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A dynamic cooperation is poised to redefine the limits of athlete safety and performance optimization in the dynamic field of sports science. A new age in sports analysis is promised by the combination of artificial intelligence (AI) and the internet of things (IoT), one in which data-driven insights not only improve our comprehension of athletic performance but also aid to reduce hazards. This academic work explores the complex interactions between AI and IoT in the context of sports. The IoT and AI integration appear to be a strong mix that has the potential to redefine the standards for athlete safety and performance improvement. This study explores the complex interactions between AI and IoT in the field of sports, emphasizing their combined potential for identifying risk factors in a variety of fields. There is a chance to proactively solve sports-related difficulties by utilizing the data-driven capabilities of IoT and the analytical power of AI, opening the door for better informed tactics and decision-making. Through an exploration of this symbiotic relationship, this paper seeks to underline the transformative potential of these technologies in fostering a safer and more performance-oriented sports environment.

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Drilling fluid is pivotal for efficient drilling. However, the gelation performance of drilling fluids is influenced by various complex factors, and traditional methods are inefficient and costly. Artificial intelligence and numerical simulation technologies have become transformative tools in various disciplines. This work reviews the application of four artificial intelligence techniques-expert systems, artificial neural networks (ANNs), support vector machines (SVMs), and genetic algorithms-and three numerical simulation techniques-computational fluid dynamics (CFD) simulations, molecular dynamics (MD) simulations, and Monte Carlo simulations-in drilling fluid design and performance optimization. It analyzes the current issues in these studies, pointing out that challenges in applying these two technologies to drilling fluid gelation performance research include difficulties in obtaining field data and overly idealized model assumptions. From the literature review, it can be estimated that 52.0% of the papers are related to ANNs. Leakage issues are the primary concern for practitioners studying drilling fluid gelation performance, accounting for over 17% of research in this area. Based on this, and in conjunction with the technical requirements of drilling fluids and the development needs of drilling intelligence theory, three development directions are proposed: (1) Emphasize feature engineering and data preprocessing to explore the application potential of interpretable artificial intelligence. (2) Establish channels for open access to data or large-scale oil and gas field databases. (3) Conduct in-depth numerical simulation research focusing on the microscopic details of the spatial network structure of drilling fluids, reducing or even eliminating data dependence.

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The enhancement of display performance and durability in polymer-stabilized vertical alignment liquid crystal and the liquid crystal are negative liquid crystals, which can be vertically aligned under the action of a vertical orientation layer and an electric field. Devices (PSVA LCDs) are crucial for advancing LCD technology. This study aims to investigate the electro-optical characteristics of PSVA LCDs by varying polymerization monomer concentrations. Using both simulations via TechWiz LCD 3D and experimental methods, such as polymer-induced phase separation, we developed an optoelectronic testing framework to assess voltage transmittance and response times. In our main findings, we show that an increase in polymeric monomer concentration from 3% to 7% resulted in a 67% increase in threshold voltage and a 44% decrease in saturation voltage. The on-state response time increased by about a factor of three, while the off-state response time decreased by about a factor of three. The alignment of our simulation results with experimental data validates our methodology, offering the potential of simulation tools as a pivotal resource in the PSVA LCDs. The alignment of our simulation results with experimental data validates our methodology, offering the potential of simulation tools as a pivotal resource in the PSVA LCDs. These advancements promise significant improvements in PSVA LCD performance and durability.

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In this research, SCAPS-1D simulation software (Version: 3.3.10) was employed to enhance the efficiency of CsSnX3 (X = Cl, Br, I) all-inorganic perovskite solar cells. By fine-tuning essential parameters like the work function of the conductive glass, the back contact point, defect density, and the thickness of the light absorption layer, we effectively simulated the optimal performance of CsSnX3 (X = Cl, Br, I) all-inorganic perovskite solar cells under identical conditions. The effects of different X-site elements on the overall performance of the device were also explored. The theoretical photoelectric conversion efficiency of the device gradually increases with the successive substitution of halogen elements (Cl, Br, I), reaching 6.09%, 17.02%, and 26.74%, respectively. This trend is primarily attributed to the increasing size of the halogen atoms, which leads to better light absorption and charge transport properties, with iodine (I) yielding the highest theoretical conversion efficiency. These findings suggest that optimizing the halogen element in CsSnX3 can significantly enhance device performance, providing valuable theoretical guidance for the development of high-efficiency all-inorganic perovskite solar cells.

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The demand for specialized hardware to train AI models has increased in tandem with the increase in the model complexity over the recent years. Graphics processing unit (GPU) is one such hardware that is capable of parallelizing operations performed on a large chunk of data. Companies like Nvidia, AMD, and Google have been constantly scaling-up the hardware performance as fast as they can. Nevertheless, there is still a gap between the required processing power and processing capacity of the hardware. To increase the hardware utilization, the software has to be optimized too. In this paper, we present some general GPU optimization techniques we used to efficiently train the optiGAN model, a Generative Adversarial Network that is capable of generating multidimensional probability distributions of optical photons at the photodetector face in radiation detectors, on an 8GB Nvidia Quadro RTX 4000 GPU. We analyze and compare the performances of all the optimizations based on the execution time and the memory consumed using the Nvidia Nsight Systems profiler tool. The optimizations gave approximately a 4.5x increase in the runtime performance when compared to a naive training on the GPU, without compromising the model performance. Finally we discuss optiGANs future work and how we are planning to scale the model on GPUs.

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In the era of ubiquitous high-throughput sequencing studies, there is a growing need for analysis tools that are not just performant but also comprehensive and user-friendly enough to cater to both novice and advanced users. This article introduces SeqKit2, the next iteration of the widely used sequence analysis tool SeqKit, featuring expanded functionality, performance optimizations, and support for additional compression methods. Retaining a pragmatic subcommand architecture, SeqKit2 represents substantial enhancement through the inclusion of 19 additional subcommands, expanding its overall repertoire to a total of 38 in eight categories. The new subcommands add functionality such as amplicon processing and robust, error-tolerant parsing of sequence records. In addition, three subcommands designed for real-time analysis are added for periodic monitoring of properties of FASTQ and Binary Alignment/Map alignment records and real-time streaming from multiple sequence files. The performance of SeqKit2 is benchmarked against the old version of SeqKit, Bioawk, Seqtk, and SeqFu tools. SeqKit2 consistently outperforms its predecessor, albeit with marginally higher memory usage, while maintaining competitive runtimes against other tools. With its broad functionality, proven usability, and ongoing development driven by user feedback, we hope that bioinformaticians will find SeqKit2 useful as a "Swiss army knife" of sequence and alignment processing-equally adept at facilitating ad hoc analyses and seamlessly integrating into larger pipelines.

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This study focuses on optimizing and designing the Delayed-Fix-Later Awaiting Transmission Encoding (DEFLATE) algorithm to enhance its compression performance and reduce the compression time for models, specifically in the context of compressing NX three-dimensional (3D) image models. The DEFLATE algorithm, a dual-compression technique combining the LZ77 algorithm and Huffman coding, is widely employed for compressing multimedia data and 3D models. Three 3D models of varying sizes are selected as subjects for experimentation. The Wavelet algorithm, C-Bone algorithm, and DEFLATE algorithm are utilized for compression, with subsequent analysis of the compression ratio and compression time. The experimental findings demonstrate the DEFLATE algorithm's exceptional performance in compressing 3D image models. Notably, when compressing small and medium-sized 3D models, the DEFLATE algorithm exhibits significantly higher compression ratios compared to the Wavelet and C-Bone algorithms while also achieving shorter compression times. Compared to the Wavelet algorithm, the DEFLATE algorithm enhances the compression performance of 3D image models by 15% and boosts data throughput by 49%. While the compression ratio of the DEFLATE algorithm for large 3D models is comparable to that of the Wavelet and C-Bone algorithms, it notably reduces the actual compression time. Furthermore, the DEFLATE algorithm enhances data transmission reliability in NX 3D image model compression by 12.1% compared to the Wavelet algorithm. Therefore, the following conclusions are drawn: the DEFLATE algorithm serves as an excellent compression algorithm for 3D image models. It showcases significant advantages in compressing small and medium-sized models while remaining highly practical for compressing large 3D models. This study offers valuable insights for enhancing and optimizing the DEFLATE algorithm, and it serves as a valuable reference for future research on 3D image model compression.

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BACKGROUND: Sports have been a fundamental component of any culture and legacy for centuries. Athletes are widely regarded as a source of national pride, and their physical well-being is deemed to be of paramount significance. The attainment of optimal performance and injury prevention in athletes is contingent upon physical fitness. Technology integration has implemented Cyber-Physical Systems (CPS) to augment the athletic training milieu. OBJECTIVE: The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM). METHODS: The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete's physical fitness status. RESULTS: The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete's physical fitness and overall performance to a considerable extent. CONCLUSION: The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions.

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The incorporation of biologically active metallic elements into nano/micron-scale coatings through micro-arc oxidation (MAO) shows significant potential in enhancing the biological characteristics and functionality of titanium-based materials. By introducing diverse metal ions onto titanium implant surfaces, not only can their antibacterial, anti-inflammatory and corrosion resistance properties be heightened, but it also promotes vascular growth and facilitates the formation of new bone tissue. This review provides a thorough examination of recent advancements in this field, covering the characteristics of commonly used metal ions and their associated preparation parameters. It also highlights the diverse applications of specific metal ions in enhancing osteogenesis, angiogenesis, antibacterial efficacy, anti-inflammatory and corrosion resistance properties of titanium implants. Furthermore, the review discusses challenges faced and future prospects in this promising area of research. In conclusion, the synergistic approach of micro-arc oxidation and metal ion doping demonstrates substantial promise in advancing the effectiveness of biomedical titanium and its alloys, promising improved outcomes in medical implant applications.

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In this study, we used the solar cell capacitance simulator (SCAPS) to analyse numerically the performance of perovskite solar cells (PSCs) containing CH3NH3PbI3. The findings indicate that P-N homologous junction processing based on traditional P-I-N PSCs can enhance the photoelectric conversion efficiency (PCE). Furthermore, the authors analyzed the effect of uniform P-N doping of CH3NH3PbI3, concluding that the photoelectric efficiency can be improved from 16.10% to 19.03% after doping. In addition, the optical properties of PSCs under solar irradiation are simulated using finite difference time-domain (FDTD) software under AM1.5. This method is applied to investigate the effect of the P-N uniform junction on the internal electric field generated within the cell. The generation of this electric field promotes carrier separation and transmission, ultimately increasing the open circuit voltage (VOC) of the solar cell from 1.03 to 1.12 V. The usage of P-N junctions enhances PSCs performance and exhibits vast potential for designing and developing PSCs.

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Silicon transistors are approaching their physical limit, calling for the emergence of a technological revolution. As the acknowledged ultimate version of transistor channels, 2D semiconductors are of interest for the development of post-Moore electronics due to their useful properties and all-in-one potentials. Here, the promise and current status of 2D semiconductors and transistors are reviewed, from materials and devices to integrated applications. First, we outline the evolution and challenges of silicon-based integrated circuits, followed by a detailed discussion on the properties and preparation strategies of 2D semiconductors and van der Waals heterostructures. Subsequently, the significant progress of 2D transistors, including device optimization, large-scale integration, and unconventional devices, are presented. We also examine 2D semiconductors for advanced heterogeneous and multifunctional integration beyond CMOS. Finally, the key technical challenges and potential strategies for 2D transistors and integrated circuits are also discussed. We envision that the field of 2D semiconductors and transistors could yield substantial progress in the upcoming years and hope this review will trigger the interest of scientists planning their next experiment.

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The review discusses the advancements in vermifiltration research over the last decade, focusing on pollution removal mechanisms, system performance, the fate of filter components, and by-products. Vermifiltration has demonstrated remarkable capabilities, particularly in treating highly contaminated wastewater with Chemical Oxygen Demand (COD) levels exceeding 92,000 mg/L and Biochemical Oxygen Demand (BOD5) levels over 25,000 mg/L, achieving removal rates of approximately 89% and 91%, respectively. Importantly, vermifiltration maintains its effectiveness even with fluctuating organic loads at the inlet, thanks to optimization of parameters like Hydraulic Loading Rate, biodegradable organic strength, earthworm density and active layer depth. Clogging issues can be minimized through parameters optimization. The review also highlights vermifiltrations' potential in co-treating the organic fraction of municipal solid waste while significantly reducing heavy metal concentrations, including Cd, Ni, Pb, Cu, Cr, and Zn, during the treatment process. Earthworms play a pivotal role in the removal of various components, with impressive removal percentages, such as 75% for Total Organic Carbon (TOC), 86% for Total COD, 87% for BOD5, 59% for ammonia nitrogen, and 99.9% for coliforms. Furthermore, vermifiltration-treated effluents can be readily utilized in agriculture, with the added benefit of producing vermicompost, a nutrient-rich biofertilizer. The technology contributes to environmental sustainability, as it helps reduce greenhouse gas emissions (GHG), thanks to earthworm activity creating an aerobic environment, minimizing GHG production compared to other wastewater treatment methods. In terms of pollutant degradation modeling, the Stover-Kincannon model outperforms the first-order and Grau second-order models, with higher regression coefficients (R2 = 0.9961 for COD and R2 = 0.9353 for TN). Overall, vermifiltration emerges as an effective and sustainable wastewater treatment solution, capable of handling challenging wastewater sources, while also producing valuable by-products and minimizing environmental impacts.

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Circadian rhythms play a pivotal role in governing various physiological processes, including physical performance. However, in individuals deprived of light perception, such as the blind, these circadian rhythms face disruption. This study aimed to explore the influence of disturbed circadian rhythms on short-term maximal physical performance in children and adolescents with visual impairment. Forty-five volunteers participated in this study, comprising 17 blind, 13 visually impaired, and 15 sighted participants. The participants underwent a series of tests assessing maximal isometric strength performance across two days. To mitigate the influence of morning session fatigue on the evening results, each participant group performed in two separate testing sessions (i.e. in the morning (7:00 h) and in the evening (17:00 h)) on non-consecutive days in a randomized and counterbalanced setting, with approximately 36 h of recovery time between sessions. To mitigate the impact of inter-individual differences on mean values and to account for the influence of age and sex on the studied variables, data were normalized. The outcomes revealed a significant diurnal variation in maximal isometric strength performance among sighted individuals, with peak performance observed in the evening. This pattern aligns with their well-entrained circadian rhythm. In contrast, blind and visually impaired individuals did not display significant diurnal variation, signaling disrupted circadian rhythms due to the absence of light perception. These findings emphasize the crucial consideration of circadian rhythms in assessments of physical performance, especially among participants with visual impairments.

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Developing thick electrodes with high-area loadings is a direct method for boosting the energy density. However, this approach also leads to a proportional increase in the resistance to charge transport. Optimizing the microstructure of the electrode can effectively enhance the charge transport kinetics in thick electrodes. Herein, a low-tortuosity nickel electrode with vertical channels (VC-Ni) is fabricated using a phase inversion method. A high-loading VC-Ni electrode (26.7 mg cm-2) delivers a superior specific capacity of 134.0 mAh g-1 at a 5 C rate, significantly outperforming the conventional nickel electrode (Con-Ni). Numerical simulations reveal the fast transport kinetics within the vertical channel electrodes. For the thick electrode, the VC-Ni electrode shows a substantially lower concentration gradient of OH- and H+ compared to the Con-Ni electrode. Notably, beyond a critical loading of 26.5 mg cm-2, the specific capacity initially increases with volume fraction, peaking at 50%, and then diminishes. The specific capacity increases as the channel size decreases, but the tendency to increase gradually decreases. The highest specific capacity is achieved with an inverted trapezoidal channel shape, characterized by larger pores near the separator and smaller pores near the current collector. This work is of guidance for the design of thick electrodes for high-performance aqueous batteries.

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Atomic layer deposition (ALD) has become the most widely used thin-film deposition technique in various fields due to its unique advantages, such as self-terminating growth, precise thickness control, and excellent deposition quality. In the energy storage domain, ALD has shown great potential for supercapacitors (SCs) by enabling the construction and surface engineering of novel electrode materials. This review aims to present a comprehensive outlook on the development, achievements, and design of advanced electrodes involving the application of ALD for realizing high-performance SCs to date, as organized in several sections of this paper. Specifically, this review focuses on understanding the influence of ALD parameters on the electrochemical performance and discusses the ALD of nanostructured electrochemically active electrode materials on various templates for SCs. It examines the influence of ALD parameters on electrochemical performance and highlights ALD's role in passivating electrodes and creating 3D nanoarchitectures. The relationship between synthesis procedures and SC properties is analyzed to guide future research in preparing materials for various applications. Finally, it is concluded by suggesting the directions and scope of future research and development to further leverage the unique advantages of ALD for fabricating new materials and harness the unexplored opportunities in the fabrication of advanced-generation SCs.

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BACKGROUND: The purpose of this scoping review was to evaluate the current use of technologies in sports settings for training adaptation and injury prevention. The review aimed to map the existing literature, identify key concepts and themes, and highlight gaps in research, thus offering guidance for future studies. METHODS: This study followed the guidelines of the PRISMA extension for scoping reviews and a search in four major databases was conducted. RESULTS: A total of 21 studies were included. The findings highlighted the widespread use of various technologies, including wearable devices and force plates, to monitor athletes' performance and inform evidence-based decision-making in training and injury prevention. Variables such as Player Load, changes of direction, and acute chronic workload ratio were identified as key metrics in injury prediction. CONCLUSIONS: This review uncovers a dynamic field of research in athlete injury prevention, emphasizing the extensive use of varied technologies. A key finding is the pivotal role of Player Load data, which offers nuanced insights for customizing training loads according to sport-specific demands, player positions, and the physical requirements of various activities. Additionally, the review sheds light on the utility of tools like force plates in assessing fatigue, aiding recovery, and steering injury rehabilitation, particularly in sports prone to knee and ankle injuries. These insights not only enhance our understanding of injury prevention but also provide a strategic direction for future research, aiming to boost athlete safety, performance, and career longevity.

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Aquatic animals such as fish and cetaceans can actively modulate their body stiffness with muscle to achieve excellent swimming performance under different situations. However, it is still challenging for a robotic swimmer with bionic propulsion mode to dynamically adjust its body stiffness to improve the swimming speed due to the difficulties in designing an effective stiffness adjustment structure. In this paper, based on the special torque mode of a motor, we propose an active variable stiffness control method for a robotic dolphin to pursue better swimming speed. Different from a variable stiffness structure design, a torque control strategy for the caudal motor is employed to imitate the physical property of a torsion spring to act as the variable stiffness component. In addition, we also establish a dynamic model with the Lagrangian method to explore the variable stiffness mechanism. Extensive experiments have validated the dynamic model, and then the relationships between frequency and stiffness on swimming performance are presented. More importantly, through integrating the dynamic model and torque actuation mode-based variable stiffness mechanism, the online performance optimization scheme can be easily realized, providing valuable guidance in coordinating system parameters. Finally, experiments have demonstrated the stiffness adjustment capability of the caudal joint, validating the effectiveness of the proposed control method. The results also reveal that stiffness plays an essential role in swimming motion, and the active stiffness adjustment can significantly contribute to performance improvement in both speed and efficiency. Namely, with the adjustment of stiffness, the maximum speed of our robotic dolphin achieves up to 1.12 body length per second (BL/s) at 2.88 Hz increasing by 0.44 BL/s. Additionally, the efficiency is also improved by 37%. The conducted works will offer some new insights into the stiffness adjustment of robotic swimmers for better swimming performance.

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The intersection of information technology (IT) and sports psychology has ushered in a transformative era for athlete development and performance optimization. This research study explores the multifaceted role of IT in reshaping the landscape of sports psychology, highlighting its impact on athletes, coaches, and sports psychologists. Beginning with an overview of the historical context of sports psychology, we trace its evolution from traditional methods to data-driven, technologically advanced approaches. We delve into key aspects of IT's role, including data collection and analysis through wearable devices and data analytics, remote counseling and support facilitated by telehealth solutions, cognitive training, and biofeedback using virtual reality and augmented reality, performance analysis and feedback enabled by video analysis software and performance dashboards, and the proliferation of mental health apps and online resources for independent mental skill practice. Communication and collaboration among athletes, coaches, and sports psychologists are vital components of this technological transformation. However, ethical considerations regarding data privacy, cybersecurity, and responsible technology use are also addressed to protection athlete integrity and well-being. As the field continues to evolve, sport psychologists are encouraged to explore emerging technologies such as artificial intelligence and biometric sensors to refine mental training and performance optimization strategies further. The research study concludes by highlighting the promising future where the synergy between human psychology and cutting-edge technology reshapes the boundaries of athletic achievement, ensuring that athletes have the mental resilience and support required to reach new heights in the digital age.(AU)

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This perspective article aims to discuss the usefulness of tools that can assist tennis professionals effectively manage the well-being of their players. This includes identifying and monitoring meaningful metrics (i.e., training load, training intensity, heart rate variability), as well as careful planning of training and competition schedules with appropriate recovery periods. The use of innovative training methods (i.e., repeated-sprint training in hypoxia and heat training), and proper dietary practices, along with biometric assessment for young players, represents should be considered. Adopting a holistic approach to decision-making about training and competition, balancing both health and performance considerations, is crucial for tennis players and their support teams. More research is needed to refine best practices for enhancing tennis performance while prioritizing the well-being of players.

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With a rising emphasis on public safety and quality of life, there is an urgent need to ensure optimal air quality, both indoors and outdoors. Detecting toxic gaseous compounds plays a pivotal role in shaping our sustainable future. This review aims to elucidate the advancements in smart wearable (nano)sensors for monitoring harmful gaseous pollutants, such as ammonia (NH3), nitric oxide (NO), nitrous oxide (N2O), nitrogen dioxide (NO2), carbon monoxide (CO), carbon dioxide (CO2), hydrogen sulfide (H2S), sulfur dioxide (SO2), ozone (O3), hydrocarbons (CxHy), and hydrogen fluoride (HF). Differentiating this review from its predecessors, we shed light on the challenges faced in enhancing sensor performance and offer a deep dive into the evolution of sensing materials, wearable substrates, electrodes, and types of sensors. Noteworthy materials for robust detection systems encompass 2D nanostructures, carbon nanomaterials, conducting polymers, nanohybrids, and metal oxide semiconductors. A dedicated section dissects the significance of circuit integration, miniaturization, real-time sensing, repeatability, reusability, power efficiency, gas-sensitive material deposition, selectivity, sensitivity, stability, and response/recovery time, pinpointing gaps in the current knowledge and offering avenues for further research. To conclude, we provide insights and suggestions for the prospective trajectory of smart wearable nanosensors in addressing the extant challenges.