Multimodal Sentiment Analysis (MSA) represents an advancing research domain focused on identifying sentiment through Audio(A), Video (V), and Text (T) modalities. A significant challenge lies in capturing joint representations by associating information across various modalities with integration techniques. Numerous existing approaches rely on acquiring joint representation through the concatenation of input features without fully exploiting interactions to ensure consistency with complementarity among modalities. To address this problem, a novel framework named Joint Representation with Optimized Transformer (JRT) has been developed to overcome the challenges in multimodal sentiment analysis through hierarchical interactions among modalities. The process begins with a diverse dataset comprising text, audio, and visual data from the CMUMOSI and CMU-MOSEI datasets. Preprocessing steps are performed with normalization, noise reduction, and feature scaling to ensure data quality. Feature extraction is used to isolate meaningful patterns from each modality for further analysis. The framework integrates these features into a unified representation through a Joint Representation Translator (JRT), which aligns heterogeneous data for compatibility using cyclic translation. This translation process captures joint representations of bimodality by translating one modality into another forward with backward passes using encoderdecoders to maintain consistency between modalities. To explore complementarity among modalities, a transformer-based prediction mechanism is optimized with the Adaptive Dragon Optimization Algorithm (ADOA), which strengthens unimodal features with common information extracted from bimodality, enhancing model accuracy and convergence. Extensive experiments conducted with CMU-MOSI and CMU-MOSEI datasets validate the effectiveness of this framework, showing superior performance compared to existing methods. The proposed method achieves 95.11% accuracy (%), 97.51% recall (%), 95.14% F-Score (%), 0.90 correlation coefficient(unitless), 0.051 false positive rate (%), 97% negative predictive value (%), and 0.0489 mean squared error (MSE). These results highlight substantial advancements in accuracy and robustness over traditional approaches.

Bioremediation of pesticides is imperative for a sustainable environment. For this purpose soil borne, pure fungal strains; Aspergillus niger and Penicillium chrysogenum were augmented in soils spiked with herbicide, Chlorsulfuron from four distinct regions of Pakistan. These strains were found to utilize Chlorsulfuron as their carbon and energy sources. Solid-liquid extraction of pesticide was followed by analysis through high performance liquid chromatography and gas chromatography mass spectrometry-SIM (selected ion monitoring mode). The use of chromatographic techniques for analysis of Chlorsulfuron and its transformation products is of paramount importance. The SIM mode enhanced the sensitivity of GCMS momentously, subsequently distinguishing chemicals even in lower detection limits. Chemical hydrolysis experiments, performed on the same soils were also found to degrade Chlorsulfuron (50%) but to a lesser extent than biodegradation (76 and 74% by both strains). Degradation rate followed first order reaction kinetics. Two major metabolites were obtained after degradation; 2-chlorobenzenesulfonamide and 2-amino-4-methoxy-6-methyl-1,3,5-triazine. Aspergillus niger degraded Chlorsulfuron (76%) slightly more than Penicillium chrysogenum (74%). R2 for degradation rates for all soil samples by both fungal strains were close to 1 and P values were less than 0.05, indicating significance of results.

In the last two decades, major car manufacturing companies are exploring the possibilities of joining magnesium with aluminium, via friction welding technique for many crucial automotive applications. Our primary objective, an experimental investigation has been carried out to study the behaviour of dissimilar joints. The microscopic structure at the welded joint interface was analysed using an optical microscopy and scanning electron microscope. It was found that, by increasing the value of friction time, the value of the tensile strength increases and the result of tensile strength is found to be 120 MPa at a friction time of 10 secs. Micro hardness was found to be higher at the interface of the weldment due to the development of a brittle intermetallic compound. Micro structural studies using SEM reveals, distinct zones such as an unaffected parent metal zone, the heat affected zone, a thermo-mechanically affected zone and a fully deformed plasticised zone.

This paper proposes use of high gain Negative self lift luo DC-DC converter to step up the low voltage from PV panel and fed to microgrid system as in marine ships to reduce to demand on fossil fuels and carbon emission control. Negative luo converter has the advantage of smaller size, weight, high frequency switching and stable output over positive luo converters and traditional Boost converters. In this paper the comparison of positive and negative luo converter is done and based on the results, negative output is concluded based on performance.

SS 430 thin plates are extensively used in medical equipment, decorative products and kitchen appliances. In this research work, the 16 straight cutting profiles on SS 430 plates were done by using Nd: YAG laser cutting processes. Experimental works were conducted to examine the influence of processing conditions of laser cutting such as power, cutting velocity and gas pressure on response characteristics like HAZ (heat affected zone) size, recast layer thickness and micro hardness. A novel integrated Taguchi-entropy weighted based Grey relational analysis (GRA) approach has been employed to attain the supreme qualities of laser cutting in this effort. The entropy method is used to measure the weight to each response characteristics. Additionally, ANOVA technique is used to identify the significant process parameters. The obtained results point out that these approaches convincing to optimize the laser cutting practices.

This article presents a new approach to compensation for manufacturing errors in real time. This approach is based on the insertion of the mathematical models into a G-code form in NC programming. Compensated errors in this work are errors due to cutting tool wear, errors due to tool path, and errors due to positioning. We used the lagrange method to model errors due to the trajectory and wear of the cutting tool and the small displacement method for positioning.

The purpose of this study was to examine the effects of warm-up intensity on total oxygen uptake and VO2 kinetics (time constants) during sub¬¬¬maximal exercise in the national female futsal player’s. The participants of this study were ten Players of Iranian futsal national team (mean± SD: age,22.5±3.2yrs;weight,56.04±6.17kg;height,163±3.68cm;bodyfat%,23.5±3.8;BMI, 21.07± 2.22kg/m2; VO2max, 46.05± 4.61 ml.kg-1.min-1 ). After measuring of VO2max and LT in first session, the subjects performed two protocols of warm up (moderate and heavy intensity) and one protocol without warm up before submaximal exercise (80%LT) in three separated sessions. Respiratory gas exchange measured by Gas Analyzer during the exercise. Repeated measures ANOVA and LSD test were applied for statistical analysis. Total oxygen uptakes were not significant difference after three conditions. After moderate and heavy warm up, third time constants significantly reduced in compare to without warm-up (p<0/05). also oxygen uptake/time ratio in third time constants significantly increased after moderate and heavy warm up respect to without warm-up (p<0/05). The results suggest that warm up causes reducing the time of reaching to steady state of O2 uptake in sub maximal exercise and this effect is independent to warm up intensity.

Fuzzy set theory plays a vital role in many fields. There are varieties of models involving fuzzy matrices to deal with different complicated aspects of medical diagnosis. Today is a world of uncertainty with its associated problems, which can be well handled by soft set theory. In this paper, a new technique named fuzzy max-min average composition method is proposed to construct the decision method for the analysis of Students Problems that affects their studies using Intuitionistic fuzzy soft matrices and its operations. Sanchez’s approach for decision making is studied and the concept is generalized by the application of Intuitionistic fuzzy soft set theory.