Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption.

Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has become crucial for both energy generation companies and consumers to predict energy consumption well in advance.

Novel transfer learning based bone fracture detection using radiographic images.

A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture can take anywhere from one month to one year, leading to significant economic and psychological challenges for patients.

pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.

Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory methods for treating cancer-affected cells, these approaches often face limitations, including high costs and substantial side effects. Recently the high selectivity of peptides has garnered significant attention from scientists due to their reliable targeted actions and minimal adverse effects.

β-caryophyllene sensitizes hepatocellular carcinoma cells to chemotherapeutics and inhibits cell malignancy through targeting MAPK signaling pathway.

Background: β-caryophyllene (BCP) is a naturally occurring bicyclic sesquiterpene extracted from various plants, and widely used as a medicinal agent for various diseases. During hepatocellular carcinoma (HCC) development, cancer cells generally exhibit increased cell proliferation due to mutations or aberrant expression of key regulatory genes. The current study determines the cytotoxic effects of BCP alone or in combination with doxorubicin (DOX) and cisplatin (DDP) on HCC cells, and elucidates the underlying mechanism of BCP to exert its anticancer activities.

A hybrid encryption framework leveraging quantum and classical cryptography for secure transmission of medical images in IoT-based telemedicine networks.

In the era of the Internet of Things (IoT), the transmission of medical reports in the form of scan images for collaborative diagnosis is vital for any telemedicine network. In this context, ensuring secure transmission and communication is necessary to protect medical data to maintain privacy. To address such privacy concerns and secure medical images against cyberattacks, this research presents a robust hybrid encryption framework that integrates quantum, and classical cryptographic methods.

Valorization of textile waste for removal of Cadmium from contaminated water.

The rapid development in agriculture and industrial sectors has raised some serious global issues like heavy metals pollution of water resources. Cadmium (Cd) is amongst the major water pollutants worldwide. In this study, two novel sorbents were prepared by using post-consumer textile waste (PCTW).

In Operando Optical Analysis of Electrolyte Colour Change and Its Correlation to Capacity Fade in Li-Ion Cells at Elevated Temperatures.

The existing body of research on battery state of health has identified various degradation modes for the electrolyte, yet very few studies have explored the role of electrolyte colour changes as a diagnostic tool for state of health (SOH). This study investigates the impact of elevated temperatures and its correlation with electrolyte colour changes and capacity fade during cycling. Specifically, the research examines whether cycling cells at elevated temperatures induces noticeable changes in electrolyte colour and whether these changes can be linked to the SOH of the cells.

Harnessing light-harvesting chlorophyll a/b-binding proteins for multiple abiotic stress tolerance in Chlamydomonas reinhardtii: Insights from genomic and physiological analysis.

Light-harvesting chlorophyll a/b-binding proteins (LHC) of photosystem II perform key functions in various processes, e.g., photosynthesis, development, and abiotic stress responses.

An innovative artificial neural network model for smart crop prediction using sensory network based soil data.

A thriving agricultural system is the cornerstone of an expanding economy of agricultural countries. Farmers' crop productivity is significantly reduced when they choose the crop without considering environmental factors and soil characteristics. Crop prediction enables farmers to select crops that maximize crop yield and earnings.

Computational investigation of the phase stability, electronic, optical, phonon spectrum, and elastic behavior of layered perovskites CaXO (X = Zr, Hf) for optoelectronic applications.

Context: A significant class of solid-state materials, Ruddlesden-Popper (RP) perovskites are well-known for their rich chemical compositions that make them excellent electrocatalysts. Therefore, in this work, we conduct thorough analysis of RP perovskites, specifically CaXO (X = Zr, Hf). We delve into the structural, electronic, optical, and mechanical properties presenting their insights for the first time.

DeepAIPs-Pred: Predicting Anti-Inflammatory Peptides Using Local Evolutionary Transformation Images and Structural Embedding-Based Optimal Descriptors with Self-Normalized BiTCNs.

Inflammation is a biological response to harmful stimuli, playing a crucial role in facilitating tissue repair by eradicating pathogenic microorganisms. However, when inflammation becomes chronic, it leads to numerous serious disorders, particularly in autoimmune diseases. Anti-inflammatory peptides (AIPs) have emerged as promising therapeutic agents due to their high specificity, potency, and low toxicity.

Doped magnetic nanoparticles: From synthesis to applied technological frontiers.

Doped magnetic nanoparticles (DMNPs) have become a fascinating class of nanomaterials with important implications in science and technology. The comprehensive review focuses on the synthetic methods, types of doping elements, distinctive properties, and extensive applications of DMNPs. The synthesis section highlights different methods, highlighting their benefits and drawbacks, such as chemical precipitation, co-precipitation, thermal breakdown, sol-gel, and other processes.

Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.

Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significantly impact metabolism and overall health. Hypothyroidism can cause a slowdown in bodily processes, leading to symptoms such as fatigue, weight gain, depression, and cold sensitivity. Hyperthyroidism can lead to increased metabolism, causing symptoms like rapid weight loss, anxiety, irritability, and heart palpitations.

Manifold roles of potassium in mediating drought tolerance in plants and its underlying mechanisms.

Drought stress (DS) is a major devastating factor affecting plant growth and development worldwide. Potassium (K) is considered a vigorous moiety and stress alleviator, which crop cultivars need for better yield. It is also helpful in alleviating the DS-induced negative consequences by regulating various morphological, physiological, biochemical, and molecular mechanisms in plants.

Dynamic Analysis and Vibration Control of Additively Manufactured Thin-Walled Polylactic Acid Polymer (PLAP) and PLAP Composite Beam Structures: Numerical Investigation and Experimental Validation.

This study primarily presents a numerical investigation of the dynamic behavior and vibration control in thin-walled, additively manufactured (AM) beam structures, validated through experimental results. Vibration control in thin-walled structures has gained significant attention recently because vibrations can severely affect structural integrity. Therefore, it is necessary to minimize these vibrations or keep them within acceptable limits to ensure the structure's integrity.

Facile assembly of flexible humidity sensors based on nanostructured graphite/zinc oxide-coated cellulose fibrous frameworks for human healthcare.

The development of flexible, cost-effective, highly efficient, and reliable humidity monitoring sensors is in high demand owing to their wide-range of applications in industrial domains. In this study, a humidity sensor was fabricated based on graphite/zinc oxide nanoparticle (G/ZnO-NP)-coated cellulose paper. A bar device was designed using computer software, and its sketch was printed on cellulose paper, with graphite bars then added using the pencil-drawing method, and then ZnO-NP paste was coated on the graphite patterns.

A facile synthesis of iron oxide nanoparticles as a nano-sensor to detect levofloxacin and ciprofloxacin in human blood and evaluation of their biological activities.

A rapid synthesis of a pH-stable magnetic nano-sensor (iron oxide nanoparticles, Fe-NPs, ∼2.6 nm) encapsulated with 3-aminobenzoic acid (3-ABA) was achieved. 3-ABA was prepared for the first time through the reduction of 3-nitrobenzoic acid (3-NBA) in the presence of HCl and tin.

Hepatocellular carcinoma antibodies preferably identify nitro-oxidative-DNA lesions induced by 4-Chloro-orthophenylenediamine and DEANO.

The widespread use of oxidative hair colouring cosmetics threatens public health. Phenylenediamine derivatives serve as the main pigment in permanent hair colours. They interact with biological macromolecules, altering their functional and structural physiology.

Expression of transglutaminase-2 (TGM2) in the prognosis of female invasive breast cancer.

Background: Transglutaminase 2 (TGM2) is a protein expressed in several isoforms in both intra- and extra-cellular tissue compartments. It has multiple functions that are important in cancer biology and several small studies have suggested expression of TGM2 in breast cancers is associated with a poorer prognosis. The aim of this study was to evaluate the role of intra-cellular and extra-cellular TGM2 expression in breast cancer and to determine whether there were any differences by hormone receptor status.

Fighting to thrive via plant growth regulators: Green chemical strategies for drought stress tolerance.

As global climate change intensifies, the occurrence and severity of various abiotic stresses will significantly threaten plant health and productivity. Drought stress (DS) is a formidable obstacle, disrupting normal plant functions through specific morphological, physiological, biochemical, and molecular mechanisms. Understanding how plants navigate DS is paramount to mitigating its adverse effects.

AI based predictive acceptability model for effective vaccine delivery in healthcare systems.

Vaccine acceptance is a crucial component of a viable immunization program in healthcare system, yet the disparities in new and existing vaccination adoption rates prevail across regions. Disparities in the rate of vaccine acceptance result in low immunization coverage and slow uptake of newly introduced vaccines. This research presents an innovative AI-driven predictive model, designed to accurately forecast vaccine acceptance within immunization programs, while providing high interpretability.

A novel meta learning based stacked approach for diagnosis of thyroid syndrome.

Thyroid syndrome, a complex endocrine disorder, involves the dysregulation of the thyroid gland, impacting vital physiological functions. Common causes include autoimmune disorders, iodine deficiency, and genetic predispositions. The effects of thyroid syndrome extend beyond the thyroid itself, affecting metabolism, energy levels, and overall well-being.

Development of novel FRET aptasensor based on the quenching ability of iron oxide-gold nanostars for the detection of aflatoxin M1.

Detecting Aflatoxin M1 (AFM1) in food products is crucial due to its high toxicity and health risks. This study introduced a fluorescence donor material using Rhodamine-B-Isothiocyanate (RBITC)-doped silica nanoparticles (RDSN) combined with iron oxide‑gold nanostars (IOGNS) as a quencher. The composite aptasensor (RDSN/IOGNS) served as a Förster Resonance Energy Transfer (FRET) nanoprobe for sensitive and selective AFM1 detection.