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Food safety and authenticity analysis play a pivotal role in guaranteeing food quality, safeguarding public health, and upholding consumer trust. In recent years, significant social progress has presented fresh challenges in the realm of food analysis, underscoring the imperative requirement to devise innovative and expedient approaches for conducting on‐site assessments. Consequently, cellulose paper‐based devices (PADs) have come into the spotlight due to their characteristics of microchannels and inherent capillary action. This review summarizes the recent advances in cellulose PADs in various food products, comprising various fabrication strategies, detection methods such as mass spectrometry and multi‐mode detection, sampling and processing considerations, as well as applications in screening food safety factors and assessing food authenticity developed in the past 3 years. According to the above studies, cellulose PADs face challenges such as limited sample processing, inadequate multiplexing capabilities, and the requirement for workflow integration, while emerging innovations, comprising the use of simplified sample pretreatment techniques, the integration of advanced nanomaterials, and advanced instruments such as portable mass spectrometer and the innovation of multimodal detection methods, offer potential solutions and are highlighted as promising directions. This review underscores the significant potential of cellulose PADs in facilitating decentralized, cost‐effective, and simplified testing methodologies to maintain food safety standards. With the progression of interdisciplinary research, cellulose PADs are expected to become essential platforms for on‐site food safety and authentication analysis, thereby significantly enhancing global food safety for consumers. [ABSTRACT FROM AUTHOR]

This research identifies the urgent need for adaptive and real-time urban weather visualization models to support city planning and decision-making. The proposed solution involves the use of digital twin technology integrated with the Fuzzy Sugeno method to handle dynamic and complex environmental data. The novelty of this approach lies in its ability to provide real-time, adaptive visualizations through a combination of IoT (Internet of Things) data and advanced fuzzy logic processing, enhancing the accuracy and relevance of urban weather monitoring. This method enables the system to process real-time weather data collected from IoT sensors distributed throughout the city, resulting in accurate and relevant visualizations. The weather data, including temperature, humidity, and wind speed, is sent to a data centre where the Fuzzy Sugeno method is used to transform this data into visualizable weather information. The weather visualizations include conditions such as heavy rain, regular rain, drizzle, overcast, cloudy, sunny cloudy, somewhat cloudy, and sunny. The results of this study demonstrate that the system can display weather conditions adaptively and in real-time, significantly contributing to the understanding and management of urban heat islands. In ten experiments with temperature ranges from 21°C to 34°C, humidity from 55% to 87%, and wind speeds from 12 km/h to 32 km/h, the dominant defuzzification value was 0.6, corresponding to sunny cloudy weather conditions. The implementation of this system also shows improvements in the quality and accuracy of weather information presented, thereby aiding in more responsive and sustainable urban planning. [ABSTRACT FROM AUTHOR]

The growing concern about air quality and its influence on human health has prompted the development of sophisticated monitoring and forecast systems. This article gives a thorough investigation into forecasting the air quality index (AQI) with an Internet of Things (IoT) device that analyzes temperature, humidity, PM10, and PM2.5 levels. The dataset used for this analysis comprises 5869 data points across six critical parameters essential for accurate air quality prediction. The data from these sensors is sent to the ThingSpeak cloud platform for storage and preliminary analysis. The system forecasts AQI using a TensorFlow-based regression model, delivering real-time insights. The combination of IoT technology and machine learning improves the accuracy and responsiveness of air quality monitoring systems, making it a useful tool for environmental management and public health protection. This work presents comparatively the effectiveness of feedforward neural network models trained with the 'adam' and 'RMSprop' optimizers over different epochs, as well as the machine learning algorithm random forest with varying numbers of estimators to forecast AQI. The models were trained using both types of regression analysis: linear regression and random forest regression. The findings show that the model achieves a high degree of accuracy, with the predictions closely aligning with the actual AQI values, thus having the potential to significantly reduce the negative health impact associated with poor air quality, protecting public health and alerting users when pollution levels are higher than allowed. Specifically, the random forest model with 100 estimators delivers the best overall performance for both AQI 10 and AQI 2.5, achieving the lowest Mean Absolute Error (MAE) of 0.2785 for AQI 10 and 0.2483 for AQI 2.5. This integration of IoT technology and advanced predictive analysis addresses the significant worldwide issue of air pollution by identifying the pollution hotspots and allowing decision-makers for quick reactions, and the development of effective strategies to reduce pollution sources. [ABSTRACT FROM AUTHOR]

The primary source of food for the growing global population is agriculture, which is frequently referred to as the basis of human civilization. It includes a wide variety of tasks that are involved in growing crops, caring for animals, and creating different raw materials for food, clothing, and industrial uses. For thousands of years, agriculture has been the foundation of human societies, producing not just food and raw resources but also impacting cultures, economies, and environments. Consider a farm where robots work together as a team, with no cables connecting them. These robots make it easier for farmers to care for their crops. In this research paper, we will look at how wireless robots are transforming the way we farm. We also investigate how they conserve energy and improve farming efficiency. The research explains how these robots can do various farming tasks such as planting and harvesting. We also discuss various issues, such as what happens when they run out of power. Finally, we consider the future and how these robots might help farmers even more. It's like having a high-tech farm worker that assures our food grows better and our farms are more sustainable. [ABSTRACT FROM AUTHOR]

Remote sensing technology is a vital component of disaster management, poised to revolutionize how we safeguard lives and property through enhanced prediction, mitigation, and recovery efforts. Disaster management hinges on continuous monitoring of various environments, from urban areas to forests and farms. Data from these observations are relayed to servers, where sophisticated processing algorithms forecast impending disasters. Remote sensing technology operates through a layered framework. The sensing layer acquires raw data, the network layer facilitates data transmission, and the data processing layer extracts meaningful insights. The application layer then leverages these insights to make informed decisions. Elevating the intelligence of remote sensing technology necessitates advancements across these layers. This paper delves into disaster management concepts and highlights the pivotal role played by remote sensing technology. It offers a comprehensive exploration of each layer within the remote sensing technology framework, detailing foundational principles, tools, and methodologies for enhancing intelligence. Addressing challenges inherent to this technology, the paper also presents future-oriented solutions. Furthermore, it examines the influence of wireless network infrastructure, alongside emerging technologies like the Internet of Things, cloud computing, virtual machines, and low-power wireless networks, in nurturing the evolution and sustainability of remote sensing technology. [ABSTRACT FROM AUTHOR]

This paper presents a novel power-efficient topology for receivers in short-range LiDAR sensors. Conventionally, LiDAR sensors exploit complex time-to-digital converters (TDCs) for time-of-flight (ToF) distance measurements, thereby frequently leading to intricate circuit designs and persistent walk error issues. However, this work features a fully differential trans-impedance amplifier with on-chip avalanche photodiodes as optical detectors so that the need of the following post-amplifiers and output buffers can be eliminated, thus considerably reducing power consumption. Also, the combination of amplitude-to-voltage (A2V) and time-to-voltage (T2V) converters are exploited to replace the complicated TDC circuit. The A2V converter efficiently processes weak input photocurrents ranging from 1 to 50 μApp which corresponds to a maximum distance of 22.8 m, while the T2V converter handles relatively larger photocurrents from 40 μApp to 5.8 mApp for distances as short as 30 cm. The post-layout simulations confirm that the proposed LiDAR receiver can detect optical pulses over the range of 0.3 to 22.8 m with a low power dissipation of 10 mW from a single 1.8 V supply. This topology offers significant improvements in simplifying the receiver design and reducing the power consumption, providing a more efficient and accurate solution that is highly suitable for short-range LiDAR sensor applications. [ABSTRACT FROM AUTHOR]

Electric vehicles need continuous monitoring of the energy supply all the time to provide the user with accurate information about the available energy. However, an increase in the power consumed by the monitoring system may cause a significant decrease in the vehicle’s autonomy. This letter presents a monitoring and estimation system for an electric car with a hybrid energy supply based on supercapacitors and batteries based on an embedded system capable of monitoring the general current, the current from the battery system, the voltage in the battery system, the voltage in the supercapacitor system, estimating the value of the state of charge from batteries, and showing the measurements to the user with an OLED display. The proposed system performs similarly in the estimation but decreases the energy consumption by 99.9% concerning the laboratory systems. [ABSTRACT FROM AUTHOR]

Hydroponic farming is a promising alternative to soil-based farming. However, it requires a precise control of the growth environment, which is hard to achieve with energy-constrained embedded systems. This letter presents an energy optimization technique for the continuous operation of energy harvesting-based hydroponics sensor nodes. The proposed technique is based on the self-tuning model, that dynamically adjust the duty cycle of the node, ensuring the autonomous operation of the Internet of Things system. The model can be programmed in a low-power microcontroller, allowing the decision-making process to reside entirely on the sensor node. Experimental results show that in the same time period, the self-tuning model allows $3.5\times $ more data transmissions than a uniform 5-min duty cycle, while ensuring a minimum voltage level in the storage device. This balance allows the stored energy to be enough for continuous monitoring, providing a clean and cost-effective alternative to perpetually power the hydroponic system. [ABSTRACT FROM AUTHOR]

Abstract The Chinese cooking process, intricate and diverse, encompasses a wide range of dishes, generating substantial data that often poses significant challenges for automation. To address these challenges, this paper introduces a versatile, highly reliable, user-friendly, and intelligent data recorder specifically designed for Chinese cuisine-the Chinese Cuisine Intelligent Cooker Recorder-V1.0 (CCICR-V1.0). This device is intended to record data generated during the cooking process and provide essential support for intelligent cooking robot systems. The core of CCICR-V1.0 is a STM32 microcontroller, equipped with sensors that monitor temperature, pressure, and attitude. These sensors facilitate the intelligent collection of data related to dishes, ingredients, and the movements of cookware throughout the cooking process. To ensure efficient data acquisition and storage, CCICR-V1.0 employs a multi-task data buffering storage method that effectively allocates CPU resources. NAND Flash is used as the storage medium, guaranteeing secure storage, management, and preservation of high-frequency, multi-channel data. Experimental results demonstrate the effectiveness of CCICR-V1.0 in achieving multi-channel, high-frequency data acquisition and storage in complex environments. This leads to an increase in automation efficiency of 89.9%. The weighing module demonstrates a maximum relative error of only 0.288%, while the attitude sensor experiment shows an attitude information error of just 0.22°C. Furthermore, the non-contact infrared temperature measurement module exhibits impressive performance with a maximum absolute error of 0.258 $$^\circ C$$ ∘ C and a maximum relative error of 0.88%. Notably, this measurement module has achieved a cost reduction of approximately 91.7% and an accuracy improvement of around 40%. Additionally, the response time of CCICR-V1.0 is less than 3 ms. With its ability to effectively capture data related to Chinese cooking, CCICR-V1.0 holds commercial value for widespread adoption and application in the field of intelligent cooking.

Abstract A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40–50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food.

Abstract Smartphones store valuable personal information, necessitating robust authentication methods to protect user data. This research proposes a lightweight bi-model fallback authentication technique that combines dynamic security questions and finger pattern recognition using inertial measurement units. The dynamic security questions are generated based on the smartphone’s usage behavior, while the owner’s finger movements are captured using four different inertial sensors: accelerometer, gyroscope, gravity sensor, and magnetometer. By combining the answers to the questions and the owner’s finger movements, the user can be authenticated even if the primary authentication method fails. In this study, data was collected from 24 participants, including 12 primary phone users and 12 close adversaries, over a span of 28 days. The dynamic security questions, derived from call, SMS, battery charging events, application usage, location, and physical activity categories, achieved high accuracy rates, with call, SMS, and application usage surpassing $$90\%$$ 90 % . Incorporating the inertial measurement units significantly improved the accuracy of all question types, increasing from a maximum of $$76\%$$ 76 % to $$90.99\%$$ 90.99 % , while also enhancing the True Positive Rate from 0.79 to 0.99 compared to a previous study. This research presents a promising lightweight bi-model fallback authentication technique that leverages dynamic security questions and inertial measurement units data, demonstrating its effectiveness for enhancing smartphone security.

Abstract With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management.

Abstract. The development and application of automated technologies in the agricultural sector have significantly transformed the management and monitoring of farm animals. This paper presents a comprehensive analysis of existing technical systems for measuring the physiological parameters of farm animals, particularly focusing on cows. The primary goal of these technologies is to enhance the accuracy and efficiency of health monitoring and to optimize livestock management through continuous data collection and analysis. Key technological solutions include the use of wireless data transmission systems, radio monitoring devices, and unmanned aerial vehicles (UAVs). These technologies enable real-time tracking of animal health parameters such as body temperature, heart rate, movement patterns, and other critical indicators. The use of various sensors, including ear, neck, and intra-body sensors, plays a crucial role in collecting primary physiological data. These sensors are integrated into a broader system that includes radio modules with built-in controllers for converting raw data into digital formats, enabling wireless transmission to central systems for processing and storage. The radio monitoring of animals allows for the continuous tracking of physiological and behavioral parameters across large areas, providing crucial insights into their well-being. It is particularly beneficial in detecting early signs of disease or health issues, ensuring timely intervention, and preventing potential economic losses for farmers. The integration of unmanned aerial vehicles (UAVs) further expands the capabilities of monitoring systems, enabling the visual assessment of animals' behavior and movement across vast pastures. Equipped with video cameras and data relays, UAVs collect video footage that is transmitted to a central processing unit for real-time analysis and storage. The combined use of radio monitoring and UAV technology forms a comprehensive framework for livestock management, allowing farmers to make data-driven decisions, optimize resource allocation, and improve overall herd health and productivity. By leveraging these automated systems, it becomes possible to reduce the dependency on manual labor, minimize human error, and ensure that animals are closely monitored without the need for constant physical presence. This represents a significant advancement in modern animal farming, aligning with global trends toward smart farming and precision agriculture. In conclusion, the integration of automated physiological monitoring technologies in farm animal management presents an innovative solution for enhancing animal welfare, improving farm efficiency, and reducing operational costs. These systems hold considerable potential for further advancements in the field of animal health monitoring, ultimately contributing to more sustainable and productive farming practices.

The failure of the building work is due to the structure failing to function and can also cause material losses and can even cause fatalities. Because of that, excellent and careful anticipation is needed. In this regard, the re-search that will be carried out is monitoring the structure's health by identifying structural damage based on vi-bration tests. Modal structural parameters are determined based on the vibration output using the Second Order Blind Identification algorithm created by numerical simulation in the MATLAB application. The method in this research is a laboratory experiment with two testing models, namely the modal parameter values of the typical truss structure and the damaged truss structure, using the Second Order Blind Identification algorithm method. Furthermore, analyzing the indications of structural damage from the estimated modal parameters, the accuser, and the effectiveness of the Second Order Blind Identification algorithm method is feasible to use. The results showed that the Hp sensor recording data that had been processed were the health15mts.mat and dam-age15mts.mat files loaded into the SOBI program, and the predicted value of the natural frequency of typical structures was 31,9072 rad/s, and damaged structures were 22,6089 rad/s. From the expected frequency, the frequency ratio value is 41.127 %. According to the Steel Frame Bridge Inspection Regulation No.005/BM/2009 Guidelines for Construction and Building of the Ministry of Public Works, Directorate General of Highways, if it exceeds 20%, it can be categorized as critical.

Abstract The 5G‐enabled sensor network systems make it possible to connect cyber and real ‘things’ in many ways. Even so, the flow of data between 5G‐enabled sensor devices brings big data environment problems, such as huge amounts of data, duplicated data, a lack of scalability etc. Some of these problems are as follows. It is hard to keep an eye on 5G‐enabled sensor systems in a ‘big data’ environment. Cyberattacks that could put the safety of the sensor network systems at risk are hard to find, which make the situation even more complicated. The security challenges of 5G‐enabled Sensor Network Systems are studied and analyzed due to some constraints associated with the sensor nodes. The proposed advanced algorithm for securing the 5G‐enabled sensor systems is a Multidimensional big data environment using artificial intelligence/machine learning (AI/ML). Using a structure that depends on both geographical and temporal data, an improved clear point selection operation may get important information from multidimensional time series data that is spread across a wide range of sensor nodes. Therefore, the actions of the 5G‐enabled sensor network can be shown accurately and a complete model of its underlying data structure is built to analysis attacking, pattern on 5G‐enabled Sensor Network Systems using the AI/ML Algorithm.

Bikes and bike friendly cities are becoming the core theme of mobility change. In the presented projects we apply and investigate data-driven and participatory approaches to enable changes in bike-friedly policy and city infrastructure. Together with Futurium - house of futures - and several fablabs we co-designed a participatory approach to empower citizens to be part of the mobility change in Berlin. 30 citizens participated in the senseBox:bike workshop, built mobile senseBoxes to measure environmental phenomena, distance of surpassing cars and road or bike-lane quality. Some of the citizens collected mobile data for over 2 years, which was published as open data on openSenseMap and visualized in an interactive “living exhibit” in the Futurium Lab. We organized an intermediate lab meeting with some of the participants to discuss feed-back, usability and the software of the protoypical device and re-co-designed the senseBox:bike to an open source product for mobile bike data collection. After two years we are meeting the citizen scientists again at Futurium and interview them about motivations, obstacles, experiences in the project. We adapted the approach with an more educational focus and designed workshops for five schools in Essen, evolving the participatory approach: the students work in the local quarters of the city, they build, program and mount the devices to their bikes, they conduct data collection and analysis, and then get into dialogue with local politicians, following the spatial citizenship approach.

Abstract For half a century, the Mallet Scale (MS) has been utilized to assess upper limb function in patients with obstetric brachial plexus palsy (OBPP). However, the correct use of the MS requires trained personnel and the MS does not measure compensatory movements. For this reason, new methods are needed to compensate for these weaknesses. This study introduces an innovative method for objective functional motion analysis using accelerometers to measure upper limb movements in thirty patients with obstetric brachial plexus lesions. Five triaxial accelerometers were positioned on the chest and each upper limb. They recorded acceleration signals during repetitive everyday tasks: hand-to-mouth (HM), hand-to-neck (HN), and hand-to-spine (HS). From these signals, 54 features were extracted and subjected to linear correlation tests to identify 5 suitable features. An algorithm was then developed to categorize patients into five groups and compute an individual movement performance score (iMPScore) assessing the patient’s upper extremity function. By using the iMPScore more than 75% of all participants have been classified correctly with respect to their MS category. Identification of MS I category patients in general and assessing upper extremity function of MS I to III in HS tasks were most challenging. We conclude that the introduced approach is a valuable tool for gauging movement limitation of upper limbs in patients with obstetric brachial plexus palsy. Compared to other clinically established methods, it becomes possible to record and even quantify the extent of compensatory movements. In this way, an objective, user- and patient-friendly method is offered, which supports significantly physicians and therapists in their evaluation of OBPP.

Musculoskeletal disorders are common among older adults, affecting mobility and quality of life. Effective rehabilitation is essential, but the implementation of programs faces challenges. Traditional methods often necessitate in-person assessments, which can be difficult for older adults with mobility limitations. Telerehabilitation offers a solution, bringing therapy closer to patients. However, the accurate remote monitoring of health and performance remains a challenge. This study addresses this gap by developing and validating the System for Tracking and Evaluating Performance (STEP). STEP is a hardware-software system that automates physical performance tests, eliminating the need for constant expert supervision. The system focuses on three standard tests: the Six-Minute Walking Test (6MWT), the Ten-Meter Walking Test (10MWT), and the 30-s Sit-to-Stand Test (30STS). Validation compared results from the STEP app with in-person assessments by physicians for patients undergoing rehabilitation after knee or hip arthroplasty. The study found strong positive correlations between the app’s results and the physicians’ assessments for all tests. These findings demonstrate the STEP system’s potential as a reliable tool for remote physical performance assessment. Further research is needed to explore its integration into clinical practice and cost-effectiveness in reducing the need for operator assistance in monitoring patients with physical limitations.

Abstract The global population is rapidly increasing, so there is a critical requirement to satisfy the food production demand. Conventional methods of agriculture are inadequate to meet building demand which leads to declining farming sector and adaptable to other industries. Most of the farming activities are highly dependent on the labor which leads to increase in cost and time of operation. The rapid growth of mechanization for all farm activities cannot completely reduce the human involvement. As a result, agricultural automation is critically important. In terms of automation, this study emphasizes the crucial role of UAVs in precision and smart agriculture. The adoption of drones for various farm operations has the possibility to minimize labor requirements as well as operational time. This review provides overview of conceptual design, command flow operation, Micro-controller boards, remote-control systems and attachments like sensors, cameras, motors in UAVs for the purpose of automation in farm activities. The Internet of Things (IoT) employed in UAVs with image processing and machine learning algorithms provides accurate and precision results in farm activities. Furthermore, this study discusses future advancements, limitations and challenges for farmers in adapting to UAVs. Graphical Abstract

Abstract A nanocomposite of CaO:MgAl2O4 was synthesized through a straightforward and cost-effective sol–gel method. The investigation of the novel CaO:MgAl2O4 nanocomposite encompassed an examination of its morphological and structural alterations, as well as an exploration of its photocatalytic activities and electrochemical characteristics. XRD analysis revealed a nanocomposite size of 24.15 nm. The band gap, determined through UV studies, was found to be 3.83 eV, and scanning electron microscopy (SEM) illustrated flake-like morphological changes in the CaO:MgAl2O4 samples. TEM, HRTEM, and SAED studies of a CaO:MgAl2O4 nanocomposite would reveal important details about its morphology, crystallography, and nanostructure. Photocatalytic activity was quantified by studying the degradation of Acid Red-88 (AR-88) dye in a deionized solution, achieving a 70% dye degradation under UV irradiation in 120 min. Plant growth examinations were carried out using dye degraded water to test its suitability for agriculture. The electrochemical energy storage and sensing applications of the prepared nanocomposite were examined using CaO:MgAl2O4 modified carbon paste electrode through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). In conclusion, the synthesized CaO:MgAl2O4 nanocomposite demonstrated promising morphological and structural characteristics, efficient photocatalytic activity, and potential applications in electrochemical energy storage, highlighting its versatility for various technological and environmental applications. Graphical abstract

Abstract The light field (LF) imaging technique can capture 3D scene information in 4D by recording both 2D intensity and 2D direction of incoming light rays. Due to this capability, LF has shown a great interest in virtual reality (VR) and augmented reality (AR) for enhanced immersion, improved depth perception and reconstruction of realistic 3D environments. This paper presents a comprehensive review of LF imaging technology and other approaches used for VR content creation. The applications of LF technology beyond VR and AR are also discussed. The challenges and limitations of other approaches for VR content creation are examined. State‐of‐the‐art research has focused on how VR experiences benefit from LF technology and identified the challenges to creating comfortable, immersive and realistic VR content such as (1) image size and resolution, (2) processing speed, (3) precise calibration and (4) depth reconstruction. Recommendations that can be considered for creating immersive VR content are provided to enhance user experience. These recommendations aim to contribute to developing more comfortable and realistic VR content, extending the potential applications of LF imaging technology in diverse fields.

The supply of macroalgal feed materials for abalone aquaculture is threatened by increasing costs, limited availability. and the allocation of seaweed resources for other purposes. The use of biofouling Sargassum horneri (SH) as additional macroalgal biomass for abalone has been studied, albeit full inclusion is discouraged due to low utilization. This study applied heat treatment and fermentation to increase the nutritive value of SH meals for the aquaculture feed industry. Untreated (UN) as control, heat treated (HT, 121[degrees]C for 20 min), and fermented (FE, 10' CFU/mL each of Bacillus sp. SJ-10 and Bacillus sp. KRF-7) SH meal was fed to the abalone (11.60 [+ or -] 0.02 g body weight and 42.98 [+ or -] 0.53 mm shell length) for 8-wk growth trial. The results showed that there is no apparent difference in the growth performance (body weight and shell length morphometries) and survival of abalone given experimental diets (P > 0.05). The crude protein, lipid, and ash composition of edible abalone flesh were similarly unaffected (P > 0.05). The gut microbiota profile of abalone was analyzed through next-generation sequencing. The [alpha]-diversity indicators (microbial richness and abundance) were statistically similar (P > 0.05). and the [beta]-diversity of operational laxonomic units revealed short distances within samples and clustering among diet groups. The core phyla (Fusobacteria, Proteobaeteria. Tenericutes) and genera (Psychrilyohacter, Vibrio, Mycoplasma) known to inhabit the gut of abalone predominantly were likewise identilied irrespective of diets; however, there is a difference in their relative abundance. Genus Psychrilyohacter was relatively abundant in UN and FE compared with HT, whereas Vibrio and Mycoplasma had higher abundance in FE and HT-fed abalone, respectively. Taste sensory attributes were discriminated in the abalone meat using an electronic tongue. Principal component analysis of sensor scores revealed several overlapping points, resulting in a negative discrimination index (-184). which means no apparent difference in the general taste profile was detected. Furthermore, the taste screening matrix through radar mapping revealed slight intensity differences in the umami. saltiness, and sourness flavor. Overall, the methods used for heat treatment and fermentation procedures herein were ineffective in improving the nutritive value of seaweed meal for abalone. KEY WORDS: abalone, gulfweed. heat treatment, fermentation. Haliotis discus harmed, Sargassum horneri, INTRODUCTION The abalone (Haliotis spp.) is an ecologically and economically important marine gastropod mollusc worldwide. They are found in tropical and temperate regions, particularly in the low intertidaj zone of [...]

Conductive MIL-101(Cr) PEDOT has been drop cast on interdigitated electrodes. When regularly deposited, the nanoparticles are randomly dispersed on the surface leading to a poor conductivity. When an electric field is applied during the drop casting, the particles are spatioselectively deposited between the finger electrodes, enhancing both the resulting conductivity and sensitivity to water vapor. [Display omitted] Precise deposition of metal–organic framework (MOF) materials is important for fabricating high-performing MOF-based devices. Electric-field assisted drop-casting of poly(3,4-ethylenedioxythiophene)-functionalized (PEDOT) MIL-101(Cr) nanoparticles onto interdigitated electrodes allowed their precise spatioselective deposition as percolating nanoparticle chains in the interelectrode gaps. The resulting aligned materials were investigated for resistive and capacitive humidity sensing and compared with unaligned samples prepared via regular drop-casting. The spatioselective deposition of MOFs resulted in up to over 500 times improved conductivity and approximately 6 times increased responsivity during resistive humidity sensing. The aligned samples also showed good capacitive humidity sensing performance, with up to 310 times capacitance gain at 10 versus 90 % relative humidity. In contrast, the resistive behavior of the unaligned samples rendered them unsuitable for capacitive sensing. This work demonstrates that applying an alternating potential during drop-casting is a simple yet effective method to control MOF deposition for greater efficiency, conductivity, and enhanced humidity sensing performance. [ABSTRACT FROM AUTHOR]

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In recent decades, massive exudation of heavy metals into natural water bodies has become prevalent world wide, posing threat to the environment, individuals and their well-being. Owing to its prolonged half-life and non-biodegradability, potential accumulation of heavy metals can cause serious health problems. Several cutting-edge diagnostic methods are being utilized for the assay of heavy/toxic metals. Certainly, these methods hold some limitations, such as requirement for extortionate devices and strenuous operations that can only be carried out in laboratories. This led to the emergence of detection methods based on sensors. Among various breakthroughs, paper-based analytical devices as well as paper-based microfluidic devices have turned up as one of the economical, convenient, easily disposable and portable substitutes for on-site detection. This study discusses the current analytical methods and paper-based devices used for heavy metal detection in the first phase followed by the discussion on comparative analysis of various analytical methods. In addition, there are techniques for heavy metal detection, which use substrates like paper, polydimethylsiloxane, etc. using colorimetric, fluorescence or electrochemical sensing, have also been discussed. The development of programmable paper-based microfluidic devices has been emphasized in the second phase of the discussion. Finally, the future progressions and developments in this field have also been proposed. [ABSTRACT FROM AUTHOR]

Water scarcity is a global issue that presents significant challenges to sustainable development. Recent Food and Agriculture Organization (FAO) evaluations revealed a concerning trend: more than half of agricultural water is wasted. The widespread use of manual irrigation systems, combined with diminishing water resources, necessitates efficient and effective irrigation methods. This study aims to address this issue by introducing a novel irrigation control system based on an Arduino mechanism. The goal of this research is to design and test an efficient and sustainable irrigation system that maximizes water usage, minimizes waste, and reduces electricity consumption. The study used an Arduino-controlled irrigation system equipped with soil moisture sensors. These sensors were strategically placed to monitor soil moisture levels and trigger irrigation when necessary. The system's performance was evaluated using a series of experimental trials on small and medium-sized farm plots. The system required approximately 20% less water than traditional manual irrigation methods. Furthermore, there was a significant reduction in electricity consumption for water pumps, with an average 20% savings per year. The ease of integration into existing farm setups and the reduction in manual labor are also significant benefits. This system assists to conserve vital water resources and promotes sustainable agricultural development by optimizing water use and reducing electricity consumption. Practical Applications: This study demonstrates a low-cost automated irrigation system for small and medium-sized farms that uses an Arduino control system. The system uses soil moisture sensors to ensure that plants receive optimal water exactly when they need it, thereby improving water efficiency and plant health. The use of a control system provides flexible programming options, making it simple to expand and integrate new control features. The system's adaptability enables the simple integration of automation into existing farm setups, and the number of soil moisture sensors can be increased to cover larger farm areas. This intelligent irrigation solution not only conserves water resources but also reduces the amount of electricity required to power water pumps, resulting in lower operating costs and maintenance needs. Therefore, implementing the proposed automated control system can significantly reduce water usage, maintenance efforts, and electricity consumption, providing farmers with a long-term and efficient irrigation solution. [ABSTRACT FROM AUTHOR]

The public health problem caused by mycotoxins contamination has received a great deal of attention worldwide. Mycotoxins produced by filamentous fungi widely distributed in foodstuffs can cause adverse impacts on humans and livestock, posing serious health threats. Particularly worth mentioning is that mycotoxins can accumulate in organisms and be enriched through the food chain. Improving early trace detection and control from the source is a more desirable approach than the contaminated food disposal process to ensure food safety. Conventional sensors are susceptible to interference from various components in intricate food matrices when detecting trace mycotoxins. The application of ratiometric sensors avoids signal fluctuations, and reduce background influences, which casts new light on developing sensors with superior performance. This work is the first to provide an overview of the recent progress of ratiometric sensors in the detection of mycotoxins in intricate food matrices, and highlight the output types of ratiometric signal with respect to accurate quantitative analysis. The prospects of this field are also included in this paper and are intended to have key ramifications on the development of sensing detection conducive to food safety. [ABSTRACT FROM AUTHOR]

3D printing (3DP) technologies have transformed the processing of advanced ceramics for small‐scale and custom designs during the past three decades. Simple and complex parts are designed and manufactured using 3DP technologies for structural, piezoelectric, and biomedical applications. Manufacturing simple or complex geometries or one‐of‐a‐kind components without part‐specific tooling saves significant time and creates new applications for advanced ceramic materials. Although development and innovations in 3DP of ceramics are far behind compared with metals or polymers, with the availability of different commercial machines in recent years for 3DP of ceramics, exponential growth is expected in this field in the coming decade. This article details various 3DP technologies for advanced ceramic materials, their advantages and challenges for manufacturing parts for various applications, and perspectives on future directions. We envision this work will be helpful to advanced ceramic researchers in industry and academia who are using different 3DP processes in the coming days. [ABSTRACT FROM AUTHOR]

A flexible capacitive three-dimensional (3D) force transducer with four bottom electrodes is proposed. A dielectric layer with ionic effects and pyramid-like microstructures is designed to improve the sensing performance of this sensor. The arrayed sensor exhibits remarkable capabilities in capturing and decoupling 3D forces, showcasing a high sensitivity, a wide response range (0–30 kPa), rapid response time (62 ms), excellent repeatability under continuous pressure, as well as static and dynamic responses. By assembling a robotic arm to grasp various objects, the sensor demonstrates exceptional sensing capabilities for object roughness, thereby highlighting its potential application as a touch recognition system in human–computer interaction scenarios. [ABSTRACT FROM AUTHOR]

In this paper, new types of autonomous systems used in agriculture were analysed. The paper shows new self-guiding systems such as AGVs with full autonomy in degrees operation. It explains internal transport and systems of autonomous vehicles in outdoor agriculture. New autonomous systems used outside such as appliance of special navigation systems and their purpose in agriculture are present in this work. Navigation systems with GPS signal and RTK technology, vehicle guidance camera and AI machine vision for manipulation are described. Light and laser technologies for fully autonomous robotic technologies such as LiDAR system in vehicle for detection of the presence of pests and diseases are presented in this paper. The paper emphasized advantages of using AGVs as result of their autonomy, clean power sources without harmful impact on the environment. Navigation in indoor spaces that uses LTE Direct protocol is explained, whereby the Wi-Fi ceiling antenna and wireless APP for horizontal movement of AGVs is shown. The ways of using UAVs for warehouse inventory through web applications with an advanced navigation system guided by AI are given in this work. [ABSTRACT FROM AUTHOR]

The article discusses an approach to analyzing processes in industrial reactors using advanced systems with feedback. The authors describe the system preparation and image reconstruction algorithms of ultrasonic tomographs (USTs) that have applications in industry. The research presented in this paper focuses on developing effective strategies for controlling and monitoring chemical reaction processes in reactors. Using advanced data processing techniques, the authors propose systems with feedback that enable accurate analysis and optimization of reactor operating conditions. A key aspect is ultrasonic tomographic imaging, which provides precise data on the state of the process in the reactor. In summary, the paper presents a state-of-the-art approach to analyzing chemical reaction processes in industrial reactors using advanced feedback systems and ultrasonic imaging technologies. The proposed solutions are essential for improving efficiency and process control in the chemical industry.

The study aims to develop a reconstruction and measurement system for data analysis using ultrasound transmission tomography (UST) and electrical impedance tomography (EIT), which can be implemented in wearable solutions for non-invasive monitoring and diagnosis of functional urinary tract disorders. Functional disorders can be associated with many urinary system defects, constipation, and central nervous system defects (spina bifida, cerebral palsy). The lack of non-invasive diagnostic methods for a functional and comprehensive analysis of the state of the urinary tract reduces the likelihood of a correct diagnosis and effective treatment. Furthermore, constructing a physical model of a tomograph is problematic due to the complexity of acoustic phenomena necessary for modeling in the case of a heterogeneous environment of propagation of waves in small, limited spaces. Nevertheless, the radial propagation model used in UST is sufficient for effectively detecting disturbances in the interior of objects and is used in medical solutions. The innovation of the solution lies in the simultaneous assessment of urinary tract function based on the analysis of images obtained using EIT and UST. As a non-invasive diagnostic, UST allows for visualization of the body's internal structures (including muscles, blood vessels, and internal organs). However, UST-based diagnostic systems of the urinary tract are not present in world markets. Moreover, building such a system requires solving many problems: limited research fields, dependence on the body structure, difficulties in imaging bone structures, and areas filled with gases.

The article discusses the design of advanced embedded algorithms that aim to manage and control the measurement process using the Electrical Impedance Tomography and Ultrasonic Tomography methods. The project aims to develop solutions that optimize the performance of embedded devices that must operate on limited resources such as memory, computing power, or bandwidth. Minimizing energy consumption is an important aspect, especially for battery-powered devices. Algorithms must also be adapted to specific hardware constraints, such as low RAM or CPU limitations, which require complex engineering and optimization. Additionally, the project assumes the implementation of algorithms that consider security aspects, which is crucial in protecting data processed and transmitted by the device. It also requires the development of effective communication methods between the embedded device and other systems, including appropriate communication protocols.

Impedance tomography (EIT) is an imaging technique that harnesses differences in electrical conductivity to visualize the interior of objects. Despite limitations such as low resolution and nonlinearity of current distribution, its potential in medicine and industry is a source of fascination. The research in this work is a step towards unlocking this potential, focusing on improving the quality of EIT image reconstruction, particularly in bladder modeling. A key element is regularization techniques, including Laplace matrices and the iterative Gauss-Newton algorithm, which balance matching accuracy and image smoothness. In the practical part, simulations were conducted on dense and sparse meshes. Modeling the urinary bladder as a rotational ellipse significantly influences the interpretation of data by algorithms, a crucial factor for the accuracy of reconstruction. Various electrode configurations were also analyzed, revealing the impact of their arrangement on electrical properties and imaging quality. Iterative testing led to identifying optimal electrode placement and grid configuration, underscoring the importance of precise modeling for obtaining high-quality images. The results underscore the critical role of appropriately selecting the regularization parameter in minimizing reconstruction errors.

In order to solve the singular fault modes of redundant position sensors in electric actuators, a fault diagnosis method based on Hall sensor analytical model is proposed on the basis of traditional redundancy comparison algorithm. Through the complete self-monitoring design of Hall sensor and the analytical model design, non-similar redundancy signals of sensors are formed. At the same time, a hypothesis testing method based on sliding data window is adopted, and by adjusting the width of the sliding window to meet the indicators of false alarm rate and missed alarm rate, a fault diagnosis algorithm for position sensors is formed as a whole. Finally, an algorithm validation was conducted for the 2∶2 singular fault mode of the position sensor. The results showed that the fault diagnosis algorithm based on the Hall sensor model can effectively solve the problems caused by 2∶2 singular faults, greatly improve the availability of sensor signals, and ensure the control accuracy and fault tolerance of the servo closed-loop.

Phononic or acoustic bandgap materials have often been made using a polymer matrix with metal inclusions such as tin and steel, which have high densities compared to the matrix material. Acoustic bandgaps are observed when waves are not transmitted at certain frequencies. These have been applied in cavity resonators, acoustic waveguides, and more. This paper introduces a concept of using cement as the surrounding matrix and carbon nanotubes as the core inclusions to develop phononic materials. The exhibition of a bandgap makes it possible for the cementitious phononic material to be used as a sensor for cement cracking and defects in oil well bores. This paper discusses ways to optimize the characteristics of the carbon nanotube core to develop gaps in transmission spectra. It shows the behavior of the cementitious material with changing filling fraction, location of core cells, and surrounding defects, creating a pathway for paradigm-shifting non-destructive sensing technologies.

Abstract Background Mycoplasma (M.) hyopneumoniae is associated with respiratory disease in pigs and is the primary agent of enzootic pneumonia. Quantification of M. hyopneumoniae-related outcome parameters can be difficult, expensive, and time-consuming, in both research and field settings. In addition to well-established methods, technological tools are becoming available to monitor various aspects of relevant animal- and environment-related features, often in real-time. Therefore, this study aimed to assess whether certain parameters, such as animal movement and body temperature using microchips (IMT), correlate with established parameters and whether the currently used parameters can be rationalized. Results The percentage of movement was significantly reduced by M. hyopneumoniae infection in pigs (p 0.30) to very strong correlations (> 0.80) were observed between the abovementioned parameters (p 0.05). Conclusions M. hyopneumoniae infection significantly reduced the movement of piglets and increased lung lesions, M. hyopneumoniae-DNA load, and anti-M. hyopneumoniae antibody levels; and, good correlations were observed between most parameters, indicating a direct relationship between them. Thus, we suggest that changes in movement might be a reliable indicator of M. hyopneumoniae infection in pigs, and that a selected group of parameters—specifically RDS, MLCL, MLL, M. hyopneumoniae-DNA load, anti-M. hyopneumoniae antibody levels, and movement—are optimal to assess M. hyopneumoniae infection under experimental conditions.

Abstract Background Freshwater mussels are important keystone and indicator species of aquatic ecosystems. Recent advances in sensor technology facilitate applications to individually track mussels and to record and monitor their behavior and physiology. These approaches require the attachment of sensor devices as “backpacks” to the outer shell surface. The interpretation of such data makes it necessary to understand the influence of these attachments on the horizontal and vertical movement behaviors of freshwater mussels. Over a series of mesocosm experiments, this study systematically investigated the effects of three size- and wiring-specific variants of artificially attached backpacks on the horizontal and vertical movement behavior of Anodonta anatina. Results Across all experiments, equipping mussels with backpacks did not result in a significant influence on horizontal movement for any of the backpack variants. In contrast to this finding, the big backpacks with a high ratio between backpack volume and mussel length resulted in a significantly negative effect on vertical movement, indicating a potential for adverse effects of such devices on mussels, especially in natural settings. Conclusions The findings of this study show that assessing the effects of attached devices on mussels requires a species-specific evaluation of potential impacts on the endpoints of interest. Especially for vertical movement patterns, selection of the smallest available devices appears mandatory.

Advances in wearable technologies now allow modern smartwatches to collect body composition estimates through bioelectrical impedance techniques embedded within their design. However, this technique is susceptible to increased measurement error when postural changes alter body fluid distribution. The purpose of this study was to evaluate the effects of postural orientation on body composition and total body water (TBW) estimates produced by smartwatch bioelectrical impedance analysis (SWBIA) and determine its agreement with criterion measures. For this cross-sectional evaluation, 117 (age: 21.4±3.0 y; BMI: 25.3±5.7 kg/m2) participants (F:69, M:48) completed SWBIA measurements while in the seated, standing, and supine positions, then underwent criterion dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance spectroscopy (BIS) assessments. In the combined sample and females, body fat percent, fat mass, and fat-free mass using SWBIA were significantly different between the supine and standing positions (all p

The escalating concern over environmental pollutants, particularly brominated flame retardants (BFRs), demands sophisticated detection methodologies for compounds like Tetrabromobisphenol A (TBBPA) and Hexabromocyclododecane (HBCD). Amidst these challenges, advancements in electrochemical detection have notably focused on the integration of inorganic modifiers within carbon electrodes. Inorganic nanoparticles, known for their catalytic and surface-enhancing properties, play a pivotal role in augmenting the sensitivity and selectivity of electrode-based detection systems. These modifiers, encompassing materials such as graphene, CeO2 nanocubes, and metal-organic frameworks, among others, have revolutionized the capabilities of carbon-based electrodes in accurately identifying specific BFRs.

This review illuminates established knowledge of root–arbuscular mycorrhizal fungi (AMF)–plant mutualism to study the uptake of phosphorus (P) as a critical element for plant nutrition. We focus on P cycling, underscoring the role of AMF in enhancing P acquisition and plant resilience in the rhizosphere. The role(s) of plant roots, root exudates, and biomolecules in relevant soil processes is emphasized in this manuscript. Enhancing P uptake efficiency through AMF interaction presents a promising avenue for sustainable agriculture, with future research opportunities focusing on understanding underlying mechanisms and developing innovative technologies as a need to transition from the use of AMF as a biofertilizer or as an inoculation alternative for seeds to being an inspiration for the development of technology adapted to different crops. This is important to promote responsible agricultural practices and improve crop yields. We provide definitions of key terms and concepts for one of the best-known natural sustainable phosphorus systems. This manuscript illuminates and aims to inspire technology development to overcome the challenge of plant nutrition under P scarcity conditions.