Acquisition Method Research Articles

Progressive Linear Programming Optimality Method Based on Decomposing Nonlinear Functions for Short-Term Cascade Hydropower Scheduling

Short-term optimal scheduling of cascade hydropower stations enhances their flexible regulation and power generation capabilities. However, nonlinear function relationships and multistage and hydraulic interdependencies present significant challenges, resulting in considerable solution errors, premature convergence, and high computational demands. This study proposes a progressive linear programming method that decomposes nonlinear functions to address these challenges. First, to accurately represent nonlinear functions and mitigate computational complexity, the entire feasible domain is partitioned into multiple contiguous subdomains in which nonconvex nonlinear functions within each subdomain can be equivalently replaced by linear relationships. Second, a progressive linear programming optimization algorithm is devised to prevent premature convergence, utilizing continuous subdomains rather than discrete points as state variables and incorporating the progressive optimality principle. Finally, to increase the solution efficiency, a dimensionality reduction strategy via the feasible domain state dynamic acquisition method is presented and optimized after excluding the infeasible states in each stage. The simulation of three cascade hydropower stations in a river basin in southwest China shows that the proposed method can achieve a superior peak regulation effect compared to the conventional mixed integer linear programming and progressive optimality algorithm. During the dry and wet seasons, the residual load peak–valley differences at the three stations are reduced by 612 MW and 521 MW compared to the MILP and 1889 MW and 2439 MW compared to the POA, which underscores the effectiveness of the method in optimizing the short-term scheduling of cascade hydropower stations.

Impact of vascular architecture, oxygen saturation, and hematocrit on human cortical depth-dependent GE- and SE-BOLD fMRI signals: A simulation approach using realistic 3D vascular networks

Abstract Recent advancements in MRI hardware, including ultra-high magnetic field scanners (≥7T) and MR data acquisition methods, have enhanced functional imaging techniques, allowing for the detailed study of brain function, particularly at the mesoscopic level of cortical organization. This has enabled the measurement of blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal changes across cortical depth in the human brain, facilitating the study of neuronal activity at laminar level. In order to better understand the generation of cortical depth-dependent BOLD signals, biophysical modeling and computational simulations permit the characterization of the impact of vascular architecture, as well as the biophysical and hemodynamic effects at the mesoscopic level. In this study, we employed four realistic 3D vascular models that mimic the human cortical vascular architecture and simulated various vessel-dependent oxygen saturation and hematocrit states, aiming to characterize the intravascular and extravascular contributions to gradient-echo (GE) and spin-echo (SE) BOLD signal changes across human cortical depth at 7T. We found that differences in the local vascular architecture between the four models, away from the pial surface, do not significantly influence the shape and amplitude of BOLD profiles. This implies that signal profiles within a cortical region of a given angioarchitecture can be averaged within a given layer without introducing substantial errors in the results. The findings futher reveal that in deeper laminae, relative relaxation rates for both GE and SE decrease linearly with increasing oxygen saturation levels, with GE showing a stronger effect. In contrast, the top lamina shows a non-linear behavior due to large vessel contributions, particularly venous, with GE displaying higher relaxation rates (4–8 times larger dependent on oxygen saturation levels) than SE. Relative BOLD signal changes also follow linear trends in deeper layers, with GE peaking at ~8% and SE at ~4%, reflecting the higher microvascular specificity of SE. However, SE does not fully eliminate large vessel contributions at the pial surface, where diffusion effects and vessel architecture play a role. Hematocrit levels linearly change the BOLD signal amplitude and significantly influence laminar contributions across cortical depth and imaging techniques. While GE signals are dominated by extravascular effects, SE retains notable intravascular venous contributions at high oxygen saturation levels, which is particularly relevant in experiments involving controlled vascular oxygenation, that is, gas challenges. These results underscore how vascular features, hematocrit, and biophysical interactions shape cortical depth-dependent BOLD signals and their specificity in ultra-high field imaging.

Exploring DUA Methodology in Teaching Technical Vocabulary for Cutting and Sewing in English: A Qualitative Study in Technical Schools of Ecuador

This qualitative study explores the application of Universal Design for Learning (DUA) methodology in teaching technical vocabulary for cutting and sewing in English within Ecuador's technical secondary education system, specifically at Colegio Fiscal Técnico Industrial Febres Cordero. The research focuses on third-year baccalaureate students (n=30) who are developing specialized vocabulary skills essential for their technical careers. Despite the growing importance of English proficiency in technical fields, research on specialized vocabulary acquisition methods in vocational education remains limited in Latin American contexts. Through semi-structured interviews with teachers (n=5), focus group discussions with students, classroom observations, and analysis of instructional materials, this research examines how DUA principles are understood, adapted, and implemented in this specific educational environment. The findings reveal that teachers at Febres Cordero creatively adapt DUA principles to accommodate diverse learning needs while negotiating challenges including limited resources and the integration of traditional terminology. Students demonstrate enhanced engagement and vocabulary retention when multiple representation modes are utilized, particularly through visual-tactile connections between English terminology and physical materials. The study identifies sociocultural dimensions affecting technical vocabulary acquisition, including local textile traditions and the practical application contexts most relevant to students' future employment opportunities. This research contributes to understanding how flexible, multimodal approaches like DUA can be contextually adapted for technical vocabulary instruction while addressing the specific educational realities and needs of students at “Colegio fiscal técnico Industrial Febres Cordero”. Implications for teacher training, curriculum development, and culturally responsive ESL pedagogy in Ecuador's technical education system are discussed.

Evaluating Challenges in Learning English with the WASPAS Approach

Learning English as a second language presents various challenges for learners. Pronunciation and grammar can be difficult due to differences from their native language. Vocabulary acquisition requires time and practice, while idioms and slang add complexity. Listening and speaking skills can be intimidating, especially in fast-paced conversations. Writing in English demands proper sentence structure and coherence. Additionally, cultural differences may impact understanding and communication. Limited exposure to English-speaking environments and a lack of confidence can further hinder progress. Despite these challenges, consistent practice, immersive learning, and effective teaching methods can help learners overcome difficulties and achieve fluency. The significance of researching challenges in learning English as a second language lies in improving language acquisition methods and enhancing educational strategies. Understanding these challenges helps educators develop more effective teaching approaches, addressing issues such as pronunciation, grammar, vocabulary, and cultural differences. It also aids policymakers in designing supportive language programs for learners. Furthermore, identifying barriers like anxiety, lack of exposure, and limited resources can lead to targeted interventions that boost learners’ confidence and proficiency. This research contributes to linguistic, cognitive, and social development, ensuring better communication skills and broader opportunities for individuals in academic, professional, and global contexts. The methodology for studying challenges in learning English as a second language involves a mixed-methods approach, combining qualitative and quantitative research. Surveys and questionnaires can be used to collect data from learners about difficulties in pronunciation, grammar, vocabulary, and communication. Interviews and focus groups help gather in-depth insights into personal learning experiences. Observational studies in classrooms assess teaching methods and learner engagement. Additionally, language proficiency tests measure progress and identify specific problem areas. Data analysis involves identifying common trends and patterns in learning difficulties. This approach helps develop effective strategies to overcome challenges and improve English language acquisition. Alternative: Pronunciation Issues, Grammar Complexity, Vocabulary Retention, Listening Comprehension, Writing Skills, Understanding Idioms & Slang, Cultural Context & Expression, Fear of Making Mistakes. Evaluation preference: Difficulty Level, Frequency, Impact on Learning, Overcoming Difficulty, Learner Confidence, Need for External Support. Fear of Making Mistakes is getting top of the table and Writing Skills is getting bottom of the table.

Anchor Characteristics and Online Purchase Intention: A Study Based on SOR Framework

With the rapid development of Chinas e-commerce industry, live e-commerce has become an important driving force for online consumption. The live streaming industry has attracted the attention of the industry and even young people awaiting employment. Based on the situation of live broadcast e-commerce, this study crawls 100 data from TikTok live broadcast platform, combining SOR model and social presence theory, discussing the influencing mechanism of anchor characteristics on consumers purchase intention, and constructs a theoretical model. The results show that the professionalism, attractiveness and the fan stickiness of the anchor can positively promote the social presence of consumers in the process of live broadcasting. Social presence will positively affect consumers purchase intention. This study has certain theoretical and practical significance. It not only makes up for the stickiness of fans in the existing research gap, but also uses new data acquisition methods ; at the same time, it provides targeted opinions for anchors, merchants and platforms, which is beneficial to improve the operational effectiveness and promote the vigorous development of the live broadcast e-commerce industry.

Nanomaterial assisted natural killer cell therapy

The rising incidence of cancer has heightened interest in immune cell therapy, particularly the role of natural killer (NK) cells, which are essential components of the immune system. Their applications in tumor treatment have expanded significantly, especially with the incorporation of nanomaterials. This review comprehensively examines NK cell biology, encompassing aspects such as classification, distribution, receptor activation, and mechanisms of cytotoxicity. It also explores various NK cell therapies, including their sources, methods of acquisition, expansion techniques, Chimeric antigen receptor-Natural Killer cell (CAR-NK) technology, gene editing strategies, and combination therapies. Additionally, the review discusses the utilization of nanomaterials in NK cell therapy, focusing on nanoparticle-assisted immune regulation and the modulation of the tumor microenvironment. While NK cell therapy holds promise, CAR-NK technology presents certain limitations. The integration of nanomaterials offers potential strategies to enhance therapeutic efficacy. Future research should prioritize the optimization of NK cell therapy, address the limitations associated with CAR-NK technology, investigate the mechanisms of nanomaterials, and develop more effective nanomaterials to improve clinical outcomes.

Acquisition method for effective electromagnetic parameters of honeycomb structures based on FNN–INN

The effective electromagnetic parameters of honeycomb composite structures are crucial for studying their electromagnetic properties. However, most existing homogenization methods are based on static or quasi-static equivalence, considering only the effects of the electromagnetic parameters and the volume fraction of each phase medium on the effective electromagnetic parameters, while neglecting the influence of honeycomb aperture parameters. To address this, this paper proposes a novel homogenization method systematically analyzing the influence of honeycomb structure parameters, particularly aperture parameters, on the effective electromagnetic parameters. This method enables the direct acquisition of the effective electromagnetic parameters of honeycomb structures from their geometric parameters and the material parameters of each phase medium. First, this paper uses the geometric parameters of the honeycomb structure and the material parameters of each phase medium as inputs to construct a forward neural network for predicting the reflection coefficient (S11 parameter). Subsequently, the S11 parameter matrix is rapidly constructed using the reflectance formula. Correlation analysis and global feature extraction in two-dimensional space yield the feature vector matrix, representing the transformed S11 parameters. These are used as inputs to construct an inverse neural network to predict the effective electromagnetic parameters of the honeycomb structure. Finally, this paper reconstructs reflectance curves for two-layer and three-layer honeycomb structures, with mean absolute error below 0.26, validating the proposed method's effectiveness and accuracy.

Intelligent Testing Method for Multi-Point Vibration Acquisition of Pile Foundation Based on Machine Learning.

To address the limitations of the conventional low-strain reflected wave method for pile foundation testing, this study proposes an intelligent multi-point vibration acquisition testing model based on machine learning to evaluate the integrity of in-service, high-cap pile foundations. The model's performance was assessed using statistical error metrics, including the correlation coefficient R2, mean absolute error (MAE), and variance accounted for (VAF), with comparative evaluations conducted across different model frameworks. Results show that both the convolutional neural network (CNN) and the long short-term memory neural network (LSTM) consistently achieved high accuracy in identifying the location of the first reflection point in the pile shaft, with R2 values greater than 0.98, MAE below 0.41 (m), and VAF greater than 98%. These findings demonstrate the model's strong predictive capability, test stability, and practical utility in supporting operator decision-making. Among the evaluated models, CNN is recommended for analyzing the integrity of in-service pile foundation based on the multi-point vibration pickup signals and multi-sensor fusion signal preprocessed by the time series stacking method.

Pathologization of LGBTQIA*individuals in psychiatry, psychosomatic medicine, and psychotherapy - historical continuities and implications for medical training and practice

The ongoing pathologization and criminalization of sexual and gender minorities means that LGBTQIA*individuals, due to minority stress, are significantly more likely to suffer from mental illness. A connection is made between the involvement of psychiatrists and psychotherapists in the systematic pathologization of LGBTQIA*individuals in the past and the associated implications for medical training and practice in the present.Heuristic methods of knowledge acquisition, using literature research, with a focus on the history of the (de-)pathologization of sexual and gender minorities as well as the associated historical continuities.Various examples of historical continuities in the pathologization of LGBTQIA*persons can be found. Although there has been an increased awareness of the importance of teaching aspects of diversity in medical school in recent years, there is still a need for a medical-historical examination of the pathologization of sexual and gender minorities.An "active de-pathologization" can only be achieved by dealing with the medical-psychiatric past in terms of content, including the implementation of such contents in medical education, as part of teaching medical history and the subjects of psychiatry, psychosomatic medicine, and psychotherapy, as well as in the context of courses on history taking.These reflexive processes must continue during psychiatric and psychosomatic training, and residents must be offered specific training on topics in this area.

Induced pluripotent stem cell-derived mesenchymal stem cells (iMSCs) inhibit M1 macrophage polarization and reduce alveolar bone loss associated with periodontitis

BackgroundPeriodontitis is a chronic inflammatory disease and macrophages play a pivotal role in the progression of periodontitis. Mesenchymal stem cells (MSCs) have emerged as potential therapeutic agents for the treatment of periodontitis due to their immunomodulatory properties and capacity for tissue regeneration. Compared to conventionally derived MSCs, induced pluripotent stem cell-derived MSCs (iMSCs) offer distinct advantages as promising candidates for MSC-based therapies, owing to their non-invasive acquisition methods and virtually unlimited availability. This study aims to investigate the effects and mechanisms of iMSCs in modulating macrophage polarization and alleviating periodontitis-related alveolar bone loss.MethodsiMSCs were generated from iPSCs and characterized for differentiation potential. The effects of iMSCs on macrophage polarization were evaluated using THP-1-derived macrophages under inflammatory conditions (LPS and IFN-γ stimulation). Co-culture assays, cytokine analysis, reactive oxygen species (ROS) detection, transcriptomic analysis, flow cytometry, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and western blot analysis were performed to elucidate the underlying mechanisms. The therapeutic potential of iMSCs was assessed in a ligature-induced periodontitis mouse model using micro-CT, histological analysis, and immunofluorescence staining.ResultsiMSCs inhibit M1 macrophage polarization through the suppression of the NF-κB signaling pathway. Additionally, iMSCs reduce the production of pro-inflammatory cytokines (IL-1β, IL-17) and reactive oxygen species (ROS), while enhancing the secretion of anti-inflammatory cytokines (IL-10) and growth factors (VEGF), thereby improving the inflammatory microenvironment. Under inflammatory conditions, iMSCs preserve the osteogenic potential of periodontal ligament stem cells (PDLSCs) and alleviate alveolar bone loss in mice with periodontitis. In vivo, iMSCs reduce the number of M1 macrophages and inhibit the activation of NF-κB in periodontal tissues, supporting their anti-inflammatory and immunomodulatory effects.ConclusioniMSCs demonstrate significant therapeutic potential in periodontitis by modulating macrophage polarization, reducing oxidative stress, and mitigating alveolar bone loss associated with the disease. These findings provide new insights into the mechanisms of iMSCs and their application as cell-based therapies for periodontal diseases.

Efficiency of Project Management Offices: An Exploratory Analysis

This study aims to critically evaluate research on the efficiency of Project Management Offices (PMOs) within the domain of Project Management. This research employs the literature-grounded theory (LGT) methodology, which focuses on generating knowledge - theories or hypotheses - through selecting, analyzing, and synthesizing existing scientific and technological literature. This approach provides a set of scientific data derived from a specific method of knowledge acquisition, fostering thematic growth for researchers in this field and highlighting key trends. This research aims to examine how PMO efficiency assessment is addressed in the literature and to understand the concept of efficiency in PMOs. The study progresses through three main stages: first presenting articles on PMO topics; second deepening the understanding of specific methods and techniques; finally a content analysis, which examines how researchers have assessed PMO efficiency and the approaches they have employed. The findings reveal a lack of conceptual rigor in the definitions of efficiency used in PMO evaluations. Numerous empirical evaluation instruments from the surveyed literature fail to address these concepts adequately. This study makes two primary contributions: first, it identifies the most significant relationship between PMO evaluations and organizational performance, wherein project efficiency is a benchmark for assessing PMO efficiency. Second, it explores key correlations by cross tabulating the identified efficiencies.

A user demand acquisition method for cloud services based on user sentiment analysis and long- and short-term preferences

A user demand acquisition method for cloud services based on user sentiment analysis and long- and short-term preferences

Quantitative Label-Free Single-Cell Proteomics on the Orbitrap Astral MS.

Quantitative Label-Free Single-Cell Proteomics on the Orbitrap Astral MS.

Exploratory Analysis of the IPF and fHP Proteome Using Data-independent Acquisition Methods

Exploratory Analysis of the IPF and fHP Proteome Using Data-independent Acquisition Methods

A Low-Cost Full-Spectrum Acquisition Method Based on Compressed Sensing and its Application in Wastewater Detection

A Low-Cost Full-Spectrum Acquisition Method Based on Compressed Sensing and its Application in Wastewater Detection

Research on the Teaching Promotion Strategy of Accounting Course in Higher Vocational Colleges Based on the Analysis of Emotional Data of Course Feedback

In view of the current situation that the research on Hybrid Teaching of accounting courses in higher vocational colleges is mostly from the perspective of concept, teaching design and teaching effect, while the research on the internal factors of the teaching process and curriculum evaluation feedback is relatively less, this paper first designs a data crawling program for online courses based on python3.9.10, and uses China MOOC as the object of data crawling to obtain the evaluation data of the target course, and then uses the combination of regular replacement and manual screening to clean the meaningless words, symbols and retrograde data, and then uses the NLPIR Chinese word segmentation toolkit of the Chinese Academy of Sciences to process Chinese text words. After word segmentation, it completes the construction of a knowledge driven multilingual sentiment analysis model, which obtains the context by word embedding. Word vector representation, Different semantic information acquisition methods are used to extract a variety of semantic information contained in the text itself, and then the feature information is fused through the double interactive attention mechanism, so as to realize the coordination and optimization of feature information between aspect words and context information. Finally, a simulation experiment is designed to classify 19837 effective curriculum reviews through the emotion classification model, and obtain the emotional word cloud of curriculum evaluation, and then give the corresponding curriculum improvement strategies.

Improving Multilingual Education in Malaysia: Tailoring Language Teaching for SJKC Schools

Multilingual education plays a critical role in linguistic and cognitive development, yet its implementation varies across Southeast Asia. While countries such as the Philippines, Indonesia, and Thailand initially mandated national language instruction, challenges in literacy acquisition among minority groups led to the adoption of Mother Tongue-Based Multilingual Education (MTB-MLE). Malaysia’s Chinese National-Type Schools (SJKC) have long provided a form of multilingual education, yet the lack of a clear distinction between first-language (L1) instruction and second-language (L2/L3) acquisition has resulted in cognitive overload and limited linguistic fluency. This paper advocates for a differentiated approach in SJKC, where Mandarin (L1) is leveraged as the primary medium for cognitive and literacy development, while Malay (L2) and English (L3) are taught using second-language acquisition methods that emphasize functional communication over content mastery. Drawing on literature reviews and best practices, this study proposes a structured framework to optimize multilingual education in SJKC through differentiated syllabi, targeted pedagogical strategies, and curriculum reform. These measures aim to enhance linguistic proficiency while fostering higher-order thinking skills, ensuring that Malaysia’s trilingual education model remains effective, sustainable, and globally competitive.

Estimating size selectivity of Calanus spp. in plankton trawls: effect of mesh size for commercial fishery

Calanus spp. are marine zooplankton that are considered as a potential source for aquaculture feed, due to their high abundance in the North Atlantic. The diminutive size (< 5 mm) of these zooplankton necessitates the use of fine-meshed trawls to harvest them efficiently. However, the use of fine-meshed trawls leads to high drag and thus energy demand to tow the trawl. Therefore, it is of importance to know the effect of mesh size on the capture efficiency of Calanus, and specifically to quantify which sizes of those that can be harvested effectively depending on mesh size in the trawl. Therefore, experimental trawl fishing targeting Calanus was conducted testing gears differing in mesh size to estimate their size selectivity. We used an image and artificial intelligence-based specially built and trained acquisition method to rapidly measure the size of large numbers of Calanus to prepare the catch data for size selectivity analysis. The results demonstrate a clear size selection process of Calanus in nets with mesh sizes considered for this fishery. Specifically, the size selectivity would result in a loss of 10%, 45% and 99% of Calanus if the trawl has a mesh size of 500, 750 and 1000 μm, respectively.

Challenges in the identification and quantitation in on-line breath analysis

The identification and quantitation of volatile organic compounds (VOCs) in exhaled human breath has attracted considerable interest due to its potential application in medical diagnostics, environmental exposure assessment, and forensic applications. Secondary electrospray ionization-mass spectrometry (SESI-MS) is a method capable of detecting thousands of VOCs. Nevertheless, most studies using SESI-MS for breath analysis have relied primarily on MS1measurements for identifications and quantification, which are susceptible to misassignments and errors. In this study, we targeted several endogenous compounds (C5 to C10 aldehydes, limonene and pyridine), known to occur in breath. These compounds were measured and quantified in exhaled breath from 12 volunteers over several days using three different acquisition methods: full scan, targeted selected ion monitoring and parallel reaction monitoring. These methods were used for identification and quantification by comparing with measurements of external standards. High-abundance features such as limonene and pyridine were successfully identified and quantified in exhaled human breath with all three methods, with MS2measurements supporting identification, albeit with limitations to separate between limonene andα-/β-pinene. For low-abundance features, the study highlights the challenges of false assignments in SESI-MS, even with MS2measurements. This was demonstrated in the case of aldehydes, which could not be reliably separated from isomeric ketones present in breath, leading to incorrect quantification.

Techniques for Respiratory Motion-Resolved Magnetic Resonance Imaging of the Chest in Children with Spinal or Chest Deformities: A Comprehensive Overview.

Quantification of the severity of chest wall deformation in children with spinal deformities is essential for understanding the effects on trunk appearance and cardiopulmonary function. Magnetic resonance imaging (MRI) is particularly valuable for this purpose, as it does not employ ionizing radiation and can provide three-dimensional (3D) imaging of thoracic anatomy. Acquiring sufficient quality images of the chest wall, lungs and airways at key stages of the respiratory cycle, such as end-inspiratory or expiratory phase, is crucial for accurately assessing chest wall deformation and pulmonary function and mechanics. Regarding image quality, low proton density and short relaxation times of the lung tissues result in poor quality images, and long acquisition times result in blurring caused by respiratory and cardiac motion. This overview summarizes strategies developed to address the inherent challenges of visualization of lung tissue and respiratory motion in MRI acquisition of the chest of pediatric patients with spinal deformities. An overview of the main methods for motion-resolved image acquisition and measurement of chest wall motion and thoracic volumes is presented and discussed. It is concluded that despite the development of multiple techniques and diverse strategies for obtaining high-quality, motion-resolved chest MRI, further validation of these methods is required before their implementation in clinics for routine evaluation of chest deformation in pediatric spinal deformity patients.