Ammonia (NH${}_3$) represents a perilous gas that poses a substantial hazard to both human well-being and the environment, particularly within agricultural regions. Agricultural activities constitute a primary source of ammonia emissions. Thus, effective monitoring and measurement of ammonia sources in agriculture are imperative for mitigating its adverse impact. However, not all existing ammonia detection methods are suitable for discerning the low concentrations typically encountered in agricultural ammonia volatilizing (ranging from 0.01 to 5 parts per million). Consequently, curtailing ammonia volatilization from farmland assumes paramount importance, with real-time monitoring serving as a crucial mechanism for assessing environmental contamination and minimizing agricultural ammonia losses. Deploying appropriate detection methodologies ensures that requisite measures are taken to safeguard human health and the environment from the deleterious repercussions of ammonia exposure. The present paper introduces a comprehensive approach to detecting and analyzing ammonia in agricultural settings. It elucidates the merits and demerits of conventional indoor and outdoor ammonia detection methods, juxtaposing them with the innovative technology of Electronic nose (E-nose). Within the paper, seven widely employed ammonia detection methods in farmland are scrutinized and compared against traditional techniques. Additionally, the constructional aspects and distinct components of E-nose are meticulously delineated and appraised. Ultimately, the paper culminates in a comprehensive comparative analysis encompassing all the aforementioned methodologies, elucidating the potential and limitations of E-nose in facilitating ammonia detection endeavors within agricultural contexts.

This investigation is centrally focused on the comprehensive evolution and enhancement of FOODIEBOT, an adaptive service automaton with a wide range of functionalities. Its capabilities encompass sophisticated image processing methods, seamlessly integrated via mobile applications (APP) and web interfaces, tailored specifically for intricate object manipulation in dining hall settings.During its developmental phase, the precise calibration of PID controller coefficients emerged as an essential requirement. The model underwent meticulous scrutiny through detailed simulations using MATLAB software. Following this phase, its operational efficiency navigating through circular, elliptical, spiral, and octagonal trajectories underwent rigorous examination, utilizing optimization methodologies like BAS, PSO, POA, and EO. The exposition emphasizes the diverse dispersion of optimized coefficients within each algorithmic framework. The pinnacle of this effort involved a comprehensive evaluation of pathway performance, amalgamating insights from each optimization paradigm. The discussion extensively delineates both simulated and real-time performance metrics of the robot, validating the accuracy and reliability of simulation in deriving PID controller values.In the comprehensive evaluation of methodologies and the robotic system’s effectiveness, the BAS technique excels in operational efficiency. This method consistently outperforms its counterparts in execution time, primarily due to its meticulous optimization of particle count. The comparative analysis across various trajectories reveals intriguing insights. The EO approach showcases outstanding accuracy in Path 1, while the POA method achieves optimal precision in Path 3. Impressively, the BAS technique demonstrates unparalleled accuracy in Path 4. Furthermore, in terms of solution optimization, the BAS method consistently displays the shortest execution times across all traversed pathways. When examining maximum velocity along these routes, the PSO method excels in Paths 1, 3, and 4, consistently achieving the highest speeds. Notably, Path 2 uniquely displays the peak velocity attained by the POA method.This article presents comprehensive insights into the constituent elements of the robotic system’s design. The inquiry delves into the intricate nuances of optimization methodologies, elucidating their profound impact on the service automaton’s performance across diverse orientations. The pragmatic implications underscore the critical role of temporal considerations in the judicious selection of these methodologies. The observed congruence between simulated and practical performance serves as a definitive validation, affirming the precision of simulation computations and the subsequent derivation of PID controller values.

Background: Sleep is a critical factor in maintaining good health, and its impact on various diseases has been recognized by scientists. Understanding sleep patterns and quality is crucial for investigating sleep-related disorders and their potential links to health conditions. The development of non-intrusive and contactless methods for analyzing sleep data is essential for accurate diagnosis and treatment. Methods: A novel system called the sleep visual analyzer (VSleep) was designed to analyze sleep movements and generate reports based on changes in body position angles. The system utilized camera data without requiring any physical contact with the body. A Python graphical user interface (GUI) section was developed to analyze body movements during sleep and present the data in an Excel format. To evaluate the effectiveness of the VSleep system, a case study was conducted. The participants’ movements during daytime naps were recorded. The study also examined the impact of different types of news (positive, neutral, and negative) on sleep patterns. Results: The system successfully detected and recorded various angles formed by participants’ bodies, providing detailed information about their sleep patterns. The results revealed distinct effects based on the news category, highlighting the potential impact of external factors on sleep quality and behaviors. Conclusions: The sleep visual analyzer (VSleep) demonstrated its efficacy in analyzing sleep-related data without the need for accessories. The VSleep system holds great potential for diagnosing and investigating sleep-related disorders. The proposed system is affordable, easy to use, portable, and a mobile application can be developed to perform the experiment and prepare the results.

The most important element of cultural heritage is the embroidery used in the ancient clothes. Embroidery used in the ancient clothes represents centuries of creative tradition, regional uniqueness, and the passing down of skills from one generation to the next. These elaborate textile practices, which have their roots in manual craftsmanship and oral instruction, are increasingly threatened by industrial production, globalization, and the slow decline of traditional artisans. By developing an automated identification structure based on MobileNetV2, an efficient yet highly effective convolutional neural network (CNN), this work presents an AI approach to support the preservation of regional embroidery. The model used in the paper was trained very carefully with a dataset of 5,200 high-resolution images that included a range of regional embroidery styles using pretrained ImageNet weights. With an identification accuracy of 97.3%, the suggested MobileNetV2 outperformed traditional CNN architectures like VGG16 and ResNet by over 2.3%. The results demonstrate how small AI models can make a significant contribution to the preservation of cultural heritage by providing a scalable method for storing and assessing textile arts for museums, scholars, and craftspeople. This methodology exhibits promise in fields such as digital archives, fashion technology, and cultural data analytics for heritage conservation. Received: 17 August 2025 | Revised: 15 December 2025 | Accepted: 3 March 2026 Conflicts of InterestThe authors declare that they have no conflicts of interest to this work. Data Availability StatementThe data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/ask1999/embroiderydataset. Author Contribution StatementAmir Sohail Khan: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Junjie Zhang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.