMO devices.

Enhancing the efficiency of high-order harmonics with two-color non-collinear wave mixing in silica

The emission of high-order harmonics from solids under intense laser-pulse irradiation is revolutionizing our understanding of strong-field solid-light interactions, while simultaneously opening avenues towards novel, all-solid, coherent, short-wavelength table-top sources with tailored emission profiles and nanoscale light-field control. To date, broadband spectra in solids have been generated well into the extreme-ultraviolet (XUV), but the comparatively low conversion efficiency in the XUV range achieved under optimal conditions still lags behind gas-based high-harmonic generation (HHG) sources. Here, we demonstrate that two-color high-order harmonic wave mixing in a fused silica solid is more efficient than solid HHG driven by a single color. This finding has significant implications for compact XUV sources where gas-based HHG is not feasible, as solid XUV wave mixing surpasses solid-HHG in performance. Moreover, our results enable utilizing solid high-order harmonic wave mixing as a probe of structure or material dynamics of the generating solid, which will enable reducing measurement times compared to the less efficient regular solid HHG. The emission intensity scaling that follows perturbative optical wave mixing, combined with the angular separation of the emitted frequencies, makes our approach a decisive step for all-solid coherent XUV sources and for studying light-engineered materials.

High-Throughput Drug Stability Assessment via Biomimetic Metalloporphyrin-Catalyzed Reactions Using Laser-Assisted Rapid Evaporative Ionization Mass Spectrometry (LA-REIMS)

Background: Building extensive drug candidate libraries as early in the development pipeline as possible, with high-throughput in vitro absorption, distribution, metabolism, and excretion (ADME) profiling, is crucial for the selection of lead compounds to guide subsequent research and production phases. Traditionally, the analysis of metabolic stability assays heavily relies on high-throughput LC-MS/MS (liquid chromatography tandem mass spectrometry) techniques to meet with the lead profiling demands. Laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) is a quick and efficient technique for characterizing complex biological samples without laborious sample preparation. Objective: In this study, using an automated LA-REIMS well plate reader, achieving an 8 s per sample measurement time, the oxidative metabolic stability of active drug agents was assessed using biomimetic metalloporphyrin-based oxidative model reactions. Results: The results obtained using the novel LA-REIMS-based protocol were compared to and corroborated by those obtained using conventional HPLC-UV-MS (high performance liquid chromatography with ultra-violet detection coupled with mass spectrometry) measurements. Conclusions: LA-REIMS emerges as a promising technique, demonstrating potential suitability for semi-quantitative high-throughput metabolic stability in an optimized solvent environment.

Out-of-phase treatment with the synthetic glucocorticoid betamethasone disturbs glucose metabolism in mice

ObjectiveEndogenous glucocorticoid levels display a strong circadian rhythm, which is often not considered when synthetic glucocorticoids are prescribed as anti-inflammatory drugs. In this study we evaluated the effect timing of glucocorticoid administration, i.e. in-phase (administered when endogenous glucocorticoid levels are high) versus out-of-phase (administered when endogenous glucocorticoid levels are low). We investigated the synthetic glucocorticoid betamethasone – which is extensively used in the clinic - and monitored the development of common metabolic side effects in mice upon prolonged treatment, with a particular focus on glucose metabolism. MethodsMale and female C57BL/6J mice were treated with the synthetic glucocorticoid betamethasone in-phase and out-of-phase, and the development of metabolic side effects was monitored. ResultsWe observed that, compared with in-phase treatment, out-of-phase treatment with betamethasone results in hyperinsulinemia in both male and female C57BL/6J mice. We additionally found that out-of-phase betamethasone treatment strongly reduced insulin sensitivity as compared to in-phase administration during morning measurements. Our study shows that the adverse effects of betamethasone are dependent on the time of treatment with generally less side effects on glucose metabolism with in-phase treatment. ConclusionsThis study highlights differences in glucocorticoid outcome based on the time of measurement, advocating that potential circadian variation should be taken into account when studying glucocorticoid biology.

NIM–TTL and TTL–NIM converters for single particle pulse counting experiments

Abstract Low-cost interface circuits are detailed that convert negative-going NIM signals to TTL and TTL to NIM, using high-speed digital and analogue components to convert between formats. By converting NIM pulses derived from single particle detectors to TTL logic, the large suite of TTL compatible devices available to manipulate, control and measure TTL signals can be exploited. Conversion of TTL pulses to NIM allows them to be used to trigger timing events, e.g. in time of flight measurements and coincidence studies. Examples using these techniques are presented.

Development of indoor CO2 monitoring system with an integrated fixed sensor and a mobile measuring robot

Abstract This paper proposes an advanced system for optimizing indoor environmental quality (IEQ) in office environments that integrates fixed sensors with a mobile measuring robot (MMR). A demand-based measurement strategy that uses human detection and predictive analytics via machine learning is used to enhance data collection accuracy and efficiency. The system incorporates voice notifications to prompt occupants to perform actions that improve IEQ. The MMR's operational capabilities and coordination with fixed sensors allow the system to achieve high precision and efficiency in office environments. The system's effectiveness is validated through empirical studies (two preliminary experiments and two main experiments) in real office settings. The first preliminary experiment identified measurement blind spots and the second preliminary experiment tested the equivalence of environmental measurements between the MMR and fixed sensors. The first main experiment showed the system's human detection function for efficient and precise environmental measurement and the voice notification function for prompting occupants to perform actions that improve IEQ. The second main experiment showed the system's predictive accuracy in forecasting CO2 levels using neural network models. The main experiments demonstrate that the system can effectively guide MMR operations, reduce measurement times, and accurately predict environmental changes. The proposed system is a comprehensive solution for IEQ enhancement in office buildings.

Monte Carlo Simulations of Spent Nuclear Fuel Dry Storage Cask Measurements with a New External Remote Monitoring System for Developing Safeguards Approaches

Until a long-term solution for the disposal of spent nuclear fuel (SNF) is available, interim dry casks will be increasingly used for the storage of SNF discharged from civilian nuclear power reactors. Dry casks containing commercial SNF may hold several significant quantities of plutonium, so appropriate nuclear material safeguards monitoring is needed. An external remote monitoring system (RMS) has been developed to advance dry cask safeguards monitoring beyond the current method of containment and surveillance used to maintain continuity of knowledge. In this study, neutron transport simulations of SNF assemblies in a dry cask were performed for several special nuclear material diversion scenarios. The simulations considered various loading patterns and fuel storage durations as long as 100 years. For each fuel loading pattern and storage time investigated, the simulation results were used to calculate the required measurement time to achieve a nondetection probability β ≤ 10% for the diversion of any single fuel assembly in the cask. The calculations were performed for false alarm probabilities α as low as 0.0001% (or 10−6). A Monte Carlo postprocessing approach was developed to consider the impact on the required measurement time of uncertainty in the burnup of fuel assemblies. The study found that the external RMS is well suited for the surveillance of SNF in dry cask storage for nuclear safeguards or other purposes and is able to detect the diversion of a single SNF assembly even after decades of storage and with a very low false alarm probability.

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. This paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241Am, 60Co, 137Cs, 192Ir, 239Pu, and 235U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

Real-time detection of cerebral edema in mice based on low-field nuclear magnetic resonance

Cerebral edema is a secondary symptom of several conditions, including stroke, liver, failure, and severe craniocerebral injury. This study aimed to propose a Low-Field Nuclear Magnetic Resonance (LF-NMR) method for rapid and nondestructive measurement of brain water content and water phase state evaluating animal brain edema models. The water transverse relaxation time (T2) distribution in brain tissues was ascertained by measuring the T2 spectrograms of mouse brain tissues following various drying durations, in accordance with the shifting law of T2 peaks. A methodological study was carried out for the determination of water content in mouse brain tissue by the LF-NMR Peak Area (LF-NMR-PA) method using CuSO4 solution as standard solution. This method was studied in comparison with the wet/dry weight method and the Partial Least Squares Regression (PLSR) prediction method in normal and MCAO model mice, and further applied to the study of mannitol treatment of cerebral edema to validate the precision and accuracy of the hydration measurements as well as the method’s impact on subsequent experiments of cerebral infarct area measurement. In the T2 spectra of mouse brain tissues, the separate peaks T21 (0–10 ms), T22 (10–100 ms), and T23 (100–1000 ms) correspond to fat and bound water, water, and free water in brain tissue, respectively. The LF-NMR-PA method had good specificity, linearity, precision, stability, reproducibility, and recovery. The accuracy and range of use of the LF-NMR-PA method were better than those of the PLSR method, and its results were not significantly different from those of the wet/dry weight method. However, the short measurement time of the LF-NMR-PA method ensured the integrity of the tissues, and the determination of the moisture by this method did not have a significant effect on the subsequent determination of the area of cerebral infarction, which ensured the consistency of the experimental results.

Short- and long-term effects of concurrent aerobic and resistance training on circulating irisin levels in overweight or obese individuals: a systematic review and meta-analysis of randomized controlled trials.

Concurrent training (CT) is emerging as a practical and effective approach to enhance body composition, cardiovascular function, and muscle mass, thereby elevating overall individual health. This study aims to systematically investigate the effects of short- and long-term concurrent aerobic and resistance training on circulating irisin levels in overweight or obese individuals. The electronic databases, including China National Knowledge Infrastructure, PubMed, Embase, Wan Fang Database, and Web of Science, were systematically searched for articles on "concurrent training" and "irisin" published from their inception to 30 November 2023. The pooled effect size was determined using standardized mean difference (SMD) and corresponding 95% confidence intervals (CIs). The study protocol received registration with the International Prospective Register of Systematic Reviews (CRD42023494163). All nine studies, encompassing a total of 264 participants, were randomized controlled trials and met the eligibility criteria. Results indicate that short- and long-term concurrent training moderately increased circulating irisin levels compared to the control group (SMD = 0.56, 95% CI [0.33-0.80], p=0.00; I 2 = 36.6%, heterogeneity p=0.106). Subgroup analyses revealed that both equal to or less than 10 weeks (SMD = 0.78, 95% CI [0.18-1.37], p=0.01; I 2 = 62.3%, heterogeneity p=0.03) and more than 10 weeks (SMD = 0.45, 95% CI [0.14-0.76], p=0.00; I 2 = 0%, heterogeneity p=0.54) of concurrent training significantly increased circulating irisin levels in overweight or obese individuals. There were no significant between-group differences (I 2=0%, p=0.34). Additionally, concurrent training significantly increased irisin levels in overweight or obese participants (SMD = 1.06, 95% CI [0.34-1.78], p=0.00; I 2 = 50.6%, heterogeneity p=0.13) and in type 2 diabetes patients (SMD = 0.70, 95% CI [0.30-1.10], p=0.00; I 2 = 0%, heterogeneity p=0.99). However, no significant effect was observed in patients with metabolic syndrome (SMD = 0.21, 95% CI [-0.25-0.68], p=0.37; I 2 = 38.7%, heterogeneity p=0.18). There were significant between-group differences (I 2=53.9%, p=0.11). Lastly, concurrent training significantly increased circulating irisin levels in overweight or obese individuals aged 45-60 years (SMD = 0.56, 95% CI [0.25-0.86], p=0.00; I 2 = 6.5%, heterogeneity p=0.38), and a significant increase in irisin levels was observed 12 h post-intervention (SMD = 0.70, 95% CI [0.35-1.05], p=0.00; I 2 = 0%, heterogeneity p=0.74). However, none of the above categorical variables showed significant between-group differences. Short- and long-term concurrent training can effectively improve circulating irisin levels in overweight or obese individuals. However, the effects of short- and long-term concurrent training should consider the participants' health status, age, and the timing of post-exercise measurements to maximize health benefits.

User engagement with home blood pressure monitoring: a multinational cohort using real-world data collected with a connected device.

Connected blood pressure (BP) monitors provide reliable data when used properly. Our objective was to analyse the engagement of real-world users with self-measurements. We included adult first-time users of a connected BP monitor from July 2019 to March 2021. They were categorized as persistent users if they continued to use the device between 311 and 400 days after inclusion. We defined a criterion to analyse the timing of self-measurements: at least 12 measurements performed within three consecutive days, at least once every 90 days. Persistent users were clustered by state sequence analysis according to the consistency of their BP monitor measurement timing with this criterion during 1 year of follow-up. Among the 22 177 included users, 11 869 (54%) were persistent during the first year. Their use was consistent with the timing criterion 25% (median) of this time (first and third quartiles: 0%, 50%) and four patterns of use were identified by clustering: 5215 persistent users (44%) only performed occasional sparse measurements, 4054 (34%) complied at the start of follow-up up to eight cumulated months, 1113 (9%) complied at least once during later follow-up up to eight cumulated months, and the remaining 1487 (13%) complied nine or more cumulated months of follow-up. Although connected BP monitors can collect a high volume of data, the real-life timing of self-measurements is far from recommended schedules. We must promote the use of BP monitors as recommended by guidelines and/or learn to analyse more occasional and sparse measurements.

High-accuracy 3D reconstruction of step edge with ray aliasing based on projective parallel single-pixel imaging

Optical 3D measurement technology especially fringe projection profilometry (FPP) is widely utilized in industrial production. However, invalid mixed phases are common when measuring step edges in industrial parts which leads to outliers in reconstructed point cloud and ultimately causes reduction of valid point cloud and dimensional measurement errors. This paper first analyzes reasons for failure of step edge measurement in FPP and illustrates feasibility of parallel single-pixel imaging (PSI) applied to eliminate ray aliasing of step edge. Further for a good balance of accuracy and efficiency, we propose an optimized projective parallel single-pixel imaging (pPSI) method combing with edge detection in wrapped phase generated by pPSI patterns and adaptive bi-direction projection strategy to accurately identify direct correspondence matching points of step edge. Experimental results verified that the proposed method can achieve high accuracy of 3D reconstruction on step edge while taking less measurement time.

Carbon sink of forest ecosystems: Concept, time effect and improvement approaches.

The widespread utilization of fossil fuels has emitted large amounts of CO2 into the atmosphere since the Industrial Revolution, leading to climate warming and frequent occurrence of extreme climate events. To effectively alleviate climate change, the international community has made various efforts to reduce carbon emissions and eliminate CO2 from the atmosphere. In 2020, the Chinese government announced that carbon emission peaking and carbon neutrality will be achieved by 2030 and 2060, respectively. According to the current forecast, by the time carbon neutrality is achieved in 2060, even under the minimum conditions of fossil energy use, production, and living emissions, China will still have to emit about 1/4 of the current total emissions. These carbon must primarily be absorbed by ecosystems. Furthermore, approximately 140 ppm increase in CO2 in the atmosphere since the Industrial Revolution still needs to be removed by ecosystems. Forests are the main component of terrestrial ecosystems, contributing more than 80% of the carbon sequestration capacity of all terrestrial ecosystems. However, due to the long periodicity, complexity and dynamic variability of forests, the basic concepts of ecosystem carbon sink and its time effect are still unclear, leading to problems, such as lacking technologies for improving carbon sink capacity and disorganized rules in the carbon sink trading market. In this review, we introduced carbon sink concept according to the processes of absorbing and fixing CO2 by plant photosynthesis in forest ecosystems. Then, we analyzed the processes of time-scale-dependent carbon sinks of forest ecosystems, discussed the time effects of forest carbon sinks, and suggested using "t-year" as the unit of carbon sink (taking 3-6 months as the minimum measurement time, i.e., the beginning of carbon sequestration). Third, we proposed the approaches to improve the carbon sink capacity of forest ecosystems. One way is to improve the carbon sink capacity (expanding forest area, improving forest quality, and increasing forest soil carbon storage) of forest ecosystems. Another approach is to maintain the carbon sink of forest ecosystems as long as possible, i.e., to reduce temporary carbon sink (definition: carbon in the forest ecosystems emit into the atmosphere for a certain period) and to increase persistent carbon sink (definition: carbon in the forest ecosystems no longer emit into the atmosphere for a certain period; according to the relevant provisions of the Paris Agreement, the upper time limit for carbon sink measurement can be considered to be the year 2100. In order to maintain the persistent carbon sink, strateges such as efficient use of wood products (replace steel, cement, plastic with wood), control of forest fires or other disturbances-induced emissions, and turning forest biomass into biochar should be taken. Finally, we proposed to develop climate-smart forestry driven by artificial intelligence (AI), which would provide new theoretical and technical support for improving the carbon sink of forest ecosystems and facilitating sustainable forest management.