Design of Fully Automatic Specification Selection System for Resistance Welding Equipment

A system for fully automatic selection of welding specifications in resistance welding equipment has been developed to address the problem of workers frequently choosing the wrong specifications during manual welding of multiple parts on a single machine in automobile factories. The system incorporates an automatic recognition system for different workpiece materials using the added machine fixture,visual detection system for nuts and bolts,and secondary graphical confirmation to ensure the correctness of specification calling. This system achieves reliable,fully automatic selection of welding specifications in resistance welding equipment and has shown significant effects in improving welding quality for massproduced workpieces,while solving the problem of specification calling errors that can occur with traditional methods involving process charts and code adjustments. This system is particularly suitable for promoting applications in manual welding of multiple parts on a single machine in automobile factories,ensuring correct specification calling and welding quality.

Basic regulatory science behind drug substance and drug product specifications of monoclonal antibodies and other protein therapeutics

In this review,we focus on providing basics and examples for each component of the protein therapeutic specifications to interested pharmacists and biopharmaceutical scientists with a goal to strengthen understanding in regulatory science and compliance.Pharmaceutical specifications comprise a list of important quality attributes for testing,references to use for test procedures,and appropriate acceptance criteria for the tests,and they are set up to ensure that when a drug product is administered to a patient,its intended therapeutic benefits and safety can be rendered appropriately.Conformance of drug substance or drug product to the specifications is achieved by testing an article according to the listed tests and analytical methods and obtaining test results that meet the acceptance criteria.Quality attributes are chosen to be tested based on their quality risk,and consideration should be given to the merit of the analytical methods which are associated with the acceptance criteria of the specifications.Acceptance criteria are set forth primarily based on efficacy and safety profiles,with an increasing attention noted for patient-centric specifications.Discussed in this work are related guidelines that support the biopharmaceutical specification setting,how to set the acceptance criteria,and examples of the quality attributes and the analytical methods from 60 articles and 23 pharmacopeial monographs.Outlooks are also explored on process analytical technologies and other orthogonal tools which are on-trend in biopharmaceutical characterization and quality control.

Product Specification Analysis for Modular Product Design Using Big Sales Data

Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.

Efficacy of a Diabetes Specific Nutrition Supplement on Glycemic, Anthropometric, Dietary and Gut Health Markers in Adults with Type 2 Diabetes: An RCT

With increasing incidence of diabetes, use of diabetes specific nutrition supplements (DSNS) is common for better management of the disease. To study effect of 12-week DSNS supplementation on glycemic markers, anthropometry, lipid profile, SCFAs, and gut microbiome in individuals with diabetes. Markers studied were glycemic [Fasting Blood Glucose (FBG), Post Prandial Glucose (PPG), HbA1c, Incremental Area under curve (iAUC), Mean Amplitude of Glycemic Excursions (MAGE), Time in/above Range (TIR/TAR)], anthropometry [weight, Body Mass Index (BMI), waist circumference (WC)], lipid profile, diet and gut health [plasma short chain fatty acids (SCFAs)]. N = 210 adults were randomized to receive either DSNS with standard care (DSNS + SC;n = 105) or standard care alone (SC alone;n = 105). After 12 weeks, significant differences between DSNS + SC versus SC alone was observed in FBG [−3 ± 6 vs 14 ± 6 mg/dl;p = 0.03], PPG [−35 ± 9 vs −3 ± 9 mg/dl;p = 0.01], weight [−0.6 ± 0.1 vs 0.2 ± 0.1 kg;p = 0.0001], BMI [−0.3 ± 0.1 vs 0.1 ± 0.1 kg/m2;p = 0.0001] and WC [−0.3 ± 0.2 vs 0.2 ± 0.2 cm;p = 0.01]. HbA1C and low-density lipoprotein (LDL) were significantly reduced in DSNS + SC [−0.2 ± 0.9;p = 0.04 and −5 mg/dl;p = 0.03] respectively with no change in control. Continuous Glucose Monitoring (CGM) reported significant differences between DSNS + SC versus SC alone for mean glucose [−12 ± 65 vs 28 ± 93 mg/dl;p < 0.01], TAR 180 [−9 ± 42 vs 7 ± 45 mg/dl;p = 0.04], TAR 250 [−3 ± 27 vs 9 ± 38 mg/dl;p = 0.05], iAUC [−192 (1.1) vs −48 (1.1) mg/dl;p = 0.03]. MAGE was significantly reduced for both DSNS + SC (−19 ± 67;p < 0.001) and SC alone (−8 ± 70;p = 0.04), with reduction being more pronounced for DSNS + SC. DSNS + SC reported a decrease in carbohydrate energy % [−9.4 (−11.3, −7.6) %;p < 0.0001] and amount [−47.4 (−67.1, −27.7) g;p < 0.0001], increased dietary fiber [9.5 (7.2, 11.8) g;p < 0.0001] and protein energy % [0.9 (0.5, 1.3) %;p < 0.0001] versus SC alone. DSNS + SC reported significant increases versus SC alone in total (0.3 ng/ml;p = 0.03) and individual plasma SCFAs. The consumption of DSNS significantly improves the glycemic, anthropometric, dietary, and gut health markers in diabetes.

Efficacy of a Diabetes Specific Nutritional Supplement (DSNS) on Glycemic Response in Prediabetic Adults: A Two-Armed, Open-Labelled Randomized Controlled Study

It is well known that Diabetes Specific Nutritional Supplements (DSNSs) are linked to improved glycemic control in individuals with diabetes. However, data on efficacy of DSNSs in prediabetics is limited. This was a two-armed, open-labelled, randomized controlled six-week study on 199 prediabetics [30 - 65 years;Glycosylated Hemoglobin (HbA1c) 5.7% - 6.4% and/or Fasting Blood Glucose (FBG) 100-125 mg/dl]. Two parallel phases were conducted: Acute Blood Glucose Response (ABGR) and Intervention phase. Prediabetic participants were randomized into test (n = 100) and control (n = 99). The primary objective was to assess the ABGR of DSNS versus an isocaloric snack, measured by incremental Area under the Curve (iAUC). Test and control received 60 g of DSNS and 56 g of isocaloric snack (cornflakes) respectively, both in 250 ml double-toned milk on visit days 1, 15, 29 and 43. Postprandial Blood Glucose (PPG) was estimated at 30, 60, 90, 120, 150 and 180 minutes. During the 4 weeks intervention phase, the test group received DSNS with lifestyle counselling (DSNS + LC) and was compared with the control receiving lifestyle counselling alone (LC alone). Impact was studied on FBG, HbA1C, anthropometry, body composition, blood pressure, nutrient intake, and physical activity. The impact of DSNS was also studied using CGM between two 14-day phases: CGM1 baseline (days 1 - 14) and CGM2 endline (days 28 - 42). DSNS showed significantly lower PPG versus isocaloric snack at 30 (p 12, and chromium were reported by DSNS + LC versus LC alone. No other significant changes were reported between groups. It may be concluded that DSNS may be considered as a snack for prediabetic or hyperglycemic individuals requiring nutritional support for improved glycemic control.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

Comparative Analysis of Statistical Thickness Models for the Determination of the External Specific Surface and the Surface of the Micropores of Materials: The Case of a Clay Concrete Stabilized Using Sugar Cane Molasses

In this work, four empirical models of statistical thickness, namely the models of Harkins and Jura, Hasley, Carbon Black and Jaroniec, were compared in order to determine the textural properties (external surface and surface of micropores) of a clay concrete without molasses and clay concretes stabilized with 8%, 12% and 16% molasses. The results obtained show that Hasley’s model can be used to obtain the external surfaces. However, it does not allow the surface of the micropores to be obtained, and is not suitable for the case of simple clay concrete (without molasses) and for clay concretes stabilized with molasses. The Carbon Black, Jaroniec and Harkins and Jura models can be used for clay concrete and stabilized clay concrete. However, the Carbon Black model is the most relevant for clay concrete and the Harkins and Jura model is for molasses-stabilized clay concrete. These last two models augur well for future research.

Development of donor specific antibodies after SARS-CoV-2 vaccination:What do we know so far?

Vaccination against Coronavirus disease-19(COVID-19)was pivotal to limit spread,morbidity and mortality.Our aim is to find out whether vaccines against COVID-19 lead to an immunological response stimulating the production of de novo donor specific antibodies(DSAs)or increase in mean fluorescence intensity(MFI)of pre-existing DSAs in kidney transplant recipients(KTRs).This study involved a detailed literature search through December 2nd,2023 using PubMed as the primary database.The search strategy incorporated a combination of relevant Medical Subject Headings terms and keywords:"COVID-19","SARS-CoV-2 Vaccination","Kidney,Renal Transplant",and"Donor specific antibodies".The results from related studies were collated and analyzed.A total of 6 studies were identified,encompassing 460 KTRs vaccinated against COVID-19.Immunological responses were detected in 8 KTRs of which 5 had increased MFIs,1 had de novo DSA,and 2 were categorized as either having de novo DSA or increased MFI.There were 48 KTRs with pre-existing DSAs prior to vaccination,but one study(Massa et al)did not report whether pre-existing DSAs were associated with post vaccination outcomes.Of the remaining 5 studies,35 KTRs with pre-existing DSAs were identified of which 7 KTRs(20%)developed de novo DSAs or increased MFIs.Overall,no immunological response was detected in 452(98.3%)KTRs.Our study affirms prior reports that COVID-19 vaccination is safe for KTRs,especially if there are no pre-existing DSAs.However,if KTRs have pre-existing DSAs,then an increased immunological risk may be present.These findings need to be taken cautiously as they are based on a limited number of patients so further studies are still needed for confirmation.

RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

Genetic regulation of cell type-specific chromatin accessibility shapes brain disease etiology

Nucleotide variants in cell type-specific gene regulatory elements in the human brain are risk factors for human disease.We measured chromatin accessibility in 1932 aliquots of sorted neurons and non-neurons from 616 human postmortem brains and identified 34,539 open chromatin regions with chromatin accessibility quantitative trait loci(caQTLs).Only 10.4%of caQTLs are shared between neurons and non-neurons,which supports cell type-specific genetic regulation of the brain regulome.Incorporating allele-specific chromatin accessibility improves statistical fine-mapping and refines molecular mechanisms that underlie disease risk.Using massively parallel reporter assays in induced excitatory neurons,we screened 19,893 brain QTLs and identified the functional impact of 476 regulatory variants.Combined,this comprehensive resource captures variation in the human brain regulome and provides insights into disease etiology.

Identity Study of English for Specific Purposes Teachers in the Context of Shifts in Public English Teaching Positioning

This study investigates the identity of English for Specific Purposes(ESP)teachers within the dynamic landscape of shifts in public English teaching positioning.Adopting a mixed-methods approach,qualitative and quantitative analyses were employed to explore the complexities of ESP teacher identity construction and adaptation.Qualitative findings revealed key themes including strong professional identity grounded in specialized expertise,the impact of changing educational policies and curriculum reforms,and the importance of cultural competence and intercultural communication.Quantitative analysis of survey data indicated high levels of job satisfaction among ESP teachers,with significant correlations between variables such as professional development participation,perceived efficacy in technology integration,and self-perceptions of identity as ESP educators.

渐近平均伪轨跟踪性质、弱specification性质和分布混沌

证明了有渐近平均伪轨跟踪性质的非平凡紧致动力系统具有一致分布混沌或者按序列分布混沌。此外,在具有渐近平均伪轨跟踪性质系统中的分布混沌在测度中心是一致和稠密的,即有一个不可数的一致分布混沌集是由这样的点组成,它们的轨道闭包包含测度中心。作为一个推论,具有弱specification性质的系统也有类似的结果。

Coherence cube enhancement based on local histogram specification

Coherence analysis is a powerful tool in seismic interpretation for imaging geological discontinuities such as faults and fractures. However, subtle faults or fractures of one stratum are difficult to be distinguished on coherence sections （time slices or profiles） due to interferences from adjacent strata, especially these with strong reflectivity. In this paper, we propose a coherence enhancement method which applies local histogram specification （LHS） techniques to enhance subtle faults or fractures in the coherence cubes. Unlike the traditional histogram specification （HS） algorithm, our method processes 3D coherence data without discretization. This method partitions a coherence cube into many sub-blocks and self-adaptively specifies the target distribution in each block based on the whole distribution of the coherence cube. Furthermore, the neighboring blocks are partially overlapped to reduce the edge effect. Applications to real datasets show that the new method enhances the details of subtle faults and fractures noticeably.

Comparative Analysis of Machine Learning Models for Customer Churn Prediction in the U.S. Banking and Financial Services: Economic Impact and Industry-Specific Insights

Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.

关于弱Specification性质的一个注记

该文证明具有弱specification性质的系统在其测度中心的限制系统也具有弱specification性质,并且其逆不真.作为推论得到在未有满射的假设下,弱specification性质蕴含渐近平均跟踪性质.最后证明具有弱specification性质的满射系统是一致分布混沌的.

弱Specification和逆极限空间上移位映射(英文)

对度量空间上连续自映射引进弱Ｓｐｅｃｉｆｉｃａｔｉｏｎ概念，研究了它与浑沌和拓扑熵的联系，此外还讨论了逆极限空间上移位映射的弱Ｓｐｅｃｉｆｉｃａｔｉｏｎ性质。

Study on specifications of farmland shelterbelt net in Northeastern Plain of China

s: The farmland shelterbelts in Northeastern Plain of China have formed relatively complete net system. The func-tions of shelterbelt net in omnibearing wind check and prevention of frostbite as well as the field of integrated climate effect within shelterbelt net were analyzed, through located observation of meteorological factors. Within the shelterbelt net, the area with more than 10% efficiency of omnibearing wind check was determined as benefited area. The analysis of yield and quality of crops indicated that the sheltering range of shelterbelt net was 25 times tree height. The mature heights of the various varieties of poplar composed the shelterbelts were determined according to their height growth. Based on the comprehensive analysis above, the suitable size of farmland shelterbelt net in Northeastern Plain of China was decided to be 400 m×400 m.

连续映射的弱specification性质与混沌

specification性质是一种重要的动力性质,对连续的区间映射和树映射,该性质与拓扑混合等价。对紧致度量空间,满足POTP性质的拓扑混合映射具有specification性质。文章对紧致度量空间上连续自映射,研究了弱specification性质与各种混沌之间的关系,证明了具有弱specification性质的系统是Li-Yorke意义下混沌的,是Ruelle-Takens意义下混沌的,是处处混沌的,并且具有性质P。

弱Specification性质与不变概率测度

对紧致度量空间上连续自映射,研究了弱Specification性质与不变概率测度之间的关系,证明了具有弱Specification性质的系统一定存在f:X→X的不变概率测度m,使得Suppm=X,并且f:X→X有满测度中心,即M(f)=X.

Remote Control of Unitary Air Conditioner Based on Manufacturing Message Specification

Traditional air conditioner used to be in local control mode. However, with the development of modern information technology, the control of air conditioner will have to support many more functions. As a first step in such a development, we have implemented a remote monitoring system of unitary air conditioners, using the manufacturing message specification (MMS). MMS is an OSI application protocol designed for the remote control and monitoring of real devices. The functions supported are those that an operator would do normally at the console of the unitary air conditioner. Due to the accomplishment of UMCS (Unitary air conditioner MMS Control System), there will be a leap of the application of MMS and the control technology of unitary air conditioner.