bluuebunny/crossref_metadata_2025 huggingface dataset:
Utilizing Machine Learning for Natural Language Processing to Conduct Sentiment Analysis on Twitter Data in Multiple Languages
Authors: Mishra Rahul
Published: February 2025

Retailers, market analysts, and other users of the web are greatly influenced
by user views. Arranging the unstructured data gathered from various social media
networks correctly is necessary for doing relevant analysis. Emotional evaluation as a
method for cross-lingual data classification has received considerable attention. Textual
organization is a subfield of natural language processing, or NLP, that may be used to
classify an individual's emotional or mental condition as positive, negative, beneficial,
or detrimental, like a thumbs up or thumbs down, etc. A combination of sentiment
analysis as well as deep learning techniques might be the key to solving this kind of
problem. Deep learning models, which are capable of machine learning, are
particularly useful for this. One of the most widely used deep learning architectures for
analyzing sentiment in text is called Long Short Term Memory. These frameworks
have potential applications in NLP. In this study, we provide algorithms to solve the
problem of multilingual sentiment analysis, and we evaluate their precision factors to
determine which one is the most effective.
The Use of Machine Learning to Analyze the Sentiment for Social Media Networks
Authors: Grover Darleen
Published: February 2025

The amount of textual information on the internet has increased significantly
with the debut of social media platforms like Twitter, including news stories and
historical records. This is due to the growth of Web 2.0. More individuals are using the
internet and different forms of social media to share their thoughts and feelings with
the world. As a result, more phrases with emotional nuance were created by the general
public. It is natural that researchers will look into new approaches to understanding
people's emotions and reactions. In addition to providing a novel hybrid system that
combines text mining and neural networks for sentiment categorization, this study
evaluates the efficacy of many machine learning and deep learning techniques. More
than a million tweets from across five different topics were utilized to create this
dataset. Seventy-five percent of the dataset was used for training, while the remaining
twenty-five percent was used for testing. When compared to traditional supervised
learning methods, the system's hybrid approach displays a maximum accuracy of
83.7%.
Sentiment Classification of Textual Content using Hybrid DNN and SVM Models
Authors: Singla Abhishek
Published: February 2025

The proliferation of Web 2.0 has resulted in a deluge of real-time,
unstructured data such as user comments, opinions, and likes. The lack of structure in
the data makes it difficult to create a reliable prediction model for sentiment analysis.
There have been promising applications of several DNN architectures to sentiment
analysis, however, these methods tend to treat all features identically and struggle with
high-dimensional feature spaces. In addition, existing techniques fail to effectively
combine semantic and sentiment knowledge for the purpose of extracting meaningful
relevant contextual sentiment characteristics. This paper proposes an integrated
convolutional neural network, or CNN, architecture that takes sentiment as well as
context into consideration as a means of intelligently developing and highlighting
significant components of relevant sentiment contextual in the text. To start, we use
transformers' bidirectional encoder representations to create sentiment-enhanced
embeddings of words for text semantic extraction using integrated emotion lexicons
with broad coverage for feature identification. The proposed approach then adjusts the
CNN in a way that it can detect both word order/contextual text semantics data as well
as the long-dependency relationship in the phrase sequence. Our approach also
employs a system to prioritize the most important portions of the phrase sequence. One
last step in sentiment analysis is the use of support vector machines (SVMs) to reduce
the complexity of the space of features and identify locally significant characteristics.
The accuracy of existing text sentiment categorization is greatly improved by the use of
the proposed model, as shown by an evaluation of real-world benchmark datasets.
Big Data Analysis and Information Quality: Challenges, Solutions, and Open Problems
Authors: Suri Sahil
Published: February 2025

Big Data is here, thanks to the proliferation of social media and IoT devices.
As a result of the immense benefit that Big Data has brought to the public as well as
businesses, the question of how to handle and utilise it more effectively has captivated
people from every walk of life. Big Data processing has been plagued by difficulties
due to the 4V features of Big Data. Solving the data quality issue is crucial to Big Data
processing, as is ensuring data quality, which is a precondition for Big Data to play its
worth. Two examples of where Big Data has been put to good use are in
recommendation and prediction systems. In this research, we investigate Big Data
recommendation and prediction systems and endeavour to ascertain the quality of data
at each stage of the process: collection, preprocessing, storage, and analysis. The
proposed remedy follows from a thorough description and examination of the issue at
hand. We have left a few questions unanswered at the conclusion of the report.
Using Deep Learning Techniques to Detect Traffic Information in Social Media Texts
Authors: Rampal Sourav
Published: February 2025

Given the real-time and pervasive nature of social media, data mining for
traffic-related insights is a newly emerging area of study. In this work, we discuss the
challenge of mining social media for traffic-generating microblogs on Sina Weibo, the
Chinese equivalent of Twitter. It is recast as a classification issue in short texts for
machine learning. In the first step, we use a dataset of three billion microblogs and the
continuous bag-of-word model to learn word embedding representations. Word
embedding, as opposed to the standard one-hot vector representation of words, has
been shown to be useful in natural language processing tasks because of its ability to
capture semantic similarity between words. Then, we suggest feeding the learned word
embeddings into convolutional neural networks (CNNs), long short-term memory
(LSTM) models, and their combination LSTM-CNN to extract traffic-related
microblogs. We evaluate the suggested techniques against state-of-the-art methods
such as the support vector machine (SVM) model using a bag of n-gram features, the
SVM model using word vector features, and the multi-layer perceptron model using
word vector features. The proposed deep learning methods are shown to be useful in
experiments.
The Prediction of Faults Using Large Amounts of Industrial Data
Authors: Singh Jagtej
Published: February 2025

An essential feature of an intelligent workplace is error-free manufacturing.
The data-driven approaches rely on fundamental status data with minimal volume,
while the conventional model-based methods rely heavily on accurate equipment
models. Not only are these aspects problematic, but they also prevent them from
meeting the real-time need of evaluating industrial big data in an IoT setting. In this
research, we provide a fault prediction approach that uses industrial big data to unearth
the connection between data (such as status and sound data) and equipment failures
using machine learning techniques. Not only that but the breakdown could be
investigated promptly since the equipment's status could be tracked in real-time. Our
solution outperforms the current ones in terms of accuracy and real-time capabilities,
according to the simulation results.
Comparison Analysis of Logical Regression and Random Forest with Word Embedding Techniques for Twitter Sentiment Analysis
Authors: Singh Dhiraj
Published: February 2025

In order to carry out categorization and generate new categories, the huge
volumes of textual materials produced nowadays must be immediately organised. The
fundamental method of gaining insights from organising textual data is text
classification. Then, we further classify the classes based on the discovered text types.
Separated into four stages—pre-treatment, text representation, classifier execution, and
classification—we use a wide range of machine learning approaches to classify texts.
In this study, we utilise real-world data from Twitter to evaluate and compare several
sentiment analysis approaches. We clean the data and divide it into train and text set
before developing models using various vectorising approaches and compare the
outcomes. Based on a comparison of the models with various vectorizations, it was
found that the best performance was provided by the Logical Regression (LR) models
using TF-IDF, with an f1 value of 0.81 and good accuracy and recollection values.
The Classification of News Articles Through the Use of Deep Learning and the Doc2Vec Modeling
Authors: Makhija Himanshu
Published: February 2025

The exponential growth in internet use has also led to the proliferation of
textual information in large quantities. Since handling unstructured material manually
is difficult, there is a need to explore novel techniques for automated categorization of
textual information. The primary goal of text categorization is to teach a model to
correctly categorise an unseen text. In this research, the Doc2vec word embedding
technique was used to classify stories in Turkish from the TTC-3600 database of
Turkish news and BBC news stories in English. In addition to the CNN based on deep
learning, traditional machine learning classification methods including Gauss Naive
Bayes (GNB), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine
(SVM) are used. The best classification results using CNN were achieved with the
proposed model, scoring 94.17% on the Turkish database and 96.41% on the English
database.
Exploring the Role of Big Data in Predictive Analytics
Authors: Mahesh T. R.
Published: February 2025

Cardiovascular illness is afflicting enormous monetary and psychological
costs. The development of an ASHRO-based model for forecasting healthcare resource
use and its link with clinical outcomes was driven by a desire to improve the economy
and provide a high-quality evaluation of the healthcare system. Data included in this
analysis were taken from a big database that included doctor visits, insurance claims
across several years, and results of preventive health screenings. Hospitalized patients
with heart illness (ICD-10 I00-I99) comprised the study population. Broadly defined
composites compliance served as the explanatory variable, while medical as well as
long-term care costs served as the objective variable. Using a combination of random
forest learning (AI) and multiple regression analysis, predictive models were
calibrated. These models were then used to create ASHRO scores. Two measures, the
area under the curve as well as the Hosmer-Lemeshow test, were used to assess the
prediction model's effectiveness. After controlling for clinical risk variables, we
compared the two ASHRO 50% threshold groups' total morbidity at 48 months of
follow-up using matching propensity scores. Heart disease affected 61.9% of the
48,456 patients surveyed, with an average age of 68.3 9.9 years at hospital release. For
the purpose of adherence score classification, machine learning was employed to
combine eight factors into a single index: generic drug rate, interconnecting outpatient
visits/clinical laboratory as well as physiological tests, the proportion of days
addressed, secondary mitigation, rehabilitation magnitude, direction, and a single index
that adjusted for eight factors. In the end, the multiple regression study yielded a 0.313
(p 0.001) coefficient of determination. Medical as well as long-term care expenditures
had a statistically significant total coefficient of determination (p 0.001) in a logistic
regression study using 50% along with 25%/75% cut-off values. At the 50% level of
significance (2% vs. 7%; p 0.001), the relationship between ASHRO score and
mortality rate was statistically significant.
Implementing Automated Reasoning in Natural Language Processing
Authors: Sengottaiyan N. , Naaz Rohaila
Published: February 2025

One deep learning method is the Convolutional Neural Network (CNN).
Natural language processing problems like text classification are simplified using this
approach. In this study, we use a deep learning strategy, namely the CNN method to
deal with the issue of text classification. CNNs, which require a large deal of time as
well as finances to train and use, have been greatly impeded by the rise of Big Data and
the increased complexity of tasks. To get around these problems, we introduce a
MapReduce-based CNN that rethinks what a CNN has learned by breaking it down into
a series of smaller networks and training them in parallel. Subsets of incoming text are
analysed by many autonomous networks.
Investigation on moisture susceptibility of asphalt mixtures containing ground tire rubber and de-vulcanized rubber
Authors: Alasfour Fatemah
Published: February 2025

Abstract. The growing environmental threats of accumulating waste tires have raised the need to recycle and utilize them effectively, such as incorporating recycled crumb rubber in asphalt pavement mixtures. This paper aims to assess the incorporation of processed rubber from recycled tires on the moisture susceptibility of asphalt mixtures. For this purpose, two forms of recycled rubber were utilized; Ground Tire Rubber (GTR) and De-Vulcanized Rubber (DVR), with a constant content of 12.5% of bitumen by weight in a wet process. These modified binders were used to prepare asphalt mixture samples, which were then examined using the Indirect Tensile Strength test (ITS) in wet and dry conditions. In addition, the Tensile Strength Ratio (TSR), Marshall Stability, and flow were calculated. The results for all examined mixes were compared to the control mixture, namely the 60/70 pen Type 3 Marshal Mix. The results demonstrate that the measured TSR values, stability, flow, and volume of voids for all tested mixes were within acceptable limits. By meeting the objective of this study, it is well proven that the incorporation of recycled rubber was found to be effective in improving the moisture susceptibility of asphalt mixtures. Future studies are necessary to optimize the percentage of recycled rubber utilization, and to perform life cycle assessments of such mixtures.
Structural design analysis of hybrid multi-rise buildings, with CLT and LGS light weight floor system, at Neom-The Line
Authors: ALOTAIBI Danah
Published: February 2025

Abstract. Aligned with Neom's vision, this study focuses on the structural design of advanced multi-rise buildings at The Line (Neom) and compares the integration of Light Gauge Steel (LGS) and Cross-Laminated Timber (CLT) floor systems within Reinforced Concrete (RC) structures to determine their structural efficiency and suitability. The research involves design and analysis of structural elements (slabs, beams, columns, shear walls) and study their performance verification using ETABS and SAP2000 software. The aim is to identify the optimal solution for structural performance and sustainability within the context of Saudi Vision 2030 to meet the structural demands while achieving sustainable development goals.
Solutions for maintenance delays in Saudi oil and gas: Integrating interpretive structure modeling and relative importance index
Authors: ALSHIBANI Adel
Published: February 2025

Abstract. Delay in maintenance and repair of oil and gas plants are crucial issue that require great attention to ensure that activities are conducted as planned. This paper introduces Interpretive Structural Modelling (ISM) based approach to identify and correlate reasons of delay in this type of operations. The potential reasons for delaying schedule of maintenance operations are initially identified through review of the literature and experts’ interview. Then feedback from the targeted professional is gathered via survey questionnaires. The perspective of contractors and owners in the Saudi Arabia Eastern province of are considered. The identified possible reasons are ranked based on the feedback received from a total of 57 respondents. The ISM approach then is employed with the assistance of two experts to explore the correlation among the identified reasons. The relative importance index (RII) revealed that the greatest critical possible reasons for delaying schedule of maintenance operation in oil and gas are material related issues (items spare parts), delivery of spare parts, inexperienced contractor, skill levels and efficiencies of manpower, and unclear scope of work. In contrast, ISM uncovered that the most driving power possible reason for delaying maintenance operations is communication issues followed by site condition and then unclear scope of work.
Building information modelling (BIM)-based solar path and shadow analysis for energy-efficient building design
Authors: TUSHAR Fazlul Hoque
Published: February 2025

Abstract. This study investigates using BIM for solar path and shadow analysis to enhance energy-efficient building design in the tropical climate of Dhaka, Bangladesh. It focuses on a site area of 6910 m² in Mohammadpur, aiming to optimize solar gain, minimize unwanted heat, and improve natural daylighting in buildings. Utilizing tools like SketchUp with Skelion and Curic Sun, detailed analyses of solar paths and shadows were conducted for key dates across different seasons. The findings demonstrate that strategic building orientation, site layout, ventilation, and the implementation of shading devices can significantly enhance energy efficiency, reducing both heating and cooling demands throughout the year. Moreover, the study underscores the importance of integrating solar and shadow data into the design process to develop sustainable buildings that balance functionality with energy conservation. These results offer valuable insights and practical guidelines for architects and engineers aiming to promote sustainable building practices.
A Comprehensive bibliometric review of circular economy in the building sector: Integrating sustainable practices
Authors: MOUSTAFA Zahwa
Published: February 2025

Abstract. This study conducts a bibliometric literature review to analyze the sustainable performance of circular economy practices in the building sector, aiming to identify key research trends, influential publications, and collaborative networks. A systematic search was performed on the Scopus database, yielding 779 relevant articles. We used VOSviewer to conduct a bibliometric analysis focusing on co-authorship networks, citation patterns, keyword co-occurrence, co-citation, and bibliographic coupling. These analyses provided a detailed visualization of the research landscape. The analysis revealed significant clusters of research activity, highlighting key authors, influential journals, and widely cited works. The study identified prevalent themes and concepts, illustrating the development of circular economy practices in the building sector. Network visualizations showed strong collaborations and citation linkages among leading researchers and institutions. This bibliometric review offers a comprehensive overview of research trends in circular economy practices within the building sector. Uncovering patterns and key contributions provides valuable insights for researchers and practitioners focused on sustainability and resource efficiency in the built environment.
A study case: Seismic evaluation of existing six-story office building with flat plate structural system
Authors: ANDREW Jonathan
Published: February 2025

Abstract. Indonesia is one of the countries with a relatively high frequency of earthquakes, which can impact buildings and infrastructure. With the ongoing updates to regulatory codes, there will be an elevation in the level of safety factors in building design. Concurrently, there will be an increased prevalence of incorporating seismic hazards into the design of new buildings. Apart from that, earthquake loads can cause the performance of flat-plate structures to decrease until they collapse. Therefore, in this research, a case study of the existing office building with a flat plate structural system built in 1998 will undergo a seismic evaluation based on ASCE 41-17 to determine whether the building's performance is still safe. The office building was evaluated at the Collapse Prevention performance level for the BSE-2E seismic hazard (975-year period) according to ASCE 41-17. The evaluation occurred in three stages: tier 1, tier 2, and tier 3. The pushover analysis demonstrates the use of linear and nonlinear static procedures in structural analysis. The evaluation indicated that the current office building could meet the specified performance level; however, several beam and column structural components still required fixing.
Experimental evaluation of the masonry infilled reinforced concrete frame under reverse cyclic loading
Authors: GALATAGE Abhijeet A.
Published: February 2025

Abstract. The design of structures that resisting the earthquake induced forces has become increasingly important, especially as masonry infilled reinforced concrete (RC) frames are crucial in resisting seismic forces. This study focuses on the performance of RC frames infilled with masonry walls under extreme seismic loading conditions, which can cause severe damage to support columns, potentially leading to partial or complete building failure. The research investigates how the seismic loading affects the structural behaviour of RC frames infilled with AAC block masonry walls and how performance parameters such as frequency, storey drift, relative stiffness, component strength, energy dissipation and local construction methods influence the overall seismic performance. To simulate earthquake conditions, cyclic loading test was performed on eight half-scale, single-story, single-bay RC frames, designed in compliance with IS 456:2000, IS 1893:2016 and IS 13920:2016 standards. These frames, with 1.5 m x 1.5 m masonry infills, were subjected to displacement-controlled cyclic loading following a standardized protocol. The study aimed to evaluate the stiffness degradation and energy dissipation of RC frames in cyclic load test. The experimental results, demonstrated through hysteretic curves, stiffness degradation trends and energy dissipation, revealed that RC infilled frames show significant potential for use in earthquake-prone regions. Key findings include energy dissipation differences of 17-18% between the first and second halves of the energy loop and a 70-75% reduction in stiffness as displacement increased by 60% at a constant frequency. The frames initially exhibited high stiffness, with slower degradation rates observed under cyclic loading. The research concludes that the seismic performance of masonry-infilled RC frames can be significantly improved by understanding stiffness degradation, load distribution mechanisms and the composite interaction between the infill and the RC frame. These findings are essential for creating more robust designs, reducing the risk of collapse and enhancing safety in earthquake-prone areas.
Thermal optimization of two-way joist slabs: A comparative study using finite element analysis
Authors: AL-MEKHLAFI Galal
Published: February 2025

Abstract. Efficient thermal management in building structures is crucial for energy conservation, particularly in hot climates. A comparative thermal analysis was presented in this paper on twenty two different two-way hollow block slabs using the finite element method (FEM). The study considered two types of hollow block materials which are normal concrete and clay, each with eleven block recesses configurations. The thermal properties including thermal transmittance, thermal resistance and equivalent thermal conductivity among various slab arrangements were investigated. The obtained results suggest that the number of vertical and horizontal intermediate bulkheads and the constituent material determine the thermal properties of the slabs. Clay-based slabs showed better thermal insulation than concrete-based slabs. This research contributes towards energy efficient building components development as well as informing selection of optimal slab configurations for enhanced heat performance.
Nonlinear ground performance under a high shaking scenario similar to Boumerdes earthquake
Authors: KHIATINE Mohamed
Published: February 2025

Abstract. This paper studies the ground response at the Dar El Beida site in Algiers, Algeria, under a strong earthquake scenario equivalent to the Boumerdes earthquake using FLAC2D software. The input motion's two horizontal components (EW, NS) and vertical component (V) are applied independently at the bedrock level. By comparing numerical predictions with recorded data, we can assess the performance of nonlinear analysis compared with the equivalent traditional linear approach. Furthermore, the nonlinear response spectrum takes into account the nonlinear behavior of materials and structures, which is crucial when analyzing structures that may undergo significant deformations during an earthquake. This type of spectrum is particularly relevant for ductile structures, which can undergo plastic deformation under heavy loads. The first simulation of this communication is carried out by DEEPSOIL software, which implements equivalent linear and nonlinear procedures; the results show good agreement in terms of acceleration. The second simulation was carried out by FLAC2D software using the Ramberg-Osgood model limited by the Mohr-Coulomb criterion. The examination focuses on the first 30 meters of soil. This depth is crucial for determining the dynamic behavior of the soil during seismic occurrences. The Ramberg-Osgood model limited by the Mohr-Coulomb criterion used in the nonlinear analysis is quite good at predicting ground motion.
Physical modeling in calibration chamber of the evolution of the soil stiffness around a piezocone probe under cyclic axial loading in saturated clay
Authors: KHOUAOUCI Mohammed
Published: February 2025

Abstract. Cone Loading Test (CLT) is a new in-situ geotechnical soil test, considered as an improvement of the piezocone penetration test (CPTu). The principle is to carry out a conventional static penetration test with a piezocone up to a given depth, then static and/or cyclic loadings are performed. The interpretation of the CLT at this depth allows a better soil characterization by providing the stiffness parameters as well as the strength parameters, which are complementary of those obtained during the conventional static penetration test. The aim of this paper is to present the results of physical modeling of cyclic CLT tests carried out in the calibration chamber of the Navier Laboratory at the Ecole Nationale des Ponts & Chaussées, France. A piezocone probe of 36 mm in diameter is jacked into saturated Kaolinite clay and subjected to cyclic axial loading up to a very large number of cycles (105 cycles). The evolution of skin friction and tip stiffness at the soil-pile interface during cyclic loading is studied. A parametric study is conducted to examine the influence of number of cycles as well as stress level on the measured parameters.
Effectiveness of marble powder to improve compaction of low-water content soil
Authors: AICHE Oubaida
Published: February 2025

Abstract. Arid environmental conditions in arid areas are characterized by little precipitation and substantial evaporation, which substantially influence soil water content, which is essential for soil compaction. The present study evaluates how incorporating powder marble impacts the compaction of low-grade soil under conditions of low water content. Modified Proctor and California Bearing Ratio (CBR) tests were conducted to assess these effects. The results revealed that incorporating 15% marble powder into the soil significantly improved maximum dry density and CBR values, indicating improved compaction and load-bearing capacity.
Investigation of the density, ultrasonic pulse velocity and strength properties of metakaolin based geopolymer mortars
Authors: HAWA Layal
Published: February 2025

Abstract. Ordinary Portland Cement (OPC) remains a cornerstone of construction, but its production carries a significant environmental burden due to greenhouse gas emissions. This paper investigates Metakaolin-based geopolymer mortar (GPM) as a promising replacement for OPC mortars, focusing on its potential for reduced environmental impact. The study explores the influence of two main factors on the properties of geopolymer mortar: the Alkaline to Metakaolin ratio (A/M) and the Sodium Silicate to Sodium Hydroxide solution ratio (SS/SH). The molarity of sodium hydroxide was fixed at 16 for all samples, and the curing temperature was maintained at 60°C for 24 hours after demolding. The effect of these variations on the unit weight, ultrasonic pulse velocity, and compressive strength of the geopolymer mortar was evaluated. Notably, geopolymer mortars have been shown to exhibit compressive strengths exceeding those of traditional OPC mortars. The investigation focuses on the interplay between A/M and SS/SH ratios. A higher A/M ratio, with a higher amount of alkaline activator in the mix, can enhance the mechanical properties of geopolymer mortars. Moreover, the additional alkalinity promotes further geopolymerization and the formation of crystalline materials within the geopolymer structure, ultimately leading to improved strength. However, maintaining a controlled amount of silica is crucial. While sodium silicate is a necessary component of the activator solution, an excessive amount of soluble silicate can hinder the formation of these geopolymer mortars for achieving desired mechanical properties and durability while contributing to the development of more sustainable construction materials. The findings will highlight the delicate balance between the A/M and SS/SH ratios for maximizing the potential of geopolymer mortars as a viable and environmentally friendly alternative to traditional cement-based materials.
Enhancing the durability of fine-grained soils with stabilizers under freeze-thaw cycles
Authors: RABABAH Samer
Published: February 2025

Abstract. The low strength and weak resistance to freezing-thawing cycles of expansive soil present a substantial engineering challenge. However, different stabilizers can improve soil properties under such conditions. This study determines how freezing-thawing cycles affect the behavior of fine-grained soils stabilized with lime, cement, and Zeolitic Tuff (ZT). Seventeen combinations of these three materials were employed and compared with natural soil. Soils were tested for unconfined compressive strength after performing several freezing-thawing cycles ranging from 0 to 20 to examine environmental conditions' impact on durability. According to the outcomes of the experiments, as the number of freezing-thawing cycles increased, both stabilized and natural soil strength reduced. However, even after 20 freeze and thaw cycles, all treated soil mixtures maintained a higher strength than natural soil. Adding ZT to soil already treated with lime or cement makes them more resistant to strength loss and volume changes due to freezing and thawing actions and helps them maintain the increased strength they gained, more than using lime or cement. Utilizing ZT in combination with lime and cement-treated soil has proven to enhance the durability of soils exposed to freezing temperatures in all the properties that have been evaluated, making its addition an eco-friendly option for lowering life-cycle costs and improving soil performance of earthwork construction.
Correlation of soft clay and shear strength in West Jakarta
Authors: Zaki M.
Published: February 2025

Abstract. Most geotechnical problems are soft soils, where geotechnical science is very unique, due to limited data, engineer experience, multi-phase, heterogeneous and variable attachment to one another. West Jakarta has 56 urban villages as one of the strategic cities that has a large distribution of soft soil with a thickness that varies more than 4 meters. Sampling was conducted randomly in all urban villages, with testing in the form of sondir, boring machine, uds, soil properties and soil engineering. the results obtained are a well-bound relationship between water content and soft soil depth, friction angle with PI and soft soil depth of r2> 0.60, while the relationship of cohesion value to soft soil depth, PI, NSP, and soft soil depth is not well bound. For the regression equation approach obtained are y = -0.8266x + 54.597 (friction angle with PI); y = -1.0209x + 56.032 (friction angle with soft soil depth) and y = 1.848x + 22.151 (soft soil depth with water content).
Effect of ground motion frequency content on nuclear power plants including soil-structure interaction
Authors: ELSHARAWY Mohamed
Published: February 2025

Abstract. The study presented in this paper examines the impact of earthquakes with different frequency content on containment nuclear structures. Three sets of strong ground motion records (i.e., nine ground motions in total) were analyzed with low, medium and high Acceleration/Velocity ratio. The selected earthquakes all fall in the west coast of North America Region. Comparison with the design spectra for the west coast given in the US-NUREG (2001) has shown that the average spectrum of the nine selected earthquakes is in good agreement with the WUS design spectrum. Two cases of the foundation have been considered namely, fixed case and flexible case. In the flexible case the soil-structure interaction is considered rock and clay (i.e., firm to stiff clay soil) soil profiles. The effect of all earthquakes on the structure responses has been examined in the two foundation cases. The results show that there is correlation among the earthquakes with different A/V ratio and the estimated responses. Additionally, the soil structure interaction has significant impact on the responses, and it increases with increasing the design ground motion intensity.
Development model of small orifice flow using simple linear and multilinear regression
Authors: HIDAYAT Dina P.A.
Published: February 2025

Abstract. The concept of "small orifice flow" describes the movement of a fluid through a tiny hole or orifice. This is a typical occurrence in engineering and fluid mechanics. In machine learning, linear regression is a fundamental and extensively used technique for modelling the connection. In this research, an analysis of the influence of the variables H1, H2 and Q was carried out on the output value of the discharge coefficient (Cd). Model evaluation is done by calculating calibration parameters like the coefficient of determination. R square (R2), also known as the coefficient of determination, describes the extent to which dependent data can be described by independent data. There are a total of 47 datasets that will be included in the development of linear and multilinear models. In this study, 90% (42) of the training data and 10% (5) of the total test data were used. Based on simple linear model, Q parameter having a stronger linear relationship with the Cd parameter compared to the H1 and H2 parameters with a strong multiple R value of 0,754 and moderate R2 correlation value of 0.569. There is a significant influence between the variables Q and Cd based on significant F value. The multilinear regression method was chosen because of its statistical data handling capabilities and ease of creation, implementation, and operation. the multilinear regression model with H1, H2 and Q values as independent variables can explain the Cd value as the dependent variable very well with the higher value of multiple R and R2, and small error value.
Preliminary study of water quality in the Jragung river
Authors: Toffin L.
Published: February 2025

Abstract. The concentration of pollutants in the Jragung River differs in the dry season and the rainy season. The purpose of this study was to analyze the effect of the dry season on the water quality of the Jragung River in the Upper Zone. The method used was grab sample with reference to SNI 698957:2008. Meanwhile, water quality data analysis was carried out using quantitative data analysis. Testing was carried out at the Environmental Engineering Laboratory of Trisakti University. The concentration of pollutants in the Jragung River differs in the dry season and the rainy season. The purpose of this study was to analyze the water quality of the Jragung River in the Upper Zone. The method used was grab sample with reference to SNI 698957:2008. Meanwhile, water quality data analysis was carried out using quantitative data analysis. Testing was carried out at the Environmental Engineering Laboratory of Trisakti University. The results showed that the concentration of detergent in the Jragung River at station 1 was 0.07 mg/L and 0.1 mg/L at station 2, both below the quality standard of 0.2 mg/L set by Government Regulation Number 82 of 2001. The Pollution Index (IP) of 7.77 indicates moderate pollution. The pH of the water ranges from 8.4 to 8.7, still suitable for aquatic life. However, several parameters, including COD, DO, Phenol, Phosphate, Oil and Fat, Sulfide, Lead, Total Coliform, and Fecal Coli, exceed the river water quality standard limits so that the use of water from the Jragung River requires a treatment process to meet the class 1 raw water quality standard.
Application of artificial neural networks (ANN) to evaluate centrifugal pump characteristics
Authors: Mohammed N.
Published: February 2025

Abstract. This paper uses an Artificial Neural Network (ANN) technique to give an experimental and comparative examination of centrifugal pump characteristics. Comprehensive physical testing is a common component of traditional pump performance evaluation techniques, which may be expensive and time-consuming. In this study, we created an ANN model to forecast important performance metrics, such as speed, torque, pressure based on input data. These metrics include flow discharge, height, hydraulic power, motor power, and efficiency. The ANN model was trained and validated using experimental data that came from a series of carefully monitored experiments conducted on a typical centrifugal pump. Using a Levenberg Marquardt backpropagation approach, the ANN model was trained to attain high prediction accuracy by refining the topology of the network found to be high R-squared value of 0.98 and low RMSE by effectively predicting important parameters. Next, in order to assess the ANN model's predictive power, its performance was contrasted with the outcomes of the experiments. The results show that there is a strong degree of correlation between the predicted and experimental data, indicating that the ANN technique offers a dependable and effective way to forecast centrifugal pump characteristics.
Leveraging AI for Environmental Solutions addressing Climate Change, Pollution Monitoring and Sustainable Resource Management
Authors: AKBAR Noreen Sher
Published: February 2025

Abstract. Artificial Intelligence (AI) has emerged as a transformative tool in addressing critical environmental challenges, including climate change, pollution monitoring, and sustainable resource management. This review synthesizes recent advancements and applications of AI techniques in these domains, highlighting key findings and identifying future research directions. In climate science, AI has significantly enhanced the accuracy of climate models and optimized renewable energy deployment, contributing to effective mitigation strategies. For pollution monitoring, AI-driven systems offer real-time detection and analysis, surpassing traditional methods in precision and scalability. In sustainable resource management, AI optimizes agricultural practices, forest conservation, and water resource allocation, promoting efficiency and sustainability. Despite these promising advancements, challenges such as data quality, model interpretability, integration with existing systems, and ethical considerations remain.
Peter E. Gordon: Prekäres Glück. Adorno und die Quellen der Normativität.
Authors: Rößler Jonathan
Published: February 2025

Rezension zu Peter E. Gordon, Prekäres Glück. Adorno und die Quellen der Normativität. Berlin: Suhrkamp, 2023.
Ist das gute Leben im Alter zu vernünftig? (zu: Mooren, Nadine: Leben im Alter)
Authors: Hiekel Susanne
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.
Classifying chronic kidney disease using selected machine learning techniques
Authors:   College of Technology, Cebu Technological University, Cebu, Philippines, Anqui Abrahem P.
Published: February 2025

Chronic kidney disease (CKD) is a serious global health problem with high mortality rates, often due to late diagnosis. Early detection and classification are essential to improve treatment outcomes and slow disease progression. This study evaluates the performance of four machine learning algorithms—linear discriminant analysis (LDA), Naïve Bayes, C4.5 decision tree, and Random Forest—in classifying CKD using a Kaggle dataset containing 1,659 instances and 52 features, covering demographic, lifestyle, and clinical data. After data pre-processing, the classification accuracies of the algorithms were assessed. LDA showed the highest accuracy at 92.8%, followed by Naïve Bayes (92.1%), C4.5 (92.0%), and Random Forest (91.9%) before hyperparameter tuning. After tuning, C4.5 achieved the highest accuracy of 92.5%, followed by Random Forest (92.2%), with Naïve Bayes remaining at 92.1%. However, even after tuning, LDA remained the most accurate, demonstrating superior performance. The key features contributing to CKD classification were serum creatinine, glomerular filtration rate (GFR), muscle cramps, protein in urine, fasting blood sugar, itching, systolic blood pressure, blood urea nitrogen (BUN), HbA1c, edema, total cholesterol, body mass index (BMI), and gender. These findings confirm that LDA outperforms other algorithms in CKD classification without the need for tuning, emphasizing the value of machine learning in improving early diagnosis and management of CKD.
Exploring the Potential Role of Generative AI Chatbots in Diabetes Management: Insights into Patients’ Health Information Seeking and Clinician Perspectives (Preprint)
Authors: Jeon Soyun , Lee Seolhee , Kim Esther Hehsun , Eun Jinsu , Lee Kwangwon , Lim Hajin , Lee Joonhwan
Published: February 2025

BACKGROUND

Diabetes requires continuous management to prevent complications. Many patients rely on online search engines and mobile health apps for information, but these tools often lead to information overload, complex navigation, and lack of personalization. Generative AI chatbots have emerged as promising alternatives by providing accessible and tailored health recommendations, addressing each patient's unique needs more effectively.

OBJECTIVE

This study explores the potential of generative AI chatbots in diabetes management. It investigates the types of questions diabetes patients frequently ask, evaluates user experience, and compares chatbot interactions with online health information-seeking behaviors and physician consultations. Additionally, the study gathers expert insights by having family medicine specialists review chatbot conversation logs and interact with the chatbot to assess its utility, limitations, and future applications in diabetes care.

METHODS

A two-part evaluation study was conducted. In Part 1, 24 patients with diabetes participated in structured sessions, followed by surveys and interviews to gather user insights. In Part 2, four family medicine specialists reviewed conversation logs to evaluate response accuracy and provided feedback on the chatbot’s potential role in diabetes care. Both parts of the study utilized DTalksBot, a GPT-4-based chatbot enhanced with Retrieval-Augmented Generation (RAG), to ensure reliable and contextually relevant responses.

RESULTS

Patients valued the chatbot’s real-time and personalized guidance, emphasizing its ability to simplify health information-seeking and reduce cognitive burden. Family medicine specialists acknowledged its potential to support routine care by addressing lifestyle-related questions, reducing provider workload, and tracking patient concerns for follow-up discussions. However, they noted the need for more tailored responses and integration with real-time patient data, such as glucose monitoring or treatment history, to better align with personalized care standards.

CONCLUSIONS

Generative AI chatbots exhibited promise as supplementary tools for diabetes management, providing reliable, personalized, and accessible health information. Unlike traditional online searches that often lead to information overload, these chatbots mitigate this issue by offering verified guidance, supporting informed decision-making, and enhancing patient engagement in self-management. However, their full potential requires further developments, such as dynamic personalization, integration with verified clinical data, and alignment with healthcare workflows to ensure safety and effectiveness. Future enhancements could establish generative AI chatbots as comprehensive, patient-centered tools for managing chronic diseases.
Effect of Organic Fertilizers and Plant Growth-Promoting Rhizobacteria (PGPR) on Nutrient Residue and Growth of Rice (Oryza sativa L.)
Authors: Tawakal Muhammad Bimasakti , Tyasmoro Setyono Yudo , Wicaksono Karuniawan Puji
Published: February 2025

This study aims to evaluate the effects of organic fertilizers and Plant Growth-Promoting Rhizobacteria (PGPR) on rice (Oryza sativa L.) growth and fertilizer residues. The research was conducted at Jatimulyo Experimental Farm, Malang, using a split-plot design with 12 treatment combinations of organic fertilizers and PGPR. The results showed that the application of organic fertilizers increased soil organic carbon content, while PGPR contributed to improving nitrogen and potassium efficiency by reducing nutrient losses. However, the efficiency of phosphorus from inorganic fertilizers remained low, with most of the phosphorus retained in the soil and not absorbed by plants. Principal Component Analysis (PCA) revealed that plant growth parameters were more influenced by inorganic fertilizer treatments than organic ones. This research provides important insights for the development of sustainable agricultural technologies that enhance soil quality and reduce dependence on inorganic fertilizers.
Reason and Passion in Thomas Middleton's The Changeling
Authors: Karo Hasan Hussein
Published: February 2025

This paper discusses the effect of passion when deciding the important life matters and judging others through emotional desire instead of reason. First, it demonstrates a short background of Jacobean drama and tragedy as a prominent dramatic genre of that time. The purpose of this study is to examine and analyzes characters’ behaviour that leads to their own destruction. The methods that are employed in this study are the crucial notions of French psychoanalyst-feminist Julia Kristeva and her study of human behavior. Through play’s textual analysis, the study has reached certain findings regarding the presentations of complex characters in Middleton’s play, especially the main female characters. Finally, this study’s conclusions elaborate the Kristeva’s notions concerning certain behaviour that women characters perform in the play that show the motives behind their actions, whether driven by reason or passion. It is worth mentioning that Kristeva’s critical notions related to desire and passion are used as the main tools of analysis to clarify the actions of one of the main female characters in the play, Beatrice, which result from her passional personality. The way Beatrice makes use of her body to achieve certain desires or even used as a means of seduction is analyzed through Kristeva’s scope of studying feminine body.