bluuebunny/crossref_metadata_2025 huggingface dataset:
Fragmentation of international trade: interim results, challenges and policy issues
Authors: Smirnov E. N.
Published: October 2024

The article analyzes key issues related to the fragmentation of international trade at the current stage of global economic development. The impact of economic globalization on disruptions in modern international exchange is discussed, including in the chronological context of the formation of the modern structure of international trade. The main channels of trade fragmentation are identified, including the ambiguous dynamics of cross-border direct investment, the aggressiveness of export control measures taken by key countries, as well as stresses observed in the global payment system. A separate aspect of the study was the analysis of national security considerations dictating the use of various subsidies, which is an additional determinant of the fragmentation of international trade. It is proven that fragmentation has led to uncertainty and increased volatility in global raw materials markets, and has strengthened the speculative and "financialized" nature of their development. In general, international trade under the influence of fragmentation should become more adaptive, and it is necessary to continue developing multilateral cooperation, but it should take place in a fundamentally different format and with the help of alternative instruments aimed at developing new trade models.
Bispidine-based Ligands as Bifunctional Chelators for Radiopharmaceuticals
Authors: Li Rui , Yan Yuting , Ren Xiyan , Li Hongyu , Huang Jianhui
Published: October 2024

Novel bispidine skeleton has been extensively designed as bifunctional chelators
(BFCs) for their special stereochemical structure and variable dentate numbers. Bispidinebased
ligands (BBLs) as BFCs generally integrate the benefits of conventional acyclic and
macrocyclic BFCs, demonstrating exceptional radiolabeling kinetics, thermodynamic stability
and kinetic inertness for their metal complexes. The accessible inherent spatial
asymmetry in bispidine skeleton is well-suited for Jahn-Teller active metal ions, notably
Cu(II). Currently, BBLs have already been studied to coordinate with radionuclides such as
52Mn, 64/67Cu, 68Ga, 111In, 133La, 177Lu, 212Pb, 212/213Bi, and 225Ac for radiopharmaceuticals
application. Among them, the 64Cu, 52Mn, 111In, 177Lu, and 225Ac complexes with BBLs have
particularly made significant research progress. In this review, we introduce the synthesis
of BBLs and their applications in chelating the above five metallic radionuclides for the development of radiopharmaceuticals
are discussed.
Correlations between Cerebrospinal Fluid Biomarkers and Gray Matter Atrophy in Alzheimer's and Behavioural Variant Frontotemporal Dementia
Authors: Scianatico Gaetano , Manippa Valerio , Zaca Domenico , Jovicich Jorge , Tafuri Benedetta , Rivolta Davide , Logroscino Giancarlo
Published: October 2024

Introduction:

Distinguishing between frontotemporal dementia (FTD) and Alzheimer’s
disease (AD) in their early stages remains a significant clinical challenge. Cerebrospinal fluid (CSF)
biomarkers (total Tau, phosphorylated Tau, and beta-amyloid) are promising candidates for
identifying early differences between these conditions. This study investigates the relationship between
grey matter density and CSF markers in the behavioural variant of frontotemporal dementia
(bvFTD) and Alzheimer’s disease (AD).

Method:

CSF and 3D T1-weighted magnetic resonance (MR) images were acquired from 14
bvFTD patients, 15 AD patients, and 13 cognitively normal (CN) matched subjects. The CSF
markers and their relative ratios (total Tau/beta-amyloid, phosphorylated Tau/beta-amyloid) were
compared across the three groups. Voxel-based morphometry (VBM) was performed to characterize
the anatomical changes in bvFTD and AD patients compared to CN subjects. Grey matter density
maps were obtained by automatic segmentation of 3.0 Tesla 3D T1-Weighted MR Images,
and their correlation with CSF markers and relative ratios was investigated.

Results:

Results demonstrated that, as compared to CN subjects, AD patients are characterised by
higher CSF total Tau levels and lower beta-amyloid levels; however, beta-amyloid and relative ratios
discriminated AD from bvFTD. In addition, AD and bvFTD patients showed different patterns
of atrophy, with AD exhibiting more central (temporal areas) and bvFTD more anterior (frontal
areas) atrophy. A correlation was found between grey matter density maps and CSF marker
concentrations in the AD group, with total Tau and phosphorylated Tau levels showing a high association
with low grey matter density in the left superior temporal gyrus.

Conclusion:

Overall, while bvFTD lacks a CSF marker profile, CSF beta-amyloid levels are useful
for differentiating AD from bvFTD. Furthermore, MR structural imaging can contribute significantly
to distinguishing between the two pathologies.
Eco-friendly Synthesis of Gold Nanoparticles of Methanolic Extract of Bauhinia vahlii Leaves and Evaluation of Tyrosinase Inhibitory Activity
Authors: Panda Pritipadma , Pal Abhisek , Dash Priyanka , Kar Biswakanth , Rath Goutam , Ghosh Goutam
Published: October 2024

Background:

The green synthesis of gold nanoparticles (AuNPs) using natural materials has
gained significant attention in recent years due to their eco-friendliness and potential applications in
various fields.

Methods:

The present study aimed at the green synthesis and anti-hyperpigmentation potential of gold
nanoparticles (AuNPs) using a methanolic extract of Bauhinia vahlii. Furthermore, the green synthesis
of the AuNPs was confirmed by UV-visible, FT-IR, XRD, and EMDEX analysis. The size and surface
topological significance of green synthesized AuNPs were evaluated through Scanning Electron Microscope
and Field Emission Scanning Electron Microscopy.

Results:

The size of the synthesized AuNPs was found to be in the range of 100-1000 nm. The tyrosinase
inhibitory activity of AuNPs was evaluated through the mushroom tyrosinase inhibitory assay
method. The tyrosinase inhibitory activities of crude extract, AuNPs, and Kojic acid were found to be
98.7 ±0.7, 75±1.3 and 41±1.1 μg/ml, respectively.

Conclusion:

Hence, green synthesized AuNPs of B. vahlii leaf extract may be used as a potent antihyperpigmentation
agent in the market of nano cosmeceuticals.
Current Insights into Therapeutic Potential of Terpenoids as Anticancer Agents
Authors: Sahoo Biswa Mohan , Banik Bimal Krishna , Sharma Shikha , Singh Bhupendra
Published: October 2024

Background:

Cancer is regarded as one of the main causes of death globally. Future predictions indicate
that the death rate from cancer will keep rising, which may reach 11.4 million in 2030. Carcinogenesis refers
to the phenomenon of transforming a normal cell into a cancer cell. Cancer is characterized by unregulated and
uncontrolled cell division due to alterations at the molecular and genetic levels. Gene mutations can speed up the
rate of cell division, which leads to cancer. Metastasis entails the dissemination of cancer cells from the primary
site to distant regions of the body via the circulatory or lymphatic systems.

Objective:

This review is mainly focusing on the anticancer properties of terpenoids. In the case of human beings,
several types of cancers can be treated clinically based on the form and phase of the cancer. So, there are different
types of treatment regimens available for the management of cancer, such as immunotherapy, hormonal therapy,
radiation therapy, and chemotherapy.

Methods:

Several problems are associated with cancer therapy, including chemoresistance, severe toxicity, relapse,
and metastasis. To minimize these complications, natural products like terpenoids seem to be beneficial for
the effective management of cancer.

Results:

Experimental results revealed that the anticancer potential of terpenoids is due to activation of apoptosis
and stimulation of cell cycle arrest. Some of the terpenoids exhibit anticancer effects by inhibiting angiogenesis
and metastasis via the regulation of several signaling pathways intracellularly. Certain terpenoids have been
shown to work in concert with anticancer medications (doxorubicin, cisplatin, paclitaxel, and 5-fluorouracil) to
provide synergistic effects. These terpenoids have also been shown to be effective against cancer cells that are
resistant to several drug therapies.

Conclusion:

The current study will focus on signaling pathways and mode of action of several types of terpenoids
as anticancer agents. Further, it will provide insights into the ongoing clinical trials and prospective pathways
for the advancement of terpenoids as possible anti-cancer agents.
Unlocking the Impact of Phyto-bioactive Compounds against Neurodegenerative Disorders
Authors: Rahaman Syed Mustafizur , Ashique Sumel , Bhowmick Mithun , Mollick Mirazuddin , Bhowmick Pratibha , Kumar Prashant
Published: October 2024

Neurodegenerative disorders (NDs) are one of the prominent worldwide issues recently.
Neurodegenerative disorders, like amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's
disease, and Huntington's disease, are characterized by ongoing neuronal failure and loss of structure
and function, which ultimately results in neuronal death. There are many established synthetic approaches
to target pathogenesis of NDs and to mitigate it but having several challenges researchers
are now focusing the significant use of plant derived bio-active constituents against several NDs
which ultimately leads safer and potent results. Among these phyto-compounds such as carotenoids,
essential oils, essential fatty acids, polyphenols, have attracted great animus due to their strong antioxidant
and anti-incendiary properties effective against NDs. Considering these green compounds
having significant role to manage various NDs as potent therapeutic approach, we reviewed the
sources, application, safety and clinical aspects of phyto-constituents against NDs.
Chromatography and Spectroscopic Technique-Based Rapid Characterization of Nano-Carrier Pharmaceuticals
Authors: Shamim  , Ali Tarmeen
Published: October 2024

A nanocarrier is a novel colloidal system whose particle size ranges between 1-100 nm. It
is extensively utilized in drug delivery and various other sectors, such as the pharmaceutical, food,
and dairy industries. The nanocarrier systems, including solid lipid nanoparticles, micelles, liposomes,
and other encapsulated compounds, have improved stability, solubility, bioavailability, and
quality. Nanocarriers offer therapeutic effectiveness with low toxicity because of their biocompatibility
and ability to cross body barriers. Various analytical techniques, such as chromatography and
spectroscopy, are crucial in qualitative and quantitative analysis of nanocarrier-based formulations.
Molecular identification and drug content determination require chromatographic techniques, particularly
HPLC. Spectroscopic techniques such as LC-MS, NMR, GC-MS, CE-MS, Raman, and IR are
used to analyze the interaction and molecular structure of the sample. Nanocarriers have several
benefits but face various challenges like stability, drug loading, regulatory standards, and biocompatibility.
Future surface engineering and nanocarrier design advancements could improve targeted
drug delivery and sustained diagnostic applications, significantly impacting healthcare.
Studying the Antioxidant and Antimicrobial Activity of Lipsticks Made with Phycoerythrin Under the Influence of Different Magnetic Fields
Authors: Nowruzi Bahareh , Talapour Mohammadtaha
Published: October 2024

Background:

Several studies have shown the benefits of magnetic treatment on the
productivity of secondary metabolites, growth, and the state of microalgae cultures

Objective:

This study examined the antioxidant and antimicrobial activity of lipsticks made with
phycoerythrin (PE) extracted from the cyanobacterium Nostoc sp. under the influence of different
magnetic fields (30 mT and 60 mT).

Material and Methods:

After cyanobacterial culture under magnetic fields of 30 mT and 60 mT,
the PE was extracted and lipsticks were formulated. The primary evaluation methods used in this
study are melting point, breaking point, linoleic acid peroxidation assay, force of application,
stability, surface abnormalities, skin irritation, thixotropy character, dispersibility test, perfume
stability, colorimetric assay, antioxidant, and microbial analysis

Results:

The 30 mT treatment showed the highest concentration, purity, dry weight, antioxidant
activity, and percentage of PE extraction compared to control cultures. No significant differences
were found in the melting point, stability, thixotropy character, dispersibility, or perfume stability
tests. The breaking point and force of application decreased significantly during 30 days. Peroxidation
assay tests revealed lipstick increased oxidation and antioxidant activity after 30 days of
30 and 60 mT treatments compared to non-PE cultures. The study found that the amount of ΔE
increased significantly in cultures without PE over time, while this increase was lower in magnetic
field-treated cultures. However, no signs of crystal formation, surface wrinkles, liquid secretion,
itching, or skin irritation were observed in 30 days of 30 and 60 mT magnetic treatments
compared to control cultures with PE. Microbial analyzes over 30 days showed a significantly
lower number of bacteria under magnetic fields than control cultures. In addition, the results of
counting Escherichia coli and coliform bacteria were negative for thirty days. The antioxidant
activity of PE was significantly higher in magnetic field-treated cultures. The number of Staphylococcus
aureus decreased significantly in all cultures under magnetic field influence.

Conclusions:

The overall results of this study showed that magnetic fields had a significant effect
in many evaluation tests on the culture of cyanobacteria Nostoc sp. As a result, lipsticks made
with extracted PE have more antioxidant and antimicrobial activity
Pharmacological Potential of Bioactive Phloroglucinol Compounds of the Plant Kingdom
Authors: Tchangoue Ngandjui Yvan Anderson , Das Niranjan , Mandal Subhash C. , Kouam Simeon Fogue
Published: October 2024

Historically, natural products, which are substances generated by living
organisms found in nature, have made an important contribution to pharmacotherapy,
especially those from plant sources. Phloroglucinols are significant bioactive
polyphenolic compounds that are found in plants, marine and microbial sources. Their
chemical structures include an aromatic phenyl ring with three hydroxyl groups and are
usually made of two or more rings linked together through methylene bridges. They
exist widely in several plant families and are known for their significant biological
potentials, such as antibacterial, antifungal, anti-inflammatory, antileishmanial,
antiplasmodial, antiproliferative and cytotoxicity activities. This book chapter provides
an overview of phloroglucinol compounds in the world, their location in the plant, and
their pharmacological applications.
Raging the War against Rheumatoid Arthritis with Plant-Derived Products: Skepticism to Clinical Development
Authors: Hazarika Sangeeta , Borah Pobitra , Deb Pran Kishore
Published: October 2024

Rheumatoid arthritis (RA) is a chronic, debilitating autoimmune disease
manifested by progressive articular and extra-articular disabilities. Pieces of evidence
hint that tremendous research efforts have increased the likelihood of attaining a better
treatment goal. However, the associated risks, drug failures, and high costs of the
available drugs or regimens still limit their widespread use. Even though natural
products are rarely considered for lead identification, which is counterintuitive to
traditional drug development, natural products occupy a large chemical space, unlike
synthetic screening methods. Although skepticism towards the use of natural products
continues, the growing attention of researchers towards the diverse flora has led a
plethora of molecules and therapies to reach clinical trial settings. This chapter sheds
light on the challenges in the development of plant-derived compounds or therapies for
the management of RA. It discusses the plant-derived products (including diet-based
therapies) undergoing clinical development for the treatment of RA.
Exploration of Plant Phytomolecules’ Potential for Antiviral Activity
Authors: Pandey Rupesh Kumar , Kumar Sokindra , Singh Lubhan , Pathak Manish , Mishra Ganesh Prasad , Pandey Priyanka , Kalyani Gunjan , Shukla Shiv Shankar , Pandey Ravindra Kumar
Published: October 2024

Viral infections are increasing continuously, and we do not have proper
treatment. Currently, the COVID-19 pandemic is an emerging threat globally. If we
look into the Indian perspective against COVID-19, plant-based medicine available in
ancient literature has been used, like Charaka Samhita and current ayurvedic
pharmacopeia. Many viral diseases will come in the future, for which, there is a need to
establish concept-based treatment with scientifically-proven pharmacological action.
The plant's primary and secondary metabolites are responsible for pharmacological
activities. Many plants have shown their efficacy in viral infections through their
phytochemicals. In this chapter, we have conceptualized the same and identified the
plants with their metabolites, which can be a direction for future research on viral
disease. Currently available allopathic treatments have efficacy but toxicities too.
For a better understanding of the diseases, the pathophysiology of the same is one of
the components, as it gives a complete idea about how the viruses affect us. In the
Indian traditional drug system, many folk medicines are available that need to establish
the correlation with the targeted sites for a disease, which can give us the direction for
future viral infections. The urgency is also to standardize these drugs for proper use
among the global population. For identification, isolation of primary and secondary
metabolites can help in treatment and drug targeting. The beauty of traditional
medicine is that it is affordable because of its availability in different regions across the
globe.
Current Pharmacological Perspectives of Herbal Antidiabetic Drug Formulations
Authors: Singh Loushambam Samananda , Roux Sumer Peter De , Debbarma Bimal , Lapasam Phaibiang , Sutnga Innocent
Published: October 2024

People of all ages are affected by the chronic complications of diabetes
mellitus. Numerous synthetic medications have been developed to treat diabetes, which
has become more common across the globe. These medications, while effective as
antihyperglycemic medicines, come with a number of side effects. They are expensive
and out of reach for the vast majority of people who live in underdeveloped nations.
Due to their accessibility and harmless nature, medicinal plants have already been
traditionally utilized to cure a variety of illnesses throughout history. Phytochemicals
found in medicinal plants provide a number of health advantages. Healthcare
professionals are looking into plant-based medicines as a potential supply of
antidiabetic pharmaceuticals because of their high efficacy and lack of adverse effects
as diabetes prevalence rises. The active phytoconstituents are being identified and
thoroughly studied in order to gain a better understanding of the mechanism of action
of therapeutic plants. Here, we focus on the perspectives of pharmacologically active
phytomolecule formulations produced from medicinal plants that demonstrate
antidiabetic action and the role they now play in managing and treating diabetes in the
present scenario. These natural molecules might be suitable for developing new
approaches to treatment or potent treatment options for diabetes.
Digital Twin for Sustainable Farming: Developing User-Friendly Interfaces for Informed DecisionMaking and Increased Profitability
Authors: Dhasarathan Chandramohan , B. Ramachandra Reddy. , S. Ashok Kumar. , Gnanasekaran Sambasivam
Published: October 2024

This chapter endeavors to develop a robust digital model for farm
optimization with the primary objectives of enhancing resource utilization, minimizing
waste, and increasing productivity while mitigating environmental impact. The
proposed digital twin will leverage data from diverse sources, including sensors,
weather data, soil moisture levels, and crop yields. Methodologically, the integration
and processing of this varied data will be achieved through advanced algorithms,
ensuring a comprehensive and accurate representation of the farm. The simulation
aspect of the digital twin will explore different scenarios, allowing for a nuanced
understanding of the impact of interventions on farm productivity and sustainability.
Specific scenarios, such as testing the effects of varied irrigation strategies on crop
yields or optimizing fertilizer inputs, will be explored. Methodological considerations
will be discussed, addressing challenges related to data integration, format disparities,
and accuracy variations across different data sources. Crucially, collaboration with
farmers and stakeholders will be a cornerstone of this research. Their insights and realworld experiences will be actively incorporated throughout the development process,
ensuring that the digital twin is tailored to the practical needs and challenges faced in
agricultural operations. In tandem with this, the development of user-friendly interfaces
will be emphasized, providing farmers and stakeholders with accessible tools for
interacting with the digital twin. Specific functionalities, tailored to inform periodic
decisions and processes, will be integrated into the interfaces, fostering usability and adoption. The chapter will examine the assessment of environmental impact. A detailed
examination of the criteria and indicators used to measure and minimize the farm's
environmental footprint will be discussed. By addressing these methodological
considerations comprehensively, this research aims to not only optimize resource use
and reduce waste but also contribute to the transformative advancement of sustainable
and efficient farming practices.
Prediction for Increasing Yield Production with IoT and AI Using Soil Properties
Authors: S. Aravind H. , Chandrappa Savitha Ambliihalli , Venkatesh Neha , Basavaiah Kalyan Kumar
Published: October 2024

'Wireless Sensor Networks and 'The Internet of Things' are the two
imminent commonalities in agricultural science that allow the development of less
exclusive systems to install, control, and maintain low-power standard protocols. The
work makes use of low-cost sensors and IoT platforms to help farmers improve
agricultural systems with better yield and reduce insufficient usage of water. Precision
agriculture helps in terms of quality of yield, efficiency of product, decrease in the
environmental harness, and minimal usage of natural assets. The proposed precision
model obtains raw properties of the given soil and achieves an overall accuracy of
93.33% in predicting the ideal crop that can be cultivated for the given soil sample
using the KNN algorithm and develops a continuous crop monitoring system for the
expected crop based on the predefined crop properties.
Pesticide Prediction and Disease Identification with AIoT
Authors: Dharmireddy Ajay Kumar , Silva Lorraine Kambham Jacob , Maddumala Ravi Kumar , Lavanya Kotha
Published: October 2024

Agriculture is vital to human survival and has a significant impact on the
economy of any nation. Crop protection costs millions of dollars per year. Insects and
other pests pose a serious threat to the health of a harvest. Excessive use of chemical
fertilizers and pesticides negatively affects the crop and soil quality. Therefore, one
way to safeguard the harvest and mitigate potential losses is through early
identification of the pests. Examining the crop at the right moment is the best technique
to determine its overall health. While manual inspection is the standard way of
conducting field inspection, it becomes challenging for large fields. In addition, manual
inspection would be exceedingly expensive and tedious. To address this, an automated
system is needed to detect pests, identify them, and recommend appropriate fertilizers
using an IoT system. Therefore, automated pest detection has become a major focus for
researchers globally, as it offers a more efficient and cost-effective alternative to
manual inspection. In this work, a smart agriculture system has been proposed that
monitors crops, identifies pests, and allows remote control. The dataset comprises over
4000 images of corn leaves, categorized into rust, blight, grey spots, and healthy
leaves. By employing Convolutional Neural Networks (CNN), the system has achieved
a remarkable 99% accuracy in pest detection.
Framework Based on IoT, AI, and Blockchain for Smart Access to Government Agricultural Schemes
Authors: Vinodha D. , Buvana M. J. , Rajalakshmi S. , Jenefa J. , Mary Anita E. A. , Lapina Maria
Published: October 2024

Agriculture plays an important part in most countries, such as India. A
survey says that 54.6% of the total labor force of India is engaged in agriculture and its
connected activities. The government is announcing many schemes to facilitate
agriculture and support farmers. But most of the farmers are from poor families and are
not able to reach the government schemes when they are really in need. Also, it is
required to observe and measure the inter and intra-field variability in crops to enjoy
the complete benefits of government schemes. This can be done with the advancements
in the field of the Internet of Things. Information related to the impact of natural
calamities on the agricultural field, malfunctions in the machinery used for cropping,
yielding level, and health status of crops can be measured using the technology of IoT
(Internet of Things) and analyzed using AI (Artificial Intelligence). Blockchain plays a
critical role in replacing traditional means of data storage and exchanging agricultural
data with a more trustworthy, immutable, transparent, and decentralized approach. By
keeping all the transactions related to government schemes in blockchain, the possible
crimes in the form of false data by the intermediate dealers acting between the farmers
and the government can be addressed. This, in turn, allows useful government schemes
to reach the farmer in time. We propose to develop a theoretical model using IoT, AI,
and blockchain, which can assist the farmers in benefitting from the appropriate
schemes announced by the government in time and achieving precise agriculture.
Survey, Collection, Preservation and Identification of Weeds
Authors: Kumar Rakesh , Kaur Pardeep , Robin
Published: October 2024

A weed survey provides valuable insights into the prevalence of various
weed species, the interplay between weeds and crops, and the dynamic patterns of these
associations in field conditions. A roving survey is implemented within a vast
geographical region that includes either a single district or multiple districts. A weed
survey with identical objectives to the roving survey can be conducted on a farm.
Periodic evaluation of the weed control treatments is necessary to assess their impact
on both the crop and weed populations. Visual estimation is a technique employed to
quickly gather data when dealing with a substantial number of plots and plant species
while facing resource constraints. The European Weed Research Council (EWRC) has
developed a rating system to assess the efficacy of weed control. Weed assessments
should be conducted during the initial stages of crop growth when weed competition is
at its peak and weeds are in their early and manageable stages for counting. The
collection and preservation of weed specimens are essential for future reference and
study, as not all weed species are readily accessible at all times and locations. An
herbarium is a collection of dried, pressed, and preserved plants, including weeds,
organized according to an accepted classification system such as Bentham & Hooker or
Hutchinson. In addition to this, accurate identification of weed plants is a fundamental
requirement for the development of efficient management strategies.
Integrated Weed Management in Pulses
Authors: Kumar Rakesh , Kaur Pardeep , Robin
Published: October 2024

The cultivation of pulses in India is carried out across 23 million hectares,
which encompasses a wide range of agroecological conditions, cropping techniques,
and management practices. They are planted throughout both the Kharif and Rabi
seasons. The average productivity of pulse crops in India is somewhat lower than the
productivity levels reported in several other pulse-growing countries globally. The
problem of weed infestation is a substantial hindrance to attaining the highest possible
crop output. This chapter discusses the critical period of crop-weed competition in
different pulse crops. The list of predominant weed species systematically classified
based on their seasonal occurrences, along with corresponding herbicides
recommended for their control, is also included.
Fingerprint Recognition System Study
Authors: Prerna  , Yadav Rishika , Awasthi Shashank , Yadav Satya Prakash , Upadhyay Prashant
Published: October 2024

Due to its broad use in fields including law enforcement, access control, and
personal identity, fingerprint recognition systems have attracted a lot of interest in
recent years. This paper offers a thorough examination of fingerprint recognition
systems, concentrating on their underlying ideas, methods, and performance
assessment. The study starts by giving a general review of the biometric identification
sector while highlighting how distinct and stable fingerprints are as a trustworthy form
of personal identification. Highlighting significant turning points and developments, it
examines the historical evolution of fingerprint identification systems from manual
fingerprint analysis to automated digital systems. This research seeks to increase the
knowledge of biometric identification technologies by offering a thorough review of
fingerprint recognition systems. Researchers, professionals, and politicians interested in
the creation and deployment of safe and effective fingerprint recognition systems for a
variety of applications can benefit from the discoveries and insights offered.
Neuromorphic Computing: Forging a Link between Artificial Intelligence and Neurological Models
Authors: Bajaj Madhvan , Rawat Priyanshu , Sharma Vikrant , Vats Satvik
Published: October 2024

By emulating the design and operation of the human brain, neuromorphic
computing promises to close the gap between artificial intelligence and brain-inspired
technologies. Researchers create specialized hardware and software to mimic the
brain's processing speed, capacity for learning, and energy economy. This chapter
examines the drivers, difficulties, and prospective uses of neuromorphic computing,
with a focus on robotics, sensory processing, and pattern recognition. The entire
potential of brain-inspired systems will be unlocked by ongoing research,
revolutionizing the field of AI and paving the way for the creation of cutting-edge,
intelligent machines that follow the principles of the brain.
Glaucoma Detection with Retinal Fundus Images
Authors: Mehta Shreshtha , Gupta Amit , Sahu Deepti , Singh Pawan Kumar , Yadav Satya Prakash
Published: October 2024

This paper discusses numerous methods for glaucoma detection. Because of
its impact on the optic nerve and the loss of ganglion cells, which eventually results in
vision loss, glaucoma has emerged as the leading cause of blindness worldwide. In this
article, we provide a few methods for recognizing glaucoma in its earliest stages, which
can prevent irreversible damage to a person's vision. We explore ROI (region of
interest), optic cup and disc ratio, LSACM, and LSACM-SP techniques in this
research, all of which help us achieve significant segmentation results. The
development of diagnostic methods for several eye illnesses began with the discovery
of the “optical disc (OD)”. To produce circular OD milestones, this methodology
rounds extrinsic morphology and detecting methodologies. The OD's pixels must be
provided as raw data. To achieve this, a methodology based on the chosen voting
method is devised.
Detection of Lung Cancer using Image Processing Methods
Authors: Mehta Shreshtha , Bordoloi Dibyahash , Yadav Satya Prakash , Singh Pawan Kumar , Upadhyay Prashant
Published: October 2024

The largest cause of cancer-related fatalities globally is lung cancer. Lung
cancer treatment results and survival rates can be considerably enhanced by early
identification and diagnosis. Image processing techniques have attracted attention as
useful tools for the early identification and diagnosis of lung cancer because of
improvements in medical imaging technology. This review study offers a thorough
examination of the various image-processing methods used in lung cancer diagnosis.
The importance of early detection and the difficulties in conventional diagnosis
techniques are covered in the first section of the paper. The potential of image
processing methods to solve these issues and boost diagnostic precision is then
highlighted. The review discusses several feature extraction, segmentation, and
classification techniques used in lung cancer diagnosis. The precise detection and
delineation of lung tumors from computed tomography (CT) scan or chest X-ray
images is investigated using image segmentation algorithms. To get pertinent data and
traits from the segmented tumor areas, feature extraction techniques are next examined.
In the end, classification methods are looked at for separating benign and malignant
tumors based on the data retrieved. The research also examines the combination of
image processing methods with machine learning and deep learning algorithms for
improved lung cancer diagnosis. It draws attention to the benefits and drawbacks of
these algorithms in terms of increasing diagnostic precision and lowering false-positive
or false-negative outcomes. The study concluded with a discussion of the potential
applications of image-processing techniques in the diagnosis of lung cancer. It
emphasizes how computer-aided diagnostic methods and artificial intelligence have the potential to revolutionize the detection and treatment of lung cancer. In conclusion, this
paper offers a thorough overview of the image processing techniques used in lung
cancer diagnosis. It clarifies how these methods could aid in the early detection of lung
cancer, improve the design of the appropriate course of therapy, and eventually
improve patient outcomes.
Web User Access Path Prediction using Recognition with Recurrent Neural Network
Authors: Prerna  , Chamoli Sushant , Singh Pawan Kumar , Chauhan Sansar Singh , Yadav Satya Prakash
Published: October 2024

This research introduces a novel technique for predicting web user access
paths based on Recognition with Recurrent Neural Network (RNN). The study focuses
on utilizing user access paths as the primary research goal and explores the application
of RNN in addressing the path forecasting problem. A network model is developed and
examined for predicting access paths by enhancing the feature layer. This approach
effectively leverages contextual information from user conversation sequences, learns
and memorizes user access patterns, and obtains optimal model parameters through
training data analysis. Consequently, it enables accurate prediction of the user's next
access path. Theoretical analysis and experimental results demonstrate the higher
efficiency and improved accuracy of path forecasting achieved by this technique,
making it well-suited for solving web user access path prediction problems.
A Neural Network Study of Face Recognition
Authors: Saklani Rishabh , Purohit Karan , Upadhyay Santosh Kumar , Upadhyay Prashant , Yadav Satya Prakash , Verma Aditya , Garg Ashish
Published: October 2024

The difficult subject of automatic recognition has attracted a lot of interest
lately since it has so many uses in so many different industries. Face recognition is one
of those difficult problems, and as of right now, no method can offer a reliable response
in every circumstance. A novel method for recognizing human faces is presented in this
research. This method employs a two-dimensional discrete cosine transform (2D-DCT)
to compress photos and eliminate superfluous data from face photographs utilizing an
image-based approach to artificial intelligence. Based on the skin tone, the DCT
derives characteristics from photos of faces. DCT coefficients are calculated to create
feature vectors. To determine if the subject in the input picture is “present” or “not
present” in the image database, DCT-based feature vectors are divided into groups
using a self-organizing map (SOM), which uses an unsupervised learning method. By
categorizing the intensity levels of grayscale images into several categories, SOM
performs face recognition. An image database including 25 face pictures, five
participants, and five photos with various facial expressions for each subject was used
to complete the evaluation in MATLAB. This method's primary benefits are its highspeed processing capacity and minimal computing demands, both in terms of speed and
memory use.
Time Sequence Data Monitoring Method Based on Auto-Aligning Bidirectional Long and Short-Term Memory Network
Authors: Rajpoot Abha Kiran , Awasthi Shashank , Naruka Mahaveer Singh , Bordoloi Dibyahash , Garg Neha
Published: October 2024

This research proposes a time sequence data monitoring method that utilizes
a auto-aligning bidirectional long and short-term memory network (LSTM) for
efficient and accurate monitoring of equipment. The method involves several steps,
including data preprocessing, bidirectional LSTM modeling, attention scoring,
prediction probability calculation, and real-time monitoring. By leveraging the
capabilities of auto-aligning and bidirectional LSTM, the proposed method aims to
enhance the accuracy and effectiveness of equipment monitoring based on time
sequence data.
Multi-Resolution Image Similarity Learning: A Method for Extracting Comprehensive Image Features
Authors: Jauhari Sheradha , Chauhan Sansar Singh , Aggarwal Gunajn , Gupta Amit , Garg Navin
Published: October 2024

This research presents an image similarity learning method that focuses on
extracting multi-resolution features from images. The proposed method involves a
series of steps, including image collection, normalization processing, image pairing
based on visual judgment and a Hash algorithm, and division of data into training and
testing sets. Furthermore, a network model is constructed using a deep learning
framework, and a specific objective function and optimizer are designated for
similarity learning. The network model is then trained and tested using the prepared
data sets. This method addresses several challenges encountered in conventional image
similarity learning, such as limited feature information extraction, inadequate
description of image features, limitations imposed by data volume during network
training, and susceptibility to overfitting.
Tensor Singular Value Decomposition-Based Multiple View Spectral Segmentation
Authors: Dixit Ashish , Singh Pawan Kumar , Yadav Satya Prakash , Bordoloi Dibyahash , Aswal Upendra Singh
Published: October 2024

This research presents a novel approach called “Multiple View Spectral
Segmentation based on Tensor Singular Value Decomposition” for the segmentation of
multi-view data. The algorithm utilizes three-rank tensors and constructs a probability
transfer matrix for all view data. By exploiting the low-rank nature of tensors in the
lateral, longitudinal, and vertical directions, the proposed procedure characterizes the
tensor's low-rank properties in each dimension using a multi-rank approach based on
tensor singular value decomposition (Tensor-SVD). Tensor-SVD decomposition, being
based on tube convolution, enables the model to capture spatial correlations more
effectively compared to other tensor resolution techniques and procedures based on
two-dimensional structure relationships. Furthermore, the use of Fourier transformation
allows for efficient calculations, thereby improving computational efficiency.
Experimental results demonstrate that the proposed tensor resolution model based on
Tensor-SVD achieves improved segmentation performance for multiple-view data.
Enhanced CNN-Based Failure Integrated Assessment Procedure for Energy Accumulator Packs
Authors: Jain Sachin , Singh Kamna , Upadhyay Prashant , Gupta Richa , Garg Ashish
Published: October 2024

This research presents a failure-integrated assessment procedure and
structure for energy accumulator packs using an enhanced Convolutional Neural
Network (CNN). The proposed approach involves wavelet packet decomposition
processing of voltage change and State of Charge (SOC) signals from a lithium
accumulator to extract energy values as input features. The assessment network
performs a preliminary failure assessment on the energy accumulator pack, followed by
evaluating whether the preliminary assessment result satisfies the assessment
confirmation condition. If met, an assessment result for the energy accumulator pack is
obtained. Otherwise, an auxiliary assessment using a CNN network is conducted for
further analysis. The primary assessment result and auxiliary assessment result are then
fused using the D-S evidence theory procedure to generate a comprehensive integrated
assessment result. Finally, the integrated assessment result is evaluated, and the
ultimate assessment result is determined. The proposed procedure improves the
assessment accuracy of energy accumulator packs by enhancing the structure of the
CNN network, determining the optimal size of the convolution kernel based on the
Bayesian Information Criterion (BIC), and incorporating auxiliary assessment networks
for enhanced accuracy and integrated assessment.
From Chips to Systems: Exploring Disruptive VLSI Ecosystems
Authors: Shah Owais Ahmad , Tiwari Devesh
Published: October 2024

This chapter provides an insightful exploration of the evolution and
disruptive impact of Very Large Scale Integration (VLSI) technology. The work traces
the development of VLSI from the integration of a few transistors to the intricate
ecosystem of today, highlighting notable developments in architectural innovations,
system-level design, and process technology. The growing impact of neuromorphic
circuits in the VLSI environment is highlighted. In addition to discussing the
difficulties the field faces, from scale constraints to design complexity, it also provides
an outlook of the future by speculating on the potential of quantum computing,
Artificial Intelligence (AI) driven design and ethical issues. The narrative underscores
the imperative of innovation, collaboration, and interdisciplinary approaches to
navigate the dynamic realm of VLSI, promising a future where VLSI continues to
revolutionize technology and shape our world.
Investigating the Acceptability of an Interactive Television Intervention Promoting Social Links Among Older Adults Living at Home and in Care Institutions: Qualitative Interview and Questionnaire Study
Authors: Naudé Bérangère , Rigaud Anne-Sophie , Pino Maribel
Published: October 2024

Background

When older adults (OAs) can no longer live independently at home, they have the option to choose from various types of geriatric care institutions, such as residential facilities or nursing homes. For several years now, thanks to the development of interactive television (iTV), social link functions have been accessible directly on televisions, tools that are already integrated into residents’ rooms. The acceptance of technologies specifically targeting older users, as well as iTV, has been widely documented in the literature, incorporating factors from the innovation resistance model.

Objective

This research aims to enrich the acceptance of existing models of innovation by OAs living in different settings.

Methods

User tests were carried out to evaluate OAs’ experiences with iTV and identify the factors involved in its acceptance. A total of 32 OAs living at home, in nursing homes, or in residential facilities in France were interviewed between November 2022 and June 2023. iTV acceptance was examined using an interview grid based on the technology acceptance model and included the following factors: intention to use, perceived usefulness, perceived ease of use, user resistance, anxiety, facilitating conditions, and user characteristics.

Results

The deductive qualitative analysis based on the technology acceptance model helped to identify 33 concepts.

Conclusions

This study has contributed to the literature on the acceptance of iTV by OAs living at home and in geriatric institutions, particularly by enriching existing models and proposing new avenues for reflection.
Exploring Health Care Professionals’ Perspectives on the Use of a Medication and Care Support System and Recommendations for Designing a Similar Tool for Family Caregivers: Interview Study Among Health Care Professionals
Authors: Ashimwe Aimerence , Davoody Nadia
Published: October 2024

Background

With the aging population on the rise, the demand for effective health care solutions to address adverse drug events is becoming increasingly urgent. Telemedicine has emerged as a promising solution for strengthening health care delivery in home care settings and mitigating drug errors. Due to the indispensable role of family caregivers in daily patient care, integrating digital health tools has the potential to streamline medication management processes and enhance the overall quality of patient care.

Objective

This study aims to explore health care professionals’ perspectives on the use of a medication and care support system (MCSS) and collect recommendations for designing a similar tool for family caregivers.

Methods

Fifteen interviews with health care professionals in a home care center were conducted. Thematic analysis was used, and 5 key themes highlighting the importance of using the MCSS tool to improve medication management in home care were identified.

Results

All participants emphasized the necessity of direct communication between health care professionals and family caregivers and stated that family caregivers need comprehensive information about medication administration, patient conditions, and symptoms. Furthermore, the health care professionals recommended features and functions customized for family caregivers.

Conclusions

This study underscored the importance of clear communication between health care professionals and family caregivers and the provision of comprehensive instructions to promote safe medication practices. By equipping family caregivers with essential information via a tool similar to the MCSS, a proactive approach to preventing errors and improving outcomes is advocated.