Smart System Research Articles

A DNA-free and genotype-independent CRISPR/Cas9 system in soybean

Abstract Here, we report a smart genome editing system for soybean (Glycine max) using the in planta bombardment-ribonucleoprotein (iPB-RNP) method without introducing foreign DNA or requiring traditional tissue culture processes such as embryogenesis and organogenesis. Shoot apical meristem (SAM) of embryonic axes was used as the target tissue for genome editing because the SAM in soybean mature seeds has stem cells and specific cell layers that develop germ cells during the reproductive growth stage. In the iPB-RNP method, the RNP complex of the CRISPR/Cas9 system was directly delivered into SAM stem cells via particle bombardment, and genome-edited plants were generated from these SAMs. Soybean allergenic gene Gly m Bd 30K was targeted in this study. Many E0 (the first generation of genome-edited) plants in this experiment harbored mutant alleles at the targeted locus. Editing frequency of inducing mutations transmissible to the E1 generation was approximately 0.4 to 4.6 % of all E0 plants utilized in various soybean varieties. Furthermore, simultaneous mutagenesis by iPB-RNP method was also successfully performed at other loci. Our results offer a practical approach for both plant regeneration and DNA-free genome editing achieved by delivering RNP into the SAM of dicotyledonous plants.

Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic

Theoretical mathematics is a key evolution factor of artificial intelligence (AI). Nowadays, representing a smart system as a mathematical model helps to analyze any system under development and supports different case studies found in real life. Additionally, the Markov chain has shown itself to be an invaluable tool for decision-making systems, natural language processing, and predictive modeling. In an Internet of Things (IoT), Machine-to-Machine (M2M) traffic necessitates new traffic models due to its unique pattern and different goals. In this context, we have two types of modeling: (1) source traffic modeling, used to design stochastic processes so that they match the behavior of physical quantities of measured data traffic (e.g., video, data, voice), and (2) aggregated traffic modeling, which refers to the process of combining multiple small packets into a single packet in order to reduce the header overhead in the network. In IoT studies, balancing the accuracy of the model while managing a large number of M2M devices is a heavy challenge for academia. One the one hand, source traffic models are more competitive than aggregated traffic models because of their dependability. However, their complexity is expected to make managing the exponential growth of M2M devices difficult. In this paper, we propose to use a Markov-Modulated Poisson Process (MMPP) framework to explore Human-to-Human (H2H) traffic and M2M heterogeneous traffic effects. As a tool for stochastic processes, we employ Markov chains to characterize the coexistence of H2H and M2M traffic. Using the traditional evolved Node B (eNodeB), our simulation results show that the network’s service completion rate will suffer significantly. In the worst-case scenario, when an accumulative storm of M2M requests attempts to access the network simultaneously, the degradation reaches 8% as a completion task rate. However, using our “Coexistence of Heterogeneous traffic Analyzer and Network Architecture for Long term evolution” (CHANAL) solution, we can achieve a service completion rate of 96%.

Investigation of Innovative High-Response Piezoelectric Actuator Used as Smart Actuator–Sensor System

This paper presents an experimental analysis of a high-response piezoelectric actuator system for the modular design of hydraulic digital fluid control units. It focuses on determining static and dynamic characteristics, forming the basis for developing a smart Industry 4.0 component that incorporates both actuator and sensor function. The design process examines the main challenges, advantages, disadvantages, and working principles to define parameters that impact the actuator’s behaviour and performance. The new piezoelectric actuator system features three piezoelectric stack actuators in series, enabling simultaneous actuation and sensing by applying and measuring the electrical voltage at each piezo element. The experimental setup and test methodology are explained in detail, revealing that the new design, combined with an appropriate open-loop or closed-loop control method, offers superior actuator stroke control, high stroke resolution, and a high-dynamic step response. This paper proposes a concept of a smart piezo actuator system focused on I4.0 and an actuator administration shell, integrated with 5G and RFID technology, which will allow automatic plug-and-play functionality and efficient interconnection, communication, and data transfer between the hydraulic valve and the piezoelectric actuator system.

Coaxial tribonegative yarn TENG with aromatic polyimide as charge entrapment layer for real-time edge ball assessment in cricket sports

Energy harvesting yarns working on triboelectric effect by converting mechanical energy into electricity for running wearable electronics as well as operating as self-powered sensors, are superb choices for wearable electronics of current and future generations. However, current yarn-based triboelectric nanogenerators (TENGs) designed through electrospinning process are limited in achieving higher outputs, comfort, mechanical strength, washability, and industrial scalability. In this study, we introduce a flexible and durable tribonegative yarn TENG (TNY) with an electrospun polyimide (PI) intermediate charge trapping layer between the electrode and electrospun superhydrophobic polyvinylidene difluoride (PVDF)/Poly(dimethylsiloxane) (PDMS). In this coaxial arrangement, the PI charge entrapment layer enabled the TNY to exhibit an output voltage of 14 V in a 2.5 cm length, compared to only 8 V for TNY without PI layer. The TNY can be successfully woven and knitted into electronic textiles (E-Textiles), with their outputs compared based on various knitted and woven patterns. The E-Textile maintained considerable outputs after washing and withstood 5000 Martindale abrasion cycles. The superhydrophobic behavior also enabled TNY to harvest water energy from a running tap. We demonstrated the potential of these E-Textiles for edge ball sensing in cricket sports in real-time by leveraging the benefits of triboelectrification, thereby avoiding the need for complex camera setups and sound systems for judging ball movements at ± 140 km/hr in real sports. We also employed E-Textiles for smart switch functions, which can be beneficial in smart systems. This study demonstrates the improved performance of yarn-based TENGs and further extends the applications of TENGs in sports analysis. It provides a facile and high-efficiency approach for designing triboelectric sensors, offering a cost-effective alternative to complex and expensive sports equipment.

Sustainable Waste Management: Innovations and Best Practices

The expanding worldwide trash challenge, caused by urbanization, rising populations, and financial growth, necessitates innovative and long-term waste management techniques. This article investigates several sophisticated techniques and standards of excellence in the management of green waste, with a focus on the use of cutting-edge technologies, community involvement, and comprehensive policy frameworks. It highlights the waste hierarchy, circular economy, and life cycle thinking as essential components to efficient waste management. Developments in technology such as modern waste-to- energy technologies, sophisticated recycling methods, and smart waste management systems are assessed for their potential contribution to sustainability. The paper also includes case studies from San Francisco, Freiburg, and Kami katsu, which demonstrate successful environmentally conscious waste practices. Furthermore, it emphasizes the importance of legislative frameworks, public-private partnerships, and community involvement in achieving sustainable waste management. Regardless of constant obstacles such as affordability, technological boundaries, behavioral barriers, and regulatory inconsistencies, the article concludes that a strategic combination of innovative technologies, strong policies, and active involvement by the public is essential for addressing the worldwide waste disaster and obtaining a cleaner happier more likely and more sustainable future.

User-centric approach to optimizing thermal comfort in university classrooms: Utilizing computer vision and Q-XGBoost reinforcement learning

The ’one-size-fits-all’ setting of heating, ventilation, and air conditioning (HVAC) systems in university classrooms not only compromises occupant comfort but also leads to potential energy wastage. This study proposes a user-centered approach to optimize the thermal environment in university classrooms by integrating computer vision and Q-XGBoost learning. Computer vision facilitates non-intrusive and real-time sensing of thermal comfort in dynamic university classroom environments. Simultaneously, the Q-XGBoost model identifies optimal operations for HVAC systems by learning from the interactions between occupants and their environment, ensuring a balance between comfort and energy efficiency. These innovative methodologies are harmoniously integrated into a smart air conditioning control box (SACC-Box), achieving intelligent operation of HVAC systems in the real world. Subsequently, a controlled study was conducted to test the user-centered approach’s performance in improving thermal comfort and energy efficiency. The results reveal that classrooms equipped with the SACC-Box significantly improve occupant satisfaction with thermal comfort by approximately 13.5 % while concurrently achieving up to a 5 % reduction in energy consumption compared to classrooms operating with traditional built-in smart control systems. This investigation offers a viable solution to balance energy efficiency and user comfort in university classrooms, underlining the transformative potential of integrating intelligent technologies that inspire a greener, more adaptable, and user-centric futures in higher education establishments.

Solving the imbalanced dataset problem in surveillance image blur classification

Surveillance videos taken in unconstrained environments can be tampered with due to different environmental factors and malicious human activities. They often blur the video content and introduce difficulty in identifying the events in the scene. The problem is particularly acute for smart surveillance systems that need to make real-time decisions based on the video. Automatic detection and classification of the blur anomalies in the video are crucial to these systems. Traditional learning-based classification methods often face imbalance problems in the sample numbers and distributions among the data classes in the dataset that severely affect their training and hence the classification performance. In this paper, a new learning-based approach for surveillance image blur classification is proposed. The imbalanced dataset problem is tackled both at the data and algorithm levels. At the data level, two synthesizers are developed to generate the required negative surveillance images to balance the sample numbers for all classes. At the algorithm level, an attention-based structure making use of the special feature of the minority class is proposed to improve the classification accuracy. Our experiment results show that the proposed approach significantly outperforms state-of-the-art methods for blur classification while keeping the model size small for edge applications.

Decomposing maintenance actions into sub-tasks using natural language processing: A case study in an Italian automotive company

Industry 4.0 has led to a huge increase in data coming from machine maintenance. At the same time, advances in Natural Language Processing (NLP) and Large Language Models provide new ways to analyse this data. In our research, we use NLP to analyse maintenance work orders, and specifically the descriptions of failures and the corresponding repair actions. Many NLP studies have focused on failure descriptions for categorising them, extracting specific information about failure, or supporting failure analysis methodologies (such as FMEA). Whereas, the analysis of repair actions and its relationship with failure remains underexplored. Addressing this gap, our study makes three significant contributions. Firstly, we focused on the Italian language, which presents additional challenges due to the dominance of NLP systems that are mainly designed for English. Secondly, it proposes a method for automatically subdividing a repair action into a set of sub-tasks. Lastly, it introduces an approach that employs association rule mining to recommend sub-tasks to maintainers when addressing failures. We tested our approach with a case study from an automotive company in Italy. The case study provides insights into the current barriers faced by NLP applications in maintenance, offering a glimpse into the future opportunities for smart maintenance systems.

Titanium Dioxide Nanomaterials: Progress in Synthesis and Application in Drug Delivery.

Background: Recent developments in nanotechnology have provided efficient and promising methods for the treatment of diseases to achieve better therapeutic results and lower side effects. Titanium dioxide (TiO2) nanomaterials are emerging inorganic nanomaterials with excellent properties such as low toxicity and easy functionalization. TiO2 with special nanostructures can be used as delivery vehicles for drugs, genes and antigens for various therapeutic options. The exploration of TiO2-based drug delivery systems shows great promise for translating nanotechnology into clinical applications; Methods: Comprehensive data on titanium dioxide were collected from reputable online databases including PubMed, GreenMedical, Web of Science, Google Scholar, China National Knowledge Infrastructure Database, and National Intellectual Property Administration; Results: In this review, we discuss the synthesis pathways and functionalization strategies of TiO2. Recent advances of TiO2 as a drug delivery system, including sustained and controlled drug release delivery systems were introduced. Rigorous long-term systematic toxicity assessment is an extremely critical step in application to the clinic, and toxicity is still a problem that needs to be closely monitored; Conclusions: Despite the great progress made in TiO2-based smart systems, there is still a great potential for development. Future research may focus on developing dual-reaction delivery systems and single-reaction delivery systems like redox and enzyme reactions. Undertaking thorough in vivo investigations is necessary prior to initiating human clinical trials. The high versatility of these smart drug delivery systems will drive the development of novel nanomedicines for personalized treatment and diagnosis of many diseases with poor prognosis.

Smart tools for archaeological survey in different frameworks and contexts: approaches, analysis, results

The world of geomatics today provides new tools, smart and low cost, for archaeological survey, from geographical positioning to detailed drawing and graphic documentation of past evidence. These instruments, light and not bulky, interface directly with tablets, iPads or smartphones via intuitive applications and speed up the data collection in the field: archaeological data collection seems not to be a big issue today and 3D survey appears to be accessible even to less experienced archaeologists. We have tested the Trimble Catalyst DA2 smart system for centimetric georeferencing at the Curiae Veteres in Rome (Italy), at the Sun Temple of Niuserra, in Abu Ghurab (Egypt) and inside the ancient site of Eridu in Iraq: it was a good opportunity to check the accuracy of this tool, especially when associated to detailed and close-range survey activities, related to landscape archaeology and stratigraphic excavations. As we will see, if the instrument cannot replace more precise measurement tools still today (such as total station or laser scanner), in the field of georeferencing can be alternative to more expensive and complex GNSS systems, and it proved to be quite effective in systematic archaeological survey of large areas, especially in association with digital aerial photogrammetry made by UAV-UAS.

Real-Time Monitoring and Control with Wireless Sensor and Actuator Technology

This paper explores the implementation of a smart monitoring system within a wireless sensor network, with a particular emphasis on developing a robust routing framework using the Routing Protocol for Low-power and Lossy Networks (RPL). This protocol, is designed to address the unique challenges of low-power and lossy environments. Our approach involves using a streamlined version of the Representational State Transfer (REST) architecture, implemented through a binary web service. This setup minimizes overhead and maximizes efficiency, which is critical for resource-constrained networks. Additionally, we use a publish/subscribe model, where each node in the network makes its resources—such as environmental sensors—available to other nodes interested in them. This model enhances the flexibility and responsiveness of the network. A significant part of our research involves a detailed performance evaluation of RPL. We conducted a series of experiments to understand how various parameters of the RPL protocol affect its performance in a smart grid scenario. Our analysis looks at key metrics such as routing efficiency, energy consumption, and overall network reliability. Through these experiments, we aim to provide valuable insights into how different configurations of RPL can impact its effectiveness. Our findings are intended to guide the optimization of RPL for specific applications, offering practical recommendations for deploying smart monitoring systems in similar low-power, lossy environments. This research not only sheds light on RPL’s performance but also contributes to the advancement of more efficient and reliable wireless sensor networks for smart grids and other related applications.

SMART CROWD MONITORING SYSTEM USING IOT-BASED YOLO-GHOST

Internet of Things (IoT) devices offer a smart, sophisticated, real-time surveillance solution for public spaces. However, due to instantaneous lighting changes and varying viewing angles, counting and tracking the people in crowded scenes is a challenging problem. To combat these issues, a novel YOLO-CROWD is proposed for a smart crowd-monitoring system using YOLO-GHOST. Initially, an Internet Protocol (IP) based camera was used to monitor and capture crowds in a video sequence. The captured video sequences are converted into frames and passed to the server via the Internet. The recorded frames are given to the YOLO-GHOST classification module to perform people detection and counting. Finally, the detected output results are transferred to the surveillance monitoring center's server. The YOLO-CROWD technique is simulated by using MATLAB. The effectiveness of the proposed YOLO-CROWD technique is assessed using evaluation metrics such as accuracy, precision, recall, sensitivity, F1-score, and mean average precision. The experimental results show that the accuracy of the YOLO-CROWD has increased to up to 99.95 %, proving that its intended use is for accurate crowd detection. The detection accuracy of the proposed method is 84.9 %, 87.58 %, 93.91 %, and 97.72 % better than existing EABeD, LCDnet, CDEM-M, and Public Vision, respectively.

Design and Implementation of Touch Display Accuracy Optimization Software Based on Deep Learning

This paper mainly introduces the touch display accuracy optimization software based on convolutional neural network to enhance the touch accuracy of the smart board system. Convolutional neural networks are used to train touch data, which enables the software to automatically correct the position of touch points and reduce touch bias. The system architecture includes a data acquisition module, a deep learning model training module, and a touch accuracy optimization module, which is expected to be applied to more fields with the continuous application and development of technology. On the whole, the launch of the software provides a new technical way to realize the smart board book system.

Research on Security System of Smart Home Based on ZigBee

In view of the drawbacks of traditional smart home using wired layout, and the problem that it can not accurately transmit and integrate home information, this paper proposes a new type of smart home using ZigBee protocol in wireless sensor network compared with the advantages of traditional smart home. This paper introduces a smart home system based on CC2530 wireless MCU ZigBee network nodes. The remote monitoring system is applied to the smart home environment under the ZigBee protocol, which is used to better deal with the security problems of smart home.

Developing a Machine Learning 'Smart' Polymerase Chain Reaction Thermocycler Part 2: Putting the Theoretical Framework into Practice.

The introduction of PCR into forensic science and the rapid increases in the sensitivity, specificity and discrimination power of DNA profiling that followed have been fundamental in shaping the field of forensic biology. Despite these developments, the challenges associated with the DNA profiling of trace, inhibited and degraded samples remain. Thus, any improvement to the performance of sub-optimal samples in DNA profiling would be of great value to the forensic community. The potential exists to optimise the PCR performance of samples by altering the cycling conditions used. If the effects of changing cycling conditions upon the quality of a DNA profile can be well understood, then the PCR process can be manipulated to achieve a specific goal. This work is a proof-of-concept study for the development of a smart PCR system, the theoretical foundations of which are outlined in part 1 of this publication. The first steps needed to demonstrate the performance of our smart PCR goal involved the manual alteration of cycling conditions and assessment of the DNA profiles produced. In this study, the timing and temperature of the denaturation and annealing stages of the PCR were manually altered to achieve the goal of reducing PCR runtime while maintaining an acceptable quality and quantity of DNA product. A real-time feedback system was also trialled using an STR PCR and qPCR reaction mix, and the DNA profiles generated were compared to profiles produced using the standard STR PCR kits. The aim of this work was to leverage machine learning to enable real-time adjustments during a PCR, allowing optimisation of cycling conditions towards predefined user goals. A set of parameters was found that yielded similar results to the standard endpoint PCR methodology but was completed 30 min faster. The development of an intelligent system would have significant implications for the various biological disciplines that are reliant on PCR technology.

Developing a Machine-Learning 'Smart' PCR Thermocycler, Part 1: Construction of a Theoretical Framework.

The use of PCR is widespread in biological fields. Some fields, such as forensic biology, push PCR to its limits as DNA profiling may be required in short timeframes, may be produced from minute amounts of starting material, and may be required to perform in the presence of inhibitory compounds. Due to the extreme high-throughput of samples using PCR in forensic science, any small improvement in the ability of PCR to address these challenges can have dramatic effects for the community. At least part of the improvement in PCR performance could potentially come by altering PCR cycling conditions. These alterations could be general, in that they are applied to all samples, or they could be tailored to individual samples for maximum targeted effect. Further to this, there may be the ability to respond in real time to the conditions of PCR for a sample and make cycling parameters change on the fly. Such a goal would require both a means to track the conditions of the PCR in real time, and the knowledge of how cycling parameters should be altered, given the current conditions. In Part 1 of our work, we carry out the theoretical groundwork for the ambitious goal of creating a smart PCR system that can respond appropriately to features within individual samples in real time. We approach this task using an open qPCR instrument to provide real-time feedback and machine learning to identify what a successful PCR 'looks like' at different stages of the process. We describe the fundamental steps to set up a real-time feedback system, devise a method of controlling PCR cycling conditions from cycle to cycle, and to develop a system of defining PCR goals, scoring the performance of the system towards achieving those goals. We then present three proof-of-concept studies that prove the feasibility of this overall method. In a later Part 2 of our work, we demonstrate the performance of the theory outlined in this paper on a large-scale PCR cycling condition alteration experiment. The aim is to utilise machine learning so that throughout the process of PCR automatic adjustments can be made to best alter cycling conditions towards a user-defined goal. The realisation of smart PCR systems will have large-scale ramifications for biological fields that utilise PCR.

A Support Tool for Emergency Management in Smart Campuses: Reference Architecture and Enhanced Web User Interfaces.

In the context of smart campuses, effective emergency management is crucial for ensuring the safety and well-being of students, staff, and visitors. This paper presents a comprehensive support tool designed to enhance emergency management on smart campuses, integrating a low-cost people-counting system based on cameras and Raspberry Pi devices. It introduces a newly designed architecture and user interfaces that enhance the functionality and user experience of a smart campus disaster management system. Finally, a usability evaluation has been carried out to validate the brand-new user interfaces devoted to emergency management.

LiPro: displaying concurrent image viewing and imperceptible positioning using visible light

Visible light can be used to achieve effective human interaction with projectors. However, current visible light-based projectors for the target positioning either lose the image viewing function or cause abnormal luminance changes perceived by human eyes, which is not practical for real-world applications. Therefore, we propose a smart lighting-based projector system, LiPro, which can display imperceptible target positioning and any image viewing, simultaneously. In a nutshell, we propose what we believe to be a novel color space conversion modulation scheme to transmit the landmark information, which adds δ and −3δ color intensity value on the red (R) channel and blue (B) channel according to the YCbCr color space analysis, so that human eyes are imperceptible to light intensity changes. Then, we propose the dual-channel decision demodulation scheme, which demodulates the received light signals on both the R channel and B channel at the same time to decide and increase the correctness of the landmark recognition. In the end, we use off-the-shelf devices to implement LiPro to evaluate its performance, and results from extensive experiments show that our LiPro can achieve centimeter-level target positioning under any image viewing conditions.

A hierarchical framework for integration of smart buildings in fully-renewable multi-microgrids and distribution systems: Towards more sustainable societies

Using smart buildings would be a crucial step towards sustainability. Although in the literature, some research effort has focused on integration of smart buildings into power systems; the integration of fully-renewable smart buildings into fully-renewable microgrids and distribution systems has not been addressed before. In this paper, a tri-level hierarchical stochastic framework is developed for the integration of fully renewable smart buildings in fully renewable multi-microgrids (MGs) and distribution systems. The studied smart buildings have electric water heater, air conditioner, PV and battery. In the first level, the model of each smart building is separately solved to find optimal schedule of its resources and the desired electricity exchange with its corresponding MG. In the second level, each MG solves its operational planning model, considering its transactions with the connected smart buildings and finally, in the third level, distribution system operator solves its operational planning model, accepting the electricity exchange requests taken from all MGs. The proposed methodology is applied to a 69-bus 100 % renewable distribution system with 4 100 % renewable microgrids, integrated with 100 % renewable smart buildings. The results confirm the efficacy of the proposed methodology for the integration of 100 % renewable smart buildings in 100 % renewable multi-MGs. The results testify that that building-to-grid (B2G) capability decreases the operation cost of all smart buildings; the average decrease in operation cost of buildings, caused by B2G, is 38 %. The results on the studied case indicate that that the impact of batteries on operation cost of smart buildings is marginal. The results also show that the higher threshold of comfort index in smart buildings, results in their higher operation cost. The results of this research may be helpful for both smart building operators and power system operators. This research is in line with the global energy transition and decarbonisation targets as the considered smart buildings, microgrids and distribution system are fully renewable.

A smart system for student performance assessment (SPA)

The proposed smart system for Student Performance Assessment (SPA) is a system that evaluates students' knowledge and skill attainment in a specific course by measuring their achievements of the Course Learning Outcomes (CLOs). The instructor defines the aspects, weights, and rating scale used by SPA to analyze each course. The system calculates the average of students' marks in each learning outcome and compares them with the CLO targets and scores to determine the effectiveness of the teaching and learning methods used. The system uses facts and rules extracted from the course syllabus and Bloom's Taxonomy to build its knowledge base. This paper presents the development of the SPA inference engine, which is used to find CLO targets based on the course level. The inference engine uses efficient procedures and a prediction process to determine the correct target and score, providing a reliable and understandable methodology for reasoning about the information in the knowledge base and formulating conclusions. SPA is a highly responsive and intelligent system that can be a valuable tool for measuring students' achievements. Its characteristics include high performance, reliability, and intelligibility, and its combination of cognitive systems and cognitive theory has led to remarkable progress in measuring student performance. Limitations include dependency on accurate course content and initial setup time, potential bias in CLO weight assignments, challenges in integrating SPA with existing institutional databases, the need for continuous updates to the knowledge base to reflect curriculum changes, and potential resistance from educators to adopt new technologies. Future improvements could involve adaptive learning integrations, enhanced user interfaces, and broader applicability across diverse educational settings.