bluuebunny/crossref_metadata_2025 huggingface dataset:
The Shift of Global Political Power from London to Washington (1919-1945): Geopolitics of the Inter-War Period
Authors: Negi Vansh , Shrivastava Dr. Aparna
Published: March 2024

Executive Summary: The current international order is based on the historical factors that unfolded in the past, one such major historical factor that shaped our current world is the shift of global power from the British Empire to the United States of America. The shift took place in the 20th century due to the unstable and ever-changing circumstances in the geopolitical realm. A host of different factors formed the rise of nationalism in Europe, anti-imperialist sentiments in colonies, and the First World War amongst others had a role to play in the unfolding of the event in question. The inter-war era i.e. the period of 1919-1945 which was the period of peace between the two World Wars could be said to be the most crucial period in this regard. The objective of this paper is to study the shift of political power from London to Washington in the inter-war period and to understand the effects of this seismic geopolitical change on the world at large. This paper will try to examine the shift by analyzing the economic, political, social, and geopolitical realities of the time. The various important events that occurred in the period like the Treaty of Versailles, humiliating conditions placed on Germany, formation of the League of Nations, etc. to understand their direct or indirect impact on the establishment of the United States of America as the global powerhouse that it today and how it first became a contender and them the victor of the cold war. The research uses several different research methods ranging from primary research, secondary sources, and comparative analysis of a few reliable sources that offer insight into the subject.
Drug Recommendation System on Sentiment Analysis of Drug Reviews by Using Machine learning
Authors: Sivaiah B. , Grishma  , Tharun S , Shashi A
Published: March 2024

Abstract: With the healthcare system facing increased challenges due to the COVID-19 pandemic, there's a big demand for new ideas to help doctors and nurses. This paper suggests a new way of using computers to help doctors decide which medicines to give to patients. By using smart computer programs, we can make it easier for healthcare workers to handle their workload and provide better care for patients. By analyzing patient reviews, we employ sentiment analysis using advanced vectorization methods like Bag of Words, Term Frequency-Inverse Document Frequency (TF-IDF), and Manual Feature Analysis. These techniques allow for the identification of subtle sentiment nuances crucial for tailoring personalized drug recommendations. We utilize various classification algorithms such as Naive Bayes, Support Vector Classifier (SVC), and Random Forest to predict sentiments. Among these, the LinearSVC classifier coupled with TF-IDF vectorization demonstrates superior performance in sentiment prediction. We tested our system using various measures like precision, recall, F1-score, and Area Under the Curve (AUC) to ensure its effectiveness. By incorporating advanced machine learning techniques, our system offers a strong foundation for enhancing the accuracy of drug prescriptions, which is a key issue in today's healthcare. Through better drug recommendations, our goal is to ultimately enhance patient health outcomes and play a part in advancing healthcare practices, especially in the face of complex and evolving healthcare challenges.
Smart Medical Health Prediction System Application Using Data Mining
Authors: Nikhade Sima , Moon Prof. Mrunalini P. , Dhanjode Rani , Dey Riya , Selokar Pratiksha , Karemore Pallavi , Kushwaha Prof. Dr. Anil
Published: March 2024

Abstract: This project can benefit from the use of data mining techniques in a variety of fields, including science, medicine, and education. Because of laws and computer accessibility, a lot of data is now available in the medical and health care sectors. It is no longer necessary to handle such a large amount of data at once because of the significant advancements in computer technology made by modern technology. Evaluating data mining methods for clinical and healthcare applications is one of the paper's main goals, which aims to facilitate informed decision-making. In the world of computers, this well-known and potent technology is highly sought after. It is a branch of computer science that creates new research and findings from data that is already available in various databases. It extracts new patterns from massive data sets and the knowledge associated with these patterns by utilizing Artificial Intelligence, machine learning, and database management techniques. This method can be used to extract data automatically or semi-automatically. Clustering, forecasting, path analysis, and predictive analysis are some of the various parameters used in data mining.
Positive or Negative Sentence Feedback Identifier
Authors: Kirubadevi Dr. M , V Naveen , D Seenivasan , P Yogeshwar
Published: March 2024

Abstract: The "Positive or Negative Sentence Feedback Identifier Project" is a natural language processing (NLP) initiative designed to automatically classify and analyse sentences or text- based feedback as either positive or negative. This project aims to streamline sentiment analysis and opinion categorization by employing machine learning and NLP techniques. Its potential applications include sentiment analysis in customer reviews, social media monitoring, and opinion mining, ultimately providing valuable insights to businesses and individuals. It's crucial to gauge the overall sentiment and tone of the feedback. Positive feedback typically conveys satisfaction, using enthusiastic language and specific compliments about at's working well. In contrast, negative feedback often carries a critical tone, highlighting issues or areas of concern with the project. Pay attention to emotional indicators, the specificity of comments, and whether the feedback offers constructive suggestions for improvement. Evaluating feedback within the context of the project's goals and objectives is essential to understanding its impact and guiding necessary actions for project enhancement. Enhance security with two-factor authentication and encryption, while implementing user authentication and authorization controls. Integrate third-party services for expanded functionality and ensure mobile responsiveness for an improved user experience. Explore AI for automation and data analysis, offer robust analytics, and gather user feedback. Prioritise accessibility, optimise performance, and gamify the project with badges and leaderboards. Enable offline functionality, foster community engagement, and align features with userneeds and scalability goals. The project's development encompasses a comprehensive range of solutions, including the implementation of a sophisticated sentiment analysis model, efficient data collection and preprocessing methods, and the incorporation of scalable real-time processingarchitecture. Furthermore, the project's innovative facets encompass a dynamic blend of emotion analysis, contextual analysis, and predictive sentiment analysis, which significantlyaugment its adaptability and effectiveness across diverse domains and use cases.
Emotion Recognition from Brain EEG Signals
Authors: Kanchi Lohitha Lakshmi , Narra Chandana Sri , Mantri Guna , Palepogu Madhupriya , Mettu Chandra Sekhar Reddy
Published: March 2024

Abstract: The recognition of emotions plays a vital role in various fields such as neuroscience, cognitive sciences, and biomedical engineering. This particular project is centered on the development of a system for recognizing emotions through EEG signals. The main goal is to accurately classify different emotional states like valence and arousal by analyzing EEG brain wave patterns. The study is based on the DEAP dataset, which contains EEG and peripheral physiological signals recorded as participants interacted with video clips and music. The main objective is to explore and compare the efficacy of two classification techniques: Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). A total of 20 electrodes are used to identify and differentiate among 12 emotional states. The principal focus is on arousal and valence-related trends utilizing Russell's Circumplex Model, which depicts emotions on a two-dimensional plane determined by these two factors. This model allows for the visualization of emotions within this framework based on their levels of arousal and valence. By conducting extensive training and testing on the DEAP dataset, the accuracy of each classifier in predicting emotional states, including valence and arousal, is evaluated. This comparative assessment helps in understanding the strengths and weaknesses of each method for emotion recognition using EEG signals.
A Conceptual Study on Implications of Green Finance in India
Authors: Vennila Dr. R. , S Dr. Sudha B.
Published: March 2024

Abstract: The financial industry can help reduce the risk of climate change and lessen the effects of unfavourable climate occurrences, which is a significant contribution to the battle against climate change. Maintaining financial stability and spreading climate-related risks can be aided by long-term institutional investors. Other financial tools, such as green stock indexes, green bonds, and voluntary de-carbonization programmes, can assist reallocate investment to "green" industries. Hedging products, such as catastrophe bonds and indexed insurance, help insure against increasing natural disaster risk. For the foreseeable future, climate change is expected to remain the central political and economic issue of this century. Globally, governments, corporations, investors, and private citizens are starting to act on climate change, particularly with regard to decarbonisation strategies. It would take exceptional amounts of new capital investment—green financing, in particular—to support the kinds of initiatives that reduce greenhouse gas emissions and help businesses adjust to the effects of climate change in order to transition to a low-carbon or green economy. For this reason, it's critical to comprehend what green finance is and why it matters. In order to handle the complex risks that climate change poses, central banks and other regulators are modifying their frameworks and procedures from an oversight standpoint. Among the strategies for enhancing climate risk disclosure and categorization requirements are those that will assist investors and financial institutions in evaluating their exposure to climate risk, as well as regulators in assessing systemic hazards. Supporting the development of climate-related stress tests and broader promotion of green finance, the IMF is collaborating with standard-setting organisations such as the Network of Central Banks and Supervisors for Greening the Financial System. The researcher concludes that it is important to pursue this investigation in light of the aforementioned facts
Phishing URL Detection using Machine Learning
Authors: Nallamala Sri Hari , Namitha Kommu , Raviteja Kunchanapalli , Sumanth Kadiyam Sai , Kota Jyothi Sri
Published: March 2024

Abstract: Phishing attacks pose a significant threat in cyberspace, Cybercrimes such as Phishing means accessing personal data and violating security through the Internet and its main aim is to steal the information from the users using different techniques in that the primary one is Phishing which demands effective detection mechanisms. This study evaluates the performance of Gradient Boosting Classifier, Random Forest, and Decision Tree machine learning models in conjunction with feature selection techniques namely SelectKBest and Chi-Square. Initially, a comprehensive feature set of 30 attributes achieved a baseline accuracy of 97.4%. Through SelectKBest and Chi-Square feature selection methods, 13 key features were identified, leading to a slightly decreased accuracy of 95.6% upon model retraining. This research highlights the importance of feature selection in enhancing phishing detection accuracy while maintaining model interpretability. Technologies such as NumPy, Pandas, Matplotlib, Scikit-learn, and Flask drive. The project, emphasising the exploration of ML models, EDA (Exploratory Data Analysis) on phishing data, and understanding feature importance. The Machine Learning Models like Gradient Boosting Classifier, Random Forest and Decision tree are used to detect whether the given URL is Malicious URL or Legitimate URL using Comprehensive Feature set and Key Feature set. This Models and Feature sets are compared based on the Performance Metrics namely Accuracy, Precision, Recall and F1- score. This study contributes to advancing cyber defence mechanism through the fusion of sophisticated Machine Learning algorithms and meticulous Feature Selection methodologies.
An Overview of the Effect of Rising Traffic and Congestion in Bangalore on the Lives of General Public
Authors: Gupta Vivek , Agrawal Anshul , Madhu Dheeraj K , Agrawal Yash , Bhayani Jeet , Adhlakha Sidharth
Published: March 2024

Abstract: Bangalore, India's Silicon Valley, is experiencing widespread traffic congestion, which has a significant impact on the urban environment of the city. This study looks at the complex effects of traffic congestion on public health, social well-being, economic productivity, and air quality in Bangalore's metropolitan environment. By conducting a thorough examination of extant literature and statistics, the research clarifies the adverse consequences of traffic congestion, including elevated levels of air pollution, an increase in traffic accidents, extended travel times, and reduced productivity. The research also looks at possible mitigating measures, such as bettering the infrastructure for public transit, enacting congestion pricing plans, encouraging the use of alternate forms of transportation, and implementing clever urban design techniques. Policymakers can create successful measures to address traffic congestion by having a thorough understanding of the intricate relationship between the two.
The Impact of Flexible Work Arrangements on Job Satisfaction
Authors: Rajaram Harini , Jha Abhishek Mohan
Published: March 2024

Abstract: This study examines the effects of flexible work arrangements on employee satisfaction, looking closely at elements like work-life balance, productivity, and job satisfaction. Understanding how these arrangements affect personnel is crucial in a dynamic corporate environment marked by flexible schedules and remote options. Telecommuting, flextime, and job sharing are just a few examples of flexible work arrangements that give employees more control over their professional lives. Numerous studies have examined how they affect organizational performance, work-life balance, and job satisfaction. Our study carefully examines prior research in an effort to provide insight into the complex interactions between flexible work schedules and employee satisfaction. Our goal is to educate organizations and decision-makers on how to design workspaces that promote employee well-being and lead to increased productivity
Next Generation Sequencing and Insilico Identification of Human Anaplastic Lymphoma Kinase in Human Cancer with Biopython
Authors: Kumari Uma , Acharya Purnima
Published: March 2024

Abstract: In this comprehensive molecular investigation, the focus centers on the anaplastic lymphoma kinase (ALK) within the context of lung cancer. Specifically, utilizing the sample with the PDB code 3AOX, the study employs a multifaceted approach combining advanced bioinformatics tools and structural biology methodologies.Commencing with RasMol, the analysis of the protein sample reveals a complex network of 196 hydrogen bonds within the ALK structure. This foundational information sets the stage for subsequent investigations. Visual representation is meticulously addressed, with RasMol utilized for color-coding alpha helices in magenta, beta helices in yellow, and the remaining residues in white. The presentation mode emphasizes helices in red, sheets in yellow, and loops in green, providing a vivid depiction of the structural elements within the ALK protein.Moving beyond visualization, PyMOL is engaged to represent the surface form, providing insights into the interactions between ALK and its environment. This is particularly crucial in understanding the structural dynamics of ALK in the context of lung cancer, where the protein sample serves as a representative model.Active site identification in Chain A is pursued using tools such as PyMol, offering a glimpse into potential functional regions crucial for ALK's role in lung cancer pathogenesis. The study extends to biophysical aspects, incorporating protein-ligand docking studies with Mektovi and Almita through CB DOCK. This approach sheds light on the intricate molecular interactions between ALK and specific ligands, providing valuable information for targeted therapies.Structural validation becomes paramount in ensuring the reliability of the 3AOX sample. The ERRAT structure validation server evaluates the overall quality factor, while Procheck on the SAVES server scrutinizes the Ramachandran plot, ensuring the conformational consistency of ALK's backbone dihedral angles.Biopython scripts play a pivotal role in extracting and analyzing data related to the alpha-beta class representation within the CATH database. This bioinformatics analysis adds another layer of understanding to ALK's structural classification, offering insights into its role in lung cancer.
Plant Disease Detection Using YOLOv7 Algorithm
Authors: Praveen Rokkam Sai , Sravani Pothuri , Akhil Savalam , Reddy Tiyyagura Poojithaa , Babu J M
Published: March 2024

Abstract: Plant diseases pose a serious danger to global food security and can result in huge financial losses for the agricultural sector. Earlier Plant disease detection and precise diagnosis are essential for putting management measures into place. Recent advancements in computer vision techniques have demonstrated encouraging outcomes in automating activities related to illness identification. This study uses the You Only Look Once (YOLOv7) object identification algorithm to present a novel method for plant disease diagnosis. The primary goal of this research is to create a reliable and effective system that can quickly and reliably identify plant diseases. YOLOv7, a highly accurate and speedy algorithm, will serve as the main underpinning for detection. The project's primary goal is to train the Yolov7 model to identify distinct citrus plant illnesses by using a large dataset that includes pictures of both healthy and diseased plants. This project categorizes leaf images recorded from a file or webcam into four categories: healthy, greening, blackspot, and canker. Early disease prediction allows farmers to take required security measures for their plants.
Decentralized E-Commerce for Digital Assets
Authors: Rajashekarappa Dr. , Hegde Gautam , Hegde Gowtam Anant , S Tanishq Hegde K , Kattimani Vishal
Published: March 2024

Abstract: In the digital age, the creation and exchange of digital assets have become integral to various domains, including art, music, education, and more. However, the existing centralized marketplaces for these digital assets pose significant challenges, such as high fees, cross-border transaction delays, and limited control for creators and artists. This project introduces a gamechanging solution – a Decentralized E-Commerce platform powered by ERC1155 tokens. The project scope encompasses a comprehensive range of functionalities, including digital asset listing, buying, selling, and secure transaction processing. It aims to create a user-friendly interface that reduces the complexities and costs associated with traditional centralized marketplaces. Moreover, the platform extends its impact across various domains, from music distribution and licensing to educational material sales and NFT marketplaces. It simplifies cross-border e-commerce for digital goods, ensuring secure transactions without the need for currency conversion. The platform's adaptability opens new horizons for creators and entrepreneurs in the digital asset space.
Review of Machine Learning Techniques based Skin Microstructure Segmentation and Aging Classification
Authors: Sultan Farheen , Kaur Prof. Navneet
Published: March 2024

Abstract: Advancements in computer vision and image processing have fueled a growing interest in understanding the intricate details of skin microstructure for various applications, particularly in the context of aging. This review systematically examines recent developments in skin microstructure segmentation and aging classification methodologies, shedding light on the evolving landscape of research in this domain. Skin aging is a complex process characterized by various morphological and topological changes in the skin microstructure. Analyzing these changes can provide valuable insights into skin health and aging progression. This paper focuses on recent research advancements in skin microstructure segmentation and aging classification using convolutional neural network (CNN)-based models.
Development of Hydroelectric Power Generation from Water Pipelines
Authors: Burange Dr. R. A. , Mishra Ayush , Katre Dhanashri , Shinde Sakshi
Published: March 2024

Abstract: Pico hydroelectricity is a good way to make clean energy. We're talking about making a system that gets water from the tanks in houses. The water moving through household pipes can make electricity that we can store. To make this system work even better, we've thought of some new ideas: an air bladder to keep the water pressure steady, U-tube piping, and a big nozzle pipe at the end. This system doesn't need any fuel and doesn't need much fixing to keep working. Our plan is to make a small hydro system that can use the energy stored in a water tank to make power. This power can then be used in homes for different things
Leveraging Blockchain and IPFS for Secure and Privacy-Preserving Patient Medical Record Management: A Government-Controlled Approach
Authors: Gandhi Jyotiraditya , Maurya Krishnakumar , Panwar Viveksingh , Patel Utpalkumar , Bhattacharjee Sujaya , Patel Meetkumar
Published: March 2024

Abstract: In recent years, the integration of blockchain technology with healthcare systems has garnered considerable attention due to its potential to enhance security, privacy, and interoperability in managing patient medical records. This paper proposes a novel approach to patient medical record management by leveraging Ethereum blockchain and InterPlanetary File System (IPFS) for storage, within a government-controlled framework. The system ensures secure and immutable storage of patients' medical records, accessible only by verified medical professionals, thus facilitating informed diagnosis and treatment. Additionally, the platform provides mechanisms for patient recourse in case of inaccuracies, as well as potential integration with insurance agencies. Furthermore, the proposed system envisages a future extension to facilitate anonymized data sharing with the scientific community, thereby contributing to advancements in medical research. This paper provides a comprehensive academic description of the proposed approach, discussing its technical architecture, security measures, regulatory framework, and potential impact on healthcare delivery and research.
Next-Generation Captcha: Enhancing Security Through Hand Gestures and Gaming
Authors: Dayanand  , Jeberson Wilson , Jeberson Klinsega
Published: March 2024

Abstract: In the digital age, the threat of automated attacks on online platforms continues to evolve, necessitating innovative approaches to ensure cybersecurity. Traditional text-based CAPTCHA systems are becoming increasingly vulnerable to sophisticated attacks, prompting the exploration of alternative solutions. This research proposes a novel approach to enhance online security through the integration of hand gestures and gaming elements into CAPTCHA mechanisms. By leveraging the unique biometric characteristics of hand gestures and the engaging nature of gaming, this next-generation CAPTCHA system aims to provide robust protection against automated bots while ensuring a user-friendly experience. The study explores the design, implementation, and evaluation of this hybrid CAPTCHA solution, assessing its effectiveness in mitigating various challenges associated with existing CAPTCHA methods. Through empirical analysis and user feedback, the research aims to demonstrate the feasibility and efficacy of incorporating hand gestures and gaming into CAPTCHA systems to enhance security and usability in online environments.
Viral and Non-viral Episodes of Wheezing in Early Life and the Development of Asthma and Respiratory Phenotypes Among Urban Children
Authors: Havens Tara N. , LeBeau Petra , Calatroni Agustin , Gern James , O’Connor George T. , Wood Robert , Lamm Carin , Krouse Rebecca Z. , Visness Cindy , Gergen Peter , Jackson Daniel , Bacharier Leonard
Published: March 2024

Background: Viral wheeze is an important risk factor for
asthma, which comprises several respiratory phenotypes. We sought to
understand if the etiology of early life wheezing illnesses relates to
childhood respiratory and asthma phenotypes. Methods: Data were
collected prospectively on 429 children in the Urban Environment and
Childhood Asthma (URECA) birth cohort study through age 10 years. We
identified wheezing illnesses and the corresponding viral etiology (PCR
testing of nasal mucus) during the first three years of life. Six
phenotypes of respiratory health were identified at 10 years of age
based on trajectories of wheezing, allergic sensitization, and lung
function. We compared etiology of early wheezing illnesses on these
respiratory phenotypes and the development of asthma. Results:
In the first three years of life, at least one virus was detected in 324
(67%) of the 483 wheezing episodes documented in the study cohort.
Using hierarchical partitioning we found that non-viral wheezing
episodes accounted for the greatest variance on asthma diagnosed at both
7 and 10 years of age (8.0% and 5.8% respectively). Rhinovirus
wheezing illnesses explained the most variance on respiratory phenotype
outcome followed by non-viral wheezing episodes (4.9% and 3.9%
respectively) at 10 years of age. Conclusion and Relevance:
Within this high-risk urban-residing cohort early life, non-viral
wheezing episodes were frequently identified and associated with asthma
development. Though rhinovirus wheezing illnesses had the greatest
association with phenotype outcome, the specific etiology of wheezing
episode in early life provided limited information about subsequent
wheezing phenotypes.
Tailoring beneficial microbe-plant interactions for “one health”, benefits
Authors: Foyer Christine , Srivastava Ashish , Singh Reema D. , Pandey Girdhar , Mukherjee Prasun K.
Published: March 2024

Plants are an intrinsic part of the soil community and the “one health
concept” considers that human health is intimately connected to the
health of animals, plants, and microbial environments. Plant-microbe
interactions are a cornerstone of one health, the soil microbiome being
comprised of a diverse range of organisms, interacts in the rhizosphere
through continuous molecular communications. Soils are a source and
reservoir of pathogens, as well as beneficial microorganisms. Hence, the
molecular dialogue at the rhizosphere interface is crucial not only for
successful plant-microbe interactions but also for crop resilience and
stress tolerance. The plant-microbe continuum forms a network of
underground “nutrient highways” that benefit both plant and microbial
communities. It also serves as a significant sink for atmospheric CO
. While microbial diversity is generally positively
associated with one health, the host range of beneficial microbes
currently limits their successful exploitation with a wide range of
microbial communities. We consider the possibility of increasing the
host range of beneficial microbes, including arbuscular mycorrhiza fungi
(AMF) and rhizobia, and how current genetic incompatibility and/or
activation of robust plant defenses, can be overcome while accepting
that significant challenges exist in translating laboratory findings
into the field. We consider why AMF inoculants and plant
growth-promoting microbes are not always beneficial under field
conditions and suggest possible approaches for tailoring plant-microbe
interactions to assist plant breeding efforts in crop resilience.
Assessing the effects of habitat loss and deterioration on a red squirrel translocation site; insights for future conservation management.
Authors: Reilly Emily , Lawton Colin
Published: March 2024

Translocations, a conservation tool used to conserve and restore
dwindling species, are often associated with high failure rates.
Inadequate long-term monitoring of both populations and their
introduction sites beyond the initial years post-translocation creates a
gap in our understanding of the factors that determine translocation
success or failure, resulting in less informed projects in the future.
This lack of long-term monitoring is partly caused by the absence of a
well-defined framework by which the success of the translocation can be
measured, leading to premature and sometimes inaccurate assessments of
their outcome. We investigated the long-term outcome of a red squirrel
translocation in the west of Ireland, specifically assessing the habitat
changes in the translocation site since the introduction in 2005, and
their impact on carrying capacity. Using digitised historical map data,
we showed that the translocation site experienced a 53% reduction in
suitable habitat. Additionally, there was a 41-81% reduction in the red
squirrel carrying capacity of the forest, according to feeding survey
data. Clear-felling, a forest fire and a shift in tree species
composition collectively contributed to this decline in site
suitability. This investigation underscores the complexity of
translocation projects and emphasises the pivotal role of habitat
quality in their outcomes. We advocate for detailed habitat assessments
during the planning phase, avoidance of unstable habitats as
translocation sites, and the implementation of long-term monitoring
practices.
Incorporating Transgender and nonbinary Participants in Phase 1 Clinical Drug Trials: Current Knowledge Gaps and Considerations for Phase 1 Studies
Authors: Walker Lauren , Stackpoole Michael , Hodge Daryl , FitzGerald Richard
Published: March 2024

Phase 1 clinical drug trials critically depend on the participation of
healthy volunteers to evaluate the safety and pharmacokinetics of new
medicinal products. Current selection criteria and health definitions
often overlook the unique health profiles of transgender and nonbinary
individuals, potentially excluding them from participating in these
essential early-stage studies. This review aims to identify and discuss
current knowledge gaps and considerations regarding the inclusion of
transgender and nonbinary participants in Phase 1 clinical drug trials.
We highlight the need for research on how gender-affirming hormone
therapy may affect drug pharmacokinetics and call for the development of
inclusive biological reference ranges that account for the physiological
effects of hormone therapies.
Aqueous Two-Phase Extraction-Assisted Distillation for Cost-Effective 2,3-Butanediol Purification
Authors: M Pramod Gawal
Published: March 2024

2,3-Butanediol (2,3-BD), known for its various applications and
potential microbial production, faces challenges in commercial viability
due to the high energy intensity nature of the conventional purification
method. This study focuses on recovering and purifying 2,3-BD from
glucose-based fermentation broth (FB) using aqueous two-phase
extraction-assisted distillation (HATPED). The selected isobutanol/NaCl
system studies were optimized with the parameter effect of salt,
solvent, and extraction temperature on extraction efficiency of 2,3-BD
through central composite design (CCD) and response surface methodology
(RSM). Under Optimal conditions (30% (w/v) NaCl, 35% (v/v) isobutanol,
and 40°C extraction temperature) yielded the highest partition
coefficient ( K) (6.8) and extraction efficiency
(87%) of 2,3-BD exceeding the theoretical value of 86.93%, by 0.080%.
The extract phase further proceeds in a distillation column to recover
the 2,3-BD. In the scale-up study, the extraction and purification were
conducted in two cycles, resulting in an overall 2,3-BD extraction and
recovery of 97.63% and 95.88% (>99% purity),
respectively. NaCl recovery from the aqueous phase reached 86.66%
through evaporation. This cost-effective and scalable method provides
valuable insights for developing an organic solvent/ inorganic salt
ATPS-assisted distillation for 2,3-BD separation and recovery from
complex fermentation broth.
Cardiovascular outcomes and molecular targets for the cardiac effects of Sodium-Glucose Cotransporter 2 Inhibitors: A systematic review
Authors: Madonna Rosalinda , Biondi Filippo , Alberti Mattia , Ghelardoni Sandra , Mattii Letizia , D'Alleva Alberto
Published: March 2024

Sodium-glucose cotransporter 2 inhibitors (SGLT2i), a new class of
glucose-lowering drugs traditionally used to control blood glucose
levels in patients with type 2 diabetes mellitus, have been proven to
reduce major adverse cardiovascular events, including cardiovascular
death, in patients with heart failure irrespective of ejection fraction
and independently of the hypoglycemic effect. Because of their favorable
effects on the kidney and cardiovascular outcomes, their use has been
expanded in all patients with any combination of diabetes mellitus type
2, chronic kidney disease and heart failure. Although mechanisms
explaining the effects of these drugs on the cardiovascular system are
not well understood, their effectiveness in all these conditions
suggests that they act at the intersection of the metabolic, renal and
cardiac axes, thus disrupting maladaptive vicious cycles while
contrasting direct organ damage. In this systematic review we provide a
state of the art of the randomized controlled trials investigating the
effect of SGLT2i on cardiovascular outcomes in patients with chronic
kidney disease and/or heart failure irrespective of ejection fraction
and diabetes. We also discuss the molecular targets and signaling
pathways potentially explaining the cardiac effects of these
pharmacological agents, from a clinical and experimental perspective.
The interplay between host-specificity and habitat-filtering influences sea cucumber microbiota across an environmental gradient of pollution
Authors: Chung Sheena Suet-Wah , Cheung Khan , Arromrak Bovern , Li Zhenzhen , Tse Simon , Gaitán-Espitia Juan Diego
Published: March 2024

Environmental gradients can influence morpho-physiological and
life-history differences in natural populations. It is unclear, however,
to what extent such gradients can also modulate phenotypic differences
in other organismal characteristics such as the structure and function
of host-associated microbial communities. In this work, we addressed
this question by assessing intra-specific variation in the diversity,
structure and function of environmental-associated (sediment and water)
and animal-associated (skin and gut) microbiota along an environmental
gradient of pollution in one of the most urbanized coastal areas in the
world. Using the tropical sea cucumber Holothuria leucospilota, we
tested the interplay between deterministic (e.g., environmental/host
filtering) and stochastic (e.g., random microbial dispersal) processes
underpinning host-microbiome interactions and microbial assemblages.
Overall, our results indicate that microbial communities are complex and
vary in structure and function between the environment and the animal
hosts. However, these differences are modulated by the level of
pollution across the gradient with marked clines in alpha and beta
diversity. Yet, such clines and overall differences showed opposite
directions when comparing environmental- and animal-associated microbial
communities. In the sea cucumbers, intrinsic characteristics (e.g., body
compartments, biochemistry composition, immune systems), may underpin
the observed intra-individual differences in the associated microbiomes,
and their divergence from the environmental source. Such regulation
favours specific microbial functional pathways that may play an
important role in the survival and physiology of the animal host,
particularly in high polluted areas. These findings suggest that the
interplay between both, environmental and host filtering underpins
microbial community assembly in H. leucospilota along the pollution
gradient in Hong Kong.
Assimilation of Satellite Albedo to Improve Simulations of Glacier Hydrology
Authors: Bertoncini André , Pomeroy John
Published: March 2024

Wildfires and heatwaves have recently affected the hydrological system
in unprecedented ways due to climate change. In cold regions, these
extremes cause rapid reductions in snow and ice albedo due to soot
deposition and unseasonal melt. Snow and ice albedo dynamics control net
shortwave radiation and the available energy for melt and runoff
generation. Many albedo algorithms in hydrological models cannot
accurately simulate albedo dynamics because they were developed or
parameterised based on historical observations. Remotely sensed albedo
data assimilation (DA) can potentially improve model performance by
updating modelled albedo with observations. This study seeks to diagnose
the effects of remotely sensed snow and ice albedo DA on the prediction
of streamflow from glacierized basins during wildfires and heatwaves.
Sentinel-2 20-m albedo estimates were assimilated into a
glacio-hydrological model created using the Cold Regions Hydrological
Modelling Platform (CRHM) in two Canadian Rockies glacierized basins,
Athabasca Glacier Research Basin (AGRB) and Peyto Glacier Research Basin
(PGRB). The study was conducted in 2018 (wildfires), 2019 (soot/algae),
2020 (normal), and 2021 (heatwaves). DA was employed to assimilate
albedo into CRHM to simulate streamflow and was compared to a control
run (CTRL) using off-the-shelf albedo parameters. Albedo DA benefited
streamflow predictions during wildfires for both basins, with a KGE
coefficient improvement of 0.18 and 0.20 in AGRB and PGRB, respectively.
Four-year DA streamflow predictions were superior to CTRL in PGRB, but
DA was slightly better in AGRB. DA was not beneficial to streamflow
predictions during heatwaves. These results show that albedo DA can
reveal otherwise unknown albedo and snowpack dynamics occurring in
remote glacier accumulation zones that are not well simulated by model
predictions alone. These findings corroborate the power of observational
tools to incorporate near real-time information into hydrological models
to better inform water managers of the streamflow response to wildfires
and heatwaves.
Dis-playing Gender: The Ludic Value of Gender-Swap Apps
Authors: Piluso Francesco
Published: March 2024

An increasing number of AI-based gender-swap applications have become a
popular trend in our social media platforms and communities, rising
controversial issues beyond their apparently playful intent. The primary
ludic function informing the use/consumption of such apps opens to
further signification and valorization of the produced images. In many
cases, the portrait pictures, modified through a system of filters,
assume an existential and utopic value for (transgender and nonbinary)
users who have the possibility to reimagining and reperforming their
gender identity. At the same time, the social availability and
disposability of such images risk to trivialize the subjective and
highly complex process of sexual and gender transition undergone by many
people on their own material body in the real life. The aim of this
paper is to point out the ambivalences and the possibilities opened by
gender-swap apps for what concerns the issue of gender identity. Through
the analysis of a corpus of promotional messages and users’ responses to
the gender-swap apps, it is argued that these trans-faces, far from
operating a flawless passing from a gender to the other, constantly
dis-play the social construction and deconstruction of sexual and gender
binary identities.
Secure Intelligent Agent for Bahrain's e-Government Call Center
Authors: Elmedany Wael Mohamed , AL-Abbasi Latifa Mohammed , Hewahi Nabil M.A.
Published: March 2024

The National Contact Center (NCC) in the Kingdom of Bahrain serves as a
pivotal platform for government clients to voice inquiries, requests,
suggestions, and complaints regarding various government services. This
paper introduces a secure platform for an Intelligent Agent (IA) named
“Ali”. It is driven by Artificial Intelligence, engages in
conversational interactions with e-Government customers. The platform
incorporates Natural Language Processing (NLP) and Deep Learning (DL) to
train the IA in understanding queries and formulating responses through
Natural Language Generation (NLG). Ali is tasked with addressing
incoming calls and executing various functions such as answering
questions, executing actions, integrating with call center systems like
Client Relationship Management (CRM), directing inquiries to appropriate
government departments, completing forms, and scheduling appointments.
The platform is tailored for a Bahraini IA proficient in Arabic and
English, equipped with intelligent capabilities. To ensure customer
privacy and security and to comply with Bahrain’s laws and regulations,
the platform boasts a comprehensive security framework covering all
aspects of security layers, network security, storage security,
operating system security, system software and application security,
data security, identity and access management, and service level
agreements. This research delves into the architecture of conversational
AI and the Machine Learning (ML), Deep Learning (DL), Natural Language
Processing (NLP), Natural Language Understanding (NLU), and Natural
Language Generation (NLG) components that collaborate to enable the IA’s
functionality. The proposed project entails the development of an
Intelligent Agent Framework in partnership with the Information and
eGovernment Authority (iGA). The initiative involves initiating the IA
project, delineating its scope, planning its phases, designing the
solution architecture and requirements, and establishing security
requirements for the chosen cloud model.
Exploration of synthetic dyes degrading bacteria from Siak River, Riau Province
Authors: Linda Tetty Marta , Alamsyah Novelia Dinda Putri , Juliantari Erwina
Published: March 2024

The presence of dyes causes a decrease in river quality. Degradation using bacteria could be used as an alternative method to overcome the dyes substances waste. This research aimed to isolate bacteria that can degrade dyes such as congo red (CR) and methylene blue (MB) from Siak River. The result of this study identified a total of 27 isolates of bacteria that can degrade CR with a concentration of 100 ppm in a Minimal Medium with three days of incubation. The ability of bacteria to degrade CR was known by forming clear zones around the colonies. The degradation index ranged from 1.54 to 5.61 after three days of incubation. The isolate CR26 had the highest degradation. Meanwhile, the number of bacteria that degrades the MB in the concentration of 25 ppm are three isolates (MB1, MB2, and MB3) in the degradation index range from 1.32-1.41. One isolate (MB2) was obtained from the concentrations of 50 ppm and 100 ppm with a degradation index of 1.42 and 1.34. Identification results isolate CR 26 as Bacillus sp and isolate MB2 as Enterobacter sp. This bacteria can be developed to overcome water pollution caused by CR and MB dyes.
Anti-Hepatitis C virus activity of Garcinia lattissima Miq. stem barks methanol exctract
Authors: Apriyanto Dadan Ramadhan , Hartati Sri , Dewi Beti Ernawati
Published: March 2024

Herbal medicine treatment for Hepatitis C Virus (HCV) infection is a promising alternative to common medical HCV treatments because it has low unwanted side effects and production costs. This study aimed to evaluate the methanol extract of Garcinia lattissima stem barks as an antiviral compound against strain JFH1 (HCV) genotype 2a. Garcinia lattissima stem bark methanolic extract (GL-SB) was added to Huh7it-1 cells infected with JFHI. Anti HCV activity was determined by Focus-forming unit (FFU) assay and the cytotoxicity activity was analyzed using the 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) assay. The GL-SB showed its efficacy as the anti-HCV agent with a 50% cytotoxicity concentration (CC50) of 34.2 µg/mL and a 50% effective concentration (EC50) of 4.7 µg/mL. The co-addition and post-infection phases of GL-SB showed an anti-HCV activity, according to a time-of-addition investigation. These findings imply that GL-SB might be a promising candidate for add-on therapy in the treatment of HCV infections.
The Use of probiotics in fermenting food wastes for production of black soldier fly larvae (Hermetia illucens L.; Diptera: Stratiomyidae)
Authors: Witriana Nifa Indah , Ardyati Tri , Jatmiko Yoga Dwi
Published: March 2024

Larvae of the black soldier fly (BSF) can process a wide variety of organic wastes and convert them into larval body primarily consisting of protein. Production of larvae with high-quality protein content is affected the rearing system, one of them is quality of feeding substrates. This study was aimed to evaluate the effect of food wastes fermented by probiotic culture as the feeding substrates for producing the BSF larvae. The probiotic cultures used were Lactiplantibacillus plantarum E2 and Limosilactobacillus fermentum F5, while the food wastes were derived from a seafood restautrant. Four rearing substrates were compared: food wastes fermented by Lactiplantibacillus plantarum E2 (P1), food wastes fermented by Limosilactobacillus fermentum F5 (P2), food wastes fermented by consortium (Lactiplantibacillus plantarum E2 + Limosilactobacillus fermentum F5) (P3), and food wastes without probiotics (P0). Before the feeding substrates were applied, the food wastes were fermented by the probiotics for 10 days. Six-day-old BSF larvae were reared for 14 days on each fermented substrate. The larval growth performance observed were fresh weight, length and width, while the nutrient content of the larvae was protein and fat content. The waste reduction index was also measured. The data showed that the best larval growth performance was shown in P3 substrate, with fresh weight of 287.27 g, larva length and width of 19.93 mm and 3.53 mm, respectively. In P3 substrate also could produce the larvae with the best nutrient content, with protein and fat content by 12.53% and 16.09%, respectively. The waste reduction index value of P3 substrate was 6.12%. The duration of BSF larvae rearing required to be extended to obtain an increased average protein content. This finding of this study could be helpful for improving the quality of BSF larvae by applying consortium of probiotic cultures in fermenting the feeding substrates.
Fundamental Models of Consumer Purchasing Behavior: An In-Depth Analysis Since the 1960s.
Authors:   Université Sultan Moulay Slimane Beni Mellal, Maroc, El gozmir Hasnaa , Makhrout Samir ,   Université Cadi Ayyad Marrakech, Maroc, Chouhbi Abderrahmane ,   Université Sultan Moulay Slimane Beni Mellal, Maroc
Published: March 2024

This paper explores established models of the consumer decision-making process, originated from the 1960s, and assesses the viewpoints of Nicosia, Howard and Sheth, Engel, Kollat and Blackwell, and Fishbein and Ajzen's theories of reasoned and planned behavior. Subsequently, it demonstrates their significance and linkages with current marketing practices, encompassing the effects associated with digitalization and the augmentation of consumer requirements. Finally, the paper puts forth a theoretical model for the decision-making process. These models provide diverse perspectives on how consumer attitudes, purchasing decisions, and interactions with marketing stimuli are formed. However, it is emphasized that these models must continually evolve to remain relevant in an ever-changing environment, as they are critical in comprehending consumer behavior and developing marketing strategies.