Volume 13 | Issue 11 |

 Abstract

In smart homes, predicting energy consumption is vital, as accurate forecasting enables effective energy utilization, achievement of sustainability targets, and lower operating costs. In this research, we explore and design a hybrid model forecasting framework that combines machine learning (ML) and deep learning (DL) methodologies to predict electricity demand on a household level in the short and long runs. The hybrid employs Random Forest (RF) to examine and interpret complex non-linear interactions, and Long Short-Term Memory (LSTM) to account for the distinctive consumption behavior temporal patterns. The environmental data used include temperature and humidity, as well as simulated hourly smart meter data and appliance-level usage records.Experimental we see that our hybrid model does better than which which is the standard in the field in terms of performance we also present.

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 Keywords

Smart Homes, Energy Forecasting, Machine Learning, Deep Learning, LSTM, Random Forest, IoT, Energy Management.

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Creative Commons Attribution 4.0 and The Open Definition

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Epilepsy is a widespread neurological condition characterized by repeated, unprovoked seizures, affecting millions of people worldwide. Despite available treatments, a significant number of patients do not respond to conventional medications, highlighting the need for improved diagnostic and therapeutic approaches. This review summarizes recent progress in diagnostic strategies, including advanced neuroimaging, high-resolution EEG and MEG, biomarkers, and artificial intelligence-based seizure prediction. It also discusses therapeutic advances, covering new-generation antiepileptic drugs, innovative mechanisms of action, drug combinations, and precision medicine tailored to individual patients. Non-drug interventions, such as minimally invasive surgeries, neurostimulation devices, gene and stem cell therapies, dietary strategies, and nanotechnology-driven targeted drug delivery, are explored as complementary options. Emerging research on microRNAs, epigenetics, gut microbiota, 3D brains organoids, and CRISPR-based gene editing provides insights for future personalized treatments. Challenges such as drug resistance, treatment costs, accessibility, and ethical concerns are addressed, emphasizing the need for a multidisciplinary approach. Integration of omics technologies, digital health, and precision neurology offers hope for more effective therapies and improved quality of life for people living with epilepsy.

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 Keywords

Epilepsy, Seizure disorders, Neuroimaging, EEG, Drug-resistant epilepsy, Nanotechnology, Epigenetics, Neurostimulation, Artificial Intelligence.

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Annona squamosa are belong to annonacea family. Various parts of this plant seed , bark, leaves, roots they contains much chemical constitutes which are useful in treating disease. There are varities of annona squamosa such as Arka Neelanchal Vikram, Arka sahan. It contain various nutritional values such as fats, calories, carbohydrates, vitamins and minerals. Annona species are mostly found at tropical and subtropical parts worldwide. Mostly it shows anticancer, anti-oxidant, antidiabetic, antihypertensive, Hepatoprotective, antiparasitic, antimalarial, insecticidal, microbicidel and molluscicidal conditioning. The main corridor of the factory is fruits, leaves, seeds, roots and dinghy, which are used in the different conditions and also have medicinal and nutraceutical values. It shows ethno- medicinal uses.

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Pharmacological Properties, Custard Apple, Annona Squamosa, Annonaceae, benefits.

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Sericulture is a vital agro-based, labor-intensive industry that sustains millions of rural livelihoods, especially in developing countries. Despite its economic significance, sericulture workers are frequently exposed to a variety of occupational health hazards. The worker engaged in sericulture is using traditional tools and techniques across different stages or tasks such as silkworm feeding, cleaning, cocoon boiling, reeling etc. The sericulture production system requires high demanding physical strength due to repetitive nature of tasks, awkward postures, extended period of sitting and standing thus, women face physical, mental and environmental challenges. The occupational risks prompted the conditions of stress, strain, work related muscular-skeletal disorders in various parts of worker's body. All the above challenges pose greater risk during working and overall reduce health of the women engaged in silkworm rearing. The drudgery involved in sericulture has significant influence on the productivity of the scheduled tribe women which will ultimately have a discouraging impact on their quality of life. This review synthesizes recent literature on the occupational risks faced by sericulture workers, categorizing them into physical, chemical, biological, and ergonomic hazards.

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Sericulture, occupational risks, ergonomic hazards, muscular-skeletal disorders, drudgery

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The impact of conversational AI agents on consumer choices and their degree of adoption are examined in this study. The results, which are based on survey data backed by prior research, indicate that interest in AI-driven calls is growing, with the majority of respondents indicating conditional openness rather than rejection. There were no apparent differences in acceptability by geographic area or gender, according to statistical research, indicating that users' perceptions are constant. Participants favored the option to escalate complex concerns to a human, but they also valued speed and convenience. When people felt in control and the AI's identity was clear, trust increased. All things considered, conversational AI agents that are intended to supplement rather than replace human service can improve customer experience and provide competitive benefits.

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 Keywords

Agentic AI, Customer Experience, Strategic Impact

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This study investigates the intermolecular interactions in a Reported Manganese(II) Complexes with the (NSAID) Tolfenamic Acid with secondary ligand bipyam [bis(?2-Tolfenamato-O,O')-bis(tolfenamato-O,O')-bis(bis(2-pyridyl)amine-N,N')-di-manganese(ii)] using Hirshfeld surface analysis and 2D fingerprint plots. The Hirshfeld surface provides a qualitative understanding of molecular packing and non-covalent interactions, while 2D fingerprint plots enable a quantitative breakdown of interaction contributions, revealing key features such as hydrogen bonding, van der Waals interactions, and ?-? stacking. In addition, dnorm, shape index, curvedness, and fragment patch analysis are discussed to highlight the molecular environment .

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Reported Manganese(II) Complexes with the (NSAID) Tolfenamic Acid with secondary ligand bipyam, Hirshfeld Surface Analysis , 2D Fingerprint Analysis

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Ion-exchange chromatography (IEC) is one of the most versatile and widely used chromatographic techniques for the separation, purification, and quantitative analysis of charged biomolecules. This review highlights the fundamental principles, operational mechanisms, and diverse applications of IEC in pharmaceutical, biochemical, environmental, and industrial research. Ion-exchange resins containing functional cationic or anionic groups interact with oppositely charged analytes, allowing highly selective and efficient separation based on charge density, ionic strength, pKa, and molecular structure. Advancements in stationary phase chemistry, membrane-based ion exchangers, monolithic columns, and coupled techniques such as ion-exchange chromatography-mass spectrometry (IEC-MS) have significantly improved resolution, speed, and sensitivity. IEC has become indispensable for purification of therapeutic proteins, plasmid DNA, monoclonal antibodies, peptides, and complex biological mixtures, as well as for water quality and environmental monitoring. Recent developments in high-performance ion-exchange materials, mixed-mode exchangers, and continuous chromatography have expanded its role in bioprocessing and analytical science. This review compiles updated research findings, technological progress, and future prospects of IEC.

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Ion-Exchange Chromatography (IEC), Charged Biomolecule Separation, Stationary Phase Functional Groups, Protein Purification, Biopharmaceutical Analysis, Chromatographic Techniques, Anion and Cation Exchanger

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Creative Commons Attribution 4.0 and The Open Definition

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Artificial Intelligence (AI) in diagnostic imaging has progressed from proof-of-concept classifiers to clinically integrated, regulated software that augments radiologists across screening, triage, quantification, and care coordination workflows, with multiple FDA-cleared products in routine use and nationwide prospective evaluations demonstrating increased detection without higher recalls in mammography screening programs. This expanded paper details model families (CNNs, vision transformers, foundation models), clinical performance in breast imaging, chest radiography, neuroimaging, cardiothoracic CT, and MRI, integration into PACS/EHRs, governance and bias mitigation, federated learning for privacy-preserving collaboration, and future trajectories including imaging foundation models, multimodal decision support, and physics-informed reconstruction at scale.

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Artificial Intelligence (AI), Diagnostic Imaging, Medical Image Analysis, Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Imaging Foundation Models, Federated Learning, Computer-Aided Diagnosis (CAD), Radiology Workflow Integration, Chest Radiography (CXR), Mammography Screening, MRI Enhancement, CT-Based Detection, Explainable AI (XAI), Saliency Maps, Grad-CAM, Data Annotation, Bias Mitigation, Model Governance, Software as a Medical Device (SaMD), PACS/EHR Integration, Real-W

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