Research Work Research Articles

Hypothesizing the Green Synthesis of Tamoxifen Loaded Magnetic Nanoparticles for the Treatment of Breast Cancer.

Breast cancer is the second leading cause of death all over the world and is not only limited to females but also affects males. For estrogen receptor-positive breast cancer, tamoxifen has been considered the gold-line therapy for many decades. However, due to the side effects associated with the use of tamoxifen, its use is only limited to individuals in high-risk groups and limits its clinical application to moderate and/or lower-risk groups. Thus, there is a necessity to decrease the dose of tamoxifen, which can be achieved by targeting the drug to breast cancer cells and limiting its absorption to other body parts. Artificial antioxidants used in the formulation preparation are assumed to upsurge the risk of cancer and liver damage in humans. The need of the hour is to explore bioefficient antioxidants from natural plant sources as they are safer and additionally possess antiviral, anti-inflammatory, and anticancer properties. The objective of this hypothesis is to prepare tamoxifen-loaded PEGylated NiO nanoparticles using green chemistry, tumbling the toxic effects of the conventional method of synthesis for targeted delivery to breast cancer cells. The significance of the work is to hypothesize a green method for the synthesis of NiO nanoparticles that are eco-friendly, cost-effective, decrease multidrug resistance, and can be used for targeted therapy. Garlic extract contains an organosulfur compound (Allicin) which has drug-metabolizing, anti-oxidant, and tumour growth inhibition effects. In breast cancer, allicin sensitizes estrogen receptors, increasing the anticancer efficacy of tamoxifen and reducing offsite toxicity. Thus, this garlic extract would act as a reducing agent and a capping agent. The use of nickel salt can help in targeted delivery to breast cancer cells and, in turn, reduces drug toxicity in different organs. This novel strategy may aim for cancer management with less toxic agents acting as an apt therapeutic modality.

Penalizing the Hard Example But Not Too Much: A Strong Baseline for Fine-Grained Visual Classification.

Though significant progress has been achieved on fine-grained visual classification (FGVC), severe overfitting still hinders model generalization. A recent study shows that hard samples in the training set can be easily fit, but most existing FGVC methods fail to classify some hard examples in the test set. The reason is that the model overfits those hard examples in the training set, but does not learn to generalize to unseen examples in the test set. In this article, we propose a moderate hard example modulation (MHEM) strategy to properly modulate the hard examples. MHEM encourages the model to not overfit hard examples and offers better generalization and discrimination. First, we introduce three conditions and formulate a general form of a modulated loss function. Second, we instantiate the loss function and provide a strong baseline for FGVC, where the performance of a naive backbone can be boosted and be comparable with recent methods. Moreover, we demonstrate that our baseline can be readily incorporated into the existing methods and empower these methods to be more discriminative. Equipped with our strong baseline, we achieve consistent improvements on three typical FGVC datasets, i.e., CUB-200-2011, Stanford Cars, and FGVC-Aircraft. We hope the idea of moderate hard example modulation will inspire future research work toward more effective fine-grained visual recognition.

Computational Studies and Synthesis of New Heterocyclics as CNS Agents.

This research work aimed to design and synthesize some new molecules of phenothiazine. The work's emphasis was on forming new phenothiazines in two series, 1-(10H-phenothiazin- 10-yl)-2-((4-(1-(phenylimino)ethyl)phenyl)amino)ethan-1-one derivatives (4a-4j) and 1-(4-((2-oxo-2- (10H-phenothiazin-10-yl)ethyl)amino)phenyl)-3-phenylprop-2-en-1-one derivatives (P1-P5). Chloroacetylation of phenothiazine was done to afford 2-chloro-1-(10H-phenothiazin-10- yl)ethan-1-one, which was further reacted with 4-amino acetophenone to produce 2-((4- acetylphenyl)amino)-1-(10H-phenothiazin-10-yl)ethan-1-one. Then, it was treated with substituted anilines and substituted benzaldehydes to produce the final derivatives 4a-4j and P1-P5, respectively. All 15 derivatives (4a-4j and P1-P5) were characterized by evaluating their Rf value, melting point, solubility, IR spectroscopy, and 1HNMR spectroscopy. Molecular docking was performed by using AutoDock Vina v.1.2.0 (The Scripps Research Institute, La Jolla, CA, USA) docking software, and the anxiolytic activity of the derivatives was assessed by using the elevated plus maze model. The designed scheme was executed in the departmental laboratory. The chemical structure of the compounds was confirmed on the basis of TLC, IR, and 1HNMR analyses. The docking study revealed a good docking score of the compounds. The Log P value of the compounds indicated their good penetration into CNS. The compounds were also screened for anxiolytic activity. Among them, compounds 4f, 4h, and P3 showed maximum activity as anti-anxiolytic agents.

Internet of Things assisted Unmanned Aerial Vehicle for Pest Detection with Optimized Deep Learning Model

IoT technologies & UAVs are frequently utilized in ecological monitoring areas. Unmanned Aerial Vehicles (UAVs) & IoT in farming technology can evaluate crop disease & pest incidence from the ground’s micro & macro aspects, correspondingly. UAVs could capture images of farms using a spectral camera system, and these images are been used to examine the presence of agricultural pests and diseases. In this research work, a novel IoT- assisted UAV- based pest detection with Arithmetic Crossover based Black Widow Optimization-Convolutional Neural Network (ACBWO-CNN) model is developed in the field of agriculture. Cloud computing mechanism is used for monitoring and discovering the pest during crop production by using UAVs. The need for this method is to provide data centers, so there is a necessary amount of memory storage in addition to the processing of several images. Initially, the collected input image by the UAV is assumed on handling the via-IoT-cloud server, from which the pest identification takes place. The pest detection unit will be designed with three major phases: (a) background &foreground Segmentation, (b) Feature Extraction & (c) Classification. In the foreground and background Segmentation phase, the K-means clustering will be utilized for segmenting the pest images. From the segmented images, it extracts the features including Local Binary Pattern (LBP) &improved Local Vector Pattern (LVP) features. With these features, the optimized CNN classifier in the classification phase will be trained for the identification of pests in crops. Since the final detection outcome is from the Convolutional Neural Network (CNN); its weights are fine-tuned through the ACBWO approach. Thus, the output from optimized CNN will portray the type of pest identified in the field. This method’s performance is compared to other existing methods concerning a few measures.

Optimization of PEMFC pressure control using fractional PI/D controller with non-integer order: design and experimental evaluation

The fuel-based proton exchange membrane (PEM) fuel cell is a promising technology for clean energy production owing to the several advantages including high efficiency (around 80% theoretical), quiet in operation, and almost zero emission as compared to conventional internal combustion engine. Only hydrogen and oxygen are supplied at the anode and cathode, respectively to generate power and water is produced as by product. However, it suffers to achieve its maximum theoretical efficiency due to lack of flow/pressure management of hydrogen and oxygen in the PEMFC stack which also causes flooding within the cell and reduce the performance of the catalyst and reduces the efficiency. The higher efficiency can be achieved with the proper control of the hydrogen and oxygen inlet flow rate and pressure at the PEMFC. Since it’s crucial to maintaining a consistent supply of exponential pressure, the main focus of this work is pressure regulation at the PEMFC cathode side. A fractional PI/D controller is designed to operate the PEMFC system more realistically. There are three primary objectives of this research work. In the first step, monitoring the PEMFC operating pressure to find out the suitable fractional PI-D controller for a given resilience level, which has the lowest Integration Absolute Error (IAE) to disturbances. The robustness level and/or threshold peak is considered as a tuning parameter for the evaluation. Second, compare the best IAE performance of the fractional PI-D controller with that of simple SIMC rules, where a certain level of resilience is achieved by varying the SIMC tuning variable. Through this comparison, the effectiveness of the recommended controller in achieving the optimal plant performance is evaluated. Thirdly, design a non-integer order PEMFC plant with a fractional controller using MATLAB software and compare the results with existing models. This comparison provides insight into the practical performance of the proposed controller. The results shows that the developed fractional PI/D controller is able to control the pressure very efficiently at the PEMFC cathode side. The findings further emphasise on the important to consider the resilience and robustness levels at the time of developing control systems for PEMFCs. The efficacy of the suggested unique technique is further confirmed by contrasting the suggested controller with the developed models.

CommGNAS: Unsupervised Graph Neural Architecture Search for Community Detection

Graph neural architecture search (GNAS) has been successful in many supervised learning tasks, such as node classification, graph classification, and link prediction. GNAS uses a search algorithm to sample graph neural network (GNN) architectures from the search space and evaluates sampled GNN architectures based on estimation strategies to generate feedback for the search algorithm. In traditional GNAS, the typical estimation strategy requires using labeled graph data to generate feedback, which plays a fundamental and vital role in the search algorithm to sample a better GNN architecture during the search process. However, a large portion of real-world graph data is unlabeled. The estimation strategy in traditional GNAS cannot use unlabeled graph data to generate feedback for the search algorithm, so the traditional supervised GNAS fails to solve unsupervised problems, such as community detection tasks. To solve this challenge, this paper proposed CommGNAS, an effective node representation learning method with unsupervised graph neural architecture search for community detection. In CommGNAS, we design an unsupervised evaluation strategy with self-supervised and self-representation learning. It represents the first research work in literature to solve the problems of unsupervised graph neural architecture search for community detection. The experimental results show that CommGNAS can obtain the best performance in community detection tasks on real-world graphs against the state-of-the-art baseline methods.

Study of Structural Elucidation, Degradation Kinetics and Antimicrobial Analysis of Copper (II) Sesame-Soap Complexes with Urea and Thiourea Ligand in Non-aqueous Solvents

Abstract: Surfactants have great importance in biological and drug industries and the complexes of metallic soaps with various ligands are used in approximately every region of national economy. Therefore, our keen interest to study of degradation kinetics and biological importance of Cu (II) surfactants in non-aqueous and non-polar solvent benzene. Present research work has been initiated with synthesis, systematic study of structural elucidation, thermal degradation, kinetics and biocidal activities of copper (II) sesame-soap complexes with macrocyclic nitrogen and sulphur containing donar ligands like urea and thiourea. The thermal degradation of copper (II) sesame-soap complexes were carried out for analysis of degradation kinetics and estimation of kinetic and thermodynamic parameters using different methods at heating rate 10oC min-1. Copper (II) sesame-soap complexes of such ligands have also been analysed against Staphylococus aureus. This research work consolidates the synthesis of copper (II) sesame soap-urea and thiourea complexes by conventional methods and the structure of these complexes were assigned according to elemental analysis and molecular weight determinations. IR, NMR and ESR spectral studies have also been done to understand structural aspects. The anti-microbial activities of copper (II) sesame-soap urea and thiourea complexes have been evaluated by testing against Staphylococus aureus. The present research work includes information of thermal analysis using TGA technique to find out their kinetic and thermodynamic parameters by using diverse equations such as Coats-Redfern equation, Horowitz-Metzger equation, Broido equation, Piloyan–Novikova equation. Moreover, the results obtained from anti-microbial screening have been used to analyze the anti-microbial activities of copper (II) sesame-soap urea and thiourea complexes against gram-positive bacteria Staphylococus aureus. These results show that complexes of copper ion co-ordinated with different nitrogen, oxygen and sulphur containing ligands, are very important in the field of pharmaceutical chemistry due to its significant role in the inhibition activity. The study of these complexes concludes that the synthesized complexes were found to possess appreciable bactericidal properties at different concentrations because chelation increases the anti-microbial potency.

A systematic review of in-memory database over multi-tenancy

The significant cost and time are essential to obtain a comprehensive response, the response time to a query across a peer-to-peer database is one of the most challenging issues. This is particularly exact when dealing with large-scale data processing, where the traditional approach of processing data on a single machine may not be sufficient. The need for a scalable, reliable, and secure data processing system is becoming increasingly important. Managing a single in-memory database instance for multiple tenants is often easier than managing separate databases for each tenant. The research work is focused on scalability with multi-tenancy and more efficiency with a faster querying performance using in-memory database approach. We compare the performance of a row-oriented approach and column-oriented approach on our benchmark human resources (HR) schema using Oracle TimesTen in-memory database. Also, we captured some of the key advantages on optimization dimension(s) are the traditional approach, late-materialization, compression and invisible join on column-store (c-store) and row-base. When compression and late materialization are enabled in a query set; it improves the overall performance of query sets. In particular, the paper aims to elucidate the motivations behind multi-tenant application requirements concerning the database engine and highlight major designs over in-memory database for the tenancy approach on cloud.

NRMG: News Recommendation With Multiview Graph Convolutional Networks

The emergence of a news recommendation system can effectively improve the news reading experience of users. The most important task of the system is how to learn news and user representations accurately. In the process of learning news representations, most of the current research works do not fully utilize the news features, which makes it difficult to learn more comprehensive news representations. Most research work only learns user representations from a single perspective, which may not be sufficient to learn diverse and dynamic user representations. Therefore, we propose a news recommendation system with a multiview graph convolutional network (NRMG). It contains two parts: news representation and user representation. The knowledge–content collaboration network is adapted to learn news representations from news content and entities, while the multiview graph convolutional network (GCN) is utilized to learn user representations from the user’s click history. The advantage of the NRMG system is that we not only expand the available features by constructing a subclass knowledge graph (KG), but also effectively improve the ability of the news recommendation system to accurately learn news and user representations. Experimental results on the real dataset MIcrosoft News Dataset (MIND) show that the NRMG achieves a 2.25% improvement in area under the receiver operating characteristic (ROC) curve (AUC) value compared with state-of-the-art methods.

Biological Importance and Therapeutic Potential of Calycopterin From Dracocephalum kotschyi: An Overview of Current Scientific Research Work.

Dracocephalum kotschyi Boiss is an important medicinal plant of Asia, Europe and Iran. The therapeutic potential of Dracocephalum kotschyi has been mentioned in traditional as well as in modern medicine, mainly due to its therapeutic effectiveness against different types of human disorders and associated secondary complications. Calycopterin is a flavonoid class phytochemical commonly called 5,4-dihydroxy-3,6,7,8- tetramethoxyflavone, which has been isolated from Dracocephalum kotschyi. It is widely used for the treatment of human disorders and associated secondary complications. Medicinal importance and therapeutic potential of calycopterin was investigated in the present work through scientific data analysis of different research works. Scientific data on calycopterin published in different research articles were collected from PubMed, Google, Google Scholar, Science Direct, SpringerLink, and Scopus and analyzed in the present work. Further, scientific data was collected from books and book chapters. Analytical data on calycopterin was also collected and analyzed in the present work. Scientific data analysis of different research work revealed the biological importance and therapeutic potential of calycopterin. Calycopterin showed therapeutic effectiveness against different types of cancerous disorders, including human prostate cancer, breast cancer, cervical cancer, lymphocyte proliferation and hepatoblastoma cancer. Moreover, the biological effectiveness of calycopterin on neuroprotection and Pseudomonas aeruginosa has also been discussed in the present work with their pharmacokinetic parameters. Nevertheless, more scientific investigations must be conducted to explore the healthbeneficial aspects of calycopterin in medicine. Present work signified the biological importance and therapeutic potential of calycopterin for the treatment of human disorders and complications, including different types of cancerous disorders.

Putting science to work for women's health.

The Office of Research on Women's Health (ORWH)'s whole health paradigm expands the scope of women's health research, incorporating a life-course perspective that recognizes the profound influences of sex and gender on health. From childhood through adulthood, external and societal factors along with internal factors and biology shape women's health and influence access to quality healthcare. This comprehensive approach integrates data-driven sex- and gender-aware strategies to prevent, diagnose, and treat disease, focusing on the unique needs of women. Acknowledging the historical lack of timely research and data on women's health, an initiative led by First Lady Dr. Jill Biden and the White House Gender Policy Council, ushers in a new era of women's health research that offers unprecedented opportunities to enhance the health of women through biomedical and behavioral research. The initiative fosters interdisciplinary collaboration, supporting research on autoimmune diseases, menopause, oral health, and chronic pain conditions. ORWH serves as the focal point for National Institutes of Health (NIH) women's health research. With a commitment to advancing holistic outcomes, ORWH engages in partnerships, outreach, and educational initiatives to disseminate critical research findings and support women's health researchers. Here we describe the convergence of this initiative with the National Institute of Dental and Craniofacial Research's work to advance the understanding of sex as a biological variable for conditions such as Sjogren's disease and temporomandibular disorder. This transformative approach to women's health research propels the United States toward innovative solutions, ensuring that science works for the health and well-being of every woman.

An overlapping conscious relief-based feature subset selection method

Feature selection is considered as a fundamental prepossessing step in various data mining and machine learning based works. The quality of features is essential to achieve good classification performance and to have better data analysis experience. Among several feature selection methods, distance-based methods are gaining popularity because of their eligibility in capturing feature interdependency and relevancy with the endpoints. However, most of the distance-based methods only rank the features and ignore the class overlapping issues. Features with class overlapping data work as an obstacle during classification. Therefore, the objective of this research work is to propose a method named overlapping conscious MultiSURF (OMsurf) to handle data overlapping and select a subset of informative features discarding the noisy ones. Experimental results over 20 benchmark dataset demonstrates the superiority of OMsurf over six existing state-of-the-art methods

Design and analysis of wideband four-port multiple input multiple output antenna using defective ground structure for 5G communication

This research work describes a compact four-port multiple input multiple output (MIMO) antenna using defective ground structure (DGS), and it perfectly supports the n77, n78, and n79 frequencies in 5G new radios (NR) bands. It can cover a wideband from 3.4 to 5.4 GHz with good impedance matching. The pair of antenna elements are orientated opposite to another pair with DGS. Due to this technique, it has minimal complexity, is less expensive, and improves isolation. It has also improved the frequency band's reflection coefficient and range using MIMO antenna with different stub lengths. Because it is less expensive, FR-4 substrate is used in the implementation of all antennas. Each antenna element has two identical stubs linked to the primary radiator. On the primary radiating element, a ''HI'' slot is created. The partial ground enhances impedance matching and radiation properties throughout the targeted band. <span>The total dimensions of the four-port MIMO antenna are 46×30×1.6 mm<sup>3</sup>. The array elements' mutual coupling in the simulation is -14 dB. The ECC value is below 0.01, and the diversity gain (DG) is less than 10 dB. The suggested designs' measured gain ranges from 10 to 11.0 dB, and the radiation efficiency is nearly 91%.</span>

In silico Identification of HDAC Inhibitors for Multiple Myeloma: A Structure-based Virtual Screening, Drug Likeness, ADMET Profiling, Molecular Docking, and Molecular Dynamics Simulation Study

Background: Multiple myeloma (MM) is a hematological malignancy of plasma cells that produce a monoclonal immunoglobulin protein. Despite significant advances in the treatment of MM, currently available therapies are associated with toxicity and resistance. As a result, there is an increasing demand for novel, effective therapeutics. Inhibition of histone deacetylases (HDACs) is emerging as a potential method for treating cancer. HDAC6 is one of 18 different HDAC isoforms that regulate tubulin lysine 40 and function in the microtubule network. HDAC6 participates in tumorigenesis and metastasis through protein ubiquitination, tubulin, and Hsp90. Several studies have found that inhibiting HDAC6 causes AKT and ERK dephosphorylation, which leads to decreased cell proliferation and promotes cancer cell death via the PI3K/AKT and MAPK/ERK signaling pathways. Objective: The objective of this study is to target HDAC6 and identify potent inhibitors for the treatment of multiple myeloma by employing computer-aided drug design. Materials and Methods: A total of 199,611,439 molecules from five different chemical databases, such as CHEMBL25, ChemSpace, Mcule, MolPort, and ZINC, have been screened against HDAC6 by structure- based virtual screening, followed by filtering for various drug-likeness, ADME, toxicity, consensus molecular docking, and 100 ns MD simulation. Results: Our research work resulted in three molecules that have shown strong binding affinity (CHEMBL2425964 -9.99 kcal/mol, CHEMBL2425966 -9.89 kcal/mol, and CSC067477144 -9.86 kcal/mol) at the active site HDAC6, along with effective ADME properties, low toxicity, and high stability. Inhibiting HDAC6 with these identified molecules will induce AKT and ERK dephosphorylation linked to reduced cell proliferation and promote cancer cell death. Conclusion: CHEMBL2425964, CHEMBL2425966, and CSC067477144 could be effective against multiple myeloma.

Design Aspects of Lower Limb Exoskeleton

Aim: This research work aimed at the design, simulation, and validation of a lower limb exoskeleton for rehabilitation. The device can provide regressive gait training for patients suffering from lower limb mobility disorders. Background: People suffering from mobility disorders, such as spinal cord injuries, and other related diseases are in high proportion. Exoskeletons play a vital role in enhancing the lifestyle of people with disorders. Devices that provide locomotion assistance and help in reducing the burden of therapists through effective and repetitive gait training are in high demand. Exoskeletons have further extended to the fields of the military to enhance the performance of physically abled persons. Prototype development of lower limb exoskeletons is too expensive and many of them are patented. The requirement for this system to perform human trials is subjective to several medical and ethical norms. Thus, there exists a need to evaluate and validate the exoskeleton designs. Methods: In this work, the design has been made inclusive of different body shapes and sizes. The device has been modeled in SOLIDWORKS and its structural integrity has been analyzed using the ANSYS software. Later, the model has been subjected to environmental assessment and then motion analysis using the ADAMS software. Results: The structural integrity analysis has revealed the design to be adequate to carry the applied load as the stresses induced were less than the yield strength of the material. The sustainability analysis showed that LLE made of aluminium alloy had less impact on the environment relative to the other two materials. Conclusion: The kinematic simulation revealed that the angular amplitudes, the reaction force of the right hip and knee joint, and the contact force between the shoe and the ground of the exoskeleton agreed well with the experimental findings of the literature.

Years lived with disabilities of the working-age population owing to injuries

Introduction: Cases of mortality and illness caused by injury in Mongolian population have increased drastically in recent years, and in 2022, 5th cause of illness and the 3rd cause of mortality. On average, more than 146,000 new cases of accidents and injuries are registered in Mongolia every year. More than 69% of it is a working-age population. Purpose and objectives: The purpose of the research work is to calculate the number of years lived with disability due to accidents among the working-age population of Mongolia. Research materials and methods: The study design is a Retrospective Cohort Study. The research work was carried out according to the WHO's burden of disease determination method. We collected quantitative data on disease numbers in Mongolia from the year 2016 to 2020 from the database of the Health Development Centre and National Center for Traumatology. The number of years lived with a disability was calculated using 306,823 quantitative information of the working-age (15-61) population of Mongolia in 2016-2020. Results: In Mongolia, 61.0% (n=187047) of the causes of accidents and injuries among the working-age population over 5 years were men and 39.0% (n=119776) were women. Considering the causes of accidents and injuries among the working-age population, 49.3% of all illnesses are falls, 25.3% are homicides, 22.9% are road accidents, and the rest are suicides, intoxication/exposure to toxic substances.120469 years of living with disability due to accidents caused by external causes of the working-age population of Mongolia over 5 years. The number of years lived with a disability was 77,135 years (64.0%) for men and 43,334 (36.0%) years for women. Conclusion: The working-age population of Mongolia has 120,469 years of living with disabilities caused by accidents caused by external causes. 1.8 times more years are lost than men and women.

Assessment of density and specific gravity tests of some selected Ayurvedic medicinal plants

The Guna(Static properties) in common terms known as properties or qualities. There are 41 Gunas in Ayurvedc classics. Among them the Gurvadi Gunas and Paradi Gunas are used in the various treatment measures. The Guru is known as heaviness. Gurutwa is correlated with Gravity in modern science. Parthiva and Apya Mahabhuta are responsible for this Guru Guna. The Guru drugs act as Brumhana(Nourishing as well as help to increase the mass of the tissues), Anuloman(Mild laxative). The exact opposite action of Guru drug is seen in Laghu Dravya. The Laghu drugs give Lightness to the body, as it is light in nature. The Akashiya, Vayavya, Agneya, Laghu Gunas are predominant in this drugs. These drugs act as Agnideepan(Appetizer), Strotoshodhan(Cleanses the channels). The more Parthiva Dravya, more the Guru can be considered. Less Parthiva Dravya more the Laghu can be considered. To assess the Parthivatwa by its correlation with Density and Specific gravity. Same can be considered for Jaliyatwa and Vayaviyatwa. Assessment of objective Parametric measures of Guru and Laghu Guna can be done by Density(Bulk), Specific Gravity(Liquid and solid). The Specific gravity, more formally known as relative density, is a measure of the density of a substance in comparison to the density of water. In this present research work there are 16 drugs for specific gravity for solids, 12 drugs for specific gravity for liquids, 20 drugs for Bulk density and the same 20 drugs for True density drugs have been selected.

Analysis of preferences of households for choosing heating and cooling systems: A discrete choice analysis in Iran

The residential sector uses a large fraction of energy for heating and cooling to provide comfort conditions for households. Technical features of appliances are determining factors of the demand for energy. The present paper thus aims to study the determinants of household adoption decision by applying the discrete choice model. Households face with unordered choice, and multinomial and mixed logit models are employed to study the heterogeneity of choices of households related to heating and cooling technologies. While most of the research works have considered preferences based on cross-sectional data collected through discrete choice experiments, the present study employs micro-level household survey data including observed preferences in order to contribute to better understanding of the actual choice behavior of households. In addition, the dynamic effect of variation of variables on the household preferences have been investigated over time. The results show that heterogenous preferences of families in choosing appliances are affected by socio-demographic characteristics. Analysis of marginal effects indicate that urbanization, education, building type and households expenditure are the most effective parameters. The level of education has the most positive impact on the adoption of wall-mounted package and split air conditioner where the probability of using them has increased by 1.65% and 4.87%, respectively. On the other hand, high education level reduces the probability of gas heater and fan adoption by 3.49% and 3.43%. The estimated results show that urbanization increases the probability of choosing central heating by 3.73% and split air conditioner by 6.1% while it decreases the adoption probability of oil heater by 5.02% and fan as by 8.9%.

Enhancing fall prediction in the elderly people using LBP features and transfer learning model

In an era where the detection and prevention of falls are crucial for the well-being of elderly and vulnerable individuals, achieving high accuracy in identifying such incidents is of utmost importance. This study introduces a modified NASNet, a novel transformer learning model designed for the classification of fall and not fall people based on local binary patterns (LBP) features. The modified NASNet model demonstrates high performance, achieving an accuracy, precision, recall, and F1 score of 99% with LBP features in differentiating fall from not fall. To assess the performance of NASNet, this research work conducted a comparative analysis with other prominent deep learning, and transfer learning models, as well as state-of-the-art machine learning frameworks. The findings indicate NASNet's enhanced performance in fall detection, highlighting its potential for application in real-time fall detection systems, contributing to a safer environment for individuals at risk. This research sets the stage for enhanced healthcare and assistance for vulnerable populations, addressing a critical concern in the healthcare sector.

Integrated Intelligent Computing Models for Cognitive-Based Neurological Disease Interpretation in Children: A Survey

INTRODUCTION: This piece of work provides the description of integrated intelligent computing models for the interpretation of cognitive-based neurological diseases in children. These diseases can have a significant impact on children's cognitive and developmental functioning. 
 OBJECTIVES: The research work review the current diagnosis and treatment methods for cognitive based neurological diseases and discusses the potential of machine learning, deep learning, Natural language processing, speech recognition, brain imaging, and signal processing techniques in interpreting the diseases.
 METHODS: A survey of recent research on integrated intelligent computing models for cognitive-based neurological disease interpretation in children is presented, highlighting the benefits and limitations of these models.
 RESULTS: The significant of this work provide important implications for healthcare practice and policy, with strengthen diagnosis and treatment of cognitive-based neurological diseases in children.
 CONCLUSION: This research paper concludes with a discussion of the ethical and legal considerations surrounding the use of intelligent computing models in healthcare, as well as future research directions in this area.