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Recycling of paper sludge waste is crucial for establishing a sustainable green industry. This waste contains valuable sugars that can be converted into important chemicals such as ethanol, poly hydroxybutyrate, and lactic acid. However, the main challenge in obtaining sugars in high yield from paper sludge is the high crystallinity of cellulose, which hinders hydrolysis. To address this, pretreatment using phosphoric acid was optimized using response surface methodology to facilitate cellulose hydrolysis with minimal energy and chemicals. The created prediction model using the response surface method considered factors such as acid concentration (ranging from 60% to 85%), consistency (ranging from 4% to 10%), temperature (ranging from 25°C to 80°C), and time (ranging from 0.5 to 4 h). The results revealed that the model's significant factors affecting the yield were acid concentration, reaction time, temperature, and the product of acid concentration and temperature, while the model's significant factors affecting the crystallinity were the consistency, the temperature, and their product. The results showed that the optimum conditions for pretreatment were using an acid concentration of 64%, temperature of 25°C, consistency of 10%, and time of 30 min. The hydrolysis of the conditionally pretreated paper sludge resulted in a weight loss of 42%, compared to only 18% weight loss in non-pretreated paper sludge. Furthermore, the optimized conditions led to low levels of furfurals and acetic acid, which are undesirable by-products that can interfere with sugar fermentation. The total sugar obtained under the optimized conditions was 0.43 g glucose/g sample (10.46 g/L), while the contents of methyl furfural, furfural, and acetic acid were 21.65, 235.7, and 4.57 mg/L, respectively. This study demonstrates the potential of phosphoric acid for pretreatment and hydrolysis of paper sludge, enabling efficient saccharification and the production of sugars with minimal undesired by-products. [ABSTRACT FROM AUTHOR]

In the realm of Human-Computer Interaction (HCI), the importance of hands cannot be overstated. Hands serve as a fundamental means of communication, expression, and interaction in the physical world. In recent years, Augmented Reality (AR) has emerged as a next-generation technology that seamlessly merges the digital and physical worlds, providing transformative experiences across various domains. In this context, accurate hand pose and shape estimation plays a crucial role in enabling natural and intuitive interactions within AR environments. Augmented Reality, with its ability to overlay digital information onto the real world, has the potential to revolutionize how we interact with technology. From gaming and education to healthcare and industrial training, AR has opened up new possibilities for enhancing user experiences. This study proposes an innovative approach for hand pose and shape estimation in AR applications. The methodology commences with the utilization of a pretrained Single Shot Multi-Box (SSD) model for hand detection and cropping. The cropped hand image is then transformed into the HSV color model, followed by applying histogram equalization on the value band. To precisely isolate the hand, specific bounds are set for each band of the HSV color space, generating a mask. To refine the mask and diminish noise, contouring techniques are applied to the mask, and gap-filling methods are employed. The resultant refined mask is then combined with the original cropped image through logical AND operations to accurately delineate the hand boundaries. This meticulous approach ensures robust hand detection even in complex scenes. To extract pertinent features, the detected hand undergoes two concurrent processes. Firstly, the Scale-Invariant Feature Transform (SIFT) algorithm identifies distinctive keypoints on the hand's outer surface. Simultaneously, a pre-trained lightweight Convolutional Neural Network (CNN), namely MobileNet, is employed to extract 3D hand landmarks, the hand's center (middle finger metacarpophalangeal joint), and handedness information. These extracted features, encompassing hand keypoints, landmarks, center, and handedness, are aggregated and compiled into a CSV file for further analysis. A Gated Recurrent Unit (GRU) is then employed to process the features, capturing intricate dependencies between them. The GRU model successfully predicts the 3D hand pose, achieving high accuracy even in dynamic scenarios. The evaluation results for the proposed model are very promising that the Mean Per Joint Position Error in 3D (MPJPE) is 0.0596 between the predicted pose and the ground truth hand landmarks, while the Percentage of Correct Keypoints (PCK) is 95%. Upon predicting the hand pose, a mesh representation is employed to reconstruct the 3D shape of the hand. This mesh provides a tangible representation of the hand's structure and orientation, enhancing the realism and usability of the AR application. By integrating sophisticated detection, feature extraction, and predictive modeling techniques, this method contributes to creating more immersive and intuitive AR experiences, thereby fostering the seamless fusion of the digital and physical worlds. [ABSTRACT FROM AUTHOR]

In this study, human-safe lactic acid microorganisms (LAM) were isolated from food samples to be used as potential additives for human food. Samples from various food sources (artisanal cheeses, fermented chickpeas, fermented rice, natural yogurt, pickles, and raw milk) were used to inoculate de Man, Rogosa, and Sharpe (MRS) and malt agar plates, which were incubated at 30°C for 48 h or 37°C for 72 h to isolate lactic acid bacteria and yeast, respectively. Out of 120 isolates (85 bacteria and 35 yeast), 75 isolates showed γ-hemolytic activity and were considered “Generally Recognized As Safe” (GRAS) isolates. After testing their growth rate under the gastrointestinal tract (GIT) conditions, including acidic pH and specific bile salt concentrations, we selected 30 isolates. Then, we evaluated the fermentative abilities of these 30 isolates on nine types of carbohydrates, their total acidity, and their antagonistic activity against five human pathogens. Based on the results of these tests, four isolates were selected for identification using the Biolog program and 16S rRNA sequencing for bacteria and 18S rRNA sequencing for yeast and found to be Bacillus bingmayongesis (FJAT-13831), Lacticaseibacillus paracasei (R094), Pediococcus pentosaceus (DSM 20336), and one yeast isolate as Saccharomyces cerevisiae (SaCe1 26S). [ABSTRACT FROM AUTHOR]