Microbiome management is becoming an increasingly interesting strategy for soil bioremediation and the substitution of chemical fertilizers. However, variations between in-lab experimentation and field testing have demonstrated that understanding interactions within the microbiome is crucial for the success of synthetic consortia applications.

Current approaches to restoring soil properties in our local environment are limited to reforestation and traditional soil retention practices. To integrate microbiome management and harness beneficial dynamics for soil restoration, this study aims to dissect interactions among the soil microbial community from a forest area in the south of Nuevo León, Mexico, affected by wildfire.

In this study, we evaluated a bioinformatic pipeline to identify, characterize, and select key taxa within the bacterial communities of soil samples from burned and unburned areas. Using QIIME2, the workflow employs sequences of the molecular marker 16S to identify community taxonomic composition. Subsequently, with PICRUSt2, we integrated abundances with genetic and enzymatic information from publicly available data to predict metabolic pathways in the community. We then used a statistical method for sparse data to infer the ecological network. We expect that identifying core species of the post-fire microbiome will allow us to harness their metabolic potential for bioremediation.

Finding simplified and accurate pipelines for the analysis of soil microbial communities is essential to accelerate ecological characterizations and optimize expenses in strain isolation, which represents an advantage for budget-limited research. This work also establishes a foundation for harnessing key members of the local soil microbiome, which is crucial for future investigations into soil bioremediation and reforestation.

This paper introduces a novel method for time series forecasting by leveraging Quantum-inspired Non-dominated Sorting Genetic Algorithm II (QNSGA-II) to optimize Echo State Networks (ESN), complemented by the evaluation of reservoir dynamics through the Separation Ratio Graph (SRG). The integration of QNSGA-II enables the simultaneous optimization of multiple ESN hyperparameters, aiming to reduce forecast error while enhancing the diversity and performance of the reservoir. SRG is employed as a metric to assess the quality of the reservoir's internal states, allowing for the identification of configurations that improve the model's ability to capture the complex dynamics inherent in time series data.

The proposed approach is validated using the Mackey-Glass time series dataset, a benchmark known for its nonlinear dynamics. Results show that the QNSGA-II optimized ESN with SRG evaluation significantly outperforms traditional ESN models, yielding a lower Mean Squared Error (MSE) in predictive performance. Additionally, the use of SRG offers deeper insights into reservoir behavior, facilitating more informed decision-making in the selection of optimal configurations.

The combination of QNSGA-II and SRG not only enhances the robustness of the ESN but also provides a comprehensive framework for improving the accuracy and reliability of time series forecasting. This method represents a step forward in leveraging quantum-inspired optimization techniques for neural networks, demonstrating the potential of hybrid approaches in addressing the challenges of nonlinear and chaotic time series prediction.

Context: Autism Spectrum Disorder (ASD) is a developmental disorder that affects communication, social interaction and behaviour. By building a machine learning model that predicts the probability of ASD through certain behaviours, demographic information and clinical history. We will be able to contribute to moving forward with getting a diagnosis for individuals with ASD even earlier. The study and resulting neural network that exists were created to be a universally available, scalable approach that can help with early diagnosis in both clinical and non-clinical situations. Objective: The main objective of this project is to build a comprehensive AI-based model for the early detection of ASD. Our approach is designed to augment early intervention efforts using a cloud-based web interface and machine learning techniques that deliver insights in an easy-to-use manner. Methods: The dataset used for the study was the "Autism Dataset for Toddlers". High-dimensional assessment of ASD traits was done using several machine learning techniques, like K-Nearest Neighbors (KNN), Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), XGBoost and LightGBM. We created and evaluated the performance of the models using accuracy, precision, recall, F1-score and ROC AUC after feature selection-based techniques such as ANOVA-SVM. Results: XGBoost was the best classifier as it had 99.6% accuracy and ROC AUC was even better than the Decision Tree, and Random Forest even though they achieved an accuracy of 98.8%. With a close 98.10% with Support Vector Machine followed up, with K-Nearest Neighbors at 96.68%. Because the system runs on a cloud-based interface, this processing occurs in real time and enables early ASD screening. Altogether, our XGBoost model holds great potential for early autism screening as it provides a viable option for both clinicians and families.

Drying agricultural crops is essential for preserving them and extending their shelf life. Incorporating drying technology in food production has improved product quality and helped meet increasing food demands. Corn (Zea mays) is a major crop grown in Southeast Asia, used for food and livestock. The preservation of crop grains, such as rice and corn, heavily relies on efficient drying processes. Common corn varieties like sweet corn, wild violet corn, waxy corn, white corn, purple corn, and young corn are cereal grains that are often dried for various food products. The study of the drying kinetics of these crops is crucial, because drying parameters significantly impact the drying process. This review article discusses various factors affecting drying, including airflow, temperature, relative humidity, sample size, and initial moisture content. Understanding these parameters helps optimize the drying process to achieve better quality and efficiency. The review also examines several mathematical models that are used to describe drying kinetics. Models such as the Weibull and Peleg models, Midilli Kucuk model, and Page and Modified Page models are analyzed for their effectiveness in evaluating design parameters. These models provide a scientific basis for improving drying techniques and ensuring consistency in food production. By presenting a comprehensive review of these aspects, this review aims to enhance the understanding of how to utilize drying technology effectively in food manufacturing and preservation, which can be vital for developing better preservation methods, improving product quality, and ultimately meeting the growing food demands.

This research paper introduces Accessible Vision as an innovative assistive technology meant to improve the independence and reliance of people with visual impairments on context-aware assistive technologies by employing intricate computer vision algorithms. This system is composed of the following functional components: YOLOv8 model for real-time object detection, MiDaS for distance measurement together with stereo vision, and a TTS for real-time audio feedback for the blind. Precisely, this helps visually impaired people to achieve an improved picture of their environment by giving them accurate information on the objects in the vicinity and their relative position in space. The main focus of Accessible Vision is to respond to the unique difficulties that people with vision impairment have to face in order to make it through daily environments. Many conventional assistive devices are not capable of delivering the processing of real-time features nor how accurate they are regarding the recognition of objects and space. Since YOLOv8 yields high performance, our approach enables the recognition of numerous objects with high speed and recall accuracy. Moreover, for estimating depth information for monocular cameras or for stereo vision, the applicability of MiDaS is again beneficial since distance measurements are critical for orientation. The working procedure of the system has been described in our methodology section, which outlines the following steps: Firstly, the YOLOv8 model\cite{lou2023dc} was trained and optimized on a broad dataset of objects in different settings to increase the algorithms’ adaptability to various conditions. It also provides comparison between MiDaS and stereo vision as well as the geeks and ticks of both approaches under different context. The incorporation of the TTS model is explained in this paper’s context, with a focus on its function of availing satisfactory and contextually relevant sound prompts to the user.

For a lot of microbrewers, the use of lactic acid bacteria for sour beer production could be an interesting product differentiation strategy. However, the subsequent difficulties in containing contaminations between products could lead to problems regarding product stability and batch reproducibility. The potential use of non-Saccharomyces yeast for sour beer production can be an interesting alternative. In this study, the strain L. thermotolerans was used for its ability to lower the pH during fermentation. The yeast was tested with an all-grain wort of an original gravity (O.G.) of 12 ^oP and 5.1 pH, under different conditions like temperature (13, 18, 24, 30) ^oC and supplemented with glucose (at 16, 20 ^oP). It was shown that L. thermotolerans has a great ability to ferment at different conditions (albeit slower than S. cerevisiae, up to ~14 days at 12 ^oP O.G.) and could lower the pH at ~3.5 by day three. It completed the fermentations in all different temperatures and original gravities. Lower temperatures resulted in longer fermentation periods (~30 days) and higher pH levels (~4.0). Furthermore, higher original gravities did not slow the fermentation rate; to the contrary, the addition of higher amounts of glucose resulted in a more rapid pH drop by day two and lower overall pH (~3.0). In conclusion, L. thermotolerans seems to be a very capable souring yeast that had no negative effect on color, turbidity, foam stability and other beer characteristics. The sensory profile of the produced beers was different depending on the O.G. and fermentation temperatures, but did not exhibit any sensory faults.

Background: According to Earth.org’s report 600 million people in India faced an acute water shortage, which also significantly affects agricultural productivity as the country’s agricultural sector heavily relies on groundwater for irrigation. According to UN News in Breadbasket of India Punjab, groundwater levels have dropped significantly. India’s annual groundwater consumption is 90%. Global Citizen reported groundwater loss in India threatens millions of farmer’s ability to grow. Also, Environmental changes have strained India’s water resources. IoT-based smart irrigation systems can help minimize these challenges by optimizing water usage in agriculture.

Objective: This System’s objective is to optimize the irrigation system for maximum water efficiency and less water wastage and to enhance crop yields. This system helps to save costs by lowering water bills and energy consumption. This system provides Remote monitoring and control for farmers’ convenience. This system is scalable, configurable, and adaptive to another system by real-time data.

Methods/Materials:Soil Moisture Sensors, Temperature and Humidity Sensors, Rain Sensors, and Other Weather data collection sensors are used to collect the data so the system will provide the water when needed. Arduino or Raspberry Pi-like microcontrollers are used to process the data from sensors and control the irrigation valves based on parameters. We have used different machine learning models like SVM, Linear Regression, Decision Tree, Random Forest, Boosting, Bagging, and two hybrid ensemble models LRBoost(linear regression and boosting) and LR²F (linear regression and random forest )to predict the water usage.

Results:So after using different kinds of regression models, we have found that among all the models that we have used Ensemble Liner regression and Random Forest model outperformed the other models by acquiring an accuracy of 96.34% MSE score of 0.0016 and RMSE score of 0.040. So we have chosen the respected model.

This work focuses on the centrality of NLP and TTS in promoting communication for the Urdu-speaking population where there is a dearth of language assets in the regional languages. While English and other languages of European origin have reliable computational assets available, Urdu is still considered relatively illiterate in this aspect, and hence restricted.
Therefore, to address this problem, we constructed our own dataset using audio from a YouTube playlist that contains an Urdu novel reader for more than 100 hours. This dataset was carefully preprocessed for our use and different errors were corrected to provide high-quality input for our TTS models. Our work constitutes one of the first research attempts at creating a large-scale Urdu speech dataset and at employing unique techniques of Automatic Speech Analysis. To achieve this purpose, the linguistic and cultural characteristics of the Urdu language are incorporated in this approach to guarantee that the voices generated are sincere.
In view of this, our project was aimed at developing TTS systems for creating natural voice outputs that take into consideration cultural differences and youthful appeal by building upon well-established neural network models in speech synthesis and by incorporating new techniques.
The results of our work are promising: we also managed to create a TTS model for accurately reading Urdu text, which was also marked to have perfect native-speaker-like pronunciation. These are the practical implications of our research across education, digital accessibility and media, possibly shifting popular culture. What we are trying to achieve is more friendly and natural biometrics for speech interfacing for Urdu users.

As a deadly lung disease, pneumonia remains a leading cause of mortality in children under five years old.
Machine learning, especially deep learning, has played a significant role in improving the detection and identification of various diseases in the field of healthcare.
Neural networks, especially the recent developments in newer architectures, have revolutionized object identification and classification applications in the clinical diagnosis of various diseases.
This study presents the application of Convolutional Neural Networks (CNNs) for the timely and accurate detection of pneumonia using chest X-rays, a development with considerable potential for aiding clinical diagnosis.
This study deployed dropout regularization in model design to mitigate overfitting and relied on recall and F1 scores for thorough model evaluation.
Although comparable studies achieved higher overall accuracy, our models registered a recall rate of 98\%, crucial in reducing false negatives and enhancing patient safety.
This suggests the potential of our CNN model as a vital tool for healthcare professionals in early pneumonia detection in children and adults, with the capacity to process a high volume of X-ray images rapidly and accurately.
The successful construction of our model was enabled by various parameter-tuning techniques, thus enhancing patient care efficiency and the potential to decrease mortality rates.

INTRODUCTION
Olive leaves, a byproduct of olive tree pruning, are considered an industrial residue with significant potential due to their content of bioactive compounds, such as oleuropein and hydroxytyrosol, which exhibit antioxidant, anti-inflammatory, and antimicrobial properties. Traditional extraction methods for these compounds typically require large volumes of solvents, prompting the need for innovative technologies that minimize solvent use and reduce processing times. Conventional methods often involve the use of heat and agitation to enhance the transfer of compounds from olive leaves into the solvent. A new extraction technique has been developed, using high temperature, ultrasound, and vacuum to improve the efficiency of compound extraction.

METHODS
To optimize the process, response surface methodology was employed, evaluating the influence of key variables (% EtOH, temperature, and number of cycles) at three levels (low, medium, high). A total of 27 assays were conducted. The samples were analyzed by HPLC-DAD and Folin-Cocolteau method for the assay of total phenols; DPPH and FRAP methods for antioxidant activity methods.

RESULTS
The highest yield of the extrations of oleuropein and hydroxytyrosol was observed at medium level of temperature, high level of hydroalcoholic solution, and medium level of number of cycles obtaining 43.53 mg/g and 2.73 mg/g for oleuropein and hydroxytyrosol, respectively. In contrast, samples processed without the new technology, yielded 35.08 mg/g and 2.70 mg/g for oleuropein and hydroxytyrosol, respectively. The antioxidant activity, assessed by the FRAP and DPPH methods, showed that untreated samples yielded 1.5 mg TE/g, which increased to 3.31 mg TE/g under conditions of high % EtOH, low temperature, and high flash cycles.

CONCLUSIONS
The optimal combination of variables enhanced the extraction yield by up to 24% for bioactive compounds like oleuropein, compared to unprocessed samples.