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The Aloe Genome Genetics, Genomics and Breeding
(2026) Beniwal, Vikas
Aloe vera is the oldest known, and the most widely applied medicinal plant worldwide. It is a
unique plant with a wealth of historical and cultural value, which is bolstered by its diverse range
of applications and vast botanical history (Foster and Hunter, 2009). Its extensive pharmacological
characteristics are supported by its varied chemical composition, which includes both gel and latex
ingredients. These pharmacological properties have been verified by multiple scientific studies, that
confirmed its efficacy in several key areas (Surjushe et al., 2008).
Azithromycin-based Albumin Nanoparticles Loaded Oral Fast dissolving Films as Superheroes in the Fight against Strep Throat
(2026) Pandey, Manisha
Streptococcal pharyngitis, strep throat, is a bacterial infection induced by group A Streptococcus. Azithromycin (AZI) is
used as the second-line drug for its treatment. The study’s goal was to formulate AZI-based albumin nanoparticles loaded
on fast oral dissolving films, which would help reduce the dosage and dosing frequency of the drug by enhancing its
local effect in the affected area. The desolvation process was utilized for the synthesis of albumin nanoparticles, while the
solvent casting approach was applied for the formulation of the oral fast-dissolving films (OFDF). A 3-factor and 3-level
CCD was implemented to ascertain the influence of critical material attributes on the critical quality attributes of the
nanoparticles employing Design-Expert software. The optimized nanoparticles exhibited particle dimension, zeta potential,
and entrapment efficiency of 250.6 ± 19.6 nm, -0.656 mV, and 98.67% ± 0.002, respectively. The in vitro release of the
optimized formulation demonstrated an initial burst release of 18.11% ± 0.0024 within the first 2 hours, and subsequently,
a sustained release profile was observed, with 96% ± 0.15 of the drug over 36 h. The AZI-loaded albumin nanoparticles
loaded oral fast-dissolving films demonstrated a drug release of 95% ± 0.659 within 1 h, and the ex vivo permeation via
goat buccal mucosa further revealed a flux of 45.36 mg/h. cm2. Moreover, the formulation was assessed for its antibacterial
potency towards Streptococcus aureus, revealing a substantial zone of inhibition measuring 38 mm ± 0.152 compared to
the marketed oral tablet. The findings collectively point to the formulation’s efficacy in addressing strep throat, suggest
ing its potential as a viable treatment option for this condition. Further preclinical studies can be performed to ensure its
successful translation.
A Review on the Fate of Emerging Contaminants in Landfill Leachate: Insights from Conventional Treatment Approaches
(2026) Kumar, Smita S.
Landfill leachate is a dark-colored,
complex liquid formed by the percolation of water
through municipal solid waste, containing diverse
array of emerging contaminants. Reported concen
trations include pharmaceuticals such as ibuprofen
(2–1,500 µg/L) and carbamazepine (up to 800 µg/L),
personal care products like triclosan (50–3,200 µg/L),
pesticides (50–1,200 µg/L), phthalates such as Di(2
ethylhexyl) phthalate (up to 5.3 mg/L), polycyclic
aromatic hydrocarbons (20–600 µg/L), polychlorin
ated biphenyls (0.1–50 µg/L), per- and polyfluoro
alkyl substances (up to 6,200 ng/L), microplastics
(102–104 particles/L), and endocrine disruptors such
as bisphenol A (0.5–1,800 µg/L). These contaminants
persist in the environment, resist natural degradation,
Highlights
• Conventional treatments only partially remove emerging
contaminants (ECs) in leachate.
• EC removal depends on physicochemical properties and
applied treatment process.
• Fate of ECs includes adsorption, partial biodegradation,
and environmental persistence.
• Hybrid and integrated treatments achieve higher EC
removal than standalone methods.
• Treatment limitations indicate the need for optimized
hybrid systems and monitoring.
and thereby posing significant ecological and health
risks. The conventional biological treatments, includ
ing activated sludge and anaerobic digestion, achieve
only partial removal (20–60% for pharmaceuti
cals; < 30% for per- and polyfluoroalkyl substances).
Physico-chemical processes such as coagulation-floc
culation, advanced oxidation, and membrane filtration
provide higher removal rates (60–95%) but remain
energy-intensive, costly, and prone to secondary pol
lution. There is no single treatment that ensures com
plete elimination, underscoring the inadequacy of tra
ditional methods. Recent advances, including hybrid
membrane bioreactors, advanced oxidation processes,
and bioelectrochemical technologies, achieve more
than 90% removal of selected contaminants. The study focuses on the occurrence and fate of emerg
ing contaminants in landfill leachate, evaluates the
performance of existing treatment technologies, and
compares regulatory frameworks across different
countries. The insights aim to guide the development
of sustainable and integrated strategies for effective
leachate management.
Weakened Gallai–Ramsey number for various graphs of order up to six
(2026) Jakhar, Jagjeet
Gallai–Ramsey theory examines how edge colorings of complete graphs avoiding rain
bow triangles inevitably yield monochromatic subgraphs. In this work, we generalize
this framework by introducing the weakened Gallai–Ramsey number grs
t(G), defined as
the smallest integer p such that every Gallai t-coloring of Kp
contains a copy of G using
at most s
Role of urban green space structure and configuration in regulating land surface temperature in NCT Delhi using explainable artificial intelligence
(2026) Kumar, Manish
Urbanization-driven heat intensification poses a serious challenge to environmental sustainability
and thermal comfort in megacities such as National Capital Territory (NCT) Delhi. The influence
of green space structure and configuration on Land Surface Temperature (LST) remains under
explored. This study presents a novel framework that integrates Fragstats-derived green space
metrics and explainable artificial intelligence (XAI) to assess the spatio-temporal impact of green
space structure and configuration on LST in NCT Delhi. The LST and nine green space metrics
were derived using the Landsat-8 (OLI/TIRS) imagery. Three machine and deep learning models i.
e., Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), and Deep Learning (DL)
were developed to regress and predict LST using H2O AutoML package in Rstudio environment.
Among these, GBM produced the highest predictive accuracy (R
2
= 0.8859) and was therefore
selected for further interpretation using XAI techniques such as SHapley Additive exPlanations
(SHAP) and Individual Conditional Expectation (ICE) plots. Results show that fragmented green
spaces intensify surface heating, while cohesive and well-connected green space corridors pro
mote cooling through enhanced evapotranspiration and shading. The findings highlight that not
only the amount but also the spatial configuration of vegetation determines its cooling efficiency.
Areas in East Delhi (Shahdara, Seelampur) and North-West Delhi (Narela, Bawana, Rohini
Extension), characterized by high fragmentation, experience higher temperatures, whereas
contiguous green space corridors of Central and South Delhi exhibit stronger cooling benefits.
These insights provide actionable guidance for urban planners and policymakers to prioritize
cohesive green space development in future urban planning for climate adaptation and
mitigation.
FSL-TM: Review onthe Integration of Federated Split Learning with TinyML in the Internet of Vehicles
(2026) Goyal, Nitin
The Internet of Vehicles, or IoV, is expected to lessen pollution, ease traffic, and increase road safety.
IoVentities’ interconnectedness, however, raises the possibility of cyberattacks, which can have detrimental effects. IoV
systems typically send massive volumes of raw data to central servers, which may raise privacy issues. Additionally,
model training on IoV devices with limited resources normally leads to slower training times and reduced service
quality. We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning (TinyML) approach,
which operates on IoV edge devices without sharing sensitive raw data. Specifically, we focus on integrating split
learning (SL) with federated learning (FL) and TinyML models. FL is a decentralised machine learning (ML) technique
that enables numerous edge devices to train a standard model while retaining data locally collectively. The article
intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the
IoV domain, coupled with FL and TinyML. Theapproachstarts with the IoV learning framework, which includes edge
computing,FL,SL,andTinyML,andthenproceedstodiscusshowthesetechnologiesmightbeintegrated.Weelucidate
thecomprehensiveoperationalprinciplesofFederatedandsplitlearningbyexaminingandaddressingmanychallenges.
We subsequently examine the integration of SL with FL and various applications of TinyML. Finally, exploring the
potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM. It is a superior method for
preserving privacy as it conducts model training on individual devices or edge nodes, thereby obviating the necessity
for centralised data aggregation, which presents considerable privacy threats. The insights provided aim to help both
researchers and practitioners understand the complicated terrain of FL and SL, hence facilitating advancement in this
swiftly progressing domain.
Deep Learning for Monitoring and Managing Oil Spill in Underwater Sensor Networks: Enabling Technologies
(2026) Goyal, Nitin
Environmental problems can significantly impact people’s lives and must be addressed.
The oil spill is one of the biggest applications of underwater wireless sensor networks
(UWSNs) that threaten aquatic life, so it is essential to focus on it. Oil spill count has risen
in recent years because of the rise in shipping and marine transportation industries. Timely
and accurate detection of oil spills can improve the response process and get the necessary
resources to the affected areas more efficiently. This work was motivated by the fact that
oil spill detection is critical to maritime protection. Since oil spill detection is a complex
process, it faces various challenges. One of the biggest challenges is similar visual
appearance which makes it difficult to identify an oil spill and a similar oil slick in
synthetic aperture radar (SAR) imagery. Various radar images are frequently utilized for
this purpose. Despite being widely used for earth observation, SAR images have noisy
and illegible image quality, which makes classification challenging. Previously, detecting
and classifying SAR images required manual involvement, which made the process time
consuming. Therefore, researchers focused on automating such tasks by incorporating
deep learning (DL) techniques. Numerous papers proposed the applications of DL in
various radar images for oil spill monitoring but faced multiple problems. This study aims
to thoroughly investigate oil spills and their detection techniques, utilizing DL techniques
applied to various radar images. These studies have originated in diverse nations with
distinct environments. However, compared to other types of radar images, synthetic
aperture radar (SAR) images are more effective in pinpointing the location of oil spills.
However, they are also rather complex.
Current trends in microplastic removal using biodegradation approaches and advancement A review
(2026) Kumar, Dhushyant
Due to improper disposal and mismanagement of plastic waste, plastic converted into microplastic when exposed
to the environment causes tremendous burdens to nature. It becomes the most persistent pollutant in air, water,
and land environments due to the inert property of plastic. It enters the environment from various sources, such
as industrial, household, and agricultural waste, affecting the lives of humans, other living beings, and the entire
ecosystem. Much research and experimentation have been conducted to eliminate microplastics from the envi
ronment with the help of conventional methods, such as physical, chemical, and biological, which remove
microplastics but are inefficient in eliminating them from the environment. Some processes are good for nature
and the environment, as they can cause secondary pollution. Therefore, there is a need for some scientifically
proven new methods to achieve the required level of results and be environmentally friendly. Bioremediation is a
sound technique used to degrade plastic with the help of bacteria, fungi, algae, and insects. However, some
studies reveal that the pretreatment (UV, thermal, chemical, and physical) can change the inert property of
plastic, making it more available to the microbes and increasing the biodegradation process without affecting the
microbes’ life. Such a treatment gives hope for removing plastic from the environment in an efficient way.
Unravelling the potential of 2-(Bromo/polybromophenylamino) substituted-4-arylthiazoles: Synthesis, characterization, anticancer, antimicrobial, molecular docking, and ADMET studies
(2026) Kumar, Aman
A total of twenty-four 2-(bromo/dibromo/tribromophenylamino)-4-arylthiazole derivatives (3a-x) were pre
pared to explore their biological potential, particularly to evaluate the effect of bromine substituent(s) on the
phenylamino group attached at position-2 of the thiazole nucleus. The reaction of α-bromoacetophenones with
different N-bromoarylthioureas at room temperature in alcohol afforded the target products in good yields. Their
structures were established based on analysis of spectroscopic data and results of single-crystal X-ray diffraction
technique. The anticancer evaluation revealed that compound 3p exhibited excellent selectivity (SI =8.86) with
an IC50 value of 129.37 ±8.0 μM toward lung cancer cells (A549), surpassing the standard drug, Carboplatin
(IC50 =128.83 ±2.0 μM, SI =1.21). It was observed that compounds possessing a 2,4-dibromophenylamino
moiety linked at position-2 of the thiazole ring demonstrated the highest selectivity for A549 cells and the
lowest cytotoxicity against Vero cells. Antimicrobial screening revealed that compounds 3g and 3h were highly
effective against the bacterial strain, Staphylococcus aureus, and the fungal strain Candida albicans, with MIC
values of 0.038 μg/mL (Ampicillin =1 μg/ml) and 0.021 μg/ml (Fluconazole =0.31 μg/ml), respectively.
Furthermore, molecular docking studies were conducted on the newly synthesized candidates, which support
their potential as effective biological agents. ADMET analysis for key pharmacokinetic and toxicological prop
erties of these compounds was also conducted to assess their drug-likeness nature.
Two-walled phthalimide extended calix[4]pyrrole: Experimental and computational anion binding investigations
(2026) Ahmed, Mukhtar
In this study, a two-walled phthalimide extended C4P (11) was probed as an anion receptor for tetrabuty
lammonium (TBA) salts of spherical halides, trigonal/tetrahedral oxoanions and linear anions with the aid of
DFT/TD-DFT calculations and UV-vis spectroscopy. Optimized geometries indicate the non-covalent interaction
present between the guest and host. Interestingly, anion receptor complex (11@F
) has been found more reactive
among various studied anion receptor complexes. Furthermore, TD-DFT analyses provided insights into elec
tronic transitions within the studied species and found the absorption spectra in the range of 180 nm to 400 nm.
UV-vis absorbance titrations of receptor 11 with various anions in CH
3
CN have revealed 1:1 binding stoichi
Oxoanions
ometry and considerably higher binding affinities with F‾, Cl‾, CH
3
COO‾, and SCN‾ as compared to the one-
walled phthalimide C4P (13) and simple C4P (1) existing in the literature. Notably, higher anion binding af
f
inities of the receptor 11 as compare to the C4Ps 1 and 13 certifies the contribution of weaker anion
in
teractions from phthalimide subunits as well as the CH–anion interactions from the aliphatic methylene (CH
protons besides primary hydrogen bonding of the pyrrolic NHs-anions as observed from
1
π
H NMR studies.