Search Results (57)

The integration of biosensors and the Internet of Things (IoT) in food packaging is gaining significant interest in rapidly enhancing food safety and traceability worldwide. Currently, the IoT is one of the most intriguing topics in the digital and virtual world. Biosensors can be integrated into food packaging to monitor, sense, and identify early signs of food spoilage or freshness. When coupled with the IoT, these biosensors can contribute to data transmission via IoT networks, providing real-time insights into food storage and transportation conditions for stakeholders across each stage of the food supply chain, facilitating proactive decision-making practices. The technologies of combining biosensors with IoT could leverage artificial intelligence (AI) to enhance food safety, quality, and security in food industries, compared to conventional existing food inspection technologies, which are limited to assessing weight, volume, color, and physical appearance. This review focused on highlighting the latest and existing advancements, identifying the knowledge gaps in the applications of biosensors and the IoT, and exploring their opportunities to shape future food packaging, particularly in the context of 21st-century food safety. The review also aims to investigate the role of the IoT in creating smart food ecosystems and examines how data transmitted from biosensors to IoT systems can be stored in cloud-based platforms, in addition to addressing upcoming research challenges. Concerns of data privacy, security, and regulatory compliance in implementing the IoT and biosensors for food packaging are also addressed, along with potential solutions to overcome these barriers. Full article

One of the health challenges in the 21st century is to rethink approaches to non-communicable disease prevention. A solution is a smart city that implements technology to make health smarter, enables healthcare access, and contributes to all residents’ overall well-being. Thus, this paper proposes an architecture to deliver smart health. The architecture is anchored in the Internet of Things and edge computing, and it is driven by artificial intelligence to establish three foundational layers in smart care. Experimental results in a case study on glucose prediction noninvasively show that the architecture senses and acquires data that capture relevant characteristics. The study also establishes a baseline of twelve regression algorithms to assess the non-invasive glucose prediction performance regarding the mean squared error, root mean squared error, and r-squared score, and the catboost regressor outperforms the other models with 218.91 and 782.30 in MSE, 14.80 and 27.97 in RMSE, and 0.81 and 0.31 in $R^2$, respectively, on training and test sets. Future research works involve extending the performance of the algorithms with new datasets, creating and optimizing embedded AI models, deploying edge-IoT with embedded AI for wearable devices, implementing an autonomous AI cloud engine, and implementing federated learning to deliver scalable smart health in a smart city context. Full article

As the 21st century advances, the demand for rare earth elements (REEs) is rising, necessitating more robust exploration methods. Our research group is using hyperspectral remote sensing as a tool for mapping REEs. Unique spectral features of bastnaesite mineral, has proven effective for detection of REE with both spaceborne and airborne data. In our study, we collected hyperspectral data using a Senop hyperspectral camera in field and a SPECIM hyperspectral camera in the laboratory settings. Data gathered from California’s Mountain Pass district revealed bastnaesite-rich zones and provided detailed insights into bastnaesite distribution within rocks. Further analysis identified specific bastnaesite-rich rock grains. Our results indicated higher concentrations of bastnaesite in carbonatite rocks compared to alkaline igneous rocks. Additionally, rocks from the Sulphide Queen mine showed richer bastnaesite concentrations than those from the Birthday shonkinite stock. Results were validated with thin-section studies and geochemical data, confirming the reliability across different hyperspectral data modalities. This study demonstrates the potential of drone-based hyperspectral technology in augmenting conventional mineral mapping methods and aiding the mining industry in making informed decisions about mining REEs efficiently and effectively. Full article

The proliferation of solar panel installations presents significant societal and environmental advantages. However, many panels are situated in remote or inaccessible locations, like rooftops or vast desert expanses. Moreover, monitoring individual panel performance in large-scale systems poses a logistical challenge. Addressing this issue necessitates an efficient surveillance system leveraging wide area networks. This paper introduces an Internet of Sensing Things (IoST)-based monitoring system integrated with sun-tracking capabilities for solar panels. Cutting-edge sensors and microcontrollers collect real-time data and securely store it in a cloud-based server infrastructure, enabling global accessibility and comprehensive analysis for future optimization. Innovative techniques are proposed to maximize power generation from sunlight radiation, achieved through continuous panel alignment with the sun’s position throughout the day. A solar tracking mechanism, utilizing light-dependent sensors and servo motors, dynamically adjusts panel orientation based on the sun’s angle of elevation and direction. This research contributes to the advancement of efficient and sustainable solar energy systems. Integrating state-of-the-art technologies ensures reliability and effectiveness, paving the way for enhanced performance and the widespread adoption of solar energy. Additionally, the paper explores anomaly prediction using Rayleigh distribution, offering insights into potential irregularities in solar panel performance. Full article

More than two-thirds of the Earth’s surface is covered by oceans and yet only a small portion of these oceans has been directly explored in detail, highlighting the need for powerful tools like remote sensing (RS) technology to bridge this gap. International frameworks, the 2030 Agenda for Sustainable Development, and Ocean Decade point out the significance of marine areas for achieving sustainable growth. This study conducts a bibliometric analysis of RS over marine areas for sustainable development to identify key contributors, collaboration networks, and evolving research themes from the beginning of the 21st century until last year. Using the Web of Science Core Collection database, 499 relevant articles published between 2000 and 2023 were identified. The bibliometric analysis showed a significant increase in scientific productivity related to the field. On an international level, China emerges as the most productive country, but international collaboration has played a crucial role, with 36.87% of articles resulting from international co-authorship, pointing to the global nature of research in this field. RS technology has continuously evolved from airborne sensors to the augmentation of Earth Observation missions. Our findings reveal a shift towards automated analysis and processing of RS data using machine learning techniques to integrate large datasets and develop robust scientific solutions. Full article

As a consequence of global climate change, sea level rise (SLR) presents notable risks to both urban and natural areas located near coastlines. For developing effective strategies to mitigate and adapt to these risks, it is essential to evaluate the potential impacts of SLR in coastal areas. While substantial research has been conducted on mapping the broad-scale impacts of SLR based on scenarios of Global Mean Sea Level (GMSL), consideration of regional scenarios, systematic classification, and distinct stages of SLR have been largely overlooked. This gap is significant because SLR impacts vary by region and by the level of SLR, so adaptations, planning, and decision-making must be adapted to local conditions. This paper aims to precisely identify the landscape and urban morphology changes caused by the impact of SLR for each foot of elevation increase based on remote sensing technologies, focusing on St. Johns County, Florida, and Chatham County, Georgia. These two counties are both situated along the southeastern coastline of the United States but with completely different urban forms due to distinct historical and cultural developments. Regional forecasting SLR scenarios covering the period from 2020 to 2100 were utilized to assess the landscape transformation and urban changes, incorporating selected landscape and urban metrics to calculate quantitative data for facilitating comparative analyses. This study investigated gradual alterations in urban morphology and green infrastructure both individually and in combination with the effect on wetlands due to SLR. The mapping outcomes of this research were generated by employing comprehensive remote sensing data. The findings of this research indicated that, when the sea level rose to 3 feet, the wetlands would experience notable alterations, and the level of fragmentation in urban built areas would progressively increase, causing most of the metric data to exhibit a pronounced decline or increase. Full article

In the present paper the humidity sensing properties of regioregular rr-P3HT (poly-3-hexylthiophene) polymer films is investigated by means of surface acoustic wave (SAW) based sensors implemented on LiNbO₃ (128⁰ Y-X) and ST-quartz piezoelectric substrates. The polymeric layers were deposited along the SAW propagation path by spray coating method and the layers thickness was measured by atomic force microscopy (AFM) technique. The response of the SAW devices to relative humidity (rh) changes in the range ~5–60% has been investigated by measuring the SAW phase and frequency changes induced by the (rh) absorption in the rr-P3HT layer. The SAW sensor implemented onto LiNbO₃ showed improved performance as the thickness of the membrane increases (from 40 to 240 nm): for 240 nm thick polymeric membrane a phase shift of about −1.2 deg and −8.2 deg was measured for the fundamental (~78 MHz operating frequency) and 3rd (~234 MHz) harmonic wave at (rh) = 60%. A thick rr-P3HT film (~600 nm) was deposited onto the quartz-based SAW sensor: the sensor showed a linear frequency shift of ~−20.5 Hz per unit (rh) changes in the ~5–~50% rh range, and a quite fast response (~5 s) even at low humidity level (~5% rh). The LiNbO₃ and quartz-based sensors response was assessed by using a dual delay line system to reduce unwanted common mode signals. The simple and cheap spray coating technology for the rr-P3HT polymer films deposition, complemented with fast low level humidity detection of the tested SAW sensors (much faster than the commercially available Michell SF-52 device), highlight their potential in a low-medium range humidity sensing application. Full article

Current spatial changes involving broad urban landscapes affect people’s perceptions of their surroundings, sense of place, and attachment to a place, constituting a disruption to these. As a result, on a social scale, they translate into people’s well-being. The following study of the impact of large-scale architectural and urban developments on the place of residence is based on the assumption that physical space determines the quality of life in the living place and the changes in the process condition residents to adapt to their new surroundings—based on the three pillars of place, people, and process (3Ps). The article consists of two parts. The first is theoretical, which conceptualises spatial change based on the theory of human dependence on place. The second part–an empirical study—discusses the transformation of the post-industrial area of Bicocca (Milan), which, 40 years after the intervention, has led to conclusions and recommendations for urban planning. The results demonstrate the different sensitivities of the urban areas to the process of functional–spatial change, the essence of the accessibility of public space, public facilities, and transport infrastructure. The planning process can positively influence social adaptation to spatial change mitigation. Residential areas may be subject to additional protection procedures. The study is relevant to a sustainable planning process in the inevitable transformation of urban areas. The interdisciplinary nature of the issue prompts the integration of research findings and knowledge transfer in the socio-technological subsystem (STS). Full article

Cyanobacterial blooms represent a common environmental issue in aquatic systems, and these blooms bring forth numerous hazards, with the generation of volatile organic compounds (VOCs) being one of them. Global climate change has led to alterations in various climatic factors affecting algal growth, indirectly impacting the quantity of VOCs released by algae. With advancements in remote sensing technology, exploration of the spatiotemporal distributions of algae in large water bodies has become feasible. This study focuses on Taihu Lake, characterized by frequent occurrences of cyanobacterial blooms. Utilizing MODIS satellite imagery from 2001 to 2020, we analyzed the spatiotemporal characteristics of cyanobacterial blooms in Taihu Lake and its subregions. Employing the LightGBM machine learning model and the (SHapley Additive exPlanations) SHAP values, we quantitatively analyzed the major meteorological drivers influencing cyanobacterial blooms in each region. VOC-related source spectra and emission intensities from cyanobacteria in Taihu Lake are collected based on the literature review and are used to compile the first inventory of VOC emissions from blue-green algae blooms in Taihu Lake. The results indicate that since the 21st century, the situation of cyanobacterial blooms in Taihu Lake has continued to deteriorate with increasing variability. The relative impact of meteorological factors varies across different regions, but temperature consistently shows the highest sensitivity in all areas. The VOCs released from the algal blooms increase with the proliferation of the blooms, posing a continuous threat to the atmospheric environment of the surrounding cities. This study aims to provide a scientific basis for further improvement of air quality in urban areas adjacent to large lakes. Full article

Indoor air quality and respiratory health have always been an area of prime interest across the globe. The significance of low-cost air quality sensing and indoor public health practices spiked during the pandemic when indoor air pollution became a threat to living beings, especially human beings. Problem Definition: Indoor respiration-associated diseases are hard to diagnose if they are due to indoor environmental conditions. A major challenge was observed in establishing a baseline between indoor air quality sensors and associated respiratory diseases. Methods: In this work, 10,000+ articles from top literature databases were reviewed using six bibliometric analysis methods (Lorenz Curve of Citations, Hirch’s H-Index, Kosmulski’s H2-Index, Harzing’s Hl-Norm-Index, Sidoropolous’s HC-Index, and Schrieber’s HM-index) to formulate indoor air quality sensor and disease correlation publication rubrics to critically review 482 articles. Results: A set of 152 articles was found based on systematic review parameters in six bibliometric indices for publications that used WHO, NIH, US EPA, CDC, and FDA-defined principles. Five major respiratory diseases were found to be causing major death toll (up to 32%) due to five key pollutants, measured by 30+ low-cost sensors and further optimized by seven calibration systems for seven practical parameters tailored to respiratory disease baselines evaluated through 10 cost parameters. Impact: This review was conducted to assist end-users, public health facilities, state agencies, researchers, scientists, and air quality protection agencies. Full article

The 21st century has seen the launch of new space-borne sensors based on LiDAR (light detection and ranging) technology developed in the second half of the 20th century. Nowadays, these sensors offer novel opportunities for mapping terrain and canopy heights and estimating aboveground biomass (AGB) across local to regional scales. This study aims to analyze the scientific impact of these sensors on large-scale forest mapping to retrieve 3D canopy information, monitor forest degradation, estimate AGB, and model key ecosystem variables such as primary productivity and biodiversity. A worldwide bibliometric analysis of this topic was carried out based on up to 412 publications indexed in the Scopus database during the period 2004–2022. The results showed that the number of published documents increased exponentially in the last five years, coinciding with the commissioning of two new LiDAR space missions: Ice, Cloud, and Land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). These missions have been providing data since 2018 and 2019, respectively. The journal that demonstrated the highest productivity in this field was “Remote Sensing” and among the leading contributors, the top five countries in terms of publications were the USA, China, the UK, France, and Germany. The upward trajectory in the number of publications categorizes this subject as a highly trending research topic, particularly in the context of improving forest resource management and participating in global climate treaty frameworks that require monitoring and reporting on forest carbon stocks. In this context, the integration of space-borne data, including imagery, SAR, and LiDAR, is anticipated to steer the trajectory of this research in the upcoming years. Full article

In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered. Full article

Cochlear implants (CIs), a revolutionary breakthrough in auditory technology, have profoundly impacted the lives of individuals with severe hearing impairment. Surgically implanted behind the ear and within the delicate cochlea, these devices represent a direct pathway to restoring the sense of hearing. Implanting hope alongside innovation, their captivating history unfolds through pivotal dates and transformative milestones. From the first human implantation by Drs. William House and John Doyle in 1961 to FDA approval in 1984, each step in their evolution mirrors a triumph of human ingenuity. The 1990s witnessed significant miniaturization, enhancing accessibility, while the 21st century brought about improvements in speech processing and electrode technology. These strides have elevated CIs beyond functional devices to life-changing instruments, enriching both auditory experiences and communication skills. This entry delves into the captivating history of CIs, spotlighting key dates that paint a vivid picture of challenges overcome and remarkable progress achieved. It explores the people and moments that defined their development, ultimately shaping these implants into indispensable tools that continually redefine the landscape of hearing assistance. Full article

In the context of sustainable manufacturing, efficient collaboration between humans and machines is crucial for improving assembly quality and efficiency. However, traditional methods for action recognition and human–robot collaborative assembly often face challenges such as low efficiency, low accuracy, and poor robustness. To solve such problems, this paper proposes an assembly action-recognition method based on a hybrid convolutional neural network. Firstly, an assembly action-recognition model is proposed using skeletal sequences and a hybrid convolutional neural network model combining Spatial Temporal Graph Convolutional Networks (ST-GCNs) and One-Dimensional Convolutional Neural Networks (1DCNNs) to sense and recognize human behavior actions during the assembly process. This model combines the joint spatial relationship and temporal information extraction ability of the ST-GCN model with the temporal feature extraction ability of the 1DCNN model. By incorporating Batch Normalization (BN) layers and Dropout layers, the generalization performance of the model is enhanced. Secondly, the model is validated on a self-constructed dataset of assembly actions, and the results show that the recognition accuracy of the model can reach 91.7%, demonstrating its superiority. Finally, a digital workshop application system based on digital twins is developed. To test the effectiveness of the proposed method, three sets of control experiments were designed to evaluate both objective and subjective aspects and verify the feasibility of the method presented in this paper. Compared with traditional assembly systems, the proposed method optimizes the recognition of human–robot collaborative assembly actions and applies them to intelligent control systems using digital-twin technology. This intelligent assembly method improves assembly efficiency and saves assembly time. It enables efficient and sustainable collaboration between humans and robots in assembly, leading to a positive and sustainable impact on the manufacturing industry. Full article

Since the 20th century, satellites have been key in remote sensing, but the 21st century saw the rise of UAVs, especially in agriculture. While both are vital tools, their implications are often misunderstood. Precision agriculture requires an understanding of its strengths and weaknesses, especially with changing climate patterns affecting crops like pistachio in southern Europe. This study evaluates the effectiveness of satellites and UAVs in measuring NDVI for pistachio orchards in Spain, utilizing Sentinel 2 and a UAV equipped with a MicaSense Altum sensor. The results show that satellite data consistently underestimated NDVI values compared to UAV data, with a correlation of r-values ranging from 0.65 in July to 0.71 in September. The correlation values were consistent and very similar in all orchards. Despite the underestimation, satellites are deemed suitable for broader trend analysis, while UAVs provide more granular, precise agronomical assessments. An integrated utilization of both technologies is recommended for comprehensive and accurate precision agriculture practices. Full article