Abstract: This paper innovatively proposes a three-roll skew rolling process for flexible forming of hollow turbine shaft, which solves the problems of long manufacturing process and low material utilization of hollow turbine shaft, the core component of aeroengine. Simufact.Forming 14.0 (SF) numerical simulation software was used to establish the finite element model of two-pass three-roll skew rolling of the GH4169 superalloy turbine shaft. The effects of process parameters on the outer diameter error, roundness error and wall thickness uniformity of the rolled piece were investigated by single factor experiments. A five-factor three-level orthogonal test was designed to explore the optimum process parameters by ' comprehensive scoring method'. The results show that the optimal process parameters are that the first pass roll rotating speed is 40 rad/min, the first pass axial speed is 15 mm/s, the second pass roll rotating speed is 50 rad/min, the second pass axial speed is 25 mm/s, and the billet preheating temperature is 1000ºC. The axial velocity of the second pass has the greatest influence on the test results, while the rotational speed of the second pass has the least influence. Under the optimal parameter combination simulation experiment, the outer diameter error, outer roundness error and wall thickness standard deviation are 0.151 mm, 0.121 mm and 0.034 mm, respectively, which are better than the results in the orthogonal test table. The research results provide a theoretical basis for realizing flexible, economical and high-quality forming of hollow turbine shaft by three-roll skew rolling.

We propose SMMF (Square-Matricized Momentum Factorization), a memory-efficient optimizer that reduces the memory requirement of the widely used adaptive learning rate optimizers, such as Adam, by up to 96%. SMMF enables flexible and efficient factorization of an arbitrary rank (shape) of the first and second momentum tensors during optimization, based on the proposed square-matricization and one-time single matrix factorization. From this, it becomes effectively applicable to any rank (shape) of momentum tensors, i.e., bias, matrix, and any rank-d tensors, prevalent in various deep model architectures, such as CNNs (high rank) and Transformers (low rank), in contrast to existing memory-efficient optimizers that applies only to a particular (rank-2) momentum tensor, e.g., linear layers. We conduct a regret bound analysis of SMMF, which shows that it converges similarly to non-memory-efficient adaptive learning rate optimizers, such as AdamNC, providing a theoretical basis for its competitive optimization capability. In our experiment, SMMF takes up to 96% less memory compared to state-of-the-art memoryefficient optimizers, e.g., Adafactor, CAME, and SM3, while achieving comparable model performance on various CNN and Transformer tasks.

Background: Many individuals undergo drug addiction treatment relapse after treatment. Additionally, a few studies have focused on the unique challenges faced by women in addiction recovery. Therefore, we aimed to identify predictors of relapse among women who have undergone drug addiction treatment to develop a more effective intervention program tailored to their needs. Methods: The present cross-sectional study evaluated 320 addicted women from Lorestan province, Iran, in 2020 who had used illegal substances. Convenience sampling was used to select participants from outpatient addiction treatment centers, triangular clinics, and rehabilitation centers. Demographic characteristics, drug use patterns, and risk factors for addiction were recorded. Predictors of relapse were evaluated using a negative binomial regression model. Results: The study included women with a mean age of 34.78±9.31 years who had a history of drug addiction relapse. Around 43.44% of the women were married and 36.88% lived with their husbands. Methamphetamine, opium, and heroin were the commonly used drugs. The study found that women with primary school education had a higher rate of relapse (IRR: 1.71, p = 0.01) and women with husbands being substance users (IRR: 1.34, p = 0.014), history of imprisonment (IRR: 1.51, p = 0.002), smoking (IRR: 1.91, p < 0.001), and alcohol consumption (IRR: 1.49, p = 0.005) were also associated with a higher rate of relapse. Conclusion: In conclusion, this study found that women with primary school education, husbands being substance users, history of imprisonment, smoking, and alcohol consumption behavior had a higher rate of relapse. By addressing the identified predictors of relapse, such programs can improve the success rate of addiction treatment and promote long-term recovery among women. In addition, this study highlights that addiction relapse is not necessarily caused by a single factor, and several different personal and environmental factors can influence this issue.

Goldberg's (2006) bass-ackward approach to elucidating the hierarchical structure of individual differences data has been used widely to improve our understanding of the relationships among constructs of varying levels of granularity. The traditional approach has been to extract a single component or factor on the first level of the hierarchy, two on the second level, and so on, treating the correlations between adjoining levels akin to path coefficients in a hierarchical structure. This article proposes three modifications to the traditional approach with a particular focus on examining associations among all levels of the hierarchy: (a) identify and remove redundant elements that perpetuate through multiple levels of the hierarchy; (b) (optionally) identify and remove artefactual elements; and (c) plot the strongest correlations among the remaining elements to identify their hierarchical associations. Together these steps can offer a simpler and more complete picture of the underlying hierarchical structure among a set of observed variables. The rationale for each step is described, illustrated in a hypothetical example and three basic simulations, and then applied in real data. The results are compared with the traditional bass-ackward approach together with agglomerative hierarchical cluster analysis, and a basic tutorial with code is provided to apply the extended bass-ackward approach in other data. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

After nanocarbon tracer was applied to thyroidectomy, we aimed to investigate the risk factors of hypoparathyroidism and the relationship between the number of exposed parathyroid glands and parathyroid function. This study retrospectively collected clinical data of patients who underwent thyroidectomy at Haining People’s Hospital from January 2019 to December 2022. Levels of parathyroid hormone (PTH) and blood calcium were observed before surgery, 1 day after surgery, and one month after surgery to assess the occurrence of postoperative parathyroid hypofunction. Patients were grouped based on their postoperative PTH levels. Furthermore, basic patient data and surgical data were collected to analyze the risk factors associated with hypoparathyroidism after thyroidectomy using single factor analysis. ANOVA analysis was used to compare the correlation between intraoperative parathyroid exposure and postoperative PTH and blood calcium levels. This study involved 80 patients. On the first day after surgery, the serum PTH and blood calcium levels of patients decreased, and 59 patients (73.75%) had normal serum PTH level after surgery, which was the normal group. Twenty-one patients (26.25%) developed hypoparathyroidism after operation and were divided into hypoparathyroidism group. Single factor analysis showed that the main risk factors of postoperative hypoparathyroidism were lesion location, lymph node dissection, nano carbon, number of lymph node metastasis, intraoperative parathyroid exposure, and inaccurate parathyroidectomy. ANOVA analysis showed that the more parathyroid exposure during operation was accompanied by the more decrease of serum PTH and blood calcium levels after operation. Therefore, the location of thyroid lesions, lymph node dissection, nano-carbon, number of lymph node metastases, intraoperative parathyroid exposure, and parathyroidectomy are risk factors for transient hypoparathyroidism after thyroid surgery. Moreover, intraoperative parathyroid exposure significantly affected serum PTH and blood calcium levels. Performing more accurate thyroidectomy and reducing parathyroid exposure help reduce the occurrence of postoperative hypoparathyroidism and hypocalcemia.

Predicting rectal cancer tumor budding grading based on MRI and CT with multimodal deep transfer learning: A dual-center study

The manipulation of cell identity by reprograming holds immense potential in regenerative medicine, but is often limited by the inefficient acquisition of fully functional cells. This problem can potentially be resolved by better understanding the reprogramming process using in vivo genetic models, which are currently scarce. Here we report that both enterocytes (ECs) and enteroendocrine cells (EEs) in adult Drosophila midgut show a surprising degree of cell plasticity. Depleting the transcription factor Tramtrack in the differentiated ECs can initiate Prospero-mediated cell transdifferentiation, leading to EE-like cells. On the other hand, depletion of Prospero in the differentiated EEs can lead to the loss of EE-specific transcription programs and the gain of intestinal progenitor cell identity, allowing cell cycle re-entry or differentiation into ECs. We find that intestinal progenitor cells, ECs, and EEs have a similar chromatin accessibility profile, supporting the concept that cell plasticity is enabled by pre-existing chromatin accessibility with switchable transcription programs. Further genetic analysis with this system reveals that the NuRD chromatin remodeling complex, cell lineage confliction, and age act as barriers to EC-to-EE transdifferentiation. The establishment of this genetically tractable in vivo model should facilitate mechanistic investigation of cell plasticity at the molecular and genetic level.

The maintenance level activity generally done after the modification in the software to check whether it is functioning right or not is termed as regression testing. Test case prioritization, a key practice, involves strategically ordering test cases based on specific criteria to enhance the efficiency of fault detection within a condensed time frame. The fuzzy rule base serves as an alternative to the conventional crisp value set, offering a nuanced approach beyond binary outcomes (Yes or No). The primary objective of this research is to address critical factors often overlooked in existing literature on prioritization. Notably, prevalent approaches focus on singular factors during test case prioritization, highlighting the need for a comprehensive technique. To enhance the prioritization of test cases, there is a demand for a method that considers multi-factors or combinations thereof, ultimately increasing effectiveness. This paper introduces an innovative approach a multi-factors regression test-case prioritization technique utilizing fuzzy rules. The methodology aims to optimize the prioritization of test cases, striking a balance between effectiveness and time efficiency. Fuzzy rules are formulated to assess the effectiveness of a prioritized set of test cases in developing the proposed approach. A user-friendly tool has been developed to facilitate the application of this technique, allowing users to input relevant factors and subsequently prioritize test cases accordingly. Through extensive experiments using the developed tool, the effectiveness of the proposed approach has been validated. The results demonstrate that the priority lists of test cases generated for different projects, considering multi-factors, show greater promise compared to techniques relying solely on a single factor for prioritization.

Monte Carlo on manifolds in high dimensions

As the core area of human activities and economic development in the Xinjiang Autonomous Region, the hilly oasis zone of Xinjiang directly affects the regional sustainable development and stability of the ecosystem. Understanding the effects of different geomorphic types on vegetation distribution is crucial for maintaining vegetation growth and development, especially the improvement in the terrestrial ecological environment in arid areas under the background of climate change. However, there are few studies on the effect of spatial differences in detailed geomorphic types on vegetation distribution patterns. Therefore, this paper divides the Xinjiang hilly oasis zone into six geomorphologic level zones and innovatively investigates the influence of detailed geomorphologic types on the spatial distribution of vegetation and vegetation cover. Further, the area proportion of detailed landform types corresponding to different vegetation coverage in each geomorphic area was quantitatively calculated. Finally, the Geodetector method was used to detect the drivers of interactions between vegetation and the environment. The findings are shown as follows: (1) In the same climate zone, the spatial differentiation of landforms has a great influence on the vegetation distribution, manifesting as the significantly different vegetation distribution in different landform types. Grassland is the main vegetation type in the erosion and denudation of Nakayama; cultivated vegetation and meadows have a larger coverage in the alluvial flood plain and alluvial plain; and the distribution of vegetation in the Tianshan economic zone is characterized by obvious vertical zoning with the geomorphology. (2) The landform type and morphological types are the strongest driving factors for vegetation coverage with q values of 0.433 and 0.295, respectively, which effectually fill the gap caused by only using two terrain indicators, slope and elevation, to study the relationship between landforms and vegetation. (3) In addition, the improved nonlinear interaction resulting from the double factor of landform type and slope is 0.486, which has a stronger control on vegetation coverage than the single factor of landform type. These findings are conducive to enhancing the supply services of vegetation to the ecosystem in arid areas as well as providing important scientific guidance for the construction of ecological civilization and sustainable development in Xinjiang.

Safe-Pass presents a user-friendly and secure solution for simplifying digital access. With a downloadable application that operates seamlessly across your devices, it eliminates the inconvenience of traditional passwords. The process begins with accessing the Master password app through a distinctive image-based authentication. Operating inconspicuously in the background, the app not only enhances the strength of your existing passwords but also manages and facilitates automatic logins. This system offers adaptable security options, enabling swift access through a single factor or heightened security through the combination of multiple factors. Addressing the persistent threat of phishing, wherein sensitive user information is compromised, we introduce an innovative approach leveraging Visual Cryptography and Steganography for enhanced online security. Our method involves the application of Visual Cryptography to confidential credentials, generating two shares. One share is stored on the server, while the other is concealed within a reCAPTCHA image or a user-defined image through Steganography. During login attempts, users provide their username along with the reCAPTCHA image or chosen image. Successful authentication grants access, while repeated failed attempts trigger email notifications. Master Login prioritizes user privacy, safeguarding passwords as individual and exclusive data. Data sharing or selling is never practiced, ensuring the confidentiality of user information.

<p class="MsoNormal" style="text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">The manufacturing sector must incorporate digitalization and greening as crucial strategies to attain high level of development in the context of the digital economy, given its significant role in quality of environment. Most of existing studies explore net effect of a single or several factors on the digitalization or greening in the manufacturing industry. Limited investigation has been done to integrate various factors impacting the connection and synchronized advancement of digitalization and greening within the manufacturing. Factors affecting the synergy of digitalization and green change within manufacturing are recognized in this article, and a theoretical framework is established to analyze. FsQCA tool is also put into use to survey configurational cases, which contains synergy degree of green digitalization from 30 provinces’ manufacturing in China. Results show that: (1) Between 2007 and 2020, the development level of digital-green integration in the manufacturing industry shows an upward trend. There are three types of collaborative modes, namely, "industrial robot driven" "market development driven" and "market development driven - industrial robot driven", with each mode consisting of at least one to four equivalent pathways. (2) The marketization level and application of industrial robots are the important external and internal pulling forces that drive the synergy degree of digitalization and green change within manufacturing industry, respectively.(3) The influencing factors of the collaborative mode choice of digital-green development within manufacturing industry are complementary, among which, the degree of marketization is similar to the role of industrial robots in the configuration, which are capable to weaken the negative impact from insufficient government R&D investment support under specific conditions; (4) The non-high collaborative state of digital green in manufacturing industry is mainly due to the low level of marketization, openness, and economic development, as well as the low application rate of industrial robots and software products. This study enriches the theoretical understanding on digitalization and green change of manufacturing industry. Meanwhile, it also provides insights to advance the integrated and synergistic advancement of digital green transformation in manufacturing practice.</span></p>

The green and low-carbon development of industrial parks plays an important role in promoting industrial and technological agglomeration, How to optimise their green and low-carbon development pathways is an important management question to be answered, requiring the examination of multiple path based on a histological perspective. Based on the technology–organisation–environment framework, this study selected 29 state-level economic and technological development zones (ETDZs) in China as research objects, and explored multiple sets of paths using fuzzy set qualitative comparison and necessity-contribution analysis. The study found that: 1) four promotion paths can be adopted for green low-carbon development of the zones: endogenous drive, balanced promotion, core support drive and comprehensive drive; 2) single environmental factor does not constitute a bottleneck hindering the green low-carbon development of the zones, which should be combined with the actual situation of the zones, and the balanced enhancement of the effectiveness of green and low-carbon should be realised through measures such as industrial policy, environmental risk prevention, and performance assessment of green policies. Finally, the study proposes the following policy recommendations: industrial parks should focus on the key role of environmental risk prevention, apply a combination of measures in accordance with their own realities, and appropriate external pressure can help promote green and low-carbon development.

Experimental and numerical investigations into the fabrication of alumina ceramic surface microchannel by electrochemical discharge machining

Most children and adolescents in Norway attend the Public Dental Service (PDS) where they are treated free-of-charge until the age of 19 years. Thus, general dentists employed in the PDS are the primary care providers for traumatic dental injuries (TDI) in young patients. This cross-sectional study assessed the knowledge of PDS general dentists on the acute management and follow-up of TDI and its socio-demographic and attitudinal covariates. All general dentists employed in the Vestland County PDS, Western Norway, (N = 170) received an online questionnaire. Socio-demographic and professional profiles of respondents as well as attitudinal indicators were queried. Clinical case scenarios on emergency treatment and further follow-ups of TDI were used to calculate a dental trauma knowledge score (DTKS; range: 0-21). Mann-Whitney U tests and Kruskal-Wallis tests determined differences between the demographic subgroups. Logistic regressions determined the potential of single factors in explaining the variability in dental trauma knowledge. The response rate was 46%. Most participants (60.5%) had not participated in a TDI course after graduation but would like such a course (84.2%). Mean DTKS was 13.82 (±2.6). Knowledge scores differed significantly between age groups (p = .014) and years since graduation (p = .0018). Younger dentists and recently graduated dentists scored highest. Dentists under the age of 30 years scored higher than 30-39- and 40-49-year-old dentists in these areas: emergency treatment of crown fractures with pulp exposure, identification of complications after avulsion, and management of severe intrusive luxation injury. Younger dentists had a higher theoretical knowledge of TDI. Continuing professional development among dentists in the Norwegian PDS is needed for emergency treatment and complication management after TDI.

The Qinghai–Tibet Railway is located in the most fragile and sensitive terrestrial ecosystem of the Qinghai–Tibet Plateau in China, and once the ecological environment is damaged, it is difficult to restore. This study, based on the Google Earth Engine platform, focuses on the section of the Qinghai–Tibet Railway from Xining to Jianghe. It utilizes Landsat series satellite imagery data from 1986 to 2020 to calculate the Remote Sensing Ecological Index (RSEI). This approach enables large-scale and long-term dynamic monitoring, analysis, and assessment of the ecological changes along the Qinghai–Tibet Railway corridor. The results indicate that (1) the average RSEI of the study area increased from 0.37 in 1986 to 0.53 in 2020, showing an overall trend of improvement. The ecological environment quality is mainly categorized as medium and good. (2) The quality of the ecological environment in the areas along the railway experienced fluctuations during different periods of railway construction and operation. From 1986 to 1994, after the first phase of the railway opened, the overall ecological environment showed a relative decline in quality. From 1994 to 2002, the ecological quality of 60% of the region saw slight improvements. During the extension construction of the second phase of the railway from 2002 to 2007, the regional ecology fluctuated again. However, from 2013 to 2020, during the operational period, a stable recovery trend was observed in the ecological environment. (3) The ecological environment in the study area is influenced by multiple factors. Different railway station areas exhibit strong spatial heterogeneity. The impact of single factors is significant, with the existence of spatial stratification and enhanced interactions among multiple factors. The strongest interactive effects are observed between land use types, the intensity of human activities, and temperature.

Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy.

This study seeks to use examine the utility of recently published, developmentally informed measures of parental active tracking for work on youth health behaviors. Information on parental active tracking and health behaviors were collected in six independent survey studies (n's = 157-408). A series of empirical questions regarding parental active tracking were examined. The recently published measures of parental active tracking in high school and college were shown to have sufficient internal consistency, to be well described by single latent factors, to be empirically distinct from helicopter parenting, to be inconsistently linked with youth risk behaviors, and to demonstrate closer linkages with youth substance use when accounting for parental motivations for tracking. The measures of parental active tracking employed provide face valid, psychometrically sound, and empirically supported indices for use examining parental influences during adolescence and young adulthood.

It is highly valuable to analyze and assess the landscape ecological risk of nature reserves to prevent and resolve ecological risks, as well as to effectively protect and maintain the sustainable development of nature reserves. Taking the forest landscape of the Lushan National Nature Reserve as its study object, this study performed grid processing for the nature reserve and classified forest landscape types using the Forest Resource Inventory Database in 2019. A landscape ecological index model was constructed to evaluate the ecological risk. Global and local Moran index values were used to reveal the autocorrelations for ecological risk. The geodetector method was used to comprehensively analyze the effects of natural and human factors on ecological risk. The results showed that, in general, the ecological risk level of the nature reserve was relatively low, as the proportion of the lowest-, lower-, and medium-risk areas to the total forestry land area accounted for 91.03%. The ecological risk ranking of each functional zone, from high to low, was in the order of the experimental zone, the buffer zone, and the core zone. The ecological risk levels of different forest landscape types were closely related to their area, spatial distribution, and succession stage, as well as human factors, such as the proximity to roads and settlements, etc. The forest landscape with the highest ecological risk was the Cunninghamia lanceolata (Lamb.) Hook. forest, and the forest landscape with the lowest ecological risk was other forestry land. Ecological risk had a positive spatial correlation and tended to be aggregated in space, demonstrating coupling with the proximity to roads and settlements. The ecological risk was affected by both human and natural factors, among which human factors played a dominant role. The proximity to roads and settlements, the relative humidity, and the temperature were the main driving factors. The interaction of pairwise factors had a stronger influence than that of single factors. Therefore, controlling the intensity of human activities and enhancing the coordination between humans and nature are beneficial for alleviating the ecological risks in the forest landscapes of nature reserves.

During the process of forming carbon fiber reinforced plastics (CFRP) in an autoclave, deeply understanding the global sensitivity of factors influencing mold surface temperature is of paramount importance for optimizing large frame-type mold thermally and enhancing curing quality. In this study, the convective heat transfer coefficient (CHTC), the thickness of composite laminates (TCL), the thickness of mold facesheet (TMF), the mold material type (MMT), and the thickness of the auxiliary materials layer (TAL) have been quantitatively assessed for the effects on the mold surface temperature. This assessment was conducted by building the thermal-chemical curing model of composite laminates and utilizing the Sobol global sensitivity analysis (GSA) method. Additionally, the interactions among these factors were investigated to gain a comprehensive understanding of their combined effects. The results show that the sensitivity order of these factors is as follows: CHTC > MMT > TMF > TCL > TAL. Moreover, CHTC, MMT, and TMF are the main factors influencing mold surface temperature, as the sum of their first-order sensitivity indices accounts for over 97.3%. The influence of a single factor is more significant than that of the interaction between factors since the sum of the first-order sensitivity indices of the factors is more than 78.1%. This study will support the development of science-based guidelines for the thermal design of molds and associated heating equipment design.