bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning Method Employed for the Objective of Identifying Text on Tweet Dataset
Authors: Pandey Sakshi
Published: February 2025

When it comes to training ML systems, internet-based data is invaluable.
Despite the difficulty in collecting this information, teams of experts from academic
institutions and research labs have created publicly accessible databases. Twitter and
other social media platforms provided large quantities of useful information throughout
the pandemic, which was used to evaluate healthcare decisions. In order to forecast
illness prevalence and offer early warnings, we suggest analysing user attitudes by
using efficient supervised machine learning algorithms. The gathered tweets were
sorted into positive, negative, and neutral categories for preprocessing. Hybrid feature
extraction is the innovative aspect of our work; we used it to correctly describe posts by
combining syntactic features (TF-IDF) and semantic elements (FastText and Glove),
which in turn improved classification. The experimental findings suggest that when
using Naive Bayes, the combination of FastText and TF-IDF achieves the best results.
Lexical Methods for Identifying Emotions in Text Based on Machine Learning
Authors: Gupta Mridula
Published: February 2025

The study of emotions has emerged as an important area of research because
of the wealth of information it can provide. Emotions can be expressed in a variety of
ways including words, facial expressions, written material, and movements. Natural
language processing (NLP) & deep learning concepts are essential to solving the
content-based classification problem that is emotion detection in a text document.
Therefore, in this research, we suggest using deep learning to aid semantic text analysis
in the task of identifying human emotions from transcripts of spoken language. Visual
forms of expression, such as makeover jargon, may be used to convey the feeling.
Datasets of recorded voices from people with Autism Spectrum Disorder (ASD) are
transcribed for analysis. However, in this paper, we specialize in detecting emotions
from all of the textual dataset and using the semantic data enhancement process to fill a
few of the phrases, or half-broken speech, as patients with Autism Spectrum Disorder
(ASD) lack social contact skills due to the patient not very well articulating their
communication.
Identification of Websites Using an Efficient Method Employing Text Mining Methods
Authors: Taneja Madhur
Published: February 2025

Herein, we introduce a method for website classification using deep neural
networks and mixed data extractors. We use iterative training as well as supervised
learning approaches to use a gradient descent methodology to simulate the website
categorization. This modern model is comprised of a webpage encoder, a convolutional
neural network (CNN) feature extraction, a bidirectional long short-term memory
(LSTM) feature extractor, as well as a fully connected classifier. It may retrieve various
website features at various granularities. Our model may quickly select a suitable
website class by concatenating mixed features obtained from mixed feature extractors.
On the realistic website dataset that has been obtained, we conduct in-depth tests. The
dataset is compiled using domains that were taken from the telecom operator's DNS
records. The proposed categorization schema outperforms state-of-the-art models in
comparison to our fresh model as well as a slew of popular machine learning
algorithms in terms of accuracy, recall, F1, and precision. Other web apps may benefit
from all of this as well, such as detecting fake websites as well as ads.
Identification of Text Emotions Through the Use of Convolutional Neural Network Models
Authors: Kaushik Vaibhav
Published: February 2025

Increasing numbers of people are using the Internet to share their feelings
and communicate with one another, and the vast majority of these expressions of
emotion take the form of text. Using sentiment dictionaries, machine learning, and deep
learning are the three most common approaches to text sentiment categorization
studies. Due to the exponential growth of textual data, it is crucial to create models that
can automatically analyse this material. Labels like gender, age, nationality, emotion,
etc., may be included in the texts. Numerous investigations of text categorization have
emerged because the use of such labels may be useful in various commercial sectors.
The Convolutional Neural Network, also referred to as CNN, was recently utilised to
the problem of text categorization, with promising results. In this study, we advocate
for the use of convolutional neural network networks for the job of classifying
emotions. Using three popular datasets, we demonstrate that our networks outperform
existing cutting-edge deep learning models by using successive convolutional layers to
process substantially longer sentences.
Classification & Clustering of Text Based on Doc2Vec & K-means Clustering based Similarity Measurements
Authors: Kapoor Prakriti
Published: February 2025

One crucial task in text processing is determining how similar two papers
are to one another. A novel similarity metric is suggested in this study. Finding a
suitable similarity metric for written materials that permits the development of coherent
groupings is a significant difficulty for document clustering. After that, we use TFIDF
to build a vector space, and then we use the ward's approach and the K-means
algorithm to accomplish clustering. WordNet is additionally employed in the process of
semantic document clustering. Visualisations and an interactive website illustrating the
connections between all clusters illustrate the findings. The existence (and quantity) of
words in texts are all that are taken into account while utilising the traditional bag-ofwords paradigm. This process might lead to texts with identical meanings but distinct
vocabulary being placed in various groups. The findings acquired using the suggested
approach are analysed for their correctness using the F-measure. Comparisons using
the sentence vectors model (Doc2vec) and the bag-of-words model are made to
confirm the edge of the suggested strategy. The suggested methodology may be used to
decipher web chat logs and client feedback posted online. We evaluate our method on a
variety of real-world data sets including examples of text classification and clustering
problems. The findings prove that the proposed measure outperforms competing
strategies.
Categorization of COVID-19 Twitter Data Based on an Aspect-Oriented Sentiment Analysis and Fuzzy Logic
Authors: Bhatnagar Tarang
Published: February 2025

During times of disaster or epidemic, social media has emerged as a vital
means of communication. It is difficult to examine the complete situational awareness
via many elements and emotions to aid authorities due to the unpredictability of these
calamities. Currently, systems for aspect recognition and sentiment analysis rely
heavily on labelled data and require human curation of aspect categories. To analyze
public opinion from a variety of angles, this study suggested a hybrid text analytical
approach. Using the popular Latent Dirichlet Allocation (LDA) topic modeling, we
first extracted and clustered the elements from the data. We then used the linguistic
inquiry and word count (LIWC) lexicon to extract the sentiments and label the dataset.
Finally, in the third layer of our structure, we mapped the elements into emotions, and
the sentiments were classified using well-known machine learning classifiers. The
comparison of our technique with other aspect-oriented sentiment analysis approaches
shows encouraging results in experiments with actual datasets, and our method with
several variants of classifiers surpasses current methods with top F1 scores of 91%.
Feature-Level Sentiment Analysis of Data Collected through Electronic Commerce
Authors: Singh Preetjot
Published: February 2025

When dealing with data in the form of text, the most popular method for
doing analysis and determining sentiment content is called “Sentiment Analysis.”
Sentiment analysis is also known as Opinion Mining. Suggestions, feedback, tweets,
and comments are all examples of the various types of text data that are being created.
Customer feedback on e-commerce sites is a constant source of new information.
Online stores may better meet client needs, improve their services, and boost sales by
analyzing E-Commerce data. Positive, negative, and neutral feedback from customers
may be separated using sentiment analysis. Numerous methods for Sentiment Analysis
have been developed by academics. Typically, only a single machine learning
algorithm is used for sentiment analysis. The purpose of this study, which makes use of
Amazon review data, is to extract positive, negative, and neutral review ratings by
locating aspect phrases, identifying the Parts-of-Speech, and applying classification
algorithms to the collected data.
Analysis of Sentiment Employing the Word2vec with CNN-LSTM Classification System
Authors: Saini Rajat
Published: February 2025

The identification of problems has become easier in sentiment categorization
using conventional neural network–based short text classification methods . Word2vec,
a convolutional neural network (CNN), and Bidirectional Long-term and Short-term
Memory networks (LSTM) are used incombination to overcome this issue. Using
Word2vec word embeddings, the CNN-LSTM model was able to attain an accuracy of
91.48%, as demonstrated experimentally. This demonstrates that the hybrid network
model outperforms the single-structure neural network when dealing with relatively
brief texts.
Text Emotion Categorization Using a Convolutional Recurrent Neural Network Enhanced by an Attention Mechanism-based Skip Gram Method
Authors: Grover Madhur
Published: February 2025

Text-based web archives have become increasingly common as technology
has advanced. For many text classification applications, classic machine learning
classifiers like support vector machines (SVMs) and naïve Bayes (NBayes) perform
well. Since short texts have fewer words and convolutional and pooling layers have
their limits, these classifiers suffer from sparsity and lack long-term dependencies. In
this study, we present a convolutional recurrent neural network architecture that makes
use of a modified skip-gram method. For the adversarial training of the skip-gram
algorithm, we employ the L2 regularization technique. It can boost the model's
performance in text sentiment classification tasks and increase its robustness and
generalizability. To extract information from the entire text while dampening the
influence of irrelevant words, we deployed a convolutional neural network equipped
with attention mechanisms. The CNN-based categorization of text emotion is complete.
When compared to other classifiers used on the Twitter dataset, our model and
algorithm were shown to be more efficient and accurate.
Machine Learning-Based Data Preprocessing as well as Visualization Techniques for Predicting Students' Tasks
Authors: Mahajan Pratik
Published: February 2025

A student's chances of landing a job after graduation are influenced by their
performance in school and their history of academic accomplishment. Students who
want to succeed in the working world need to develop skills including technical
proficiency, critical thinking, and effective communication. Here we are making an
attempt to figure out how students' academic performance affects their future
opportunities. Algorithms for data mining play a key role in analysing and forecasting
the chance for students' placement according to their previous academic achievement.
We polled students at a prominent technical institution for this piece. Multiple factors
that influence students' probabilities are included in the dataset, and these factors are
examined and shown graphically. We made an effort to assess the data and provide
visualisations and insights before running or applying algorithms for machine learning
to this dataset. Data analysis, understanding, and preparation are the key focuses of this
research.
Investigating the Utility of Data Mining for Automated Credit Scoring
Authors: Yadav Amarpal
Published: February 2025

Banks and other financial organisations rely heavily on credit scoring (CS)
as a method of risk management since it is both effective and necessary. It reduces
financial risks and gives sound advice on loan disbursement. As a result, businesses
and financial institutions are exploring innovative automated solutions to the CS
dilemma in an effort to safeguard their resources and those of their clients. The use of
various machine learning (ML) as well as data mining (DM) approaches has led to
significant progress in CS prediction in recent years. The Deep Genetic Hierarchical
Network of Learners (DGHNL) is a novel approach developed for this study. Support
Vector Machines (SVMs), k-nearest Neighbours (kNNs), Probabilistic Neural
Networks (PNNs), and fuzzy structures are just some of the many types of methods that
may be used in the suggested method. The Statlog German (1000 occurrences)
approval of credit dataset from the UCI machine learning library was used to evaluate
our model. We used a DGHNL model with five unique learner types, two feature
extraction methods, three kernel functions, and three methods for optimising model
parameters. In addition to conventional cross-validation (CV) and train-testing
(stratified 10-fold) methods, this model employs a cutting-edge biological layered
training (participant selection) approach. Using data on German credit approvals from
Statlog, we show that the suggested DGHNL model can obtain a prediction accuracy of
94.60% (54 errors per 1000 classifications) with its 29-layer architecture.
Assessment of ready-mix concrete properties in various zones of concrete samples
Authors: Mohammed N.
Published: February 2025

Abstract. In this work, the surface, core, and interface portions of concrete samples are examined to determine the characteristics of ready-mix concrete. The goal of the study is to comprehend how distinct zones inside concrete samples impact water absorption and compressive strength test. For the purpose of assessing these qualities, concrete samples were gathered and processed in accordance with conventional protocols. As the surface displays distinct properties from the core and interface areas, the results show notable differences in attributes between the zones. The concrete's top zone side was found to have the highest water absorption. In addition, site curing (SC2) has been resulted to be an improved way to cure concrete since it leads to lower water absorption and greater compressive strength as compared to Site Curing (SC1). Compressive strength in every zone remained within the expected range, indicating effective mix design and compaction. There is a significant difference between the two sites, site cures SC2 and SC1. In order to maximize the performance of concrete, zone-specific features are considered. These findings have consequences for concrete mix design and building techniques.
Numerical analysis of pavement structure on expansive subgrade stabilized with surcharge pressure
Authors: Djellali Adel
Published: February 2025

Abstract. Soils swell upon wetting and shrink when drying. This behavior can sustain damage to structures, particularly, light buildings and roads. The phenomenon is exciting to be researched, how far the effect of water contents is towards the behavior of free swelling of expansive soils. The aim study of this investigation is to discuss the findings on analysis results of pavement structure constructed on a relatively expansive material stabilized by an application of surcharge pressure. Samples were taken to the laboratory from the subgrade of a deteriorated road. The soils were classified as medium plasticity with medium to high expansion potential. The free swell oedometer test indicated a volumetric strain v = 0.12 % with a swelling pressure of 520 kPa. An adequate surcharge pressure was used according to the procedure described in the paper to stabilize the deformations induced by the subgrade. The load-displacement behavior of expansive subgrade soil is studied using the Software PLAXIS 3D. After simulation, it was observed that the swelling deformations were considerably reduced by 263% without any cracks in the pavement.
Analyzing the differences between the impacts of wind and earthquakes on base shear and drift in diagrid structures
Authors: Mohammed N.
Published: February 2025

Abstract. Diagrid structures, which are identified by their diagonal grid framework, are becoming more and more popular for high-rise construction due to their higher lateral load resistance and efficient material usage. This study offers a thorough evaluation of how base shear and drift in diagrid structural systems are impacted by wind and seismic stresses. The investigation aims to evaluate these systems' performance in seismic and wind settings in order to shed light on their different benefits and drawbacks. The distribution of base shear and drift in various situations is illustrated by the findings, which show that diagrid systems perform better than standard framed structures in terms of lowering base shear and lateral drift. The result shows, in terms of both earthquake forces in the (EQX) and (EQY) directions, the reinforced concrete (RC) frame configurations exhibit greater base shear values than the V diagrid + RC frame and the X diagrid + RC frame, respectively. Whereas, the base shear of the RC frame is greater for wind loading in the x direction (WINDX) and (WINDY) than the base shear values of the V diagrid + RC frame and X diagrid + RC frame configurations, respectively. Additionally, suggestions for further study are given, with an emphasis on improving diagrid designs for better performance in cases with coupled loads. The present research highlights the possibility of utilizing diagrid structural solutions to enhance the security and effectiveness of contemporary tall structures.
Seismic performance of automatically generated ductile moment resisting frames
Authors: TAFRAOUT Salim
Published: February 2025

Abstract. In line with the modernization trend within the Architecture, Engineering, and Construction (AEC) sector, which lags behind the advancements of the 4th industrial revolution, there is a growing focus on automating structural design at an early stage, particularly emphasizing interdisciplinary interfaces. This paper aims to refine and enhance the performance of a recently proposed method to aid in the automation and optimization of the structural design of reinforced concrete moment-resisting frames (MRFs). The proposed method involves generating an optimized layout for a given architectural configuration in the IFC (Industry Foundation Classes) format using the Hill-Climbing algorithm (HCA). Initially, the method determines potential geometric positions for columns at each level that fit the architectural configuration and ensures a continuous force path from the top floor to the foundation. This stage produces an infinite number of possible column positions. Subsequently, the HCA search algorithm is employed to identify the optimal axes positions, resulting in a finite number of column positions that define two orthogonal grids of MRFs. The performance of the derived MRFs is then assessed using push-over analysis. The results demonstrate superior performance in terms of capacity curves and performance points, indicating the method's efficacy in enhancing structural design automation and optimization.
Refinement of tuned mass damper parameters on machine support structure using dynamic cuckoo search algorithm
Authors: MAHMOOD Ahmad Muinuddin
Published: February 2025

Abstract. A classical Tuned Mass Damper (TMD) consists of a mass, spring, and viscous damper, typically optimized in the frequency domain to minimize the steady-state displacement amplitude of a primary structure under harmonic force excitation with varying frequency. This study proposes the use of a dynamic Cuckoo Search (CS) algorithm, a nature-inspired optimization method, to enhance the accuracy of TMD parameters when external factors, such as excitation frequency or structural properties, change. The research aims to optimize key TMD parameters (mass ratio, damping ratio, and frequency ratio) using the dynamic CS algorithm. The performance of this algorithm will be evaluated by comparing it to the computed values in literature, especially from the Den Hartog formula. In the dynamic CS algorithm, the value for discovery probability (pa) and the step size factor (a) are changed dynamically which changes the convergence speed to the solution. The descend rate to the final solution is a combination of exploration mode (bigger search area) and exploitation mode (more specific area). Key outcomes in the research continuation from this paper will include a parameter study that can enhance the TMD's design accuracy in mitigating harmonic vibrations. The intent is to formulate an algorithm capable of adapting to fluctuations in excitation frequencies and the structural modal parameters. These findings are hoped to contribute to the design of more robust TMDs, with broader applicability for vibration control in civil engineering structures.
An overview of the mechanical and characterization aspects of self-compacting concrete in relation to the influence of catalyst
Authors: Mohammed N.
Published: February 2025

Abstract. Self-compacting concrete (SCC) has emerged as a revolutionary material in the construction industry due to its ability to flow and fill formwork under its own weight without the need for mechanical vibration. The development of SCC has significantly improved construction efficiency and quality. One promising approach to further enhance the properties and performance of SCC is the incorporation of photocatalysts. Photocatalysts, such as titanium dioxide (TiO2) and zinc oxide (ZnO), have the ability to accelerate concrete hardening, improve mechanical strength, enhance durability, and reduce environmental pollution by degrading organic contaminants. This review paper provides a comprehensive overview of the use of different photocatalysts in SCC. It discusses the properties of various photocatalysts, their mechanisms of action in concrete, and their effects on the properties of SCC at fresh and hardened state. The review also explores the challenges and future prospects of using photocatalysts in SCC production, including issues related to cost, long-term performance, and environmental impact. Overall, this review aims to provide valuable insights into the application of photocatalysts in SCC and their potential to revolutionize the construction industry.
Structural damage localization and quantification in cantilever beam structure using modal strain parameters and artificial neural network
Authors: AZZI Rachid
Published: February 2025

Abstract. In this study, we proposed a damage identification method in two stages. In the first stage, Strain modal analysis is carried out using the finite element method on the cantilever beam model, and the first four-strain mode shapes are obtained for damaged and undamaged states of the cantilever beam along the length of its middle line. Then, the absolute difference of strain mode shapes and modal strain energy-based damage index are used to determine the damage position along the length of the cantilever beam. For this purpose, several damages are inserted in the finite element model of the beam at different positions to evaluate the performance of this damage indicators. The results obtained showed that the proposed indicators could determine the damage position at any location along the length of the beam. In the next stage, the values of the damage indicators at damaged sites of the beam are used as input parameters to train the artificial neural networks incorporated to predict the damage severity. The results of this study illustrated the accuracy and efficiency of the proposed two-stage damage detection methods for predicting the location and magnitude of damage in a cantilever beam.
Optimizing cover concrete durability in ready mixed concrete a critical review of curing methods and their impact on performance
Authors: Mohammed N.
Published: February 2025

Abstract. The durability of reinforced concrete buildings is greatly determined by the quality of the cover concrete, especially under hard conditions. Effective curing processes are required to optimize the qualities of cover concrete in ready-mixed concrete. This paper objectively assesses the influence of several curing processes, including membrane, steam, and wet curing, on the development of concrete strength, permeability, and resistance to environmental conditions. It also investigates the impact of concrete mix composition, ambient conditions, and curing time on the effectiveness of various methods. The evaluation outlines best practices and guidance for choosing appropriate curing techniques based on project specifications. Case studies also show how curing processes may be used successfully to improve the longevity of cover concrete. The challenges of applying optimal curing procedures are reviewed, as well as recommendations for future study to solve knowledge gaps and enhance cover concrete performance. Finally, the assessment emphasizes the need of adequate curing in ensuring the lifetime and sustainability of reinforced concrete buildings.
Review of stone columns, piled raft foundations, and combined stone columns and piles under raft as a ground improvement techniques for soft clay soils
Authors: Ahmed D.
Published: February 2025

Abstract. This review paper aims to provide an in-depth analysis of three commonly used foundation systems: stone columns, piled raft foundations, and combined stone columns and piles under raft. These foundation techniques have gained significant attention due to their effectiveness in improving soil conditions and enhancing the load-bearing capacity of weak or problematic soils. The aim is to evaluate their effectiveness in addressing the challenges associated with building on soft clay. The review incorporates published research and case studies to compare the performance of each method in terms of enhancing bearing capacity, reducing settlement, and improving overall performance. The findings suggest that while each approach offers distinct advantages, the hybrid solution of combining stone columns and piles beneath a raft foundation offers the most comprehensive benefits for supporting structures on soft clay sites.
Shear capacity prediction of squat flanged walls using machine learning approach with CatBoost and CTGAN
Authors: ALOTAIBI Khalid Saqer
Published: February 2025

Abstract. Reinforced concrete (RC) walls serve an important role in providing lateral strength and stiffness for structures against seismic forces and wind loads. Squat walls, defined as having a shear span ratio less than or equal to two, are commonly used in building construction. Among squat wall designs, flanged walls have gained popularity due to architectural benefits. However, the seismic behavior of flanged RC squat walls is not fully understood due to complexities introduced by their cross-sectional shape and varying shear span ratios. Current structural design codes aim to capture wall behavior but have limitations in accurately modeling flanged squat walls. Previous experimental work has enhanced comprehension, but key aspects remain unsolved, such as precise prediction of stiffness and effective flange length. Data-driven machine learning models show promise for predicting engineering properties of composite materials like concrete. However, limited publicly available data hinders developing highly predictive computational models. This study addresses prior data limitations by using conditional tabular generative adversarial network (CTGAN) to synthesize additional tabular data. CTGAN, an adversarial learning method for tabular datasets, can generate realistic synthetic rows conditioned on discrete features. Here, CTGAN was trained on a 152-specimen experimental database to synthesize 5,000 data points. The synthesized data were then used to train a CatBoost gradient boosting algorithm for predicting shear capacity of flanged squat walls. The model achieved a high R-squared value of 0.878 when tested on 152 unseen experimental specimens, demonstrating accurate prediction of shear capacity in real data.
Mechanical properties and microstructures of steel-reinforced rebar exposed to heat
Authors: ALSHAMMARI Yousef
Published: February 2025

Abstract. Reinforced steel rebars are well known for their unique properties that give the concrete its strength and durability. Concrete is one of the most composite materials that have been used in the world. Most of the buildings in the Middle East are made using reinforced concrete. The harsh, dry, and hot weather conditions in the region might cause incidents such as fire. To understand the effect of elevated temperature on the building structure and integrity, it is essential to study the impact of heat on the properties of reinforced steel rebars. Fire can effectively change the properties of steel rebars in many ways. Moreover, the cooling process of the fire is important to recognize due to the effect of cooling on the microstructural modification of the steel rebars. In the big picture, each cooling process scenario must be designed and explored. For this study, the focus is to investigate the effect of heat on the reinforced steel rebar once it has been cooled naturally. Besides the control sample, four different heat scenarios were studied. Namely, the as-received rebars have experienced heat values of 300℃, 500℃, 700℃, and 900℃ by employing a muffle furnace. The selected heat profile was chosen to simulate scenarios with low, moderate, and high temperatures. The tensile properties of the reinforced rebars subjected to heat have changed dramatically. The yield stress and the ultimate tensile strength decreased with the increment of the temperature, whereas the elongation increased, reaching almost double the value of the control sample. Likewise, the microstructures of those steel rebars have been altered as a result of heat exposure.
The relationship between the plasticity index, the friction angle and the cohesion of the soils in Jakarta during El Niño and La Niña cycles
Authors: Ngom M.B.
Published: February 2025

Abstract. The Indonesian BMKG (Meteorology , Climatology and Geophysics Agency)'s 2009 study on forecasting an El Niño cycle in the same year in Indonesia was a major step forward in understanding and recognizing the various phenomena and factors that could be behind such an event[1]. This study is based on the measurement of air pressure variations between the various Indonesian waters and the Pacific Ocean, the main source of which is the temperature difference between these different areas .An El Niño or La Niña cycles has impacts that can influence soil conditions, so geotechnical studies are needed to understand how soils behave during such a cycle and to take measures to prevent any risks. C. Kuenzer et al. studied variations in soil moisture during the El Niño and La Niña cycles in 2009, with the aim of finding out whether these events have an impact on soils in the different areas concerned. In our study, we will seek to establish relationships between the various parameters needed to describe soils during El Niño cycles. Indeed, the basic parameters for characterizing the mechanical behavior of soils are mainly related to shear strength. Therefore, we are going to study the soils of West Jakarta during the La Niña and El Niño cycles using a correlative approach, which involves understanding the evolution of properties such as the plasticity index (PI), the water content (wc) and the specific density of solid particles in the soil (Gs), as a function of the mechanical parameters (friction angle and cohesion). Referring to the studies of James et al. in 2023, we will look for a linear correlation between the parameters involved and the accepted correlation coefficient r is that r²>0,7. The relationships retained are the variation of the compression coefficient Cc as a function of the cohesion C governed by Cc = -2,7*C+0,714 ; with a correlation coefficient r²=0,748 , the variation in the angle of friction phi as a function of the cohesion C governed by the equation phi = 48,9*C+3,59.
Concrete incorporating plastic waste: Challenges and possible solutions
Authors: AHMED Almotaseembillah
Published: February 2025

Abstract. Due to huge plastic wastes (PW) accumulation, there have been considerable efforts in recycling and reuse of PW including its utilization in concrete. The developed PW concrete (PWC) has extensively been studied and its mechanical and durability properties are well-documented in the literature. This paper highlights the challenges associated with utilization of PWC and proposes possible solutions and recommendations. The identified challenges will be discussed in this study are: (a) the consistency of the fresh PWC, (b) the impact of low elastic modulus on the application of PWC in floor slabs and beams, and (c) the degradation at high temperature environment. Potential solutions and recommendations to overcome these challenges are also discussed.
The influence of fly ash characteristics as a supplementary cementitious material (SCM) on concrete's mechanical properties
Authors: SUNARNO Y.
Published: February 2025

Abstract. The utilization of fly ash as a Supplementary Cementitious Material (SCM) in concrete offers numerous benefits that support sustainable construction. These advantages include reducing CO2 emissions, minimizing waste, enhancing energy efficiency, and lowering costs. Additionally, fly ash can improve the performance of concrete. The characteristics of the fly ash used significantly influence the performance of the resulting concrete. This study aims to evaluate the impact of three types of fly ash used as SCM. The tested fly ash samples originate from three different power plants, namely FA-BSW, FA-BAR, and FA-PLN. Testing was conducted by creating concrete mixtures using variations of these three fly ash sources and comparing them with a control concrete mixture using ordinary Portland cement (OPC), labeled as FA-0. Workability was measured through slump tests on fresh concrete, while dry density and compressive strength were evaluated on hardened concrete specimens (100 mm x 200 mm cylinders) at 3, 7, 28, and 56 days. Additionally, the Strength Activity Index (SAI) was analyzed to assess the pozzolanic activity of the fly ash relative to OPC. The findings from this study are intended to provide valuable insights into the characteristics and performance of these three fly ash types in concrete, as well as their influence on the mechanical properties of the material.
Melted plastics as the exclusive binder for masonry units: A sustainable solution for the construction industry
Authors: AWOYERA Paul O.
Published: February 2025

Abstract. This study investigates the feasibility of using melted plastic wastes, specifically low-density polyethylene (LDPE) and high-density polyethylene (HDPE), as the sole binder in masonry blocks in light-load applications, addressing both environmental and construction challenges. Repurposing plastics offers a sustainable alternative owing to the increasing plastic waste crisis and high carbon footprint of traditional cement. Predetermined plastic-to-aggregate ratios (1:1.5, 1:2, and 1:2.5) were adopted to produce masonry units, and properties of hardened samples, such as compressive strength and water absorption, and the scanning electron microscopy(SEM) micrographs were assessed. From the results, the blocks made with 100% HDPE and LDPE in a 1:1.5 ratio yielded compressive strengths of 18.6 N/m2 and 15.4 N/m2, respectively, thus surpassing the control cement-based blocks by 120% and 110%, respectively. The water absorption rate of melted plastic blocks was 3% less than that of the control blocks. Although with somewhat high production cost, these melted plastic blocks offer a significant long-term environmental benefit by reducing carbon emissions and diverting plastics wastes from landfills. The study concludes that melted plastic masonry units are viable, sustainable alternative for light-load construction.
A review on sources of water contamination in the Eastern Province, Kingdom of Saudi Arabia
Authors: ALYAHYA Salma Ali
Published: February 2025

Abstract. Rapid urbanization and growth in population is creating an enormous stress on the water resources in the kingdom of Saudi Arabia (KSA). Water quality is deteriorating due to over exploitation of the resources to meet the growing demand and also contamination due to human activities. Eastern province of KSA is dominated with huge industrial complexes, therefore the demand and contamination of water sources are enhancing each day. This paper presents a short review of the possible sources of contamination to the water resources in the eastern province of the kingdom.
Renewable energy for green environment in the Eastern Province in KSA: Opportunities and sustainability
Authors: Dernayka S.D.
Published: February 2025

Abstract. The kingdom of Saudi Arabia, in pursuit of its Vision 20230 goals for sustainable energy development, is prepping to generate electricity by shifting away from fossil-fuel based energy and adopting sustainable alternatives along with its new investments. The Eastern Province ranked second in highest energy consumption in the kingdom offers great potential for establishing renewable energy-based power plants due to the abundance of sun energy and wind sources. Moreover, the Biomass process can be an optimal solution for maximum extracted rate of energy. The simulation of two power plants of (1000 KW) and (10,000 KW) capacity, driven by four renewable energy sources 1) Photovoltaic 2) Wind Turbine 3) Solar Thermal and 4) Biomass has been evaluated in RetScreen software in terms of energy performance and environmental impact. The results have shown that the Eastern province has a great opportunity to draw the highest rate of electricity from Biomass for small- and large-scale power plants. In terms of sustainable effects, Wind and Solar thermal largely contribute to the reduction of carbon footprint in the area, and Biomass can play a pivotal role in displacing harmful gases, only if carbon releases are effectively controlled during operation.
Weisheit und Älter-Werden (zu: Mooren, Nadine: Leben im Alter)
Authors: Gasser Georg
Published: February 2025

Rezension als Beitrag zum Buchsymposium zu: Mooren, Nadine: Leben im Alter. Eine philosophische Untersuchung zur Frage nach dem guten Leben. Frankfurt a. M.: Klostermann, 2023.
Blended Care Intervention for Cancer Aftercare in General Practice Centers: Protocol for a Randomized Controlled Trial
Authors: Smits Michelle J M , Bolman Catherine A W , Mesters Ilse , Lechner Lilian
Published: February 2025

Background

Combining effective eHealth programs with face-to-face consultations in general practice may help general practitioners care for survivors of cancer.

Objective

This study protocol describes a 2-armed randomized controlled trial to evaluate the cost-effectiveness of a blended intervention integrating the Cancer Aftercare Guide in general practice centers (GPCs).

Methods

A parallel-group design will compare an intervention group with a waiting list control group. Participants will be nested within GPCs and randomization will occur at the GPC level. The participants in the intervention group will receive a blended care intervention. In contrast, the participants in the waiting list control group will receive care as usual for the duration of this study and will receive the online intervention afterward. All participants will be asked to complete an online questionnaire at baseline, 6 months, and 12 months after baseline, measuring self-reported adherence to lifestyle recommendations, psychosocial well-being, and quality of life. A process evaluation and cost evaluation are also included in this study. The effects will be evaluated based on differences in residual change scores between intervention and control group participants, using multilevel linear regression analyses. Moreover, effect analyses will be supplemented with Bayes factor analyses. Finally, an economic evaluation will be conducted from a societal perspective and will include medical costs, productivity costs, and costs of the blended care intervention.

Results

This study was funded in July 2020. Data collection started in August 2022 and is likely to be completed by April 2025. As of December 2024, a total of 127 participants have been included in this study, recruited across 26 GPCs in the Netherlands. Data analysis will commence once data collection is completed. Data analysis is estimated to start in the spring of 2025. The results will likely be published in 2026.

Conclusions

The results will provide insight into the effectiveness of blended care and may be relevant to cancer aftercare, general practice, and the field of eHealth implementation in general. Potential challenges lie in recruitment due to the strain on the health care system since the COVID-19 pandemic.

Trial Registration

ISRCTN ISRCTN12451453; https://www.isrctn.com/ISRCTN12451453

International Registered Report Identifier (IRRID)

DERR1-10.2196/64662
Virtual reality for workplace violence training of healthcare workers: A mixed-methods usability study (Preprint)
Authors: Jackson Daniel Isaiah , Wongphothiphan Thipkanok , Luna John , Maa Tensing , Fristad Mary A. , Huang Yungui , Schaffner Brittany , Reese Jennifer , Wheeler Jason , Abbott Brandon , Sezgin Emre
Published: February 2025

BACKGROUND

Workplace violence (WPV) in healthcare is a significant issue that affects frontline providers, patients, and visitors. Healthcare systems and regulatory organizations seek to mitigate this problem by training frontline healthcare providers with essential skills for de-escalation procedures.

OBJECTIVE

This study assessed the usability of a proof-of-concept virtual reality (VR) training course for WPV prevention and intervention with frontline healthcare workers.

METHODS

A cross-sectional, mixed-methods study was conducted with 13 frontline healthcare providers. A multicomponent training consisting of four training modules on WPV was developed and deployed in a virtual reality environment. Participants engaged with each module while providing qualitative feedback. Qualitative feedback was analyzed using rapid qualitative analysis technique. After completing the training pilot, participants filled out surveys on usability (System Usability Scale), user experience (mini-Player Experience Inventory), and shared feedback via Reaction Cards.

RESULTS

We identified four themes from qualitative feedback: (1) Perceived Value, (2) Technical and Navigational Barriers, (3) User Preferences, and (4) Vision. Participants described the VR training modules as refreshing and noted areas for improvements in the tone and emotional responses from non-player characters. Participants perceived the VR training modules positively, with room for improvement towards the usability (M = 63.30, SD = 9.53). Participants found the VR training manageable and learned to operate VR quickly. Despite this, participants may have required technical support to initiate VR training. The user experience was overall engaging (M = 5.23, SD = 1.34), and participants described the VR content and their experience as "valuable," "engaging," and "creative” via the reaction cards.

CONCLUSIONS

VR training has the potential to be a useful WPV training tool. Refining course content may improve the usability of VR while offering an immersive, hands-on, and safe learning environment for healthcare workers. Further research may compare VR WPV trainings with traditional trainings to evaluate short- and long-term training effectiveness.
Understanding Patient Perceptions of Bacterial Vaginosis Treatments: A Mixed Methods Sentiment Analysis Study of Online Drug Review Forums (Preprint)
Authors: Watkins Eren , Cimino Andrea N , Culbertson Curtis , Amico Jennifer R
Published: February 2025

BACKGROUND

Bacterial vaginosis (BV) is the most common cause of vaginal discharge in people of childbearing age in the United States (US). More information about what patients do and do not like about the most common BV products and the extent to which they reduced BV symptoms is an important step to understanding patients’ health and the current treatment landscape for BV.

OBJECTIVE

Using data from online drug reviews forums, this study’s objectives were to (1) quantitatively characterize the patient voice via sentiments (positive to negative) and emotions about the three most common Food and Drug Administration (FDA)-approved treatments for BV: oral metronidazole (OM), vaginal metronidazole (VM), vaginal clindamycin (VC) ; and (2) to qualitatively summarize themes characterizing the patient-perceived impact of BV and BV products.

METHODS

Data for this mixed methods descriptive study came from 1,645 users reviews of BV products posted on WebMD.com and Drugs.com. Reviewer attributes, reviewer submitted star ratings, and sentiment analysis using word-emotion association were analyzed with descriptive statistics and bivariate associations. A traditional qualitative analysis using qualitative description was also examined.

RESULTS

Most reviewers were female (99%), between the ages of 18-44 years, and reported using product less than one month, though qualitative results suggested most reported recurrent BV infections. Quantitative results suggest reviewers preferred vaginal products. Mean star ratings for VC were significantly higher when compared to OM and VM. VC reviews had the highest proportion of positive emotion words compared to OM and VM. Qualitative results for VC support the quantitative findings: favorable themes related to perceptions of value, effectiveness in alleviating symptoms, and minimal side effects. Additionally, despite some concerns related to cost for VC, reviewers would use the medication again. Other qualitative findings support BV medical education campaigns for patients and providers on BV treatment.

CONCLUSIONS

Overall, people want a BV treatment that is easy to use, quickly alleviates symptoms and has minimal side effects. Patients use product reviews to inform their decision-making about BV treatment, ask and seek answers to health-related questions, and share their experiences.