Development Of Intelligent System Research Articles

The current research status of Kansei engineering in the field of emotional clothing design

PurposeThis study provides a comprehensive overview of the research landscape of Kansei engineering (KE) within the domain of emotional clothing design. It explores the pivotal technologies, challenges and potential future directions of KE, offering application methodologies and theoretical underpinnings to support emotional clothing design.Design/methodology/approachThis study briefly introduces KE, outlining its overarching research methodologies and processes. This framework lays the groundwork for advancing research in clothing Kansei. Subsequently, by reviewing literature from both domestic and international sources, this research initially explores the application of KE in the design and evaluation of clothing products as well as the development of intelligent clothing design systems from the vantage point of designers. Second, it investigates the role of KE in the customization of online clothing recommendation systems and the optimization of retail environments, as perceived by consumers. Finally, with the research methodologies of KE as a focal point, this paper discusses the principal challenges and opportunities currently confronting the field of clothing Kansei research.FindingsAt present, studies in the domain of clothing KE have achieved partial progress, but there are still some challenges to be solved in the concept, technical methods and area of application. In the future, multimodal and multisensory user Kansei acquisition, multidimensional product deconstruction, artificial intelligence (AI) enabling KE research and clothing sales environment Kansei design will become new development trends.Originality/valueThis study provides significant directions and concepts in the technology, methods and application types of KE, which is helpful to better apply KE to emotional clothing design.

Wafer-scale integration of two-dimensional perovskite oxides towards motion recognition

Two-dimensional semiconductors have shown great potential for the development of advanced intelligent optoelectronic systems. Among them, two-dimensional perovskite oxides with compelling optoelectronic performance have been thriving in high-performance photodetection. However, harsh synthesis and defect chemistry severely limit their overall performance and further large-scale heterogeneous integration. Here, we report the wafer-scale integration of highly oriented nanosheets by introducing a charge-assisted oriented assembly film-formation process and confirm its universality and scalability. The shallow-trap dominance induced by structural optimization endows the device with a distinguished performance balance, including high photosensitivity close to that of single nanosheet units and fast response speed. An integrated ultra-flexible 256-pixel device demonstrates the versatility of material-to-substrate integration and conformal imaging functionality. Moreover, the device achieves efficient recognition of multidirectional motion trajectories with an accuracy of over 99.8%. Our work provides prescient insights into the large-area fabrication and utilization of 2D perovskite oxides in advanced optoelectronics.

Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs

Periapical radiographs are routinely used in dental practice for diagnosis and treatment planning purposes. However, they often suffer from artifacts, distortions, and superimpositions, which can lead to potential misinterpretations. Thus, an automated detection system is required to overcome these challenges. Artificial intelligence (AI) has been revolutionizing various fields, including medicine and dentistry, by facilitating the development of intelligent systems that can aid in performing complex tasks such as diagnosis and treatment planning. The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on periapical radiographs. A dataset comprising 1000 periapical radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists. A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.7–0.73), implants (0.97–0.98), restored teeth (0.85–0.89), teeth with fixed prosthesis (0.92–0.94), and missing teeth (0.82–0.85). The automatic detection by the AI system was comparable to the oral radiologists and may be useful for automatic identification in periapical radiographs.

Theoretical basics of the self-learning system of intelligent locomotive decision support systems

Analysis of works in the field of artificial intelligence allows to make an assumption that today there is a sufficiently developed theoretical basis for the development of intelligent control systems for locomotive control. This will minimize the risks associated with the human factor on the railways. The paper presents the theoretical rationale for the development of a knowledge base for intelligent locomotive control systems. The approach and structure of the self-learning system of intelligent DSS is proposed, the advantage of which is the presence of a fuzzy classifier that works according to the set criteria and determines a fuzzy image of the current train situation. Learning a fuzzy classifier consists in finding a vector K that minimizes the distance between the results of logical inference and experimental data from the sample. The knowledge base is implemented using linguistic variables formalized by methods of fuzzy logic. The use of linguistic values makes it possible to design the base using the usual language of communication, which greatly simplifies both the design process itself and the analysis of the system's performance. Also, the knowledge base has the possibility of constant self-improvement. This happens in two ways. The first is by adding new rules to the knowledge base in case the current situation does not match the existing ones in the base, in which case an additional rule is created and checked for adequacy. The second way is a mechanism for ranking rules in the knowledge base. If the control action of the locomotive driver coincided with the recommendation of DSS in the current situation, then the rating of this recommendation (rule) increases, and in the future the rule selection algorithm will choose one or another control action for the current situation that has the highest rating (that is, it has already been verified several times person). The experiment has shown that the use of intelligent DSS has positive results. On average, the DSS made the correct train control decisions faster than the locomotive driver.

A Survey on Classifying Ocular Diseases Using Deep Learning and Machine Learning Techniques

In ophthalmology, using fundus images to identify ocular diseases early may pose challenges for clinicians. Manually diagnosing ocular conditions is time-consuming, difficult, and requires experimentation. As a result, technology was created to help computers differentiate between ocular diseases. It is possible to create a system of this type due to different learning algorithms based on visual capabilities. Recent breakthroughs in deep learning and machine learning have led to the development of intelligent systems that improve accuracy and efficiency in classifying eye diseases. The purpose of this study is to conduct a comprehensive survey of modern systems that classify ocular problems using different methods, including pre-trained deep learning networks, using the Ocular Disease Intelligent Recognition (ODIR) dataset. The goal is to build and train a model that can recognize and classify ocular disorders. Previous research indicates the increasing use of deep learning techniques. CNN-based methods have spread widely in this field, compared to traditional manual procedures, due to their outstanding results. The most prominent deep learning techniques are convolutional neural networks (CNNs), recurrent neural networks (RNNs), and various learning methods for increasing and transferring data. The survey highlights the potential of these systems to enhance classification accuracy and sensitivity while addressing challenges such as data availability, interpretability, and integration with clinical practice.

Application of artificial intelligence technologies at substantiation of effective control algorithms for the electrical engineering complex of urban electric transport traction electrical equipment

Background. Studies on the current state and development trends of urban ground public electric transportation in Russia highlight the urgent need for innovative technologies. These innovations should focus on designing and operating new types of rolling stock, traction electrical equipment, and promising types of electric traction. At the same time, special attention should be paid to building autonomous control systems for electric transportation using artificial intelligence technologies. Aim. This study aims to explore the application of neural networks to develop algorithms for effectively controlling the electrical engineering complex of traction electrical equipment in urban ground rail electric transport. Materials and Methods. The research utilized data from studies on the traction electrical equipment modes of rolling stock. These studies were conducted through both computer simulations and field experiments under real operating conditions of urban electric transport using different control algorithms. By applying probability theory and mathematical statistics, the analysis identified the correlations between operational and energy parameters of rolling stock movement and the operational mode of its traction electrical equipment. Results. The identified correlation dependencies informed the design of an effective network architecture, including its size and complexity, as well as the composition of their training samples. This led to the development of an original, simplified algorithm for determining effective control parameters for the electrical complex of traction electrical equipment during the movement of a vehicle on a given section of track. Conclusion. The research concluded that using “simple” neural networks for calculating the parameters of effective control of traction electrical equipment operation modes in urban electric transport provides higher speed and sufficient accuracy compared to complex neural network models. These results are valuable for developers of intelligent control systems for streetcar transportation.

Enhancing Cooperative Lane-Changing for Intelligent Connected Vehicles: A DDPG-Based Approach

Current research on lane-changing for intelligent connected vehicles (ICVs) primarily focuses on the control of a single ICV. Even studies on cooperative lane-changing among multiple ICVs often use fixed control algorithms, which are difficult to adapt to constantly changing traffic conditions. This paper proposes a method based on the deep deterministic policy gradient (DDPG) model. The DDPG algorithm has strong generalization capabilities, enabling it to handle complex traffic scenarios. Combined with the design of innovative reward functions, it successfully achieves the control of cooperative lane-changing for multiple ICVs. The novelty of this model is mainly reflected in the implementation of multiple ICVs control and cooperative strategies through the design of a speed reward function. Specifically, when two or more ICVs come within a 15-m range, a collaborative strategy is implemented. In this strategy, the original speeds of the vehicles involved are replaced with their average speed. This average speed is then used to compute the velocity reward. Moreover, an additional headway reward is introduced when two vehicles are on the same lane with a distance of less than 10[Formula: see text]m between them, further optimizing lane-changing decisions and enhancing driving efficiency. The innovative approach described in this paper has been validated through simulations in the highway-env simulation environment. The model’s effectiveness was tested under various conditions, including different road densities and numbers of ICVs. Results indicate that with an increasing number of episodes, the model consistently enhances both the fleet’s and each individual vehicle’s average speed and steps. It also ensures that the headway is maintained within a desired range of 10–20[Formula: see text]m. This paper showcases the potential applications of a new DDPG-based multi-networked vehicular cooperative lane-changing decision model across different scenarios. These findings provide important reference values for future research and development in intelligent transportation systems (ITS), particularly in optimizing vehicle cooperative behaviors and enhancing traffic efficiency. This paper emphasizes the model’s potential to significantly impact the management and operation of future vehicular networks, paving the way for more sophisticated and efficient transportation systems.

Effective knowledge representation and utilization for sustainable collaborative learning across heterogeneous systems

AbstractThe increasingly decentralized and private nature of data in our digital society has motivated the development of collaborative intelligent systems that enable knowledge aggregation among data owners. However, collaborative learning has only been investigated in simple settings. For example, clients are often assumed to train solution models de novo, disregarding all prior expertise. The learned model is typically represented in task‐specific forms that are not generalizable to unseen, emerging scenarios. Finally, a universal model representation is enforced among collaborators, ignoring their local compute constraints or input representations. These limitations hampers the practicality of prior collaborative systems in learning scenarios with limited task data that demand constant knowledge adaptation and transfer across information silos, tasks, and learning models, as well as the utilization of prior solution expertise. Furthermore, prior collaborative learning frameworks are not sustainable on a macro scale where participants desire fairness allocation of benefits (e.g., access to the combined model) based on their costs of participation (e.g., overhead of model sharing and training synchronization, risk of information breaches, etc.). This necessitates a new perspective of collaborative learning where the server not only aggregates but also conducts valuation of the participant's contribution, and distribute aggregated information to individuals in commensurate to their contribution. To substantiate the above vision, we propose a new research agenda on developing effective and sustainable collaborative learning frameworks across heterogeneous systems, featuring three novel computational capabilities on knowledge organization: model expression, comprehension, and valuation.

The impact of narcissistic leadership on organizational support: A study based on a sample of strategic emerging industries

AbstractWith the development of new science and technology, more and more strategic emerging industries have grown up close to the development of science and technology. Companies in these industries no longer emphasize the unilateral loyalty and commitment of employees to the organization but focus on building mutual loyalty and trust between the organizations and employees. Based on the leadership‐member exchange theory and organizational support theory, the study establishes a model of the influence mechanism of narcissistic leadership on organizational support and puts forward hypotheses. Using the questionnaire survey method, from the eight major fields involved in high and new technologies: electronic information, biology and new medicine, aerospace, new materials, high‐tech services, new energy and energy saving, resources and environment, advanced manufacturing and automation, and conducts empirical research on the 228 valid questionnaires recovered in a group manner. The relationship between narcissistic leadership and organizational support is explored at different power distance levels. Empirical results show that at low power distances, narcissistic leadership is negatively correlated with leadership identification. At high power shifts, narcissistic leadership is positively correlated with leadership identification. Regardless of the level of power distance, leadership identification will positively affect organizational support, and leadership identification mediates the relationship between narcissistic leadership and organizational support. This study has contributed to the study of narcissistic leadership and organizational support and has given relevant recommendations in combination with management practices. This study can help to understand the internal operation mode of the organization and provide theoretical support for system science. Besides, it guides the development of intelligent systems to evaluate the impact of leaders' behaviours accurately.

Development of a Controllable Intelligent Drug Delivery System for Efficient Treatment of Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a complex inflammatory disease of the joints, which is often accompanied by degeneration of articular cartilage and bone erosion, seriously affecting the quality of life and psychological state of patients. RA is difficult to be cured completely, and currently the main purpose of relief is through the use of anti-inflammatory and antirheumatic drugs, hormones, and biological agents. Tofacitib is a new type of small molecule inhibitor, which has a good effect in the treatment of RA. The current direct drug delivery method has serious side effects caused by the systemic distribution of the drug, so there is a need to develop an intelligent drug delivery system to realize precise treatment. In this work, tofacitib, gallic acid, targeted molecule folic acid, and Fe(III) were selected to assemble a novel type of artificial controllable nanodrug GF-TF. The self-photoacoustic/magnetic resonance imaging (self-PA/MRI) monitored the enrichment of GF-TF in the lesion in real-time, and artificially regulated the addition of deferoxamine (DFO) at the optimal enrichment. DFO strongly chelates Fe(III) in GF-TF and causes its structure to disintegrate gradually, and the self-PA/MRI signal of GF-TF became weaker while tofacitib began to be released, thus realizing the precise and artificially controlled release of the drug under the guidance of imaging. This nanodrug not only achieves efficient aggregation of drugs in inflamed joints, but also achieves real-time monitoring and precise control of drug release through self-PA/MRI, providing a new strategy for the precise treatment of RA.

Research on the Development of Intelligent Game Dialogue System and Game Experience Based on ChatGPT

Research on the Development of Intelligent Game Dialogue System and Game Experience Based on ChatGPT

On-device Training: A First Overview on Existing Systems

The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed the design and development of various intelligent systems over wide application domains. While most existing machine learning models require large memory and computing power, efforts have been made to deploy some models on resource-constrained devices as well. A majority of the early application systems focused on exploiting the inference capabilities of ML and DL models, where data captured from different mobile and embedded sensing components are processed through these models for application goals such as classification and segmentation. More recently, the concept of exploiting the mobile and embedded computing resources for ML/DL model training has gained attention, as such capabilities allow (i) the training of models via local data without the need to share data over wireless links, thus enabling privacy-preserving computation by design, (ii) model personalization and environment adaptation, and (iii) deployment of accurate models in remote and hardly accessible locations without stable internet connectivity. This work summarizes and analyzes state-of-the-art systems research that allows such on-device model training capabilities and provides a survey of on-device training from a systems perspective.

Automatic target detection in vehicle images based on YOLOv10 deep learning algorithm

In this paper, we explore the effect of its application in vehicle image detection based on the latest YOLOv10 model.YOLOv10, as the latest version in the YOLO series, inherits and optimises the advantages of the previous generations of models, aiming to provide higher detection accuracy and faster inference speed. In our experiments, we firstly divided the dataset reasonably and trained the YOLOv10 model with data from the training set. By analysing the Precision-Recall curve, we found that the area below the PR curve is larger, which indicates that the AUC value of the model is close to 1, thus reflecting the superiority of the model in classification performance. In addition, from the F1-confidence curve, the model is still able to maintain good classification results at higher confidence levels and maintains high F1 scores in most confidence intervals, which further validates the reliability of the model. Test results show that the trained YOLOv10 model is able to accurately recognise vehicles and effectively predict vehicle types. This enables the model to monitor the vehicle conditions on the road in real time and achieve accurate detection and classification of road traffic targets. Therefore, this paper demonstrates the powerful ability of YOLOv10 in the field of vehicle detection, which provides an important reference for the development of future intelligent transport systems. Overall, this study not only verifies the effectiveness of YOLOv10 in practical applications, but also lays the foundation for further improvement of target detection technology.

Enhancing the correlation between azimuth and Doppler frequency for speed estimation of nearby tangential targets

With the development of intelligent transportation systems, traffic supervision radar with wide coverage plays a crucial role in traffic management and vehicle-road coordination. The correlation between Doppler frequency and azimuth has been widely validated in wide coverage traffic supervision radar for high-precision velocity measurement. However, angular glint and noise of the nearby targets lead to a decrease in correlation between the azimuth and Doppler frequency, which negatively impacts the accuracy of velocity estimation. Currently, adopting separate filtering strategies for target azimuth and Doppler frequency has limited performance in enhancing correlation. This paper presents a joint observation model for azimuth and Doppler frequency to achieve the extraction of interrelated components from subspaces, which improves the accuracy of velocity measurement. The effectiveness of this approach is validated using data obtained from X-band and Ku-band sensors.

Bridge damage location and quantification under the moving vehicle loads based on deep learning multi-objective regression

The extraction of damage-sensitive features based on deep learning for the vibration response caused by vehicle-bridge interaction (VBI) has become a mainstream method in damage identification. However, most studies remain at the stage of qualitative damage assessment, not achieving precise numerical quantification of damage. They also typically involve multiple frequent runs using only a single dedicated vehicle. In this study, utilizing a deep neural network designed for multi-objective regression tasks, a novel method for bridge damage localization and quantification is presented with a multi-objective regressor designed to create a direct mapping between the curve of bridge deflection in time domain and the damage information matrix. The final quantitative damage values are obtained through statistical analysis. The method is evaluated on a simulated data set generated from VBI simulation using various 3D heavy vehicle models. The results demonstrate that this method can accurately locate and quantify damage even with unseen vehicle load conditions, damage scenarios, and measurement noise. The structure and depth affect the effectiveness of extracting damage-sensitive features from time-domain deflection during training. The study shows potential for real-time damage identification under normal operating conditions of real bridges in the rapid development of intelligent transportation systems.

Application of Adaptive Machine Learning in Non-Stationary Environments

In the current data-driven era, the application of adaptive machine learning in non-stationary environments has become particularly important. This paper explores the basic concepts of adaptive machine learning and its applications in financial market forecasting and industrial equipment monitoring, demonstrating its superior performance in high-noise, dynamic environments. The research results indicate that adaptive machine learning models significantly improve prediction accuracy, response speed, and robustness through strategies such as online learning and incremental learning. In financial market forecasting, the mean squared error (MSE) was reduced to 0.015, and the fault detection accuracy in industrial equipment monitoring increased to 95%. The paper also proposes future research directions, including multi-source data fusion, anomaly detection, computational efficiency enhancement, and model interpretability. Adaptive machine learning technology not only enriches the theoretical framework of machine learning but also provides new solutions for practical applications in various fields, paving the way for further development of intelligent systems.

Investigation on lightweight identification method for pavement cracks

Mastering a lightweight method for the identification of pavement surface cracks plays a vital role in enhancing the efficiency and accuracy of pavement crack detection. However, the existing crack detection methods typically concentrate on improving model performance while neglecting the study of lightweight identification methods, causing inefficient crack detection. Therefore, this study aims to investigate a lightweight identification method for pavement cracks. High-definition vehicle-mounted cameras were employed to collect pavement crack images across multiple districts in Shenzhen, China, resulting in a dataset containing 20256 images. In addition, a pavement crack detection method based on the You Only Look Once version 8 (YOLO v8) model was proposed. On this basis, a knowledge distillation model enhanced by multiple teacher-assistants (KDMTA) was established to explore the impact of different teacher assistants and distillation paths on model performance. The results indicate that in the proposed KDMTA model, each level of teacher-assistant (TA) model can absorb diverse aspects of knowledge from the teacher models, thus broadening the range of insights. Subsequently, the information is imparted to lower-level TA models and the student model, expanding the perspective from which the student model acquires knowledge. Therefore, this improves the model's generalization performance and its effectiveness in target detection. The type and number of teacher assistant models, as well as the distillation path, influence the model performance. The random learning strategy enriches the combination and transfer of knowledge among teacher models, TA models, and student models, achieving lightweight identification for pavement crack. According to the results, the crack identification accuracy reaches 95.79 %, the mAP reaches 81.07 %, and the image processing speed has been improved by 79.6 %. This study enhances the efficiency and accuracy of pavement crack detection, providing methodological support for the rapid and accurate identification of pavement cracks. This study offers a novel method for the rapid and accurate identification of pavement cracks, which can assist management authorities to timely identify and repair pavement defects, reducing maintenance costs, and extending the lifespan of roads. Moreover, it can also facilitate the development of road safety management and intelligent transportation systems.

Optimizing safe and sustainable public transit for wellness tourism: Southeast Asian case studies

This study presents the development of the Reinforcement-Learning Multi-Objective Artificial Multiple Intelligence System (RL-MOAMIS) framework, aimed at optimizing public transport systems specifically designed for wellness tourism in Southeast Asia. The framework is tailored to enhance the safety, sustainability, and economic viability of bus routes that connect key wellness destinations across Thailand, Laos, and Vietnam. By integrating safety and environmental sustainability principles through a mixed-integer programming model, the RL-MOAMIS framework introduces two innovative improvement methods inspired by metaheuristic operators and employs a reinforcement-learning-based approach to dynamically select enhancement strategies. The empirical results demonstrate that the RL-MOAMIS framework significantly outperforms conventional methods, such as the Genetic Algorithm and Whale Optimization Algorithm, achieving a 27.45% increase in optimization efficiency, a 15% improvement in safety metrics, and a sustainability index enhancement ranging from 7.35% to 13.48% across all routes. The findings underscore the potential of RL-MOAMIS as a comprehensive and adaptable solution for optimizing public transport in wellness tourism. These improvements not only provide safer and more sustainable transportation options but also support economic development in rapidly urbanizing regions. The implications of this study are significant for policymakers, urban planners, and tourism developers, offering critical insights for the design and implementation of public transport systems that cater to the unique demands of wellness tourism.

Knowledge transfer, not just true beliefs: virtue epistemology as a philosophical basis for education

Introduction. Modern pedagogical practice is being reformed due to systemic changes in the ways of social interaction. Knowledge transfer is being significantly modified under the influence of technical innovations, although it remains traditionally one of the main tasks of the educational process. The development of artificial intelligence, neural networks and other analytical computer systems shows that propositional knowledge is not the privilege of the human mind. This state of affairs changes the idea of knowledge as such. In this regard, the participants of the educational process face the task of revising the philosophical basis of modern learning in terms of finding a new definition of knowledge. It is also necessary to develop new methodological recommendations that would correspond to modern epistemological theories of knowledge, belief and understanding. Materials and research methods. The article uses the methods of conceptual analysis and comparative study characteristic of philosophy. The research materials are sources on theory of knowledge, education and also on applied epistemology. Results. Virtue epistemology is one of the most actively developing theories of knowledge in contemporary philosophy and humanities. It is based on the idea that knowledge cannot be an accidental belief, but must be the merit of a cognitive agent. This approach solves many problems of the philosophical theory of knowledge and creates a subjectoriented view of the nature of the cognitive process. This attitude allows us to use virtue epistemology in debates about applied issues, including those related to the explanation of the tasks and content of education. Discussion. A feature of the analysis within virtue epistemology is attention to cognitive skills (reliabilism) and sustained intellectual dispositions (responsibilism) in their relationship to the process of formation of true beliefs. Virtue epistemology defines knowledge through the notion of merit in the manifestation of subjective qualities, so the student's motivation and the context of belief formation should be taken into account when developing specific methodological recommendations. Such an approach explains why standardized testing cannot serve as the sole and primary means of assessing learners. At the same time, the general semantic peculiarity of the notions of virtue allows us to appeal to examples of individuals as ideal types of certain qualities (this is called exemplarism). At the same time, it is necessary to explain why these traits are the excellences that make their bearer better in epistemic terms. Conclusion. In explaining the knowledge transfer, pedagogy can be based on the philosophical principles of virtue epistemology. Such integration enriches both disciplines: pedagogy gets a conceptual scheme adequate to the modern social and technological situation, and epistemology gets a practical application.

Polyimide aerogel-based capacitive pressure sensor with enhanced sensitivity and temperature resistance

The development of intelligent electronic power systems necessitates advanced flexible pressure sensors. Despite improved compressibility through surface micro-structures or bulk pores, conventional capacitive pressure sensors face limitations due to their low dielectric constant and poor temperature tolerance of most elastomers. Herein, we constructed oriented polyimide-based aerogels with mechanical robustness and notable changes in dielectric constant under compression. The enhancement is attributed to the doping of surface-modified dielectric nanoparticles and graphene oxide sheets, which interact with polymer molecular chains. The resulting aerogels, with their excellent temperature resistance, were used to assemble high-performance capacitive pressure sensors. The sensor exhibits a maximum sensitivity of 1.41 kPa−1 over a wide working range of 0-200 kPa. Meanwhile, the sensor can operate in environments up to 150°C during 2000 compression/release cycles. Furthermore, the aerogel-based sensor demonstrates proximity sensing capabilities, showing great potential for applications in non-contact sensing and extreme environment detection.