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Abstract Background Duke Activity Status Index (DASI) is a widely used tool to assess functional capacity among patients, but there is no Sinhala version validated for patients in Sri Lanka. This study aimed to cross-culturally adapt and test the validity and reliability of the Sinhala version of DASI (DASI-S). Methods The translation and cross-cultural adaptation of the DASI questionnaire were conducted following the standard guidelines. It was pre-tested on ten pre-operative patients and further modified. The construct validity and reliability of DASI-S were evaluated by administering the modified final DASI-S, which comprised 12 items, along with the physical functioning sub-scale of the 36-item short-form health survey (SF-36), consisting of 10 items to eighty-one patients who were awaiting non-cardiac surgeries at university surgical wards, National Hospital of Sri Lanka (NHSL), and Colombo North Teaching Hospital (CNTH), Sri Lanka. Reliability was assessed through Cronbach alpha, while the validity was evaluated using factor analysis and Spearman’s correlation. The ethical approval was obtained from the Ethics Review Committee, Faculty of Medicine, University of Colombo, Sri Lanka. Results The mean age of the participants was 46.2 (± 16.6) years and the majority were females (54.3%). The mean height, weight, and body mass index of the sample were 160.5 (± 9.6) cm, 60.3 (± 11.9) kg, and 23.4 (± 4.5) kgm−2 respectively. The Cronbach's alpha coefficient for the internal consistency of DASI-S was 0.861. The concurrent validity of DASI-S was substantiated by positively correlating (p

Promoting the charge separation to improve photocatalytic performance of semiconductor photocatalysts is very important in the field of artificial photosynthesis. Here, a novel MoS2/CdS 2D–2D ultrathin nanosheet heterostructure was fabricated via a one-pot solvothermal route. The obtained 2D–2D MoS2/CdS nanojunction has not only provided large contact areas, but also shortened the charge transport distance, resulting in significantly enhanced photocatalytic H2 evolution property. The composites that were synthesized were examined using X-ray diffraction (XRD), Field emission scanning electron microscope (FESEM), Transmission electron microscope (TEM), UV-vis diffuse reflectance spectra (DRS), Photoluminescence (PL) and X-ray photoelectron spectra (XPS) analysis. By optimizing the 2D MoS2 amounts in the heterojunction, the 5 wt.% 2D/2D MoS2/CdS heterojunction displayed the maximal photocatalytic H2 evolution rate of 4449 μmolh−1g−1 under visible light irradiation in the presence of lactic acid as the sacrificial reagent, which was 6 times higher than that of pristine 2D CdS. Based on the photoelectrochemical and photoluminescence spectra tests, it could be deduced that the charge separation and transfer of 2D/2D MoS2/CdS heterojunction was tremendously improved, and the recombination of photoinduced electron-hole pairs was effectively impeded. Moreover, the 2D MoS2 was used as a cocatalyst to provide the abundant active sites and lower the overpotential for H2 generation reaction. The current work would offer an insight to fabricate the 2D/2D heterojunction photocatalysts for splitting H2O into H2.

Abstract Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall excellence in segmenting multimodal medical images. Therefore, we propose several modifications to the existing cutting-edge U-Net model. The technical approach involves applying a Multi-Dimensional U-Convolutional Neural Network to achieve accurate segmentation of multimodal biomedical images, enhancing precision and comprehensiveness in identifying and analyzing structures across diverse imaging modalities. As a result of the enhancements, we propose a novel framework called Multi-Dimensional U-Convolutional Neural Network (MDU-CNN) as a potential successor to the U-Net framework. On a large set of multimodal medical images, we compared our proposed framework, MDU-CNN, to the classical U-Net. There have been small changes in the case of perfect images, and a huge improvement is obtained in the case of difficult images. We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively.

QueryMintAI, a versatile multimodal Language Learning Model (LLM) designed to address the complex challenges associated with processing various types of user inputs and generating corresponding outputs across different modalities. The proliferation of diverse data formats, including text, images, videos, documents, URLs, and audio recordings, necessitates an intelligent system capable of understanding and responding to user queries effectively. Existing models often exhibit limitations in handling multimodal inputs and generating coherent outputs across different modalities. The proposed QueryMintAI framework leverages state-of-the-art language models such as GPT-3.5 Turbo, DALL-E-2, TTS-1 and Whisper v2 among others, to enable seamless interaction with users across multiple modalities. By integrating advanced natural language processing (NLP) techniques with domain-specific models, QueryMintAI offers a comprehensive solution for text-to-text, text-to-image, text-to-video, and text-to-audio conversions. Additionally, the system supports document processing, URL analysis, image description, video summarization, audio transcription, and database querying, catering to diverse user needs and preferences. The proposed model addresses several limitations observed in existing approaches, including restricted modality support, lack of adaptability to various data formats, and limited response generation capabilities. QueryMintAI overcomes these challenges by employing a combination of advanced NLP algorithms, deep learning architectures, and multimodal fusion techniques.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by several motor symptoms such as resting tremor, muscular rigidity, slowness of movement and different speech impairments. PD is a kind of singular, multi-system disorder that gradually worsen over the time. In this research, we classify the neurological state of the patients with Parkinson’s disease (PwPD) according to the third section of the Movement Disorders Society - Unified Parkinson’s Disease Rating Scale (MDS-UPDRS-III) using multimodal bio-signal data. As PD advances from low to advanced state, PwPD finds it difficult in their speech production and irregularities in their gait patterns. Monitoring the chaotic nature of time series data corresponding to speech and gait biomarkers can provide insights into the progression of the condition across different stages. This work for the first time analyze PD in a complex system perspective while representing the biomarkers as complex networks. The time-series corresponding to speech and gait signals are represented separately, as a complex network using the visibility graph algorithm. The characterization of the different stages of PD is explored for each modalities using different network features. Performance evaluation shows that the results obtained using the multimodal configuration of speech and gait left foot signals outperform the state-of the-art method. Moreover, performance comparison with the unimodal counterparts proves the need for multimodal assessment of PD severity. The configuration ‘speech and gait left foot’ outperforms (in terms of accuracy) that of the unimodal by 32% in speech, 3% in gait left foot, 19% in gait right foot, and 3% in gait both feet.

Automatic product recognition is crucial in advancing economic and social fronts due to its superior reliability and time-saving nature compared to manual operations. The precise organization of products on store shelves is essential for boosting sales and ensuring customer satisfaction. However, verifying that the physical arrangement aligns with the ideal plan is a costly and time-consuming task for store personnel. In the computer vision domain, detecting products in scene images poses a considerable challenge, particularly when dealing with grocery items displayed on store shelves. The arrangement of products often presents crowded environments with numerous identical objects placed closely together. This study illustrates the ongoing challenge of identifying specific objects in complex situations despite using advanced object detection systems. The proposed framework consists of a three-stage pipeline. The initial stage incorporates a cutting-edge product detection algorithm, YOLOv5, to locate multiple grocery objects. The proposed OD-Refiner layer in the second stage identifies the missed retail object and rectifies overlapping bounding boxes of YOLOv5. The OCR-based object recognizer called Batch Normalization Free Rigorous Feature Flow Neural Network (BNFRNN) is proposed in the final stage of the pipeline. The performance of the proposed framework was evaluated using a benchmark dataset, WebMarket. The proposed framework outperforms current state-of-the-art approaches by achieving a precision score of 92.56%, recall of 85.64%, and F-score of 88.97%.

Early cancer identification using gene expression data is critical for providing successful patient care. Accurate data recognition is essential to prevent improper detection because it may result in higher complexities and increased mortality rates. Gene expression data typically include numerous features, each representing distinct genes. The abundance of features introduces high dimensionality, contributing to high computational complexity and resource demands. Furthermore, the presence of redundant or highly correlated selected features may lead to multicollinearity issues. In the existing works, certain limitations, such as reduced performance due to degraded data quality, high storage space requirements, overfitting issues, and lack of robustness, can compromise overall classification accuracy. To address these challenges and enhance classification outcomes, this research employs an efficient framework based on a deep learning (DL) approach. Initially, the data is collected from five gene cancer datasets, which are then augmented to maximize the data size. Min-Max Normalization is used for data pre-processing. The Enhanced Chimp Optimization (ECO) algorithm is applied to select the most significant genes while eliminating redundant or unwanted ones. Based on the selected gene set, the Depth-wise Separable Convolutional Neural Network (DSCNN) is employed to categorize diverse cancerous and non-cancerous classes. The performance of the proposed model is greatly improved by resolving the dimensionality and overfitting problems. The implementation is carried out using PYTHON, and the overall accuracy of the DSCNN model is determined to be 99.18% across all datasets. Additionally, metrics such as precision, recall, and F1 score are evaluated to analyze the model’s overall performance. The proposed model demonstrates a significantly higher effective performance compared to existing approaches.

Abstract A drop in physical activity and a deterioration in the capacity to undertake daily life activities are both connected with ageing and have negative effects on physical and mental health. An Elderly and Visually Impaired Human Activity Monitoring (EV-HAM) system that keeps tabs on a person’s routine and steps in if a change in behaviour or a crisis might greatly help an elderly person or a visually impaired. These individuals may find greater freedom with the help of an EVHAM system. As the backbone of human-centric applications like actively supported living and in-home monitoring for the elderly and visually impaired, an EVHAM system is essential. Big data-driven product design is flourishing in this age of 5G and the IoT. Recent advancements in processing power and software architectures have also contributed to the emergence and development of artificial intelligence (AI). In this context, the digital twin has emerged as a state-of-the-art technology that bridges the gap between the real and virtual worlds by evaluating data from several sensors using artificial intelligence algorithms. Although promising findings have been reported by Wi-Fi-based human activity identification techniques so far, their effectiveness is vulnerable to environmental variations. Using the environment-independent fingerprints generated from the Wi-Fi channel state information (CSI), we introduce Wi-Sense. This human activity identification system employs a Deep Hybrid convolutional neural network (DHCNN). The proposed system begins by collecting the CSI with a regular Wi-Fi Network Interface Controller. Wi-Sense uses the CSI ratio technique to lessen the effect of noise and the phase offset. The t- Distributed Stochastic Neighbor Embedding (t-SNE) is used to eliminate unnecessary data further. The data dimension is decreased, and the negative effects on the environment are eliminated in this process. The resulting spectrogram of the processed data exposes the activity’s micro-Doppler fingerprints as a function of both time and location. These spectrograms are put to use in the training of a DHCNN. Based on our findings, EVHAM can accurately identify these actions 99% of the time.

The aim of the present study was to assess the infrared thermographic profile in relation to mastitis in lactating dairy cows. In this study, 20 lactating crossbred dairy cows each were monitored for a period of 21 days in four dairy farms. The udder skin surface temperature (USST) was recorded using infrared thermography (IRT) during the study period at diurnal time intervals. Milk samples were collected at the time of afternoon milking from each quarter and tested for California Mastitis Test (CMT). It was observed that higher USST was noticed in udders with clinical mastitis followed by non-mastitis and subclinical mastitis, and the skin surface temperature of non-mastitis and clinical mastitis udders were significantly higher than subclinical mastitis affected udders. The results of this study indicate the limitations in the use of IRT of udders for early detection of mastitis across all regions.

Despite advancements in technology, power system faults leading to electric power interruption remain a significant issue. Efficient restoration of the power system relies on the swift classification and clearance of faults. Among the various types of faults in transmission lines, open circuit and short circuit faults are commonly encountered. This study specifically focuses on the analysis of five types of short circuit faults: line-to-line, line-to-ground, double line-to-ground, triple line, and triple line-to-ground faults. Faults can cause both power failure and power loss in transmission lines. Once a fault occurs, it is crucial to restore electricity supply promptly to prevent further losses. Therefore, the development of a system capable of accurately and swiftly detecting and removing faults is essential. Traditionally, categorizing transmission line faults required sophisticated mathematical modeling, intricate signal processing techniques, and expert knowledge to interpret the output signals. In this paper, an alternative approach is proposed, utilizing deep learning techniques for transmission line fault classification. Specifically, the paper employs techniques such as artificial neural network (ANN), long short-term memory (LSTM), with and without window regression (WR). By implementing these deep learning techniques, automatic feature extraction and signal processing are achieved, streamlining the fault classification process. The experimental results obtained from this study demonstrate an accuracy of 42.98% for ANN, 99.98% for LSTM, and 99.99% for LSTM-WR. These outcomes underscore the effectiveness of the deep learning techniques employed in accurately classifying transmission line faults, with LSTM and LSTM-WR outperforming ANN in terms of accuracy.

This research paper presents a comprehensive study on a novel NAMPT (Nicotinamide phosphoribosyl transferase) inhibitor molecule, N- [4- [(4R)-1,4-dimethyl-6-oxidanylidene-4,5-dihydropyridazin-3-yl] phenyl]-5,7-dihydropyrrolo [3,4-b] pyridine-6-carboxamide], with the potential applications in cancer therapy. We performed quantum chemical calculations with DFT and topological investigations on the molecule to understand its electronic properties and chemical behaviour. The study covers basic molecular properties, molecular planarity and various electronic properties, including the HOMO (Highest Occupied Molecular Orbital), LUMO (Lowest Unoccupied Molecular Orbital) energy levels, the energy gap of 4.09 eV, ionization potential, electron affinity, electronegativity, chemical potential, chemical hardness, chemical softness, electrophilicity index, Mulliken atomic charges, Laplacian bond order, and ESP (Electrostatic Potential) properties. Moreover, we studied the nature of bonding and interactions within the molecule using ELF (Electron Localization Function), LOL (Localized Orbital Locator), RDG (Reduced Density Gradient), and NCI (Non-covalent Interaction). An analysis of the title molecule's molecular thermodynamic properties demonstrates its intrinsic stability under thermal and quantum effects. Molecular docking studies were also performed to reveal how the molecule interacts with its target protein, exhibiting a strong binding affinity for NAMPT target proteins (7PPF and 5WI0), which could help develop future cancer drugs. This multifaceted investigation provides a detailed understanding of the molecular structure and interactions of the molecule with NAMPT protein, potentially paving the way for its potential use as a cancer therapy.

Rice (Oryza sativa L.) is the staple food for more than 50% of the world’s population. Micronutrient malnutrition is a significant issue affecting approximately 2 billion people globally with severe levels of hunger. Fifteen hybrids derived through 6 x 6 half diallel mating were evaluated along with the six parents in a randomized block design at Tamil Nadu Rice Research Institute, Aduthurai during Kharif, 2022. Analysis of variance showed significant differences among the genotypes for all the traits except for the flag leaf width. The GCA effects of CO54 and Kalanamak were significant for most of the quantitative traits including grain yield, indicating the usefulness of these two genotypes for improving yield contributing traits. The parents such as Kalanamak and Chinkinikar registered high mean values for Fe and Zn and therefore these two parents can be exploited to develop Fe and Zn rich high yielding varieties. Analysis of specific combining ability and heterosis per se of the hybrids indicated that the crosses viz., ADT56/Kalanamak, CO54/ Kalanamak and CO54/Chinkinikar were best hybrids for improving yield as well as nutritional traits.

In the new approach, undoped and (0.1,0.3, and 0.5) La3+doped CoCr2O4 samples were prepared using microwave combustion methods. The Transmission Electron Microscope (TEM) and X-ray diffraction (XRD) are used to characterize the material's surface morphology and for phase identification and crystallinity. The samples were also studied with a UV–visible, diffuse reflectance spectrum (DRS) and a (VSM) Vibrating Sample Magnetometer. Spherical particles in agglomeration are visible in the TEM images. The average crystal size is about 15 - 43 nm was observed from the X-ray diffraction pattern results, and it was also confirmed that a cubic spinel structure existed. In addition to providing insight into the divalent and trivalent metal cation distributions at the A and B sites of the chromite lattice, XRD data also revealed divalent and trivalent metal cations distributions. Increased La content results in a decrease in estimated band gap energy. An increasing trend with increasing in La concentration resulting from cation rearrangement. Because La3+replaces some Cr ions in octahedral sites of cobalt chromite, there is a variation in its magnetic parameters.

Salivary gland tumors makes up 3–4% of all head and neck cancers, mucoepidermoid carcinoma is a malignant locally recurrent variant of salivary gland carcinoma. .In this short communication we are presenting a rare entity of mandible “intramedullary mucoepidermoid carcinoma” treated with wide local excision (composite resection) with modified radical neck dissection followed by reconstruction with free fibula osteocutaneous flap.

In humans, the immune system serves as a protective barrier against infection; however, when the immune system is out of balance, it can harm the host. Immunomodulators are chemicals or medications that have been employed in the clinic to treat an unbalanced immune response. The majority of immunological medicines in clinical use are cytotoxic. They harm the patient's quality of life by causing various side effects and being associated with higher production costs, longer lead times, and a high failure rate. Furthermore, obtaining single-compound chemicals with low toxicity, high efficacy, and selectivity for specified disorders is difficult for researchers. As a result, techniques based on alternative medicine are gaining traction in drug development, focusing on innovative natural compounds utilized to treat various disorders. Many plant molecules founded to have biologically beneficial properties. This review aimed to look at the immunomodulatory activity of plant-derived chemicals from widely used plants that treat a range of diseases worldwide.

In the present study, the effect of various weight percentages (0, 20, 30, and 40 wt.%) of Tungsten Carbide (WC) reinforcement in the Nickel-Chromium (Ni-Cr) alloy was investigated. The coatings were deposited on a 316L stainless steel substrate using Plasma Transferred Arc Welding (PTAW) process. Additionally, the As Welded (AW) NiCr-WC coatings were subjected to Short Time Heat-Treatment (STHT) and Laser Re-Melting (LRM) processes and a comparative study has been carried out with that of AW coatings. X-ray Diffraction (XRD), Scanning Electron Microscope (SEM), Vickers hardness and Pin on disc wear testing facilities were employed to characterize the phase structures, microstructure, dilution, crystallization, hardness and wear properties of the deposits. Wear mechanisms were studied using the 3D surface analyzer and SEM images. The STHT and LRM coatings showed an increased hardness and more crystalline phases than the As-Welded (AW) coatings. The correlation between the intermetallic phases, residual stress, microstructural evolution and friction-wear behaviour among the AW, STHT and LRM NiCr and NiCr-WC coatings were discussed in detail.

Listeriosis has emerged to be one of the life-threatening foodborne diseases worldwide, especially for immunocompromised individuals. The aim of this study was to develop a potential anti-bacterial drug with plant phytocompounds against phosphatidylinositol-specific phospholipase C from Listeria monocytogenes. A series of forty terpenoid compounds were selected based on Lipinski's rule of 5 and were subjected to molecular docking and dynamic simulation studies. From the results, Vouacapan was identified to be the promising compound out of the forty terpenoid compounds as it exhibited a good docking score of −7.7 kcal/mol and better stability with the protein in 100ns MD trajectory. Using MM/GBSA method, a total binding free energy value of −14.25 kcal/mol was observed for the Vouacapan-protein complex. The study offers a molecular level understanding of interactions of the selected compounds against the virulent protein.

The present study reports the effect of WC particle (20%, 30%, and 40%) and secondary processes (Heat Treatment and Laser Treatment) on the mechanical properties of NiCrBSi and NiCrBSi–WC deposits. A detailed comparative analysis of As Welded (AW), Heat Treated (HT) and Laser Treated (LT) deposits was made in terms of morphology, interface thickness, hardness, residual stress, tensile and flexural strength. Higher surface hardness was obtained for Laser treated NiCrBSi–WC(40%) deposits as compared to the heat-treated and as-welded deposits. The higher weight percentage of WC particles modified the solidification pattern and stress distribution in the NiCrBSi matrix, which eventually decreased the mechanical strength of NiCrBSi–WC (40%) deposits. In addition, fractographic examinations were used to delineate the various toughening mechanisms present in the post-treated NiCrBSi and NiCrBSi–WC deposits.

Objective: The present article details the preparation and characterization of bio-nanocomposite films loaded with nanoclays and chitosan which are the catalysts for retaining the fresh quality of flowers, fruits, and vegetables and extending their shelf life under packed with this film. The development of new packaging films is currently an emerging field for the consumers and edible goods trading industries. Hence, novel bio packaging films with a unique combination is essential to ensure optimum characteristics for the bio packaging films, thereby obviating synthetic plastics. Methods: Synthesis and characterization of the formulated blends of carboxymethylcellulose incorporated with chitosan, nanoclays, epoxy, and polyethylene glycol with the addition of lemon leaf extract (LLE) and analysis of their antibacterial activity. Result: The results indicate that when the jasmine flower buds were packed with the synthesized film, retain their buddy state for a minimum of 5 days at room temperature, freshness, and fragrance. Conclusion: Hence, the nano-bio packaging film will ensure the transportation of daily harvested flowers, fruits, and vegetables from farms to firms, stores, and homes.

Increasing evidence from recent research demonstrates that aberrant expressions of microRNAs (miRNAs) are linked to the development of chronic human diseases. Targeting miRNAs with bioactive small-molecules (or drugs) to regulate their activities provide an innovative insight into human disease treatment. Identifying the drugs that target particular miRNAs through the experimental study is complicated, time-consuming, and tremendously expensive. Therefore, computational researches by integrating information on drugs and miRNAs are essential for discovering potential drug-miRNA associations. Realizing the appropriate drugs that target the causal miRNAs behind diseases will contribute to miRNA mediated disease therapeutics and drug clinical applications. This study proposes an ensemble learning approach, ELDMA, that predicts novel drug-miRNA associations based on deep architecture-based classification. The method constructed features based on the integrated pairwise similarities of drugs and miRNAs and reduced the feature dimensions with principal component analysis (PCA). With the resulting features, the convolutional neural network is trained to extract intricate, high-level patterns. The deep retrieved features are given to the support vector machine classifier to infer potential drug-miRNA associations. We conducted global leave-one-out cross-validation (LOOCV), drug-fixed local LOOCV, miRNA-fixed local LOOCV, and 5-fold cross-validation to evaluate the model performance. ELMDA achieved corresponding AUCs of 0.9862, 0.7426, 0.9847 and 0.9928 for Dataset 1 and AUCs of 0.8643, 0.6742, 0.8671 and 0.8521 for Dataset 2, respectively. The results and case studies illustrate the effectiveness of ELDMA in identifying novel drug-miRNA candidates. The top predicted relationships are released for future wet-lab studies.