bluuebunny/crossref_metadata_2025 huggingface dataset:
Research of static and dynamic characteristics of a process system ‘electric drive-fluid-handling machine-pipeline’
Authors: Al-Rawashdeh Ayman Y. , Pavlov Vlademer E. , Al Zyoud Khalaf Y. , Khrisat Mohammad S.
Published: February 2024

The paper offers the obtained quantitative assessment of the performance and energy parameters of a process system composed of an asynchronous motor, a fluid-handling machine and a pipeline when using two methods of performance control, in particular, throttling and speed control methods. Taking into account nonlinearity of mathematical representation of fluid-handling machines and asynchronous drive motors, the starting conditions were analysed using nonlinear differential calculus. The calculations for the models were performed using the MATLAB software package. Transient profiles of flow and head, stator current, angular frequency and torque of an asynchronous motor were obtained at pump startup and control of pump capacity. It has been found that the developed mathematical model of a process system composed of an asynchronous motor, a fluid-handling machine and a pipeline allows obtaining quantitative estimation of the performance and energy parameters of the unit when using two methods of the pump capacity control. The use of frequency method allows to decrease the pump rotation speed and significantly reduce the power consumed by the unit and provide energy-saving mode of operation, the economic efficiency of which depends on the range of feed control.
CNN-CatBoost ensemble deep learning model for enhanced disease detection and classification of kidney disease
Authors: Bhaskar Navaneeth , Borhade Ratnaprabha Ravindra , Barekar Sheetal , Bachute Mrinal , Bairagi Vinayak
Published: February 2024

An efficient deep-learning prediction model for identifying chronic kidney disease (CKD) from exhaled breath is presented in this paper. The concentration of urea will be higher in CKD patients. Salivary urease breaks down the stored urea into ammonia, which is then excreted through breath. Thus, by monitoring the breath ammonia content, it is possible to identify the presence of high urea levels in the body. In this work, a novel sensing module is developed and applied to measure and assess the amount of ammonia in exhaled breath. Moreover, an effective deep learning prediction model that combines the CatBoost algorithm and convolutional neural network (CNN) is used to automate the prediction of disease. The proposed model, which combines the benefits of gradient-boosting and CNN, attained an exceptional accuracy of 98.37%. Experiments are conducted to evaluate the proposed model using real-time data and to assess how well it performs in comparison with existing deep learning methods. Our study's findings demonstrate that kidney disease can be accurately and noninvasively diagnosed using the proposed approach.
Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach
Authors: Yaddarabullah Yaddarabullah , Rahman Aedah Abd , Saad Amna
Published: February 2024

This study investigates the use of k-nearest neighbors (k-NN) for classifying occupant positions in under-actuated zones, aiming to enhance ventilation control. The focus is on evaluating different data preprocessing techniques, particularly cumulative moving average (CMA), Kalman filtering (KF), and their combination, to boost the k-NN model's reliability and accuracy. The research uses received signal strength indicator (RSSI) data in a controlled setting. The methodology involves dividing the dataset into training and testing subsets and using root mean squared error (RMSE) to determine the best k value for model validation. The study performs a comparative analysis of the k-NN model's performance with both original and preprocessed RSSI data, focusing on metrics such as accuracy, precision, recall, F1-score, and RMSE. The findings emphasize the significant impact of the combined CMA-KF preprocessing technique in improving the model's accuracy and reliability. Specifically, this approach achieved an accuracy of 98.58%. The RMSE values are particularly noteworthy, exhibiting a perfect fit (RMSE of 0) for training data and a remarkably low RMSE of 0.119 for testing data, confirming the model's high accuracy and predictive capability.
Automatic facial expression recognition under partial occlusion based on motion reconstruction using a denoising autoencoder
Authors: Kemmou Abdelaali , El Makrani Adil , El Azami Ikram , Aabidi Moulay Hafid
Published: February 2024

Automatic facial expression recognition (FER) plays a valuable role in various fields, including health, road safety, and marketing, where providing feedback on the user’s condition is crucial. While significant progress has been made in controlled environments (such as frontal, unconcluded, and well-lit conditions), recognizing facial expressions in unconstrained environments (natural settings) remains challenging. The presence of occlusions poses a particular difficulty as they obscure parts of the facial information captured in the image. To address this issue, researchers have proposed different solutions, broadly categorized into two approaches: those focusing on visible regions of the face and those attempting to reconstruct hidden parts. Currently, most solutions rely on texture or geometry-based methods, with only a few utilizing motion-based approaches. However, incorporating motion appears to be particularly promising in adapting to occlusions due to its unique characteristics, such as close-range propagation and local coherence. In this paper, our focus lies on leveraging motion to overcome the challenges posed by occlusions in FER tasks.
Formal validation of authentication scheme in 5G-enabled vehicular networks using AVISPA
Authors: Hamdan Mays A. , Maklouf Amel Meddeb , Mnif Hassene
Published: February 2024

Smart transportation may come from 5G-enabled cars. Traffic reports include congestion, roads, and driving. Urbanisation and population growth increase traffic accidents and travel time. Traffic accidents kill and injure most people worldwide. Intelligent transportation systems (ITS) improves driver and pedestrian safety. This study connects the VANET to 5G to create a 5G-enabled vehicle network because the road-side unit (RSU) is expensive and unsecure. This study connects numerous automobiles to TA for 5G-BS D2D communication. Data transmissions between autos are risky. Several scholars suggest authentication techniques for safe vehicle-to-vehicle communications. Overhead may enable side-channel attacks with these tactics. A secure and effective efficient and secure authentication-privacy-preserving (ES-APP) system connected TA, 5G-BS, and on-border unit (OBU) was presented. Initialization, vehicle registration, parameter renewal, message signing, single and batch verification are ES-APP steps. The formal evaluation automated verification of internet security protocols and applications (AVISPA) tool with on-the-fly model-checker (OFMC) and attack searcher (ATSE) back-ends secures the suggested ES-APP technique. ES-APP appears impervious to active and passive AVISPA assaults.
Modeling agricultural and methane emission data: a finite mixture regression approach
Authors: Thongnim Pattharaporn , Charoenwanit Ekkapot
Published: February 2024

In this paper, the method for unsupervised learning of finite mixture regression (FMR) models is presented for evaluation using agricultural and emissions data sets. The FMR models can be written as problems with incomplete data, and the expectation–maximization (EM) algorithm can be used to estimate unknown variables. The goals of this research are to find the best clustering model with different sets of training and test data and examine the relationship between crop production index and methane emissions in 22 countries from 1990 to 2019 using FMR. In this study also use machine learning process for a FMR model from real world data. According to the findings, the performance of the random training data (RDM) in time series is preferable to that of the fixed training data (FXM). In addition, both RDM and FXM are capable of classifying the 22 countries into two distinct groups and constructing the parameters for the regression model. However, selecting training and test data will result in a good prediction; it is dependent on the data collected. Picking the right training and test data is crucial for accurate predictions-it all comes down to having good data in the first place.
Cultivating excellence: a case study of enterprise architecture transformational journey in higher education
Authors: Amin Fatimah Azzaharah , Hussein Surya Sumarni , Daud Nor Aziah , Abu Bakar Nur Azaliah , Wan Ahmad Wan Azlin Zurita
Published: February 2024

An enterprise architecture (EA) is a critical framework that clarifies the complex elements of organizations, including business processes and technology. It coordinates the interaction of these elements to achieve predetermined business goals. Higher education institutions (HEIs) may become susceptible to rigidity, duplication, and intricate operations without a comprehensive EA. The primary aims of this study are to ascertain the necessary information for each EA domain: business, data, and application at Universiti Teknologi MARA (UiTM) and to design the landscape map viewpoint architecture for UiTM by utilizing the EA framework. This research uses qualitative methods, namely interviews and document analysis, to comprehensively comprehend the intricacies and prerequisites within every EA domain. Critical insights for each UiTM domain: business, data, application, and technology were uncovered through the discernments. From the thematic analysis, the frequency of issues according to the EA domain is business 13 issues, application eight issues, data seven issues, and technology seven issues. Following a thorough analysis, these findings led to the development of the landscape map viewpoint architecture diagram. In conclusion, the results of this research hold the potential to provide HEIs with invaluable knowledge for enhancing their organizational transformation via EA, thereby propelling the overall quality and efficacy of higher education systems forward.
Evaluating various machine learning methods for predicting students' math performance in the 2019 TIMSS
Authors: Elouafi Abdelamine , Tammouch Ilyas , Eddarouich Souad , Touahni Raja
Published: February 2024

The growth of a country strongly depends on the quality of its educational system. All over the world, the education sectors are experiencing a fundamental evolution of their mode of operation. The greatest challenge for education today is the low success rate of learners and the abandonment of education in institutions at a premature age. Early prediction of student failure can help administrators provide timely guidance and supervision to enhance student success and retention. We propose a performance prediction model based on students' social and academic integration using several classification algorithms. This study involves a comparative analysis of five algorithms: logistics regression, k-nearest neighbors (K-NN), support vector machine (SVM), decision tree, and random forest. They were applied to a set of data from TIMSS 2019 in Morocco, to determine their effectiveness in predicting student performance using prediction models such as logistics regression, KNN, SVM, decision-tree, and random forest, decision-makers can make data-driven decisions to enhance educational strategies and improve outcomes in mathematics education.
SWT-PCA-CNN: hyperspectral image classification with multi-stage feature extraction and parameter tuning
Authors: Goswami Tilottama , Shruthi Kandi Navya , Chokkarapu Sindhu , Kune Raghavendra , Tripathi Mukesh Kumar
Published: February 2024

Hyperspectral imaging is an increasingly popular technique in remote sensing, offering a wealth of spectral information for a range of applications. This paper presents a comparative study of hyperspectral image classification techniques using three different datasets: Indian Pines, Salinas, and Pavia University. The study employs a combination of three methods, namely stationary wavelet transforms (SWT), principal component analysis (PCA), and convolutional neural network (CNN), to develop a model for hyperspectral image classification. The proposed approach combines SWT and PCA for spatial feature extraction and dimensionality reduction, followed by classification using CNN. Furthermore, the study performs parameter tuning by changing the optimizer, activation function, and filter size of the CNN model on the Indian Pines dataset. The results demonstrate that the proposed SWT-PCA-CNN approach outperforms the conventional DWT-PCA and PCA-KNN algorithms, achieving an overall classification accuracy of 98.2%, 99.86%, 99.80% on the Indian Pines, Salinas and Pavia University datasets respectively. The study highlights the effectiveness of the proposed approaches for hyperspectral image classification and their potential for applications in remote sensing and other fields.
Precision agriculture: exploration of deep learning models for farmland mapping
Authors: Tolentino Anjela Cabrera , Palaoag Thelma D.
Published: February 2024

Precision is required for agricultural advancements to be sustainable. Traditional farming lacks effective monitoring, resulting in resource waste and environmental problems. Farmland mapping is important for agricultural management and land-use planning. The use of deep learning techniques in farmland mapping is increasing rapidly. Excellent results have been generated from deep learning approaches in a number of applications, such as image processing and prediction. Agricultural agencies are now considering different applications of deep learning including land mapping, crop classification, and monitoring of paddy fields. This paper shall explore different deep learning models that are commonly used for image processing specifically in land mapping. The three deep learning models convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) were evaluated to find out which among the deep learning models is best for land mapping. It compares the classification accuracy of the models on image processing and it can be concluded that CNN algorithm normally makes better results when compared to other deep learning models. This study offers guideline and suggestions to researchers who are interested in contributing to the field of precision agriculture with the used of deep learning techniques.
Agile fusion: developing 'Eat at Right Place' sentiment analysis tool
Authors: Prabhune Akash , Srihari Vinay R , Sethiya Neeraj Kumar , Gauniyal Mansi
Published: February 2024

This study presents the development and validation of the "Eat at Right Place Initiative," a sentiment analysis tool for restaurant reviews. Combining a user-centric approach with the Scrum framework, the mHealth agile development and evaluation framework was implemented, deviating from the initially considered Scrum framework. A multidisciplinary team navigated three phases, aligning sprints, goals, and backlogs. Phase 1 focused on product identification through interviews and surveys. Phase 2 involved development and alpha testing using a bidirectional encoder representation from transformers (BERT) rule-based sentiment analysis model. The final phase, beta testing, incorporated user feedback for usability enhancements. Ethical considerations were prioritized, ensuring participant consent and confidentiality. The study culminated in a robust aspect-based sentiment analysis model, effective in capturing nuanced insights from diverse restaurant aspects. Beta testing revealed actionable insights, marking the tool as fit for release. This sentiment analysis tool addresses consumer and owner needs, with iterative development and real-world testing laying the groundwork for future enhancements.
Fuzzy logic controller-based Luo converter for light electric vehicles
Authors: Balamurugan Rangaswamy , Vairavel Komarapalayam Subramaniam , Kalimuthu Marimuthu , Devi Veerappan Kiruthiga
Published: February 2024

A brushless DC motor (BLDC) drive fed by a Luo converter is presented in this paper for the use in electric vehicle (EV) applications. The proposed Luo converter provides stable and ripple free output for EV. An approach for getting the stable output voltage is by using a fuzzy logic controller to control the Luo converter. It helps to generate the appropriate pulse with respect to the feedback voltage. This proposed system has the advantages like voltage increase, high-gain output with low ripples and high efficiency. The performance of this proposed drive is tested through hardware prototype at varying line voltage levels and results are demonstrated. A comparative analysis is presented to justify the effectiveness of the proposed Luo converter fed EV motor.
“If I Stop Now What Will Happen Later?”: The Lived Experiences of Teenage Moms as Students and Mothers in Cebu City during the Pandemic
Authors: Amolo Raiza Mae , Calamohoy Don Eric , Congson Jan Carylle , Cubillan Gregory John , Gingoyon Hazel Mae , Lanticse Chloe , Lausa Jan Mark , Tajanlangit Lorraine Belle , del Socorro Celeste , Gagani Flordeliza
Published: February 2024

This phenomenological study was conducted to give a platform and to amplify the voices of teenage mothers who were at the same time studying during the COVID-19 pandemic. This study was conducted in Cebu City, Philippines in 2022 having teen mothers as the participants of the study employing non-probability sampling. Using Colaizzi's descriptive analysis method to evaluate transcripts of online interviews with five selected participants, the researchers identified five major themes that accurately portrayed the highlights, challenges, coping mechanisms, and overall meanings of participants' experiences. The five major themes were: (1) “Choose The Right Partner!”: Having Company; (2) “The One That Changes Me”: The Effect of Motherhood; (3) “Student/Mother/Father/Daughter”: Conflict in a Routine; (4) “If I Stop Now, What Will Happen Later?”: The Value of Education; (5) “Survival of the Fittest”: Me and My Future. The researchers discovered that teenage mothers in this situation benefit greatly from supportive relationships, form strong attachments to their children, face numerous conflicts in different aspects of their lives, value education highly, and have optimistic views of their futures and of themselves. With these results in mind, the researchers strongly recommend more programs to be implemented and policies to be developed to encourage teenage mothers to re-engage with education, especially knowing the factors that may contribute to a more positive experience.
Reading Performance of The Grade II Pupils in the Different Teaching Approaches
Authors: Mara Joann T.
Published: February 2024

This study aimed determine the level of reading performance of 210 Grade 2 pupils of Sta. Cruz District, Division of Zambales using Marungko Reading Approach and Phonic Reading Approach.

The researcher utilized a quantitative descriptive research design and comparative study which helped the researcher collect information that best describe the existing problem by asking respondents of their perceptions about the strategies of teachers in improving the reading preparedness of Grade 2 pupils during modular distance learning.

The study revealed that the level of pupils’ reading performance using the Marungko Reading Approach was outstanding while excellent in the use of Phonic Reading Approach was excellent. The level of pupils’ reading difficulty using both the Marungko Reading Approach and the Phonic Reading Approach was very easy. The level of pupils’ reading performance using the Marungko Reading Approach and Phonic Reading Approach has no significant difference. The level of pupils’ reading performance using both the Marungko Reading Approach and the Phonic Reading Approach has significant relationship to their academic performance in English and Filipino.

Based on the summary of the investigations conducted and the conclusions arrived at, Teachers use the Marungko Reading Approach and Phonic Reading Approach to improve the reading performance of pupils. Teachers should develop reading program and activities responsive to pupils who are facing difficulty in reading using the Marungko Reading Approach and Phonic Reading Approach. Teachers should seek support from stakeholders for the provision of reading materials appropriate to the garde of pupils. A follow up study may be conducted to validate the results of this research by extending the study in other schools or districts. Teachers should develop reading materials such as strategic intervention material appropriate to the level of pupils using the Marungko Reading Approach and Phonic Reading Approach.
Reading Level Vis-À-Vis Academic Performance in English of Grade 6 Learners in Subic District
Authors: Arca Kimberly B.
Published: February 2024

This study was conducted to determine the Reading Level and Academic Performance of Grade 6 Learners in Subic District. The unit of analysis of the study was the 30 grade six Teachers of the said school who serve as respondents. This study focuses on the Reading Comprehension Level and Academic Performance of Grade 6 students in Subic District. The descriptive correlational research method was used in this study. To obtain the needed data the researchers administered a survey questionnaire to identify the challenges and strategies of Teachers in Teaching Reading which consisted of 30 items and collect the Academic Performance in 1st – 4th Quarter grades and Final average in English Subject. Aside from that the researcher collects the Philippine Informal Reading Inventory or Phil-IRI result of Grade 6 Students in School Year 2021-2022.The data were tabulated, analyzed and interpreted. The statistical tools employed in this study were the percentage, weighted mean and pearson (r).

Based on the Findings, the researcher concluded the following: A typical teacher in the Grade 6 level in Subic district schools is a female mid-adult, married, has been in service to the Department of Education for at least half a decade with a general elementary education degree. The level of competencies that pupils have in letter recognition, fluency, vocabulary and comprehension; The teachers-respondents agreed that in the use of teaching reading strategies they have procedures and challenges encountered that they have remedies or strategies to address the challenges. There is a correlation between the learner’s learning competencies and teachers’ challenges and there is a significant relationship between reading level and academic performance in English of grade 6 Learners in selected schools of Subic District, Division of Zambales.
Causal Relationship Between Branched‐Chain Amino Acids and Hypertension: A Mendelian Randomization Study
Authors: Cai Shiyuan , Fu Yuanyuan , Chen Jie , Tian Mingjie , Li Xue
Published: February 2024

Background

This study aimed to investigate the causal relationships between branched‐chain amino acids (BCAAs) and the risks of hypertension via meta‐analysis and Mendelian randomization analysis.

Methods and Results

A meta‐analysis of 32 845 subjects was conducted to evaluate the relationships between BCAAs and hypertension. In Mendelian randomization analysis, independent single‐nucleotide polymorphisms associated with BCAAs at the genome‐wide significance level were selected as the instrumental variables. Meanwhile, the summary‐level data for essential hypertension and secondary hypertension end points were obtained from the FinnGen study. As suggested by the meta‐analysis results, elevated BCAA levels were associated with a higher risk of hypertension (isoleucine: summary odds ratio, 1.26 [95% CI, 1.08–1.47]; leucine: summary odds ratio, 1.28 [95% CI, 1.07–1.52]; valine: summary odds ratio, 1.32 [95% CI, 1.12–1.57]). Moreover, the inverse variance‐weighted method demonstrated that an elevated circulating isoleucine level might be the causal risk factor for essential hypertension but not secondary hypertension (essential hypertension: odds ratio, 1.22 [95% CI, 1.12–1.34]; secondary hypertension: odds ratio, 0.96 [95% CI, 0.54–1.68]).

Conclusions

The increased levels of 3 BCAAs positively correlated with an increased risk of hypertension. Particularly, elevated isoleucine level is a causal risk factor for essential hypertension. Increased levels of leucine and valine also tend to increase the risk of essential hypertension, but further verification is still warranted.
Placental Pathology Contributes to Impaired Volumetric Brain Development in Neonates With Congenital Heart Disease
Authors: Nijman Maaike , van der Meeren Lotte E. , Nikkels Peter G. J. , Stegeman Raymond , Breur Johannes M. P. J. , Jansen Nicolaas J. G. , ter Heide Henriette , Steenhuis Trinette J. , de Heus Roel , Bekker Mireille N. , Claessens Nathalie H. P. , Benders Manon J. N. L. , Nijman Joppe , Alderliesten Thomas , Bosch Rian , Wösten‐van Asperen Roelie M. , Slieker Martijn G. , van Iperen Gabrielle G. , Grotenhuis Heynric B. , van Loon Kim , Koomen Erik , van Wijk Bram , Schoof Paul H. , Talacua Hanna , van Schooneveld Monique M. J. , Sprong Maaike C. A. , Haak Monique C. , Snoep Maartje C.
Published: February 2024

Background

Neonates with congenital heart disease are at risk for impaired brain development in utero, predisposing children to postnatal brain injury and adverse long‐term neurodevelopmental outcomes. Given the vital role of the placenta in fetal growth, we assessed the incidence of placental pathology in fetal congenital heart disease and explored its association with total and regional brain volumes, gyrification, and brain injury after birth.

Methods and Results

Placentas from 96 term singleton pregnancies with severe fetal congenital heart disease were prospectively analyzed for macroscopic and microscopic pathology. We applied a placental pathology severity score to relate placental abnormalities to neurological outcome. Postnatal, presurgical magnetic resonance imaging was used to analyze brain volumes, gyrification, and brain injuries. Placental analyses revealed the following abnormalities: maternal vascular malperfusion lesions in 46%, nucleated red blood cells in 37%, chronic inflammatory lesions in 35%, delayed maturation in 30%, and placental weight below the 10th percentile in 28%. Severity of placental pathology was negatively correlated with cortical gray matter, deep gray matter, brainstem, cerebellar, and total brain volumes (
r
=−0.25 to −0.31, all
P
<0.05). When correcting for postmenstrual age at magnetic resonance imaging in linear regression, this association remained significant for cortical gray matter, cerebellar, and total brain volume (adjusted
R
2
=0.25–0.47, all
P
<0.05).

Conclusions

Placental pathology occurs frequently in neonates with severe congenital heart disease and may contribute to impaired brain development, indicated by the association between placental pathology severity and reductions in postnatal cortical, cerebellar, and total brain volumes.
Checklist of symbionts in Hyalella (Amphipoda, Hyalellidae) from South America
Authors: Rauque Carlos , Hidalgo Alejandro , De los Ríos-Escalante Patricio
Published: February 2024

Despite their ecological importance, research on symbionts of amphipods has been limited in South America. The present study provides a comprehensive overview of symbiont organisms in the freshwater amphipods of the genus Hyalella in South America. Our findings reveal various taxa such as Apicomplexa, Microspora, Peritrichia, Folliculinidae, trematodes, nematodes, cestodes and acanthocephalans, often involving complex life cycles. Studies on symbionts have been done almost exclusively in Argentinean Patagonia. The present overview allows us to know the species that use amphipods, to elucidate their lifecycles and to know their occurrence values.
The effects of physical activity on the modulation of gut microbiota composition: a systematic review
Authors: Lavilla-Lerma M.L. , Aibar-Almazán A. , Martı́nez-Amat A. , Benomar-El-Bakali N. , Abriouel-Hayani H. , Hita-Contreras F.
Published: February 2024

Exercise induces many health benefits, preventing or treating diseases. On the other hand, the intestine houses trillions of microbes with the ability to influence the normal physiology of the organism. The intestinal microbiota is immensely diverse, varies between individuals, and can fluctuate according to various factors, including physical activity. In this sense, the aim of this systematic review is to search through the recent knowledge, in order to elucidate the roles played by different exercise modalities on modulation of the intestinal microbiota of adults. A literature search was performed in the PubMed, SCOPUS and Web of Science databases. The main inclusion criteria were randomised controlled trials involving exercise and microbiota in adults. The initial search identified 1,103 publications of which 13 were finally included. The heterogeneity of the training parameters used in the studies, statistical analyses, and sequencing methods did not allow us to carry out a meta-analysis. However, the results tend to show that modulation of the gut microbiome is related to the type of exercise, the intensity and the time of intervention, where these changes are more significant at the level of specific microbial populations than richness and diversity indices.
Research progress on aluminum matrix composites reinforced by medium and high volume fraction hybrid particles
Authors: Hu Zeyi , Li Puyu , Fan Caihe , Xiao Yinchun , Huang Jingyue , Ma Wudan
Published: February 2024

Aluminum matrix composites reinforced with particles offer many advantages, including high specific strength, elevated specific stiffness, reduced thermal expansion coefficient, enhanced thermal conductivity, abrasion resistance, and dimensional stability. These composites find extensive
application in aerospace, electronic packaging, and weaponry. The concept of hybrid particle reinforcement, involving multiple reinforcing particles, optimizes the performance attributes of each phase and the synergistic reinforcement effect, leading to potentially superior hybrid particle-reinforced
aluminum matrix composites. This paper presents a comprehensive overview of the methods for preparing particle-reinforced aluminum matrix composites. It examines the toughening mechanisms in aluminum matrix composites reinforced with hybrid particles at medium and high volume fractions. These
mechanisms include fine grain reinforcement, Orowan reinforcement, and heterogeneous deformation-induced reinforcement, including geometrically necessary dislocation reinforcement. This paper elucidates the role of micronano organizational structures-such as the morphology, size, distribution,
and interfacial bonding state of hybrid particles and matrix-in determining the comprehensive performance of aluminum matrix composites. Additionally, it explores the effect of hybrid particle morphology, size, distribution, and micronano structure on the composite’s overall performance.
Finally, future research directions and trends in the development of high-performance hybrid particle-reinforced aluminum matrix composites are discussed.
Comparison of Proxy-Shortwave Cloud Albedo from SBUV Observations with CMIP6 Models
Authors: Weaver Clark , Wu Dong L. , Bhartia P. K. , Labow Gordon , Haffner David P. , Borgia Lauren , McBride Laura , Salawitch Ross
Published: February 2024

We construct a long-term record of top of atmosphere (TOA) shortwave (SW) albedo of clouds and aerosols from 340-nm radiances observed by NASA and NOAA satellite instruments from 1980 to 2013. We compare our SW cloud+aerosol albedo with simulated cloud albedo from both AMIP and historical CMIP6 simulations from 47 climate models. While most historical runs did not simulate our observed spatial pattern of the trends in albedo over the Pacific Ocean, four models qualitatively simulate our observed patterns. Those historical models and the AMIP models collectively estimate an equilibrium climate sensitivity (ECS) of ∼3.5°C, with an uncertainty from 2.7° to 5.1°C. Our ECS estimates are sensitive to the instrument calibration, which drives the wide range in ECS uncertainty. We use instrument calibrations that assume a neutral change in reflectivity over the Antarctic ice sheet. Our observations show increasing cloudiness over the eastern equatorial Pacific and off the coast of Peru as well as neutral cloud trends off the coast of Namibia and California. To produce our SW cloud+aerosol albedo, we first retrieve a black-sky cloud albedo (BCA) and empirically correct the sampling bias from diurnal variations. Then, we estimate the broadband proxy albedo using multiple nonlinear regression along with several years of CERES cloud albedo to obtain the regression coefficients. We validate our product against CERES data from the years not used in the regression. Zonal mean trends of our SW cloud+aerosol albedo show reasonable agreement with CERES as well as the Pathfinder Atmospheres–Extended (PATMOS-x) observational dataset.

Significance Statement

Equilibrium climate sensitivity is a measure of the rise in global temperature over hundreds of years after a doubling of atmospheric CO2 concentration. Current state-of-the-art climate models forecast a wide range of equilibrium climate sensitivities (1.5°–6°C), due mainly to how clouds, aerosols, and sea surface temperatures are simulated within these models. Using data from NASA and NOAA satellite instruments from 1980 to 2013, we first construct a dataset that describes how much sunlight has been reflected by clouds over the 34 years and then we compare this data record to output from 47 climate models. Based on these comparisons, we conclude the best estimate of equilibrium climate sensitivity is about 3.5°C, with an uncertainty range of 2.7°–5.1°C.
Estimation of Surface Sensible Heat Flux due to Precipitation over CONUS and Its Impact on Urban Extreme Precipitation Modeling
Authors: Tan Haochen , Kotamarthi Rao , Ray Pallav
Published: February 2024

The surface sensible heat flux induced by precipitation (QP) is a consequence of the temperature difference between the surface and the rain droplets. Despite its seemingly negligible nature, QP is frequently omitted from both meteorological and climatological models. Nevertheless, it is important to acknowledge the numerous occasions in which the instantaneous values of QP can be significant, particularly during extreme precipitation events. This study undertakes a comprehensive assessment of QP across the contiguous United States (CONUS) utilizing high-resolution reanalysis, observational data, and numerical modeling to examine the influence of QP on precipitation and the surface energy budget. The findings indicate that the spatial distribution of QP climatology is analogous to that of precipitation, with magnitudes ranging from 2 to 3 W m−2 predominantly over the Midwest and Southeast regions. A seasonal analysis of QP reveals that the highest values occurring during the June–August (JJA) period, averaging 3.18 W m−2. Peak QP values of approximately 4 W m−2 are observed during JJA over the Great Plains region. We hypothesize that the QP during an extreme precipitation event would be nonnegligible and have a significant impact on the local weather. To test this conjecture, we perform high-resolution simulations with and without QP during an extreme precipitation event over the Chicago Metropolitan Area (CMA). The results show that the QP may be a dominant factor compared to other components of surface heat flux during the zenith of precipitation hours. Also, QP has the potential to not only diminish precipitation but also alter and reconfigure the remaining surface energy budget components.
Introduction to the Special Issue: Dismantling Anti-Black Racism and Counseling Psychology
Authors: Pieterse Alex L. , Kim Bryan S. K.
Published: February 2024

The current Special Issue focuses on the role counseling psychology can play in the dismantling of anti-Black racism. This introduction provides a brief review of the articles in the Special Issue and highlights the Society of Counseling Psychology's ongoing committment to the dismantling and eradication of anti-Black racism.
Curriculum and NEP 2020: Perspectives and Inter-connections
Authors: Bhardwaj Mayank , Ranjan Ashish , Sharma Jyoti
Published: February 2024

There cannot be a more appropriate time for infusing the education system of India with flexibility, innovativeness and quality. National Education Policy (NEP), 2020, is a step in this direction which emphasises on inclusiveness, equity and quality with a clear focus on the attainment of learning outcomes. NEP 2020 promotes the adoption of learner-centric education which develops critical thinking, creativity and problem-solving skills and discourages rote learning methods. An effective education policy relies on strong curricular foundations for optimum results. An enriched curriculum should be rooted in India’s diverse culture, knowledge systems and traditions embracing local knowledge and cultural practices, on the one hand, and at the same time, should incorporate the modern-day learning perspectives developed by the curriculum theorists over the last century. This article analyses the idea of curriculum as conceived by different learning perspectives and how NEP 2020 draws inferences from them.
The nonlinear model reference adaptive impedance control for underwater manipulator operation objects in bilateral teleoperation system
Authors: Zhang Jianjun , Li Han , Liu Zhiqiang , Wu Zhonghua , Yang Jinxian
Published: February 2024

The bilateral teleoperation system is susceptible to model parameter uncertainty and unknown disturbances in both the master and slave manipulators, resulting in instability and inaccuracies in the force and position tracking performance. To address these issues, a novel nonlinear model reference adaptive impedance controller has been designed to achieve coordinated force and position synchronization of dual manipulators. The adaptive control laws, based on sliding mode functions, have been designed to compensate for the uncertainty of the manipulator model. Furthermore, an adaptive estimation law has been employed to appraise the unknown upper bound of external disturbances. This ensures that the closed-loop model parameters of the dual manipulator converge to the reference impedance model respectively. Simultaneously, it enables the position error between the reference model response and the end-effector task space position to asymptotically converge to zero. To verify the effectiveness of the proposed controller, simulations have been conducted on the MATLAB platform and experiments on a single degree of freedom teleoperation system have been performed. The results demonstrate that the controller exhibits strong robustness and has the capability of force-position tracking ability.
Moving from gender responsive to gender transformative action: What Canada achieved and missed in its Rohingya response strategy in Bangladesh
Authors: Banerjee Deeplina
Published: February 2024

Since 2018, Canada, through its Rohingya Response Strategy in Bangladesh, has been providing targeted assistance to the impoverished Bengali host communities and Rohingya refugees living in Cox's Bazar, Bangladesh. Under Canada's Feminist International Assistance Policy (FIAP), gender equality and gender-responsive humanitarian action constitute some of the core priority areas for the Canadian government. In Cox's Bazar, Global Affairs Canada (GAC) funds programmes to empower women and girls through skill-based and livelihood training and to provide sexual and reproductive health services. However, scholars like Jennifer Cadesky and Rebecca Tiessen caution against the conflation of gender equality and empowerment, arguing that it often leads to short-term, quick-fix solutions without any long-term benefit to women. Canada needs to go beyond funding recovery assistance programmes at a surface level. Without understanding social norms and cultural practices that influence women's participation, mobility, and visibility in public spaces, gender equality in Canada's response strategy will be limited to only checklist goals.
Mental health service utilization and help seeking behaviours of adult Cambodians living in Western countries: A systematic scoping review
Authors: Than Vannaral , Doroud Nastaran , O’Brien Lisa
Published: February 2024

Background:

Health disparity and under-utilization of health services is prevalent among Asian refugees and migrants in Western countries and can profoundly impact health outcomes. Cambodians who survived extreme physical and emotional trauma during the genocides enacted by the Khmer Rouge regime in the 1970’s are particularly vulnerable to poor mental health outcomes decades later. Understanding the experiences of help-seeking and service use by displaced Cambodians in Western countries may help to design more effective and culturally safe healthcare services.

Aims:

To identify characteristics or factors associated with help seeking behaviours and service utilization of displaced Cambodians with mental health issues living in Western countries.

Methods:

This review followed the Arksey & O’Malley methodological framework for scoping reviews. Articles for review were identified through searches of nine electronic databases and manual searches. Relevant articles were selected, and data was extracted and synthesized into key themes.

Results:

This review included 15 articles. Most of the studies were conducted in the USA ( n = 13) with one each conducted in Canada and New Zealand. Seven studies used qualitative interviews, five used a cross-sectional survey approach, two used a mixed-methods approach and one was a narrative review and case series. Key findings highlighted the impact of Cambodian cultural beliefs about mental health (guilt, shame and help-seeking stigma) on service utilization and the disconnect between Western models of service provision and preferred Cambodian ways of receiving support.

Conclusion:

Mental health services in Western countries are likely to be under-utilized by Cambodians due to a mismatch between health beliefs and Western models of care. Further investigation of the association between health beliefs and barriers to service utilization among adult Cambodian refugees is warranted.
The Press and the Intelligence Community: The Construction of OTRAG and Cóndor as Global Threats
Authors: Blinder Daniel
Published: February 2024

This article studies the way the US government through the Central Intelligence Agency, the Washington Post and the New York Times approached in tandem the development of rockets in Argentina and in two African countries (Zaire and Libya) during the last stretch of the Cold War. A qualitative analysis is carried out from primary government and journalistic sources, looking at how the media acted alongside the government and the intelligence community, providing the same information and a very similar interpretation of the facts, building common sense and a geopolitical imaginary. This is a geopolitical analysis of the construction of imagery of the dangerous identity of the OTRAG and the Cóndor II in the 1970s and 1980s. The conclusions show that both cases were construed as a geopolitical identity on non-core countries that ended in pressures, the projects be terminated, managing to build a sense by which the economic and political interests of the United States were projected hegemonically as universal interests.
Fabric defect image generation method based on the dual-stage W-net generative adversarial network
Authors: Hu Xuejuan , Liang Yifei , Wang Hengliang , Tan Yadan , Liu Shiqian , Pan Fudong , Wu Qingyang , He Zhengdi
Published: February 2024

Due to the intricate and diverse nature of textile defects, detecting them poses an exceptionally challenging task. In comparison with conventional defect detection methods, deep learning-based defect detection methods generally exhibit superior precision. However, utilizing deep learning for defect detection requires a substantial volume of training data, which can be particularly challenging to accumulate for textile flaws. To augment the fabric defect dataset and enhance fabric defect detection accuracy, we propose a fabric defect image generation method based on Pix2Pix generative adversarial network. This approach devises a novel dual-stage W-net generative adversarial network. By increasing the network depth, this model can effectively extract intricate textile image features, thereby enhancing its ability to expand information sharing capacity. The dual-stage W-net generative adversarial network allows generating desired defects on defect-free textile images. We conduct quality assessment of the generated fabric defect images resulting in peak signal-to-noise ratio and structural similarity values exceeding 30 and 0.930, respectively, and a learned perceptual image patch similarity value no greater than 0.085, demonstrating the effectiveness of fabric defect data augmentation. The effectiveness of dual-stage W-net generative adversarial network is established through multiple comparative experiments evaluating the generated images. By examining the detection performance before and after data augmentation, the results demonstrate that mean average precision improves by 6.13% and 14.57% on YOLO V5 and faster recurrent convolutional neural networks detection models, respectively.
A Tool Kit for Relation Induction in Text Analysis
Authors: Stoltz Dustin S. , Taylor Marshall A. , Dudley Jennifer S. K.
Published: February 2024

Distances derived from word embeddings can measure a range of gradational relations—similarity, hierarchy, entailment, and stereotype—and can be used at the document- and author-level in ways that overcome some of the limitations of weighted dictionary methods. We provide a comprehensive introduction to using word embeddings for relation induction, and demonstrate how such techniques can complement dictionary methods as unsupervised, deductive methods.
Towards a multifactorial framework of the Roma’s victimisation: Discrimination, risky situations, and acceptance of violence as correlates of physical assault and harassment
Authors: Molnar Lorena , Chopin Julien , Hashimoto Yuji Z , Vazsonyi Alexander T
Published: February 2024

The European Roma population faces violence and discrimination, but the causes of their victimisation are not well understood. This study used a multi-theoretical framework to analyse data from a representative sample of 2,913 Roma surveyed in European Union Minorities and Discrimination Survey II. The results showed that police stops perceived as ethnically motivated, exposure to risky situations, and acceptance of violence when insulted predicted physical victimisation and harassment. To reduce victimisation, recommendations include sensitising police officers, diverse police patrols, crime-reduction measures in neighbourhoods, and education on nonviolent communication. Further research is needed to understand other forms of victimisation among the Roma. The study highlights the usefulness of testing multiple risk factors from different criminological theories to address victimisation of the Roma ethnic minority.