Indirect Approach Research Articles

Accuracy comparison of terrestrial and airborne laser scanning and manual measurements for stem curve-based growth measurements of individual trees

Monitoring forest growth accurately is important for assessing and controlling forest carbon stocks that impact, for example, the atmospheric CO2 concentration and, consequently, the climate change. In prior studies, forest growth monitoring with laser scanning methods has resulted in relatively high errors. However, the contribution of reference measurement error to uncertainty in growth resolution has rarely been analysed, and the reference measurements are usually considered mostly flawless. In this study, a seven-year-long growth of individual trees was estimated using both airborne and terrestrial laser scanning (ALS, TLS) that have emerged as potential candidates for digital forest reference measurements. The growth values were derived for diameter at breast height (DBH) and stem volume between the years 2014 and 2021 using an indirect approach. The values obtained with laser scanning were paired with manual field measurements and also with each other to study pairwise errors. The pairwise comparison showed that even though all the three measurement methods produced good Pearson correlation coefficients for one-time measurements (all above 0.88), the coefficients for growth measurements were significantly lower (0.19–0.44 for DBH and 0.47–0.66 for stem volume). The best correlation and root mean squared deviation (RMSD) for DBH growth (ρ = 0.44, RMSD = 0.98 cm) and stem volume growth (ρ = 0.66, RMSD = 0.052 m3) was observed between the manual field measurements and the ALS-based growth measurement method, in which the tree stem curve was obtained from the 2021 point cloud, and the stem curve was predicted backwards for the year 2014 according to height growth. The ALS method suffered less from outlying values than the TLS-based growth measurement method, in which the growth was computed based on the difference of stem curves derived separately for the years 2014 and 2021. The study showed that observing the stem curve is a potential method for short-period growth monitoring. Using the pairwise comparison results, we further derived estimates for the mean and standard deviation of measurement error of each individual measurement method. For the manual measurements, the standard deviation of error was found to be approximately 0.4 cm for DBH growth and 0.03 m3 for volume growth, which were the lowest of the three methods but not by a large margin. This highlights the need for more accurate reference data as the accuracy of laser scanning-based growth estimation methods continues to approach the accuracy of manual measurements.

Utilization of streamflow rates for determination of precipitation thresholds for landslides in a data-scarce region (Eastern Bartın, NW Türkiye)

One of the Türkiye’s most destructive natural hazards is landslides. Although much progress has been achieved in this subject throughout the country, there are still some problems related to adequate meteorological and high-quality landslide data. The aim of this study, which was carried out in the eastern part of Bartın province in the Western Black Sea region of Türkiye, is to indirectly determine the possible threshold values for landslides known to be triggered by precipitation. For this purpose, first, data related to landslides, precipitation, and streamflow were compiled and analyzed. Although many landslides have been mapped in the area, it has been determined that the number of reliable data on the dates (only three exact dates) of landslide occurrences is quite limited in the area. The relationship between the landslides that occurred in 1985, 1998, and 2021, and the stream gauging–precipitation data was analyzed. Then, due to the data scarcity related to the precipitation data, an indirect method, called Soil Conservation Service Curve Number (SCS-CN), was used to determine the relation between runoff and precipitation. The results revealed that daily 80 mm and cumulative 160 mm could be selected as the threshold values that may trigger the landslides. This study serves as an illustration of how an indirect approach can be used to approximate potential precipitation thresholds in a data-scarce region. Therefore, it will be possible to use these precipitation thresholds as a basis for future landslide hazard and risk assessments.

AGRICULTURAL EXTENSION DURING THE NEW NORMAL ERA

Since the outbreak of COVID-19 in 2020, the pandemic has seriously impacted agriculture sectors. Coffee farmers are one of the groups significantly affected by this pandemic. In addition, agricultural extension, as one of the essential instruments in improving farmers' knowledge and skills, faces new challenges in providing services after the pandemic ends. This study examines the condition of agricultural extension after the Covid-19 pandemic in Malang Regency from the aspects of extension planning, extension materials, methods, media, and extension evaluation. The research was conducted in AMSTIRDAM sub-districts of Malang Regency. This research used a quantitative descriptive method. The research respondents were 97 coffee farmers randomly selected from 2622 coffee farmers. Primary data were obtained through observation, questionnaire distribution, and respondent interviews. Secondary data were obtained from the Agricultural Extension Office. The results showed that during the COVID-19 pandemic or the new normal era, agricultural extension officers continued to develop programs together with farmers even though it was done without face-to-face interaction (online). Agricultural extension workers also continue to facilitate the preparation of RDKK. During the COVID-19 pandemic, agricultural extension workers conveyed more information about capital. The extension method and media use an indirect approach and the use of social media. In addition, agricultural extension workers also continue to evaluate the impact of counseling during the pandemic. It can be concluded that agricultural extension workers continue to carry out extension activities for farmers even with various adjustments during the COVID-19 pandemic by implementing strict health protocols, including reducing face-to-face activities.

Efficient modelling of surface roughness effects in additively manufactured materials

3D printing defects are often used to explain the mismatch between numerical predictions and experimental results of additively manufactured acoustic materials. Although they are often perceived as desirable, as in most cases, they improve the absorptive behaviour of the system, occurring gains are unplanned and uncontrollable. Among different 3D printing inaccuracies, surface roughness provides additional acoustic losses, mostly due to the enlargement of the boundary layer thickness. On the other hand, roughness is responsible for the shift of the operating frequency compared to the numerical predictions. The goal of this research is to provide efficient modelling methods to capture the effects of surface roughness arising from the additive manufacturing process. First, a direct modelling method will be discussed, which can be computationally expensive as it relies on an accurate geometrical representation of surface roughness. Second, an alternative indirect modelling approach will be presented, which mimics the presence of roughness by adjusting specific air parameters in the locations of interest of the smooth geometry to reduce the computational cost. The two strategies will be compared with each other and to the results of an experiment where additional roughness is added to target locations within the design.

Fast initialization of the indirect optimization problem in the solar sail circle-to-circle orbit transfer

This Short Communication presents a procedure to approximate, in an analytical form, the solution of the optimization problem that gives the minimum-time trajectory of a low-performance solar sail, in a classical circle-to-circle heliocentric transfer. More precisely, the proposed procedure gives an accurate approximation of the solution of the two-point boundary value problem associated to the optimization process, when an indirect approach is exploited to obtain the optimal transfer trajectory and an ideal force model is used to describe the solar sail thrust vector. In particular, the analytical results illustrated in this work extend the model discussed in the recent literature, obtained with an ideal electric thruster, to the case of a flat, perfectly reflecting, solar sail-based scenario.

Who benefits from indirect prevention and treatment of depression using an online intervention for insomnia? Results from an individual-participant data meta-analysis.

Major depressive disorder (MDD) is highly prevalent and burdensome for individuals and society. While there are psychological interventions able to prevent and treat MDD, uptake remains low. To overcome structural and attitudinal barriers, an indirect approach of using online insomnia interventions seems promising because insomnia is less stigmatized, predicts MDD onset, is often comorbid and can outlast MDD treatment. This individual-participant-data meta-analysis evaluated the potential of the online insomnia intervention GET.ON Recovery as an indirect treatment to reduce depressive symptom severity (DSS) and potential MDD onset across a range of participant characteristics. Efficacy on depressive symptom outcomes was evaluated using multilevel regression models controlling for baseline severity. To identify potential effect moderators, clinical, sociodemographic, and work-related variables were investigated using univariable moderation and random-forest methodology before developing a multivariable decision tree. IPD were obtained from four of seven eligible studies (N = 561); concentrating on workers with high work-stress. DSS was significantly lower in the intervention group both at post-assessment (d = -0.71 [95% CI-0.92 to -0.51]) and at follow-up (d = -0.84 [95% CI -1.11 to -0.57]). In the subsample (n = 121) without potential MDD at baseline, there were no significant group differences in onset of potential MDD. Moderation analyses revealed that effects on DSS differed significantly across baseline severity groups with effect sizes between d = -0.48 and -0.87 (post) and d = - 0.66 to -0.99 (follow-up), while no other sociodemographic, clinical, or work-related characteristics were significant moderators. An online insomnia intervention is a promising approach to effectively reduce DSS in a preventive and treatment setting.

Exploring the influence of the home literacy environment on early literacy and vocabulary skills in Korean-English bilingual children.

Studies have emphasized the significance of maintaining a heritage language for various reasons such as the establishment of linguistic and cultural identity, as well as socio-emotional development. Despite the crucial role that literacy development in a heritage language plays in language preservation, there is a scant research that explores the impact of home literacy environment and literacy development in children with a heritage language. This study aimed to examine the home literacy environment and literacy-related skills in 4-to 5-year-old Korean-English bilingual children living in an English-speaking country, Australia, whose heritage language is Korean, and to investigate the relationships among the home literacy environment factors and the child-internal literacy-related skills. The study employed parental questionnaires and video analyses of parent-child shared book reading sessions to assess the Korean and English home literacy environment. Children's early literacy skills in Korean and English, along with their Korean, English, and conceptual vocabulary skills, were measured as literacy-related skills. The findings indicated that parents utilized an indirect approach for Korean literacy practices, in contrast to a more direct and explicit method for English literacy practices. However, active and direct literacy practices were found to be essential for Korean early literacy development, while indirect methods are sufficient for English early literacy skills. Moreover, the availability of abundant Korean literacy resources at home had a positive impact on the development of Korean and English, as well as conceptual vocabulary skills. In conclusion, this study underscores the importance of providing a robust literacy environment in a heritage language in bilingual families to promote language proficiency in both the heritage language and the dominant social language, while also supporting the development of conceptual language skills.

Towards an improved thermometric method: Convective and radiative heat transfer for heat flux measurement through an indirect approach

Actual thermal performance of building walls need to be assessed through on-site tests. Several measurement procedures have been proposed during the last decades. Nonetheless, among them the Thermometric (THM) method has been poorly explored in literature, hence opportunities for improvements can be highlighted. The THM technique is a quite a new and non-standardized procedure for wall thermal characterization, based on the well-known Newton’s law of cooling. This law needs the heat transfer coefficient knowledge and there is no agreement about the value to apply when the THM method is used. In this work, a low-cost experimental apparatus characterized by a heated wooden sample was realized. Sensors were installed to measure heat fluxes through a common heat flux plate. The obtained values were compared with the heat flows achieved via the THM method, where dimensionless groups analysis was performed for computing suitable heat transfer coefficients obtained from data processing. The aim of this work is to lay the groundwork for overcoming the disagreement related to the value of the total heat transfer coefficient in the THM method by proposing and consolidating an alternative approach. The results show that the proposed data processing for the THM method can provide satisfying results in terms of indirect heat flux measurements, showing extremely low percentage differences (ranging from + 1.16 % to + 3.56 %) if compared with the HFM method.

Enhanced brain distribution of Ginsenoside F1 via intranasal administration in combination with absorption enhancers

Ginsenoside F1 (GF1) is a potential drug candidate for the treatment of Alzheimer's disease. Nevertheless, its low oral bioavailability and poor solubility limit clinical application. By utilizing either a direct or indirect approach, intranasal administration is a non-invasive drug delivery method that can deliver drugs to the brain rapidly. But large molecule drug delivered to the brain through intranasal administration may be insufficient to reach required concentration for therapeutic effect. In this study, using GF1 as a model drug, the feasibility of intranasal administration in combination with absorption enhancers to increase brain distribution of GF1 was explored. First of all, the appropriate absorption enhancers were screened by in situ nasal perfusion study. GF1-HP-β-CD inclusion complex was prepared and characterized. Thereafter, in vivo absorption of GF1 after intranasal or intravenous administration of its inclusion complex with/without absorption enhancers was investigated, and safety of the formulations was evaluated. The results showed that 2% Solutol HS 15 was a superior absorption enhancer. HP-β-CD inclusion complex improved GF1 solubility by 150 fold. Following intranasal delivery, the absolute bioavailability of inclusion complex was 46%, with drug brain targeting index (DTI) 247% and nose-to-brain direct transport percentage (DTP) 58%. Upon further addition of 2% Solutol HS 15, the absolute bioavailability was increased to 75%, with DTI 315% and DTP 66%. Both nasal cilia movement and biochemical substances (total protein and lactate dehydrogenase) leaching studies demonstrated 2% Solutol HS 15 was safe to the nasal mucosa. In conclusion, intranasal administration combining with safe absorption enhancers is an effective strategy to enhance drug distribution in the brain, showing promise for treating disorders related to the central nervous system.

Using bi-temporal ALS and NFI-based time-series data to account for large-scale aboveground carbon dynamics: the showcase of mediterranean forests

ABSTRACT New remote-sensed biomass change products will transform our capacity to monitor and validate large-scale carbon dynamic in the next decade. In this study, we evaluated the use of multitemporal Airborne Laser Scanning (ALS) and the Climate Change Initiative (CCI) BIOMASS spaceborne mission to estimate AGB dynamics in different Mediterranean forest over an 8-year period (2010–2018). To do so, we evaluated different maps to estimate change in AGB, specifically indirect approach using forest-type specific ALS-based AGB maps using i) countrywide ALS coverage at 25 m resolution (2010–2018) and ii) the global, 100-m resolution CCI maps version 3 (2010–2018). The change in AGB (ΔAGB) was mapped across the study region to compute dynamics by forest type. Our results suggest that the indirect approach using ALS-model-based produced more accurate estimates in change of AGB than CCI when we compared with the design-based AGB estimation using Spanish National Forest Inventory (SNFI) at strata level. The spatial representation of the AGB change indicated that ΔAGB-ALS changes by forest type had an overall gain in biomass at regional level. The ΔAGB total and net annual changes by year and area (ΔAGB, Mg ha−1 year−1) were closed to the values obtained using SNFI at strata level. This study demonstrates the feasibility of enhancing carbon sequestration and stock capacity in Mediterranean forest using multitemporal ALS data and the limitations of global AGB maps at Regional Scale.

Indirect Calorimetry-Based Novel Approach for Evaluating Metabolic Flexibility and Its Association with Circulating Metabolic Markers in Middle-Aged Subjects.

We propose a novel method for assessing metabolic flexibility (MF) through indirect calorimetry. A total of twenty healthy volunteers (10 females; 10 males) aged 45-65 were categorized into a Low-Intensity activity group (LI, 0-1 session of 1 h per week) and a High-Intensity activity group (HI, 5-6 sessions of 2 h per week). Volunteers underwent a stepwise exercise test on a cycle ergometer, connected to a calorimeter, to examine respiratory gas exchange to evaluate peak fatty acid Oxidation (PFO) and peak carbohydrate oxidation (PCO). Circulating peroxisome proliferator-activated receptor α (PPARα) biomarkers, docosahexaenoic acid/eicosapentaenoic acid (DHA/EPA) ratio and N-oleoylethanolamine (OEA), and the endocannabinoid- 2-arachidonoylglycerol (2-AG), were evaluated. We developed two MF parameters: the MF index (MFI), calculated by the product of PFO normalized per kg of fat-free mass (FFM) and the percentage of VO2max at PFO, and the peak energy substrates' oxidation (PESO), computed by summing the kilocalories from the PFO and PCO, normalized per kg FFM. The MFI and PESO were significantly different between the HI and LI groups, showing strong correlations with the circulating bioactive substances. Higher DHA/EPA ratio (p ≤ 0.05) and OEA (p ≤ 0.01), but lower 2-AG levels (p ≤ 0.01) were found in the HI group. These new parameters successfully established a functional link between MF and the balance of PPARα/endocannabinoid systems.

Ehf and Fezf2 regulate late medullary thymic epithelial cell and thymic tuft cell development.

Thymic epithelial cells are indispensable for T cell maturation and selection and the induction of central immune tolerance. The self-peptide repertoire expressed by medullary thymic epithelial cells is in part regulated by the transcriptional regulator Aire (Autoimmune regulator) and the transcription factor Fezf2. Due to the high complexity of mTEC maturation stages (i.e., post-Aire, Krt10+ mTECs, and Dclk1+ Tuft mTECs) and the heterogeneity in their gene expression profiles (i.e., mosaic expression patterns), it has been challenging to identify the additional factors complementing the transcriptional regulation. We aimed to identify the transcriptional regulators involved in the regulation of mTEC development and self-peptide expression in an unbiased and genome-wide manner. We used ATAC footprinting analysis as an indirect approach to identify transcription factors involved in the gene expression regulation in mTECs, which we validated by ChIP sequencing. This study identifies Fezf2 as a regulator of the recently described thymic Tuft cells (i.e., Tuft mTECs). Furthermore, we identify that transcriptional regulators of the ELF, ESE, ERF, and PEA3 subfamily of the ETS transcription factor family and members of the Krüppel-like family of transcription factors play a role in the transcriptional regulation of genes involved in late mTEC development and promiscuous gene expression.

Evaluation of two deep learning-based approaches for detecting weeds growing in cabbage.

Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting weeds in cabbage: (1) detecting weeds directly, and (2) detecting crops by generating the bounding boxes covering the crops and any green pixels outside the bounding boxes were deemed as weeds. The precision, recall, F1-score, mAP0.5, mAP0.5:0.95 of You Only Look Once (YOLO) v5 for detecting cabbage were 0.986, 0.979, 0.982, 0.995, and 0.851, respectively, while these metrics were 0.973, 0.985, 0.979, 0.993, and 0.906 for YOLOv8, respectively. However, none of these metrics exceeded 0.891 when detecting weeds. The reduced performances for directly detecting weeds could be attributed to the diverse weed species at varying densities and growth stages with different plant morphologies. A segmentation procedure demonstrated its effectiveness for extracting weeds outside the bounding boxes covering the crops, and thereby realizing effective indirect weed detection. The indirect weed detection approach demands less manpower as the need for constructing a large training dataset containing a variety of weed species is unnecessary. However, in a certain case, weeds are likely to remain undetected due to their growth in close proximity with crops and being situated within the predicted bounding boxes that encompass the crops. The models generated in this research can be used in conjunction with the machine vision subsystem of a smart sprayer or mechanical weeder. © 2024 Society of Chemical Industry.

Approval of DFMO for high-risk neuroblastoma patients demonstrates a step of success to target MYC pathway.

The "undruggable" MYC oncoproteins are deregulated in 70% human cancers. The approval of DFMO, an irreversible inhibitor of ornithine oxidase (ODC1) that is a direct transcriptional target of MYC, demonstrates that patients can benefit from targeting MYC activity via an indirect approach. However, the mechanism of action of DFMO needs further studies to understand how it works in post-immunotherapy neuroblastomas. Efforts to develop a more potent and safer drug to block MYC function will continue despite challenges.

Using Diffraction Deep Neural Networks for Indirect Phase Recovery Based on Zernike Polynomials.

The phase recovery module is dedicated to acquiring phase distribution information within imaging systems, enabling the monitoring and adjustment of a system's performance. Traditional phase inversion techniques exhibit limitations, such as the speed of the sensor and complexity of the system. Therefore, we propose an indirect phase retrieval approach based on a diffraction neural network. By utilizing non-source diffraction through multiple layers of diffraction units, this approach reconstructs coefficients based on Zernike polynomials from incident beams with distorted phases, thereby indirectly synthesizing interference phases. Through network training and simulation testing, we validate the effectiveness of this approach, showcasing the trained network's capacity for single-order phase recognition and multi-order composite phase inversion. We conduct an analysis of the network's generalization and evaluate the impact of the network depth on the restoration accuracy. The test results reveal an average root mean square error of 0.086λ for phase inversion. This research provides new insights and methodologies for the development of the phase recovery component in adaptive optics systems.

Tight coupling of cell width to nucleoid structure in Escherichia coli

Cell dimensions of rod-shaped bacteria such as Escherichia coli are connected to mass growth and chromosome replication. During their interdivision cycle (τ min), cells enlarge by elongation only, but at faster growth in richer media, they are also wider. Changes in width W upon nutritional shift-up (shortening τ) occur during the division process. The elusive signal directing the mechanism for W determination is likely related to the tightly linked duplications of the nucleoid (DNA) and the sacculus (peptidoglycan), the only two structures (macromolecules) existing in a single copy that are coupled, temporally and spatially. Six known parameters related to the nucleoid structure and replication are reasonable candidates to convey such a signal, all simple functions of the key number of replication positions n(=C/τ), the ratio between the rates of growth (τ−1) and of replication (C−1). The current analysis of available literature-recorded data discovered that, of these, nucleoid complexity NC[=(2n−1)/(n×ln2)] is by far the most likely parameter affecting cell width W. The exceedingly high correlations found between these two seemingly unrelated measures (NC and W) indicate that coupling between them is of major importance to the species’ survival. As an exciting corollary, to the best of our knowledge, a new, indirect approach to estimate DNA replication rate is revealed. Potential involvement of DNA topoisomerases in W determination is also proposed and discussed.

Abstract A068: Proteomic analysis of keratin 17 positive cells from laser-capture microdissected pancreatic ductal adenocarcinoma

Abstract Introduction. Although patients with pancreatic ductal adenocarcinoma (PDAC) have one of the lowest survival of all carcinomas, there is considerable variation in survival and disease progression, even when accounting for factors such as age and pathologic stage. Our group identified keratin 17 (K17) as a negative prognostic and predictive biomarker in PDAC. Patients with high K17 expression in their tumors have shorter overall survival as well as decreased response to chemotherapeutic agents, including gemcitabine. K17 has also emerged as an established biomarker of the basal molecular subtype of PDAC. In this study, we aim to identify proteins which correlate, directly or indirectly, with K17 expression via a combined approach using immunohistochemistry (IHC) followed by laser capture microdissection (LCM) and mass spectrometry. Methods. We selected 5 PDAC cases of low K17 expression and 5 PDAC cases of high K17 expression from the archival collections from the Stony Brook Cancer Center Biobank. A modified indirect immunoperoxidase approach was performed on sections mounted on LCM-compatible membrane slides. Carcinoma cells that were either K17 positive or negative by IHC were precisely dissected using LMD7 microscope (Leica) from 10-µM thick sections, followed by a modified tissue digestion and protein extraction protocol. Collision-induced fragment mass information and peptide abundance values were obtained by a liquid chromatography-electrospray ionization tandem mass spectrometry (LC-MS/MS). Spectral cutoffs were analyzed at both <5% and 1% false discovery rates. The human UniProt dataset was used for data alignment. Results. Mass spectrometry yielded over 1600 detected proteins with approximately 50 of which correlated strongly with K17 expression. Our preliminary results show that keratin 5, a known marker of basal-like PDAC, is positively correlated with keratin 17. Genes whose increased expression contribute to the Moffitt classification of basal molecular subtype of PDAC, such as S100A2, keratin 6A and keratin 7, also showed positive correlation with K17 in our study. The remainder of proteins associated with K17 expression include those involved with cell adhesion, cell-extracellular matrix interaction and smooth muscle contraction pathways, with specific types and classes being identified, pending in-depth analysis. Proteins with inverse relationship to K17 expression included those involved in intracellular organelle trafficking and apoptosis pathways. Conclusion. Our findings demonstrate the utility of mass spectrometry-based spatial proteomic analysis of microdissected cell populations from formalin-fixed, paraffin embedded samples. These methods have the potential to uncover complementary biomarkers to K17 that could enhance prognostic accuracy or serve as therapeutic targets and provide mechanistic insight into the mechanisms through which K17 drives tumor aggression. Citation Format: Ji Dong K. Bai, John D. Haley, Natalia Marchenko, Luisa F. Escobar-Hoyos, Kenneth R. Shroyer. Proteomic analysis of keratin 17 positive cells from laser-capture microdissected pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A068.

Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners

The management of product quality is a crucial process in factory manufacturing. However, this approach still has some limitations, e.g., depending on the expertise of the engineer in evaluating products and being time consuming. Various approaches using deep learning in automatic defect detection and classification during production have been introduced to overcome these limitations. In this paper, we study applying different deep learning approaches and computer vision methods to detect scratches on the surface of microfasteners used in rechargeable batteries. Furthermore, we introduce an architecture with statistical quality control (SQC) to continuously improve the efficiency and accuracy of the product quality. The proposed architecture takes advantage of the capability of deep learning approaches, computer vision techniques, and SQC to automate the defect detection process and quality improvement. The proposed approach was evaluated using a real dataset comprising 1150 microfastener surface images obtained from a factory in Korea. In the study, we compared the direct and indirect prediction methods for predicting the scratches on the surface of the microfasteners and achieved the best accuracy of 0.91 with the indirect prediction approach. Notably, the indirect prediction method was more efficient than the traditional one. Furthermore, using control charts in SQC to analyze predicted defects in the production process helped operators understand the efficiency of the production line and make appropriate decisions in the manufacturing process, hence improving product quality management.

Remaining Useful Life Prediction of Rolling Bearings Based on ECA-CAE and Autoformer.

In response to the need for multiple complete bearing degradation datasets in traditional deep learning networks to predict the impact on individual bearings, a novel deep learning-based rolling bearing remaining life prediction method is proposed in the absence of fully degraded bearng data. This method involves processing the raw vibration data through Channel-wise Attention Encoder (CAE) from the Encoder-Channel Attention (ECA), extracting features related to mutual correlation and relevance, selecting the desired characteristics, and incorporating the selected features into the constructed Autoformer-based time prediction model to forecast the degradation trend of bearings' remaining time. The feature extraction method proposed in this approach outperforms CAE and multilayer perceptual-Attention Encoder in terms of feature extraction capabilities, resulting in reductions of 0.0059 and 0.0402 in mean square error, respectively. Additionally, the indirect prediction approach for the degradation trend of the target bearing demonstrates higher accuracy compared to Informer and Transformer models, with mean square error reductions of 0.3352 and 0.1174, respectively. This suggests that the combined deep learning model proposed in this paper for predicting rolling bearing life may be a more effective life prediction method deserving further research and application.

Optimal guidance laws for diffractive solar sails with Littrow transmission grating

The use of advanced and highly engineered membrane films in solar sail design enables the thrust vector to achieve some specific characteristics that are capable of affecting the performance of a sail-based spacecraft trajectory in a transfer mission scenario. In this field, the recently proposed diffractive sail uses metamaterial films to coat the very thin sail membrane as a potential and effective (in some mission scenarios) alternative to the more common metallic reflective coating. The purpose of this work is to study the optimal guidance law, as a function of sail pitch angle, of a diffractive sailcraft with a Littrow transmission grating film in a typical two-dimensional heliocentric transfer. In particular, the optimal steering law is obtained through a classical indirect approach, depending on the characteristics of Lawden's primer vector, either in exact (by numerical simulation) or approximate (by analytical means) form. The performance of a diffractive sailcraft with a Littrow transmission grating film is then analyzed in a number of potential mission scenarios such as, for example, a phasing in a circular orbit, an interplanetary transfer between coplanar Keplerian trajectories, a circular orbit flip maneuver, and an escape from the Solar System. Simulation results indicate that this specific diffractive sailcraft is potentially capable of executing a typical solar sail-based mission scenario with a simple steering law.