In this work, an analog front-end (AFE) circuit for Electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter- all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the $g_m/I_D$ methodology. Both the pre-layout and post-layout simulation were carried out. The layout consumes an area of 0.0058 mm$^2$ without the resistors and capacitors. Analysis of various simulation results were carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz with a variable gain between 44 to 58 dB, a notch depth of -56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of -59.65 dB (less than 1\%), an input referred noise spectral density of $

Reducing the carbon footprint of energy production is one of the most pressing challenges facing humanity today. Lignocellulosic biomass residues from fruit production industries show promise as a viable energy source. This paper presents a study of the Italian context concerning the utilization of orchard lignocellulosic residues for energy production as electricity or bioethanol. The potential of various orchard residues was assessed through chemical and physical analysis, and an equivalent electrical energy of about 6441.62 GWh or an amount of 0.48 Mt/y of bioethanol was obtained based on the average annual dry residue mass availability of about 3.04 Mt/y. These data represent the 9.30% of the national electrical energy production from renewable sources, as well as the 6.21% of the Italian demand for gasoline in 2022. Electricity generation from these residues shown its potential as a reliable and sustainable baseload power source, as well as source of renewable transportation fuel. The studied process could be a valuable reference to expand these concepts on global scale to achieve a greener and more sustainable energy future.

In everyday life, the birth of a number of blind people takes place and millions of people are already available. Visually impaired people struggle to walk safely and in- dependently because they have trouble detecting obstacles. In this work, we suggest a novel approach to detecting obstacles utilizing an Arduino UNO board and an ul- trasonic sensor (HC-SR04). The main goal of this research is to increase the mobility and security of blind people in a variety of settings. The suggested system makes use of the HC-SR04 ultrasonic sensor’s capability to precisely measure distance and identify nearby obstructions. As the main processor component, the Arduino UNO board processes algorithms for real-time obstacle detection while also collecting data from the sensor. The device is intended to give the visually impaired user immedi- ate aural input while informing them of the location and proximity of obstacles. To Extensive testing is done in controlled and real-world settings to assess the system’s performance and dependability. In the studies, the effectiveness of obstacle detec- tion, the system’s response time, and its practicality and usefulness for blind people are all measured. The findings show that the suggested obstacle detection method provides very accurate obstacle identification at a safe distance. Users who are blind or visually handicapped may successfully understand their surroundings thanks to the aural feedback system, which helps them to navigate by making judgments that are well-informed. Additionally, the system responds quickly, guaranteeing that the user receives real-time obstacle updates to increase safety and confidence while mov- ing. The study’s conclusions demonstrate the possibility of using the Arduino UNO board with the HC-SR04 ultrasonic sensor as a practical and affordable approach for detecting obstacles.

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1-measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data is expensive), and the complex relationships between anomalies not easily captured by traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model is deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

Elevated blood glucose levels, known as glycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding this link has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment, in-silico model of the human atrial node's action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on atrial node action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the sinoatrial node action potential frequency. In summary, our model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

Abstract: With the growing integration of the Internet of Things (IoT) in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of Wireless Sensor Networks (WSNs) in a commercial multi-story building, specifically addressing the difficulty of dealing with variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights that have the potential to signifi-cantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system’s performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. By leveraging the capabilities of Contiki operating system, this study employs a specialized simulator to generate simulated data, allowing for a comparison between real-world and simulated scenarios. We provide an in-depth analysis to scrutinize the disparities and commonalities between the datasets obtained from real-world deployments and simulated en-vironments. We highlight the necessity of precise simulation to validate real-life sensor data, shedding light on the challenges and opportunities associated with implementing WSNs in in-telligent buildings. Results obtained show the significance of accurate simulation models for re-liable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. The findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental pa-rameters. This includes insights into temperature, humidity, and light intensity, offering oppor-tunities for enhanced comfort and efficiency in intelligent environments.

The objective of this paper was to verify the applicability of statistical learning (SL) compared to human reasoning with respect to the Universal Thermal Climate Index (UTCI), a complex tool for the assessment of outdoor thermal stress. UTCI is an equivalent temperature index based on the 48-dimensional output of an advanced model of human thermoregulation formed by 12 variables at four consecutive 30-minute intervals, which were calculated for 105642 thermal conditions from extreme cold to extreme heat. Comparing the performance of SL algorithms to the results accomplished by an international endeavor involving more than 40 experts from 23 countries, we found that random forests and k-nearest neighbors closely predicted UTCI values, but that clustering applied after dimension reduction algorithms (principal component analysis and t-distributed stochastic neighbor embedding) were inadequate for risk assessment in relation to the UTCI stress categories. This indicates a potential supportive role for SL, as it will not (yet) fully replace the bio-meteorological expert knowledge.

Accurately predicting the distance an object will travel to its destination is very important in various sports. Acceleration sensors as a means of real-time monitoring are increasingly gaining increasing attention in sports. Due to the low energy output and power density of Triboelectric Nanogenerators (TENGs), recent efforts have focused on developing various acceleration sensors. However, these sensors suffer from significant drawbacks of large size, complexity, high power input requirements, and high cost. Here, we described a portable and cost-effective real-time refreshable strategy design comprising a series of individually addressable and controllable units based on TENGs embedded in a flexible substrate. This results in a highly sensitive, low-cost, and self-powered acceleration sensor. Putting, which accounts for nearly half of the strokes played, is obviously an important component of the golf game. The developed acceleration sensor has an accuracy controlled within 5 %. The initial velocity and acceleration of the forward movement of a rolling golf ball after it is hit by a putter can be displayed, and the stopping distance quickly calculated and predicted in about 7 seconds. This research demonstrates the application of the portable TENG-based acceleration sensor, while paving the way for designing portable, cost-effective, scalable, and harmless ubiquitous self-powered acceleration sensors.

Despite Low Power Wide Area Networks’ ability to offer an extended range, it might still encounter challenges with coverage blind spots in the network. This article proposes an innovative energy-efficient nature-inspired relay selection algorithm for LoRa-based LPWAN networks, serving as a solution for challenges related to poor signal range in areas with limited coverage. A behavior swarm-inspired approach has been utilized to select the relays’ localization in the network, providing network energy efficiency and radio signal extension. These relays help bridge communication gaps, significantly reducing the impact of coverage blind spots by forwarding signals from devices with poor direct connectivity with the gateway. The proposed algorithm considers critical factors for the LoRa standard, such as the Spreading Factor or device energy budget analysis. The simulation experiments validate the proposed scheme’s effectiveness in terms of energy efficiency under diverse multi-gateway topology scenarios involving thousands of devices. Specifically, it has been verified that the proposed approach outperforms the reference method in preventing the battery depletion of relays, which is vital for battery-powered IoT devices. Furthermore, for some large-scale problems, the proposed heuristic method achieves over twice the speed of the exact method with a negligible accuracy loss of less than 2%.

Elevated blood glucose levels, known as glycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding this link has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment, in-silico model of the human atrial node's action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on atrial node action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the sinoatrial node action potential frequency. In summary, our model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

Turbidity stands as a crucial indicator for assessing water quality, and while turbidity sensors exist their high cost prohibit their extensive use. In this paper, we introduce an innovative turbidity sensor, and it is the first low-cost turbidity sensor which is designed specifically for long-term stormwater in-field monitoring. Its low cost ($23.50 USD), enables the implementation of high spatial resolution monitoring schemes. The sensor design is available under open hardware and open-source licences, and the 3D-printed sensor housing is free to modify based on different monitoring purposes and ambient conditions. The sensor was tested both in the laboratory and in the field. The laboratory results show a strong linear relationship (R2 > 0.99) between the sensor readings and the commercial hand-held turbidimeter (Thermo Scientific AQ4500) results of the solution. In the field, the low-cost sensor measurements were statistically significantly correlated (p < 0.01) to a standard high-cost commercial turbidity sensor (GreenSpan TS-1000). Biofouling and drifting issues were also analysed after the sensors were deployed in the field for more than 6 months, showing both biofouling and drift occur during monitoring. Nonetheless, in terms of maintenance requirements, the low-cost sensor exhibited similar needs compared to the Green-Span sensor.

Grating (moiré) interferometry is one of the well-known methods for full-field in-plane displacement and strain measurement. There are many design solutions for grating interferometers, including systems with a microinterferometric waveguide head. This article proposes a modification to the conventional waveguide interferometer head, enabling the implementation of a polarization fringe phase shift for automatic fringe pattern analysis. The article presents both the theoretical consideration of the proposed solution and its experimental verification, along with the concept of the in-plane displacement/strain sensor using the described head.

Wireless Sensor networks (WSNs) are gaining more and more research interest due to their ability to monitor large areas more or less independent. However, their reliability is an important issue, since their system reliability is influenced from a hardware, a data and an energetic point of view due to different factors like loading conditions, signal attenuation and battery lifetime. Regarding these issues, the positions of sensor nodes have to be chosen correctly to maximize the system reliability, which has to be considered during the development of new products equipped with WSNs. In this paper, an approach to estimate a WSNs system reliability during the development phase based on the analysis of the measurements on the example of strain measurements in finite elements (FE) models is presented. For that, the part under consideration is divided into regions with similar strains by the use of a Region Growing Algorithm (RGA). Afterwards, the WSN’s configuration is analyzed regarding its reliability in accordance to the data paths and measurement redundancy due to the sensor positions in the found measuring regions. This methodology is tested on an exemplary WSN configuration at an aircraft wing box under bending load and was found to work very well regarding the estimation of the hardware perspective on the system reliability. Therefore, the methodology and the developed algorithm show potentials for optimizations in sensor node positions for better reliability results.

.Various synthetic nematicides are currently used effectively in the control of plant parasitic nematodes. However, these nematicides have adverse effects on humans and the environment. Neem oil is eco-friendly alternatives to synthetic nematicides. The effectiveness of repeated neem oil application in suppressing the root-knot nematodes infestation in figs seedlings was therefore examined in this study, both in its natural form (10 ml/L) and in its nano-emulsion form (5 and 7 ml/L). The recorded reduction parameters of M. incognita were J2/250g soil, galls and egg masses / 5g roots of infected fig seedlings were measured at that time, and the effects of these natural additives were compared with the Fosthiazate (Krenkel 75%EC). The impact of such soil amendments on some typical plant parameters, the total microbial count of soil, and the overall enzymatic activity of the fig rhizosphere were also included. Obtained results revealed that the soil population density of root knot nematode suppressed by 80.3% , reduction of nematode egg masses (60.4%) due to the application of nano-emulsion of neem oil (at 7ml/L). Present work recommend the use of nano-emulsion of neem oil as one of the effective and eco-friendly means to reduce the root- knot nematode on fig .

The decarbonization of the built environment, both in new construction and renovation, is crucial to mitigate its relevant impact on climate change and achieve the Paris agreement goals. This study presents a systematic LCA-based methodology to assess the whole life carbon emissions of buildings, applied to a proposal for the regeneration of one of Milan’s disused railway yards. As an entry for the 2020 Reinventing Cities competition, Scalo Lambrate is a project for a mainly residential neighborhood with a public park. Strategies to reduce carbon emissions deriving both from the operational energy and construction and maintenance were evaluated and their effects compared to a reference scenario over a time horizon of 100 years. The results show that, while the opportunities to reduce carbon emissions during the use phase are somehow limited due to the already stringent performance requirements for new builds, the use of fast-growing biogenic materials for construction materials, even if mixed with more traditional ones, can provide a significant reduction of the global warming potential over the whole life cycle, with a reduction of 70% over the baseline. The remaining emissions can be offset with afforestation initiatives that, however, must be assessed against land use issues.

Chronic kidney disease (CKD) is characterized by a continuous decline in kidney function, often persisting for years or even a lifetime. This study presents a new exploration into the extraction and application of insect chitosan (Tenebrio molitor) targeting hemodialysis (HD). Initially, the potential of insect-derived chitosan as a sustainable and effective alternative to traditional sources (i.e. crustaceans) is demonstrated. Efficient extraction methods were developed (totaling between 6 h and 12 h), producing chitosan. Firstly, chitin was efficiently isolated from T. molitor through deproteinization and demineralization, with yields of approximately 5% (w/w). Subsequently, chitosan was obtained from the extracted chitin resulting in yields of between 65.0 ± 0.8% (w/w) and 79.3 ± 0.8% (w/w). Characterization using Fourier Transform Infrared Spectroscopy (FTIR) confirmed structural similarities with commercial chitosan and degrees of deacetylation in the 73 - 75% range. The bioactive properties of chitosan obtained from T. molitor, including antimicrobial and antioxidant activities, were evaluated. All the microorganisms tested were inhibited, exhibiting minimum lethal concentrations between 2 and 8 mg/mL, confirming the antimicrobial activity of the extracted chitosan. In addition, chitosan showed antioxidant activity in the range of 60 to 65 µmol Trolox equivalent/g, suggesting its viability for various medical applications.This study also developed a chitosan-based membrane (CH - M) culminating in a proof-of-concept phase for hemodialysis. The permeation characteristics of CH-M for urea and albumin were studied in vitro to assess their suitability as HD membranes. Urea was permeable to values of over 70% and albumin was retained.

Molluscs are significant species in aquatic ecology. Molluscs are crucial for maintaining ecological balance as secondary consumers and serving as bioindicator species in biomonitoring systems. Molluscs are threatened by pesticides such as commonly used pyrethroid insecticides. Pyrethroids are typical insecticides used to control insects in agriculture and gardening. Pyrethroids are increasingly being utilized in both agricultural and human activities. Pyrethroids enter aquatic bodies through rainfall and drainage systems. The use of pyrethroids is becoming a significant concern for aquatic creatures, particularly molluscs. The discussion will focus on the increasing issue of pyrethroids becoming a nightmare for molluscs. Pyrethroids, due to their lipophilic nature, pose a significant risk to molluscs by affecting their metabolites, producing reactive oxygen species, and influencing neurotransmitter actions. Molluscs are crucial organisms in aquatic ecosystems, and their threats warrant significant discussion and attention. This review will delve into the behaviour of pyrethroids on molluscs.

Polyphenols are the most common organic compounds in wine. They improve both the sensory aspects of wine and human health by activating antioxidant systems. Therefore, there is a great need for a simple, fast and accessible method to determine the polyphenol content of wine. The aim of this study is to characterize polyphenolic compounds in natural wine samples using an electrochemical method and a modified carbon paste electrode with TiO2 nanoparticles. This work is motivated by the promising improvement of the sensing properties of a TiO2 nanoparticle-modified carbon paste electrode observed in the electrooxidation of different polyphenols. Since the voltammetric response of wine is largely irreversible, another motivation was the possibility of improving the processing of voltammograms to obtain data indicative of wine polyphenols, based on the approach described in the seminal work by Espinoza et al. The modified electrode studied provides an improved and reproducible voltammetric response of gallic acid in wine samples and provides a good basis for further development of an integrated sensor/data system with the possibility of wider application for the quantification of gallic acid as a flavor and visually relevant parameter in winemaking.

The study assessed the trapping efficacy of locally-modified 1) Gravid Aedes Trap(GAT) lined with insecticide-treated net (ITN) as a killing agent, 2) Autocidal Gravid Ovitrap(AGO) with sticky board in the semi-field system (SFS) and field setting. Fully-balanced Latin square experiments were conducted to compare GAT lined with ITN vs AGO, both with either yeast or grass infusion. Biogents-Sentinel (BGS) with BG-Lure and no CO2 was used as standard trap for Aedes mosquitoes. In the SFS, GAT outperformed AGO in collecting both nulliparous (65% vs 49%,OR=2.22,[95%CI:1.89-2.60],p

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data was uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below most of the comparable system. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

The pervasive presence of atmospheric pollution, particularly polycyclic aromatic hydrocarbons such as 7,12-dimethylbenz(a)anthracene, instigates aberrant cellular and tissue conditions, precipitating oxidative damage and inflammatory cascades conducive to the onset of cancer, notably mammary cancer. Given the hepatic metabolism of 7,12-dimethylbenz(a)anthracene, the imperative of employing natural antioxidants becomes apparent. In this study, we delve into the antioxidant and hepato-protective properties of Pistacia lentiscus against 7,12-dimethylbenz(a)anthracene-induced toxicity, alongside exploring its potential anticancer attributes. Our investigations unveil Pistacia lentiscus's anti-proliferative efficacy against human breast cancer cell lines. Notably, 7,12-dimethylbenz(a)anthracene-intoxication escalates body weight, disrupts lipid profiles, and incites serum oxidative damage. Concurrently, hepatic and renal oxidative stress ensue, accompanied by heightened antioxidant enzyme activity in the 7,12-dimethylbenz(a)anthracene-exposed-group compared to controls (p&lt;0.05). Nonetheless, Pistacia lentiscus co-administration rectifies biochemical imbalances, significantly attenuates oxidative stress aberrations, and augments antioxidant enzyme responses (p&lt;0.05). Importantly, histological analysis evinces Pistacia lentiscus's shielding effect against 7,12-dimethylbenz(a)anthracene induced hepatocyte injury and steatosis. Our findings underscore Pistacia lentiscus's robust anti-proliferative, antioxidant, and hepato-protective capacities, mitigating metabolic disturbances, oxidative stress propagation in liver and kidney functions, and potential histological alterations, thereby impeding 7,12-dimethylbenz(a)anthracene-induced mammary cancer initiation. We posit that Pistacia lentiscus may serve as a prophylactic agent against breast cancer instigated by this carcinogen.

The importance of conflict analysis and safety considerations during work zone intervals at signalized intersections cannot be overstated, as these periods pose unique challenges and safety risks for both motorists and pedestrians. Work zones introduce temporary changes to traffic patterns, lane configurations, and signal timings, which can disrupt the flow of traffic and increase the likelihood of conflicts between vehicles and pedestrians. By conducting comprehensive conflict analysis, it is possible to identify potential conflict points, such as merging areas, pedestrian crossings, and lane transitions, and implement targeted safety measures to mitigate risks. Additionally, considering safety considerations during work zone intervals is paramount to safeguarding the well-being of road users and minimizing the occurrence of conflicts or collisions. Measures such as enhanced signage, temporary traffic control devices, and increased enforcement efforts can help promote compliance with traffic regulations and ensure the safe passage of vehicles and pedestrians through work zones. This study investigates the dynamics of traffic behavior and safety implications within work zones at signalized intersections, aiming to address key challenges and enhance intersection management strategies. Through comprehensive analysis, varying frequencies of Vehicle-to-Vehicle (V2V) conflicts were observed across different movement directions, with significant occurrences identified in specific phases of the traffic signal cycle. Specifically, 69 V2V conflicts in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8 were recorded. Furthermore, the examination of Vehicle-to-Pedestrian (V2P) conflicts underscored the importance of prioritizing pedestrian safety, with notable occurrences observed in phases associated with pedestrian crossing movements. Meanwhile, despite the closure of left-turn movements, instances of red light running persisted, with 69 red light running events in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8.To mitigate these safety concerns during work zone intervals, proactive measures are essential. Strategies include improving communication through clear signage and advanced warning systems, enhancing enforcement efforts with red light cameras and increased police presence, and investing in infrastructure improvements such as temporary pedestrian facilities. Integration of real-time data from sensors, particularly LiDAR technology, offers significant advantages in detecting conflicts and red light runners during work zone intervals. LiDAR's precise, real-time data on vehicle and pedestrian movements, combined with its 360-degree coverage and high spatial resolution, facilitates accurate detection and tracking of potential conflict scenarios, contributing to enhanced intersection safety and traffic management efficiency. This study endeavored to conduct a traffic safety analysis during work zone intervals at a signalized intersection in Baltimore City, known for its high rate of traffic crashes, utilizing LiDAR sensor technology.

Unmanned Underwater Vehicles (UUVs) are pivotal in ocean exploration, research, and various industrial activities such as marine mining and offshore engineering. However, traditional methods of navigating these vehicles face significant challenges, mainly due to the inefficacy of electromagnetic waves in water, leading to signal loss. To address these limitations, researchers are increasingly turned learning, an artificial intelligence technique capable of learning from data-enhancing underwater navigation, mainly through visual Simultaneous Localization and Mapping (SLAM). This paper explores integrating deep learning methodologies and sensor technologies to revolutionize underwater navigation for UUVs. Proprioceptive sensors, along with exteroceptive sensors, are crucial in accurately measuring and comprehending the underwater environment. Additionally, the paper provides detailed insights into the processes of underwater SLAM, camera-based underwater positioning systems, sonar systems for underwater navigation, and the utilization of Lidar in underwater navigation. Furthermore, it delves into applying deep learning techniques in underwater SLAM, offering a comprehensive understanding of the innovative processes driving advancements in underwater vehicle navigation. By leveraging these advancements, this research aims to improve underwater navigation systems' precision, reliability, and adaptability, thereby unlocking new frontiers in ocean exploration and industrial applications for UUVs.

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Towards that goal we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant spikerbox, a device that measures changes in voltage differentials of plants between roots and leaves. Using machine learning, we find that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems, while also paving the way to use plants as biosensors for analyzing human movement.

This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli, an electrical voltage for ERFs and a magnetic field for MRMs, respectively. In this review article, the MRMs are classified into magnetorheological fluid (MRF), magnetorheological elastomer (MRE) and magnetorheological plastomer (MRP). To easily understand the history of sensing research using the two smart materials, the order of this review article is organized in a chronological manner of ERF sensors, MRF sensors, MRE sensors and MRP sensors. Among many sensors fabricated from each smart material, one or two sensors or sensing devices are adopted to discuss on the sensing configuration, working principle and specifications such as accuracy and sensitivity. Some sensors adopted in this article include force sensor, tactile device, strain sensor, wearable bending sensor, magnetometer, display device and flux measurement sensor. After briefly describing what have been reviewed in a conclusion, several challenging future works, which should be done for practical applications as sensors or sensing devices, are remarked in terms of new technologies such as artificial intelligence neural network in which several parameters affecting the sensor signals can be precisely tuned or controlled. It is sure that this review article is very helpful to make creative sensors using not only the proposed smart materials but also several different types of smart materials including shape memory alloys and active polymers.

This article describes an approach for optimising sensor-controlled systems through minimal intervention, utilising Fuzzy Linear Systems of Equations (FLSE). Staring with a generalised model of the system behaviour, incorporating an array of control units, environmental sensors, and an expert knowledge base. The described problems of detecting the level of intervention needed to change the system state to another is handled with the help of developed methods for solving the inverse problem for FLSE. By achieving minimal intervention, we ensure that system adjustments effective, economically optimal and non-intrusive. A MATLAB-based implementation is presented.

Ethical considerations regarding our treatment of animals have gained strength, leading to legislation and a societal focus across various disciplines. This is a subject of study within curricula related to agri-food sciences. The aim was to determine the perceptions of agronomy university students concerning animal welfare in livestock production systems. A survey was conducted to encompass various aspects, from participants' sociodemographic attributes to their attitudes and behaviors regarding animal welfare and the consumption of animal products. Statistical analysis, performed using R software, delved into the associations between participants' characteristics and their perspectives on the ethical, bioethical, and legal dimensions of animal welfare. Associations between demographic factors and ethical viewpoints among students were identified. Gender differences emerged in animal treatment perceptions, while rural and urban environments impacted perspectives on various animals. Bioethical considerations revealed distinctive disparities based on gender and education in concerns for animal welfare, value perceptions, evaluations of animal behaviors, and opinions on animal research. It is crucial to distinguish between animal welfare and ethical considerations arising from coexisting with sentient beings capable of experiencing suffering. Ethical theories provide a lens through which we perceive our obligations toward animals. The responsibility to ensure animal welfare is firmly rooted in recognizing that animals, like humans, experience pain and physical suffering. Consequently, actions causing unjustified suffering or mistreatment, particularly for entertainment purposes, are considered morally unacceptable.

Droplet-based microfluidics has revolutionized numerous fields such as biomedical research, pharmaceuticals, drug discovery, food engineering, flow chemistry, and cosmetics. This paper presents a comprehensive study focusing on the detection and characterization of droplets with volumes in the nanoliter range. Leveraging the precise control of minute liquid volumes, we introduced a novel spectroscopic On-Chip microsensor equipped with integrated microfluidic channels for droplet generation, characterization, and sensing, simultaneously. The microsensor, designed with Interdigitated-Ring-Shaped Electrodes (IRSE) and seamlessly integrated with microfluidic channels, offers enhanced capacitance and impedance signal amplitudes, reproducibility, and reliability in droplet analysis. We were able to make analyses of droplets length in the range 1.0-6.0 mm, velocity 0.66-2.51 mm/s, droplet volume 1.07nL-113.46nL. Experimental results demonstrated that the microsensor's has a great performance in terms of droplet size, velocity, and length, with a significant signal amplitude of capacitance and impedance, and real-time detection capabilities, thereby highlighting its potential for facilitating microcapsule reactions and enabling on-site real-time detection for chemical and biosensor analyses on-chip.

Human Activity Recognition (HAR) technology enables continuous behavior monitoring, particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR, were employed for classification. Results demonstrate the ear sensor's efficacy, with deep learning models achieving 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital signs monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.

This work aims to investigate the effect of process temperature and catalyst content by thermochemical degradation of municipal solid waste (MSW) fraction (organic matter + paper + plastic) on the yield of reaction products (bio-oil, biochar, H2O and gas), physicochemical properties and chemical composition of bio-oils, as well as on the morphology and crystalline phases of biochar in laboratory scale. The organic matter, paper and plastic segregated from the gravimetric composition of total waste sample were subjected to the pre-treatments of drying, crushing and sieving. The experiments were carried out at 400, 450 and 475 °C and 1.0 atmosphere, and at 450 °C and 1.0 at-mosphere, using 5.0, 10.0 and 15.0% (wt.) of FCC zeolite, bath mode, using a laborato-ry scale glass reactor. The bio-oil was characterized for acidic value. The chemical functions present in the bio-oil identified by FT-IR and the composition by GC-MS. Biochar was characterized by SEM/EDS and XRD. Thermal pyrolysis of the MSW frac-tion (organic matter + paper + plastic) shows bio-oil yields between 9.44 and 9.24% (wt.), aqueous phase yields between 21.93 and 18.78% (wt.), solid phase yields between 67.97 and 40.34% (wt.) and gas yields between 28.27 and 5.92% (wt.). The yield of bio-oil decreases with increasing process temperature. For the experiments using FCC, the biochar and gas yields increase slightly with the FCC content, while that of bio-oil de-creases and the H2O phase remains constant. The GC-MS of bio-oils identified the presence of hydrocarbons and oxygenates, as well as nitrogen-containing compounds, including amides and amines. The acidity of the bio-oil increased with increasing temperature and with the aid of FCC as a catalyst. It has been identified the presence of hydrocarbons within bio-oil by addition of FCC catalyst due to the deoxygenation of carboxylic acids, followed by decarboxylation and decarbonylation reactions, producing aliphatic and aromatic hy-drocarbons.

In this paper, a continuous power management for a decentralized DC microgrid (DCMG) is proposed to achieve voltage regulation and power balance even under system uncertainty and voltage sensor failure. The DCMG system achieves a continuous power management through only the primary controller to reduce the computational burden of each power agent. To enhance the reliability and resilience of the DCMG system under voltage sensor failure, a DC bus voltage (DCV) sensor fault detection algorithm is proposed. In this algorithm, the DCV sensor failure is detected by comparing the measured DCV with the estimated DCV. When the power agent identifies the DCV sensor failure, it changes the operation properly according to the proposed control mode decision algorithm to maintain the stability of the DCMG system. When uncertain conditions such as the power variation of the distributed generations, sudden grid disconnection, DCV sensor failure, electricity price change, and critical battery status occur, the DCMG system is changed to transitional operation modes. These transitional operation modes are employed to transmit the power agent information to other agents without digital communication links (DCLs) and to accomplish power sharing even under such uncertain conditions. In the transitional operation modes of the DCMG system, the DCV levels are temporarily shifted to an appropriate level, enabling each power agent to detect the uncertainty conditions, and subsequently to determine its operation modes based on the DCV levels. Simulation and experimental results under different test conditions confirm the reliability and effectiveness of the proposed control scheme.

Diabetes mellitus (DM) is a metabolic disorder characterized by the deficiency of insulin production and is one of the concerning health problems worldwide, especially in developing countries. Considering the systemic toxicity associated with current antidiabetic medications and their limited efficacy, there is an urgent need to discover new plant-based natural antidiabetics. Since time immemorial, medicinal plants have played an important role in the management of health disorders. In recent years, herbal medicine has experienced exponential growth in popularity worldwide, owing to its natural origin and fewer side effects, appealing to both developing and developed nations. Ecuador has a rich cultural history in ethnobotany that plays a crucial role in their people's lives. Several Ecuadorian medicinal plants have hypoglycemic or an-ti-hyperglycemic properties that are rich with bioactive phytochemicals. These medicinal plants were extensively used in traditional medicine preparation in Ecuador for many decades to treat diabetes because of their cost-effectiveness, available traditional knowledge, and low toxicity. Many Ecuadorian medicinal plants are reported to have antidiabetic properties that reduce blood sugar levels, and oxidative stress, regulate intestinal function, improve insulin resistance, inhibit α-amylase and α-glucosidase, lower gluconeogenic enzymes, stimulate glucose uptake mechanisms, and play an important role in glucose and lipid metabolism, yet there is a substantial lack of integrated approach between the existing ethnomedicinal practice and pharmacological research. Therefore, this review aims to discuss and explore the traditional medicinal plants used in Ecuador for treating DM and their bioactive phytochemicals, which are mainly responsible for their anti-diabetic properties.

Visual Sensor Networks (VSNs) have been the focus of numerous studies, emphasizing the quality of the information they generate. The primary goal of these networks is to capture data from an area, extract the pertinent information from the visual scene, and facilitate communication. However, the presence of objects in the study area can impacted the network's efficiency, linking the quality of acquired data directly to the positioning of the cameras. Strategically positioning multiple cameras in a potentially complex area, while ensuring effective communication between nodes, presents a challenging issue in VSNs applications. This paper introduces a specific method to enhance the deployment of visual sensors in indoor environments. The research addresses the theme of multi-objective optimization of VSN deployment, integrating various criteria: maximizing overall visual coverage, maximizing the observation of zones of interest, maximizing observation redundancy of zones of interest and ensuring communication connectivity for data transfers. To tackle this problem, we simulate the observed environment incorporating cameras, obstacles, and considering zones of interest. Camera positioning is performed while considering visual constraints and the scene's characteristics. The quality of positioning is evaluated based on the defined objective functions. Initially, a comparative study is conducted with a state-of-the-art approach, integrating the first three objective functions. Subsequently, we incorporate the fourth objective function to ensure data transfers. The proposed method demonstrates efficient camera network deployments in indoor areas, considering global coverage and observation redundancy. In contrast to the state-of-the-art, these results are achieved in areas with visual obstacles while ensuring communication connectivity between the different cameras.

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The rapid growth of the global wearable technologies market is projected to reach $61.30 billion by 2030 which reflects a shift towards personalised health monitoring embedded in everyday activities. This paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. Experimental results demonstrate the sensor’s capability to detect water droplets and glucose concentrations, showcasing promising applications for health monitoring. The embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture.

This paper introduces PatchFusion, an innovative approach for nonrigid tracking and reconstruction of deformable objects using a single RGB-D sensor. Existing methods face challenges in accurately capturing the rapid deformations of soft and flexible objects, thereby limiting their utility in diverse scenarios. Our approach overcomes this challenge by employing a dynamic patch-based framework that adapts to rapid inter-frame motions. Firstly, patch-wise rigid transformation fields for non-overlapping patches are solved via Iterative Closest Point (ICP) by incorporating geometric features as additional similarity constraints, thereby enhancing robustness and accuracy. Secondly, deformation optimization based on a nonrigid solver is applied to refine the coarse transformation fields. In order to enable simultaneous tracking and reconstruction of deformable objects, the patch-based rigid solver is designed to run in parallel with the nonrigid solver, serving as a plug-and-play module requiring minimal modifications for integration while enabling real-time performance. Following a comprehensive evaluation, PatchFusion showcases superior performance in effectively dealing with rapid inter-frame deformations when compared to existing techniques, rendering it a promising solution with broad applicability across domains such as robotics, computer vision, and human-computer interaction.

Hansenula polymorpha is a non-conventional, methylotrophic, and thermo-tolerant yeast well known for the industrial production of recombinant proteins. However, the research to evaluate this yeast potential for sugar fermentation to produce alcohols for biofuel applications is at an infant stage. For research advancement, the present work investigated the wild type H. polymorpha strain (DSM 70277) for the co-production of methanol, ethanol, and butanol using a batch fermentation reactor and performing experiments at an ideal temperature of 40°C with a pH of 5.5 using different sugars (fructose, glucose, and xylose) as substrates. The maximum specific growth rates (h-1) of H. polymorpha yeast were 0.59–3.02 for fructose, 0.08–2.60 for glucose, and 0.06–0.08 for xylose. The overall yields (g/g) of methanol were higher from fructose (0.132–0.226) than glucose (0.020–0.021) and xylose (0.009–0.030). Ethanol overall yield (g/g) from fructose (0.130–0.325) was more than glucose (0.054–0.167), and xylose (0.042–0.068). The overall butanol yield (g/g) ranged from 0.018–0.031 (fructose), 0.017 (glucose), and 0.009–0.010 (xylose). Due to the methylotrophic nature of H. polymorpha, a shorter time (preferably 2 h) was found suitable for co-production of methanol, ethanol, and butanol.

Driven by environmental sustainability concerns, the integration of biobased components in curtain wall systems is gaining traction in both research and the construction market. This paper explores the development and validation of a bio-based façade system within the Basajaun H2020 project. The project aimed to demonstrate the feasibility of introducing environmentally friendly bio-based components into the mature curtain wall façade industry. The paper focuses on identifying technological solutions for replacing key components such as profiles, insulation, and the tightness system with bio-based and less environmental impact alternatives presenting the results achieved in the façade system design of the Basajaun project funded by European Union. These solutions aimed at creating a bio-composite-based curtain wall façade that adheres to current building envelope standards and normative; to implement diverse façade typologies for vision panels, opaque sections, and integrated windows; to engineer the prefabrication process for industrialization, enabling wider market replication and simplified transport and installation. The results demonstrate that the Basajaun façade successfully integrates selected components and meets the performance requirements set by regulations. However, questions remain regarding the workability of bio-based profiles as a commercially viable, ready-to-market solution that can replace traditional aluminum profiles in the curtain wall façade industry.

This study aims to construct simple devices to ascertain the concentrations of ammonium (NH4+) and nitrate (NO3-). The device structure comprises a visible and near-infrared absorption meas-urement room, along with an RGB (red, green, and blue) absorption measurement room, con-stituting the two measuring rooms. Both the measurement rooms are equipped with light sources and detectors configured to operate in opposed or thru-beam modes. A tiny processor is utilized for processing the measurements, with an algorithm implemented within its memory. During the algorithm development phase, ranges for measuring the quantity of nutrients were categorized using a discriminant analysis approach. Thirty percent of the soil sample data were utilized as verification data, while the remaining 70 percent served as training data. Nutrient content values from reliable measuring instruments were incorporated into all datasets. Discriminant analysis was employed to classify the amount of NH4+ into five ranges based on the test results. When tested with soil samples, the accuracy was 66.13% compared to the reference value of a reliable measuring device. Similarly, NO3- was classified into four ranges with an accuracy of 81% com-pared to the reference value of a reliable measuring device.

In mining machines with friction discs, but also in multi-rope traction elevators, it is necessary to distribute the applied tensile load, generated by the weight of the cage and counterweight, evenly in all cross-sections of the load-bearing ropes. Hydraulic devices used for this purpose can operate on the principle of Pascal's law. The article presents a structural design, a 3D model and an implemented solution of a laboratory device capable of simulating a practical method of evenly distributing the total weight of the load into partial tensile forces of the same size acting on a selected number of load-bearing ropes. The laboratory equipment uses two pairs of three steel cables of finite length for the simulations. During the experimental measurements, tensile forces derived from the tractive force of the piston rods, pushed into the bodies of the hydraulic cylinders by the pressure of the hydraulic oil supplied through the pipeline under the pistons of the hydraulic cylinders, were detected. The resulting amount of hydraulic oil pressure in the hydraulic circuit influenced by different values of the hydraulic oil pressures in the hydraulic cylinders and by the pressure in the supply pipe was experimentally studied on the laboratory equipment. Simulations were also carried out in order to detect the hydraulic oil pressure in the hydraulic circuit caused by the change in the different magnitudes of the tensile forces in the ropes From the experiments carried out, it follows that with the appropriate choice of hydraulic elements and the design of the hydraulic circuit, the weight of the load, acting as the total pulling force in the ropes, can be evenly distributed (with a deviation of up to 5%) to all cross-sections of the load-bearing ropes. If the exact values of the hydraulic oil volumes under the pistons of all hydraulic cylinders are not known, it is not possible to calculate the pressure values in the hydraulic circuit when the valves of the hydraulic pipes are gradually opened.

Keywords: Energy harvesting, Hybrid, Medical Wearable Sensor Nodes, Health Monitoring, Power Management

In the construction sector, the integration of biobased materials has emerged as a pivotal aspect of EU policies, aimed at promoting sustainable practices throughout the value chain to mitigate resource consumption and carbon emissions. This paper explores the development and validation activities of a bio-based façade system within the Basajaun H2020 project, focusing on enhancing the utilization of biobased components within building envelopes to replace conventional solutions with eco-friendly alternatives. This paper reports the methodologies employed to detect requirements and outline the testing protocols undertaken to validate the façade system design devised within the project focusing on the original façade components as the bio composite profile. Vision and opaque façade modules are prototyped and tested following curtain wall standards standard for performance and acoustic assessments, to showcase the efficacy of the developed solution. The conducted tests demonstrate the feasibility of integrating bio-based components into building envelopes, aligning with project expectations, and prevailing standards for curtain wall façade solutions. Notably, the designed façade system meets technical conditions and project objectives. Nevertheless, the research underscores the need for further refinements to facilitate solution industrialization and explore broader market applicability. Future efforts will focus on addressing these improvements and assessing the scalability of the solution.

This paper details the comprehensive activities conducted in a laboratory setting to assess the Structural Health Monitoring (SHM) of prefabricated building envelopes. Aimed at enhancing the structural evaluation of buildings, the focus of this research lies in employing nondestructive sensors for SHM with the objective to advance in the understanding and assessment of facade conditions. The paper outlines the methodologies employed, starting with a modal analysis to define the structural vibration's modal frequency. Subsequently, tests were defined and executed to validate building envelope compliance in case of stress typical of high-rise building. Throughout these tests, continuous monitoring of the façade was conducted using both conventional (accelerometers, extensimeter washers) and non-conventional sensors (Fiber Optic Sensing) The gathered data underwent thorough processing and analysis to provide an overall perspective on the structure's behavior during the stress test. The investigation aimed to identify whether specific physical phenomena correlate with distinct signals and to evaluate if continuous façade monitoring over the building's lifespan aids in preventing potential structural damage. Moreover, two modal analyses were conducted to compare final data and frequencies with earlier data, enabling the detection of any structural alterations. The results from compliance testing and structural analysis yielded noteworthy insights contributing to the evolving field of structural health monitoring, offering valuable findings that advance our ability to proactively manage and maintain the integrity of building structures.

Single-phase-to-ground fault in non-effectively grounded systems poses serious public safety concern. Along with increasing deployment of low-voltage (LV) sensors, new opportunities and ways are created for solving this traditional problem in distribution systems. A new approach, identifying primary single-phase-to-ground line fault and locating it through measurement data from smart sensors deployed in the secondary side of distribution transformers, is proposed in this paper. When a fault occurs in primary distribution feeder, the transient aerial mode voltage (AMV) of each LV smart sensor is calculated. The fault line can be identified by an abrupt change value of AMV at LV side. Next, by finding the position of the maximum value of this AMV from LV sensors on the fault line, the fault location can be pinpointed to two neighboring distribution transformers. The proposed method has been proven to be efficient and reliable by simulation studies.

The state detection of power cable is very important to ensure the reliability of power supply. Traditional sensors are mostly based on electric field detection, the operation is complex, and its efficiency needs to be improved. This paper optimizes the design and development of the magnetic field detection sensor for AC power cables. Firstly, through the establishment of the magnetic field sensor model, it is determined that permalloy is the material of the magnetic core, the optimal aspect ratio of the magnetic core is 20, and the ratio of coil length to core length is 0.3. Secondly, the coil simulation model is established, and it is determined that the optimal number of turns of the coil is 11000 turns, the diameter of the enamelled copper wire is 0.08mm, and the equivalent magnetic field noise of the sensor is 0.06pT. Finally, the amplifying circuit based on negative magnetic flux feedback is designed and the sensor is assembled, and the experimental circuit is built for sensitivity test. the results show that the sensitivity of the magnetic field sensor is 327.6mV/μT. The sensor designed in this paper has the advantages of small size, high sensitivity, easy to carry and high reliability.

Shared public buildings have become centers of innovation, integrating advanced technologies to meet evolving societal needs. With a heightened emphasis on occupants’ health and well-being, these buildings serve as hubs for technological convergence, facilitating seamless connectivity and intelligent data analysis and management. Within this context, environmental monitoring emerges as a foundational element, pivotal to all aspects of building management. This article provides findings from the nationally funded RE-START project, which focuses on shared public buildings, with special regard to educational and medical facilities. The project explores enhanced indoor air quality monitoring, focusing in particular on CO2 concentration that is directly correlated with occupancy, as a fundamental element for developing health and safety protocols, energy efficiency strategies, integration of smart building technologies, and data-driven energy management. The intersection of environmental monitoring, energy efficiency, security, and IoT technologies in in indoor spaces are relevant. The outcomes of the study reveal the delicate nature of all the involved components, that need to be carefully developed in an integrated manner.

Biometric fingerprint identification hinges on the reliability of its sensors, however, calibrating and standardizing these sensors poses significant challenges, particularly in regard to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like shaped phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels and in the contactless fingerprint sensor setting, 3D printing and silicone filling provides the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.

Bio-inspired gyroid triply periodic minimum surface (TPMS) lattice structures have been the focus of research in automotive engineering because they can absorb a lot of energy and have wider plateau ranges. The main challenge is determining the optimal energy absorption capacity and accurately capturing plastic plateau areas using finite element analysis (FEA). Using nTop's Boolean subtraction method, this study combined walled TPMS gyroid structures with normal TPMS gyroid lattice. This made a composite TPMS-gyroid lattice with relative densities ranging from 14% to 54%. Using the ideaMaker software and the fused deposition modeling (FDM) Raise3D Pro 2 3D printer to print polylactic acid (PLA) bioplastics in 1.75 mm filament made it possible to slice computer aided design (CAD) models and fabricate 36 lattices samples precisely layer by layer technique. Shimadzu 100kN testing equipment was utilized for the mechanical compression experiments. And the validation using finite element analysis (FEA). Further, CTG was examined using a field emission scanning electron microscope (FE-SEM) before and after compression testing. The composite TPMS gyroid lattice showed potential as shock absorbers for vehicles with relative densities of 33%, 38%, and 54%. The Gibson-Ashby model showed that the composite TPMS gyroid lattice deformed mainly by bending, and the size effect was seen when the relative densities were less than 15%. The lattice's relative density had a significant impact on its ability to absorb energy. The research also explored the use of these innovative foam-like composite TPMS-gyroid lattices in high-speed crash box scenarios, potentially enhancing vehicle safety and performance.

This study introduces a Fiber Specklegram Sensor (FSS) design for temperature measurement utilizing a single-multi-single mode optical fiber configuration. FSS technology offers unique advantages for potential development into portable sensing systems. This investigation explores an FSS design compatible with miniaturization by optimizing the interrogation system based on the relationship between speckle grain size and filtering fiber area. Temperature variations are detected through fluctuations in optical power at the sensor output, offering a simple and reduced interrogation scheme. The performance of the FSS is assessed through post-calibration measurements compared to a commercial Fiber Bragg Grating (FBG) sensor, demonstrating comparable results. These findings pave the way for compact and portable fiber optic temperature sensing systems leveraging FSS advantages while maintaining compatibility with established fiber optic measurements.

The aim of this study was to assess the influence of thermocompression conditions on lignocellulosic biomass such as sugarcane bagasse (SCB) in the production of 100% binderless bio-based materials. Five parameters were investigated: pressure applied (7-102 MPa), molding temperature (60-240°C), molding time (5-30 min), fiber/fine particle ratio (0/100-100/0) and moisture content (0-20%). These parameters affected the properties and chemical composition of the materials. The density ranged from 1198 to 1507 kg/m3, the flexural modulus from 0.9 to 6.9 GPa and the flexural strength at breaking point from 6.1 to 43.6 MPa. Water absorption (WA) and thickness swelling (TS) respectively ranged from 21% to 240% and from 9% to 208%. Higher mechanical properties were obtained using SCB with fine particles, low moisture content (4-10%), high temperature (≥ 200°C) and pressure (≥ 68 MPa), while water resistance was improved using more severe thermocompression conditions with the highest temperature (240°C) and time (30 min) or a higher moisture content (≥ 12.5%). Correlations were noted between the mechanical properties and density, while the material obtained with only fine particles had the highest mechanical properties and density. Material obtained with a 30 min molding time had the lowest WA and TS due to internal chemical reorganization followed by hemicellulose hydrolysis into water-soluble extractables.

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection which detects the patient's exhaled VOCs combined with sensor arrays of Convolutional Neural Network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, due to low accuracy, high complexity computation and large number of parameters make the CNN algorithms difficult transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models such as EfficientNet, LTNet has fewer parameters (32K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than that of the five existing models’, and it also offers the shortest training (844.38s and 584.67s) and inference time (23s and 14s) in the same validation set. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

Bio-absorbable stents are a novel tool which relieve stenosis and then resorb as the patient grows, avoiding obligatory re-intervention. This case describes the novel use of an iron bio-absorbable stent in a child with surgically repaired Tetralogy of Fallot who post-operatively developed a severely stenotic right ventricular outflow tract.

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws in-spiration from the sit-and-wait strategy observed in frilled lizards during hunting. The underlying theory of FLO is presented and mathematically formulated in two phases: (i) an exploration phase, simulating the frilled lizard's attack towards prey, and (ii) an exploitation phase, simulating the lizard's retreat to the top of the tree after feeding. To assess FLO's efficacy in solving optimization problems, the algorithm's performance is evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. Comparative analyses with twelve existing metaheuristic algorithms are conducted. The simulation results reveal that FLO, distinguished by its adeptness in exploration, exploitation, and balancing them during search process, outperforms competing algorithms. Additionally, FLO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems, demonstrating its effectiveness in addressing real-world optimization applications.

In order to solve the problem of fault diagnosis for CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and ensemble learning model is proposed. First, various monitoring signals including vibration signal, noise signal and current signal are collected. Then, the monitoring signals are preprocessed and the time-domain, frequency-domain and time-frequency domain feature indexes are extracted to construct a multi-dimensional mixed domain feature set. Finally, the feature set is putting into the constructed DoubleEnsemble-LightGBM model to realize the fault diagnosis of the feed system. The experimental results show that the model can achieve good diagnosis results under different working conditions for both the public data set and the feed system test bench data set, and the average overall accuracy is 91.07% and 98.06% respectively. Compared with XGBoost and other advanced ensemble learning models, the method has better accuracy. It provides technical support for the stable operation and intelligent of CNC machine tools..

The well-being and sociability of individuals have always been part of modernity. The development of new technologies that meet these aspirations is receiving increasing attention. Thus, strengthening the desired objectives of these tools and minimizing their undesirable side effects is the subject of a growing commitment. The present contribution aims, in this context, to evaluate and analyze the desired and undesirable effects of the interaction of electromagnetic fields with living tissues in general. This involves routines reinforcing expected functions as well as those controlling and protecting against undesirable effects. First, the interactions of electromagnetic fields with tissues are analyzed, involving their thermal biological effects of desired and undesired exposures. The roles of blood and sap fluids in bio-affected tissues are then analyzed. Secondly, the governing electromagnetic, bio-heat equations, and their coupled solution are studied. Third, the thermal behavior of tissues and the exposure adverse effects are examined. Next, monitoring and defending of the effects of exposures are discussed. This contribution illustrates and discusses mathematical modeling routines based on a generalized interaction of electromagnetic fields with living tissues.

This work proposes a hollow-core photonic crystal fiber-based edible oil sensor in the terahertz (THz) range (e.g., 1.0THz ≤ f ≤ 3.0THz) and different sensing characteristics are numerically analyzed. The suggested sensor’s performance was assessed by means of COMSOL Multiphysics, a commercial program that uses the finite element approach. The computational results indicate that the relative sensitivity is 85.591%, 84.648%, 82.625%, 82.683%, and 79.161%, respectively, at f = 2.2 THz, for several types of sunflower oil, mustard oil, coconut oil, olive oil and palm oil; and the corresponding effective areas are 7.22×10-8 um2, 7.09×10-8 um2, 6.83×10-8 um2,7.09×10-8 um2, 6.5231 ×10-8 um2. In addition, the effective material loss for sunflower oil, muster oil, coconut oil, olive oil, and palm oil has been found to be 0.02561 cm-1, 0.027054 cm-1, 0.030322 cm-1, 0.028854 cm-1 ,0.035427cm-1 respectively. Moreover, the proposed sensor also has low confinement loss are 1.55×10-8 dB/m, 1.63×10-8 dB/m, 1.31×10-8 dB/m, 1.99×10-8 dB/m, 4.0345×10-8 dB/m.This proposed sensor can be fabricated using extrusion and 3D-printing technologies, and due to its augmented detecting capabilities, it can be a vital part of oil sensing devices implemented in real life such as industry fields.

The rapid increasing rate of soil and water bodies pollution is the main anthropogenic effect caused by the mismanagement of post-consumer plastics. This research evaluated the effectiveness of cattail (Typha Latifolia) fibers (CFs) as bio-adsorbents of microplastic particles (MPPs) from wastewater. The effect of the adsorption environment composition on the adsorption rate was investigated. Batch tests were conducted to evaluate the “spontaneous” adsorption of MPs onto CFs. Five MPPs materials (PVC, PP, LDPE, HDPE, and Nylon 6) were evaluated. An industrial wastewater (PW) and Type II Distilled Water (DW) were employed as adsorption environments. The batch tests results show that CFs are effective in removing MPPs from DW and PW. However, higher removal percentage of MPPs were obtained in PW, ranging from 89% to 100% for PVC, PP, LDPE, and HDPE; while the adsorption of Nylon 6 increased to 29.9%, a removal increased of 50%. These observations indicate that hydrophobic interactions drive the “spontaneous and instantaneous” adsorption process and that adjusting the adsorption environment effectively enhances the MPPs removal rate. This research demonstrates the important role that bio-substrates can play in reducing the environmental pollution as efficient, sustainable, low cost, and reliable adsorbents for the removal of MPPs from wastewaters.

Modal testing is a common step in aerostructure design, serving to validate the predicted natural frequencies and mode shapes obtained through computational methods. The strategic placement of sensors during testing is crucial to accurately measuring the intended natural frequencies. However, conventional methodologies for sensor placement are often time-consuming and involve iterative processes. This study explores the potential of machine learning techniques to enhance sensor selection methodologies. Three machine learning-based approaches are introduced and assessed, comparing their efficiency with established techniques. The evaluation of these methodologies is conducted using a numerical model of a beam to simulate real-world scenarios. The results offer insights into the efficacy of machine learning in optimizing sensor placement, presenting an innovative perspective on enhancing the efficiency and precision of modal testing procedures in aerostructure design.

We report the relative humidity (R.H.) sensing response of a resistive sensor, employing sensing layers based on a ternary nanohybrid comprising holey carbon nanohorns (CNHox), potassium chloride (KCl), and polyvinylpyrrolidone (PVP) at mass ratios 7/1/2, 6.5/1.5/2, and 6/2/2 (w/w/w/w). The sensing structure comprises a silicon substrate, a SiO2 layer, and interdigitated transducer (IDT) electrodes. The sensing film is deposited on the sensing structure via the drop-casting method. The sensing layers' morphology and composition are investigated through Scanning Electron Microscopy (S.E.M.) and RAMAN spectroscopy. The ternary hybrid-based thin film resistance increases when the sensors are exposed to R.H., ranging from 0% to 100%. The manufactured devices show a room temperature response comparable to a commercial capacitive R.H. sensor and are characterized by excellent linearity, rapid response time, and good sensitivity. The presented sensors exhibit superior performance in terms of sensitivity compared to other similarly manufactured and tested sensors that employ a CNHox-based sensing layer. We explain the sensing role of each constituent of the ternary hybrid nanocomposites based on their chemical and physical properties, electronic properties, and affinity for water molecules. Different alternative sensing mechanisms are considered and discussed, such as the decreasing number of holes in the holey CNHox at the interaction with water molecules, proton conduction, and PVP swelling.

An original approach has been proposed for designing a nanofibrous (NFs) layer using UV-cured polyvinylpyrrolidone (PVP) as a matrix, incorporating mesoporous graphene carbon (MGC) nanopowder both inside and outside the fibres, creating a sandwich-like structure. This architecture is intended to selectively adsorb and detect acetic acid vapours, which are known to cause health issues in exposed workers. The nanocomposite MGC-PVP-NFs layer was fabricated through electrospinning deposition onto interdigitated microelectrodes (IDEs) and stabilised under UV-light irradiation. To enhance the adhesion of MGC onto the surface of the nanocomposite polymeric fibres, the layer was dipped in a suspension of polyethylenimine (PEI) and MGC. The resulting structure demonstrated promising electrical and sensing properties, including rapid responses, high sensitivity, good linearity, reversibility, repeatability, and selectivity towards acetic acid vapours. Initial testing was conducted in a laboratory using a bench electrometer, followed by validation in a portable sensing device based on consumer electronic components (by ARDUINO®). This portable system was designed to provide a compact, cost-effective solution with high sensing capabilities. Under room temperature and ambient air conditions, both laboratory and portable tests exhibited favourable linear responses, with detection limits of 0.16 and 1 ppm, respectively.

This research work has been based on a previous study by the authors that characterized the behavior of a polyimide-coated FBG sensor in a structural monitoring system. Sensors applied to structural health monitoring are affected in the presence of a state of multidirectional strains simultaneously. The previous study observed the influence of transverse strain (ε_y) while keeping longitudinal strain constant (ε_x), where the direction x is the direction of the optical fiber. The present study developed an experimental methodology which has consisted of carrying out a campaign of biaxial tests on cruciform specimens with three embedded FBG sensors coated with polyimide, acrylate and ORMOCER®. Applying the Strain–Optic Theory as reference, a comparison of the results obtained with the different coatings has been studied. This experimental work has done it possible to study the influence of transverse strain (ε_y) in the longitudinal measurement of each of the FBG sensor and the influence of the coating material. Finally, the calibration procedure as well as the strain sensitivity factor K for each sensor has been defined.

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing constellation of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9, Sentinel-2, and planned future missions like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA) Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from Reference systems to Client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

Automation is the key aspect of science and technology field and refers to controlling the systems for efficient output requirements. The development of robotics and similar, technologies in 21st century is a vast topic of research and significant progress has been made on this field. This research study focuses on building a ribbon-cutting robot with a Bluetooth interface for event inaugurations. The entire system hinges on constructing a prototype that is mounted on a robotic car frame with four wheels and an Arduino controller. In this case, ribbons are cut using a nickel wire, and it may be paired with an Android device via Bluetooth or an application. The robot is one of the first steps toward the development of autonomous, cognitive robots and can be used in a variety of events. Efficient temperature monitoring is done with help of LM35 sensor and Bluetooth based communication in order to monitor the conditions and effectively control the actions for which robot is designed. The robot has significantly allowed authors for desired tests on ribbon cutting and final results obtained were very exciting for its desired application target. Ribbon-cutting robots find applications in various events, including inaugurations, exhibitions, and ceremonies. Their precision and novelty add value to such occasions.

Photovoltaic materials convert light into electrical voltages. So far, most research in the field are focused on how to refine/improve the existing or standard photovoltaic cells to yield more efficient electricity, to reduce weight, to make more flexible shape, or to minimize costs under standard and uniform sunlight illuminations. In this project, we explore and demonstrate a hypothesis that non-uniform light illumination photovoltages can also be generated due to photo doping induced mobile charge carrier density gradient between light and dark areas in either p-type or n-type photo doping semiconductors. The term non-uniform light illumination was coined to refer to subjecting our photo sensing thin film devices to areas of high and low intensities of light illumination simultaneously, with an intention of creating a charge density gradient and therefore a voltage. Specifically, a polymeric composite thin film composed of P3HT (p-type photo doping semiconductor) and PCBM (photo doping electron acceptor and trap) at varying p-doping levels were studied. We observed that the fabricated photovoltaic cells with about 80% PCBM doped P3HT thin film devices exhibited 0.5-1.2 volts photovoltages under non-uniform light illuminations.

The purpose of this study was to investigate the relationship between clinical outcomes and lateral thrust before and after unicompartmental knee arthroplasty (UKA) using inertial measurement sensor units. Eleven knees were evaluated with gait analysis. The varus angular velocity was used to evaluate lateral thrust. The femoro-tibial angle (FTA) and hip-knee-ankle angle (HKA) were used to evaluate lower limb alignment, and Oxford Knee Score (OKS) and Japanese Orthopaedic Association Score (JOA) were used to evaluate clinical outcomes. The mean pre-UKA peak varus velocity was 37.1 ± 9.8°/sec, and that of post-UKA was 28.8 ± 9.1°/sec (p=0.00003). Assuming the definition of lateral thrust is when varus angular velocity is more than 28.1°/sec, 81.8% of patients had lateral thrust preoperatively, but this decreased to 55.6% postoperatively. Both OKS and JOA improved after surgery. In addition, HKA was -7.9° preoperatively and -5.8° postoperatively (p=0.024), and FTA was 181.4° preoperatively and 178.4° postoperatively (p=0.012). There was a positive correlation between postoperative JOA and FTA, indicating that changes in postoperative alignment affected clinical outcomes. This study quantitatively evaluated the disappearance of lateral thrust by UKA, and it found that the stability can be achieved by UKA for unstable knees with lateral thrust.

In this study, a novel flexible ethanol gas sensor was created by the deposition of CoFe2O4 (CFO) thin film was on a thin mica substrate using pulsed laser deposition. Transition electron microscopy (TEM) investigations clearly demonstrated the successful growth of CFO on the mica, where a well-defined interface was observed. Ethanol gas-sensing studies showed optimal performance at 200 °C, with the highest response, 19.2, to 100 ppm ethanol. Operating the sensor in self-heating mode under 7 V applied voltage, which was corresponds to a temperature of approximately 200 °C, produced a maximal response to ethanol of 19.2. This aligned with the highest responses observed during testing at 200 °C, confirming the sensor’s accuracy and sensitivity to ethanol under self-heating conditions. In addition, the sensor exhibited good selectivity to ethanol and excellent flexibility; maintaining its high performance after bending and tilting up to 5000 times. As this is the first report on flexible self-heated CFO gas sensors, we believe that this research holds great promise for the future development of high-quality sensors based on this approach.

Recent advances in artificial intelligence combined with behavioral sciences have led to the development of cutting-edge tools for recognizing human emotions based on text, video, audio, and physiological data. However, these data sources are expensive, intrusive, and regulated, unlike plants which have been shown to be sensitive to human steps and sounds. A methodology is proposed to use plants as human emotion detectors. Electrical signals from plants are tracked and labeled based on video data. Labeled data are then used for classification. MLP, biLSTM, MFCC-CNN, MFCC-ResNet, and additionally, Random Forests, 1-Dimensional CNN, and biLSTM without windowing models are set using a grid search algorithm with cross-validation. Finally, best-parameterized models are trained and used with the test set for classification. The performance of this methodology is measured with a case study with 54 participants, who were watching an emotionally charged video, as ground truth their facial emotions were simultaneously measured using face emotion analysis. The Random Forest model shows the best performance, particularly in recognizing high-arousal emotions, achieving an overall weighted accuracy of 55.2% and demonstrating high weighted recall in emotions such as fear 61.0% and happiness 60.4%. The MFCC-ResNet model offers decently balanced results, with Accuracy(MFCC-ResNet)=0.318 and Recall (MFCC-ResNet)=0.324. With the MFCC-ResNet model fear and anger are recognized with 75% and 50% recall respectively. Thus, using plants as an emotion recognition tool seems worth investigating, addressing both cost and privacy concerns.

Short-range Internet of Things (IoT) sensor nodes operating at 2.4 GHz need to provide ubiquitous wireless sensor networks (WSNs) with energy-efficient, wide-range output power (POUT) and fully integrated on a single chip for wireless body area networks (WBANs) and wireless personal area networks (WPANs) using low-power Bluetooth (BLE) and Zigbee standards. The proposed fully integrated transmitter (TX) utilizes a digitally controllable current-mode class-D (CMCD) power amplifier (PA) with a second harmonic distortion (HD2) suppression to reduce VCO pulling in an integrated system while meeting harmonic limit regulations. The CMCD PA is divided into 7-bit slices that can be reconfigured between differential and single-ended topologies. Duty cycle distortion compensation is performed for HD2 suppression, and an HD2 rejection filter and a modified C-L-C low-pass filter (LPF) reduce HD2 further. Implemented in a 28 nm CMOS process, the TX achieves a wide POUT range of from 12.1 to -31 dBm and provides a maximum efficiency of 39.8% while consuming 41.1 mW at 12.1 dBm POUT. The calibrated HD2 level is -82.2 dBc at 9.93 dBm POUT, resulting in a transmitter figure of merit (TX_FoM) of -97.52 dB. Higher order harmonic levels remain below -41.2 dBm even at 12.1 dBm POUT, meeting regulatory requirements.

Wireless Sensor Networks are critical for modern applications ranging from environmental monitoring to smart city infrastructures. This research proposes a novel optimization learning and deployment strategy for Wireless Sensor Networks (WSNs) by 3D WUSN model is uniformly distributed M sensor nodes across diverse terrains, with clusters representing 20% of sensors, showcasing its ability to balance power consumption and extend network longevity, grounded in fuzzy clustering based protocol using the Fuzzy C-Means (FCM) method, called FCM-3DWUSN. Comparative analysis against established models including LEACH-C, K-Means and LEACH emphasized FCM-3DWUSN's outperformed performance, with average power consumption such as 134.990, 341.790, 143.895, and 192.984; respectively. The experimental result shows that FCM protocol in the 3D WUSN model can reduce energy consumption and improve the network lifetime to gather data and send the information.

Keywords: Evanescent field; photonic sensor; optical waveguide; light-matter interaction; absorption; mid-infrared.

Naturally-derived synthetic leather is a widely used and high-quality type of faux leather or synthetic leather. Many consumers prefer authentic leather goods due to their exceptional qualities, including high water vapor permeability and superior wearing comfort. Consequently, the exploration of artificial leather made from natural materials with exceptional qualities as an alternative to genuine leather has garnered significant interest and is now a highly studied subject. Synthetic leather, sometimes known as imitation leather, has gained popularity recently as a highly suitable alternative to genuine leather. Continuous advances in this area of research increase the range of uses for synthetic leather using natural fibers. Thus, this research aims to explore synthetic leather materials using orange peel waste, which can be applied to the fashion industry. This research applies several types of orange peel waste, such as Pomelo, Sweet orange, and Sunkist orange, to different compositions and strengthening materials, such as bandages and dacron. The resulting bio-composite materials' morphology, mechanics, and adhesive properties were evaluated following ASTM D 2209-00 and ASTM D 4533-04 standards. The results indicate that specimen 1A (Pomelo using bandage as reinforcement) has the most significant effects, with a tensile test value of approximately 472 N/cm2, an elongation of roughly 67.28 % was the highest value on sweet orange using bandage as reinforcement, and a tear resistance of around 14.28 N/cm2 for Pomelo with bandage as reinforcement. Furthermore, the test results also show that composition 2B still does not meet the standards of SNI 1294:2009 and ISO 3376 for tensile strength and ISO 3377-1 for tear strength. Additionally, statistical analysis showed no appreciable changes in the samples analyzed. Furthermore, statistical evaluations showed no significant alterations in the studied samples. The results of this experimental investigation can be explored further and used as a basic framework for the creation of bio-composites from orange peel waste for future fashion.

In this study, different configurations based on linear fiber lasers were proposed and experimentally demonstrated to measure the concentration of paracetamol liquid solutions in a range of 52.61 to 201.33 g/kg. The optical gain was provided by a commercial bidirectional Erbium-Doped Fiber Amplifier (EDFA) and the linear cavity was obtained using two commercial Fiber Bragg Gratings (FBGs). The main difference of each configuration was the coupling ratio of the optical coupler used to extract the system signal. The sensing head corresponded to a Single-Mode Fiber (SMF) tip that worked as an intensity sensor. The results reveal that, despite the optical coupler used (50:50, 60:40, 70:30 or 80:20), all the configurations reached the laser condition, however, the concentration sensing was only possible using a laser drive current near to the threshold value. The configurations using a 70:30 and an 80:20 optical coupler allowed to perform paracetamol concentration measurements with a higher sensitivity of (-3.00±0.24) pW/(g/kg). In terms of resolution, the highest value obtained was 1.75 g/kg, when it was extracted 20% of the output power to the linear cavity fiber laser configuration.

Structural health monitoring (SHM) is critical for ensuring the safety of infrastructures like bridges. This article presents a digital twin solution for SHM of railway bridges using low-cost wireless accelerometers and Machine Learning (ML). The system architecture combines edge on-premises computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, accelerometers stream raw vibration data, which is processed in the frequency domain and analyzed via machine learning to detect anomalies indicating potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge, where vibration data was collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks, suggesting changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warning of bridge damage and also supports the use of in-house designed sensors of lower-cost and edge computing capabilities than those used in the demonstration. The successful on-premises-cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.

With the megatrend of digitalization, the demand for sensors in previously difficult to access scenarios is increasing. Filament-shaped sensors (FSS) are ideal for this demand especially in applications in which the monitoring of textile structures is the focus. Electrically conductive bicomponent filaments based on thermoplastic polyurethane (TPU) and doped with carbon nanotubes (CNTs) offer great potential due to the flexible mechanical properties. Through the core-conducting, bicomponent structure the sensing material is protected from environmental factors such as surrounding conductive materials and external moisture. The insulating material, however, simultaneously complicates the contacting method in order to measure sensing changes in the conductive core. In this work laser cutting is employed as a technology in order to expose the conductive core of the filaments. The filament is then coated with silver and mechanically crimped, providing both a conductive interface for the data acquisition device as well as a protective layer. Laser parameters (power and speed) are investigated in order to identify the parameters with the best cutting properties which is analyzed visually as well as electrically. The work conducted here can then be used in order to provide a robust and reproducible contacting method for core-conducting TPU filaments for strain sensing applications.

The objective of this study is to evaluate how two varying amounts of sepiolite-based powder, infused with indigo extract, affect the appearance and durability of a water-based white primer. To examine the influence of this eco-friendly pigment on the coatings' overall appearance, assessments were performed for color, gloss, and surface roughness. Additionally, the coatings were investigated through optical and electron microscopic observations, to evaluate the distribution of the pigment within the polymer matrix. The effect of the pigment on the coating's durability was assessed through accelerated tests, including exposure in a salt spray chamber and a UV-B chamber. These tests aimed to evaluate the emergence of defects and changes in the appearance of the samples over time. Furthermore, the impact of different quantities of sepiolite-based powder on the coating's ability to act as a barrier was assessed using liquid resistance tests and contact angle measurements. These evaluations aimed to understand how the coating responded to various liquids and its surface properties concerning repellency or absorption. In essence, this study underscores the considerable influence of the eco-friendly pigment, demonstrating its capacity to introduce unique color and texture variations to the paint. Moreover, the inclusion of the pigment has enhanced the coating's color stability, its ability to act as a barrier, and overall durability when exposed to harsh environments.

A system to evaluate several facets of hand function for neuroprostheses-mediated tasks is proposed. It includes a platform that serves as a test bench, sensors for dynamometry, electromyography, sensorized glove, and interactive objects. The system methodically locates and assesses the motor points along the forearm, evaluates stimulation-induced fatigue, measures digit motion and forces exerted during ADL grasps, and evaluates nerve excitability to advance electrode designs. While the system is exclusively designed for neuroprostheses-mediated grasp, it can also be extended to generic grasp and object manipulation-based research.

In wireless sensor networks (WSNs) deployed in smart agriculture systems, accurately determining the locations of sensor nodes is crucial for efficient monitoring and control. However, in many cases, the exact positions of certain sensor nodes may be unknown. To address this challenge, this paper presents a novel method for localizing unknown sensor nodes in WSN-based smart agriculture systems using range measurements. The proposed method utilizes the Levenberg-Marquardt optimization algorithm to solve a nonlinear least squares problem and minimize the error in estimating the unknown node locations. By leveraging the known positions of a subset of sensor nodes and the inexact distance measurements between pairs of nodes, the localization problem is transformed into a nonlinear optimization problem. To validate the effectiveness of the approach, extensive simulations and experiments were conducted. The results demonstrate that the proposed method achieves accurate localization of the unknown sensor nodes, effectively enhancing the overall performance of the WSN-based smart agriculture system. Furthermore, the method demonstrates robustness against measurement noise and scalability for large-scale networks. The practical implications of this research extend beyond smart agriculture systems, as the proposed method can be applied to various WSN applications where precise node localization is essential.

We use COMSOL Multiphysics software to model optical processes in media and the effects of light propagation in whispering gallery mode resonators (WGMRs). Although plane wave irradiating on resonator does not create resonances, it was shown that they can be obtained when focused tangentially on the perimeter of the resonator. We demonstrate that resonances can be excited in resonators by prism coupling when the incident angle of the light is larger than the angle of total internal reflection. We determined the optimal distance between the prism and the resonator to get the strongest resonances. Bio-sensing principle of WGMRs is shown by demonstrating that the shift of resonances increases with a larger number of "bio-molecules" attached to the surface of WGMR. Surface functionalization and surface roughness were explored.

One of the challenges of deploying Wireless Sensor Networks (WSN) at large scale is to operate for prolonged periods of time relying on the limited amounts of energy stored in its nodes. Generally, data communication is the main factor of draining energy. Consequently, reducing the amount of transmitted data by the nodes is of great interest. At the same time, reducing data to be transmitted should not decrease the quality of the deduced information. Solving this problematic is what we call in this article "Ensuring the Quality of Information Service (QoIS)". This document provides a state-of-the-art for the QoIS regarding WSNs. At first, we give a quick overview of the Internet of Things (IoT) paradigm and the WSN technologies in order to understand how data is gathered and used in these networks. Also, we introduce a novel concept: QoIS, in contrast with classic QoS. Then, it is expected a summary of criteria that asses the QoIS in WSNs plus some techniques to improve it. In this article, we will focus on data management techniques. These techniques can improve raw data, information and/or knowledge quality in energy-constrained WSNs. Finally, we give some conclusions and a glimpse about our future work.

There have been many researches on the surface acoustic wave (SAW) sensors for detecting sulfur-containing toxic or harmful gases. This paper aims to give an overview of the current state of the polymer films used in SAW sensors for detecting deleterious gases. By covering most of the important polymer materials, the structures and types of polymers are summarized, and a variety of devices with different frequency, such as delay lines and array sensors for detecting mustard gas, hydrogen sulfide and sulfur dioxide are introduced. The preparation method of polymer films, the sensitivity of the SAW gas sensor, the limit of detection, the influence of temperature and humidity, and the anti-interference ability are discussed in detail. The advantages and disadvantages of the films are also analyzed, and the potential application of polymer films in the future is also forecasted.

Optical current sensors have been developed and improved in recent decades, and their use has been widespread in power systems, including smart grids. Like any measuring instrument or system, the result of its measurements is associated with a measurement uncertainty, which quantifies its precision. This article presents a review of the main uncertainty contributions that affect the quality and performance of optical current sensors.

Effective detection of isopropyl methylfluorophosphonate (GB, sarin), a type of organophosphine poisoning agent, is an urgent issue to be addressed in maintaining public safety. In this research, a gas-sensitive film material defined as poly (4-hydroxy-4,4-bis trifluoromethyl) - butyl-1-enyl) - si-loxane (SXFA) with the structure of hexafluoroisopropyl (HFIP) functional group was synthesized by using methyl vinylpropyl dichlorosilane and hexafluoroacetone trihydrate as initial materials. Synthesis process products were characterized by FTIR. SXFA was prepared on a 200 MHz shear surface wave delay line with spin coating method for GB detected. Detection limit of ＜ 0.1 mg/m3 was obtained through conditional experiments. Meanwhile, we also obtained the maximum re-sponse of 2.168 mV at 0.1 mg/m3 concentration, which indicting a much lower detection limit of SAW-SXFA sensor. Additionally, the maximum response standard deviation of 0.11 mV with a co-efficient of variation of 0.01, as well as the maximum recovery standard deviation of 0.22 mV with a coefficient of variation of 0.02 were also obtained through 5 repeated experiments. Results showed that the SAW-SXFA sensor has strong selectivity and reproducibility, good selectivity, positive detection ability, high sensitivity, and fast alarm performance for sarin detection.

Conventional methods for pathogen detection in water rely on time-consuming enrichment steps followed by biochemical identification strategies, which require assay times ranging from 24 hours to up to a week. However, in recent years, significant efforts have been made towards the development of biosensing technologies enabling rapid and close-to-real-time detection of waterborne pathogens. In previous studies, we developed a plastic optical fiber (POF) immunosensor using an optoelectronic configuration consisting of a U-Shape probe connected to an LED and a photodetector. Bacterial detection was evaluated with the immunosensor immersed in a bacterial suspension in water with a known concentration. Here, we report on the sensitivity of a new optoelectronic configuration consisting of two POF U-Shape probes, one as the reference and the other as the immunosensor, for the detection of Escherichia coli. In addition, another way of detection was tested where the sensors were calibrated in the air, before immersed in bacterial suspension and then read in the air, making the immunosensor more sensitive and with a faster detection time. This new sensor detected the presence of E. coli at 104 CFU/mL in less than 10 minutes. Currently sub 10 minutes is faster than previous studies using fiber-optic based biosensors. It is also a much simpler and quicker methodology when compared with conventional laboratory technology.

The availability of multi-rotor UAVs with lifting capacities of several kilograms, allows for a new paradigm in atmospheric measurement techniques, i.e. the integration of research-grade sonic anemometers for airborne turbulence measurements. With their ability to hover and move very slowly, this approach yields an unrevealed flexibility compared to mast-based sonic anemometers, for a wide range of boundary layer investigations that require an accurate characterization of the turbulent flow. For an optimized sensor placement, potential disturbances by the propeller-induced flow (PIF), as well as the potential impact of the sensor weight and angular momentum on the flight performance in the case of a boom-mounted sensor, have to be considered.

Recent development for synthesis of bio-based long chain aliphatic polyesters by acyclic diene metathesis (ADMET) polymerization of α,ω-dienes, derived from plant oils and bio-based chemicals, like bis(10-undecenoate) with isosorbide, using ruthenium-carbene catalysts have been reviewed. Development of subsequent (one-pot) tandem hydrogenation afforded saturated polyesters under mild conditions. The polymerizations under bulk (without solvent, 80-90 ºC) or in ionic liquids (50 ºC) under vacuum conditions enabled synthesis of high molar mass polymers (Mn &gt;30,000 g/mol). The polymerization by molybdenum-alkylidene catalyst afforded the highest molecular weight polyesters (44000‒49400 g/mol, in toluene at 25 ºC) exhibiting promising tensile properties (strength and elongation at break) beyond polyethylene, polypropylene. Depolymerizations of these polyesters including closed loop chemical recycling were also demonstrated. Catalyst developments (more active, under mild conditions) play a key role for the efficient synthesis.

In recent years, the integration of sensors in agriculture has emerged as a pivotal catalyst for transforming conventional farming practices into precision agriculture. Sensor application is impacting the everyday objects that enhance human life. In this special issue, the main objective was to address recent advantages of sensor application in agriculture covering a wide range of topics in this field. This provides overview of diverse application of sensors in agriculture, highlighting their role in enhancing productivity, sustainability, and resource management. sensors are instrumental in monitoring various agriculture parameter such as soil condition, weather, crop health and livestock wellbeing. By collecting real-time data and providing valuable insight, sensors enable precision farming, optimize resource allocation, and minimize environmental impacts. we have to explore the key sensor types used in agriculture, their applications, benefits, and future prospects, emphasizing their crucial roles in the modernization of agriculture and global quest for food security and crop production.

This article describes a novel electrochemical on-chip biosensor that utilizes anti-PSA antibody (Ab) and silver nanoparticles (AgNPs) to enhance the sensing and detection capability of prostate specific antigen (PSA) in the blood. The AgNPs are prepared, characterized, and applied onto a silicon photonic on-chip biosensing receptor platform designed to enhance the accurate detection of PSA. The AgNPs were synthesized by a chemical reduction method using silver nitrate (AgNO₃) as the precursor. Transmission electron microscopy (TEM), selected area electron diffraction (SAED), energy dispersion X-ray spectroscopy (EDS), small angle X-ray scattering (SAXS), X-ray diffraction (XRD), and light microscopy were among the methods used in the characterization and analysis of the AgNPs. Each stage of the immunosensor fabrication was characterized using cyclic voltammetry. The proposed immunosensor was applied in the detection of PSA, a prostate cancer biomarker, with a high sensitivity and a limit of detection of 0.17 ng/mL over a linear concentration range of 2.5 to 11.0 ng/mL. The immunosensor displayed good stability and was selective in the presence of interfering species like immunoglobulin (Ig) in human serum, ascorbic acid (AA), and diclofenac (Dic). The detectivity and sensitivity are significantly higher than previous reports on similar or related technologies.

In the autonomous navigation of mobile robots, precise positioning is crucial. In forest environments with weak satellite signals or in sites disturbed by complex environments, satellite positioning accuracy has difficulty in meeting the requirements of autonomous navigation positioning accuracy for robots. This article proposes a binocular vision SLAM/UWB tightly coupled localization method. The core of this algorithm is based on an Extended Kalman Filter (EKF), which utilizes the displacement increment output by binocular vision ORB-SLAM and the positioning coordinates calculated via UWB positioning as measurement information to achieve data fusion. The research utilized the constructed experimental platform to collect images and UWB ranging data in outdoor environments and experimentally validated the combined positioning method. The experimental results show that this algorithm outperforms the use of the individual UWB or visual SLAM localization methods in terms of positioning accuracy. Compared with traditional single positioning algorithms, this algorithm exhibits better practicality and stability, and can effectively suppress the impact of UWB non line of sight errors. The average error under normal line of sight conditions is 0.058 m, the root mean square error (RMSE) is 0.080, and the average error under non-line-of-sight conditions is 0.081 m, with an RMSE of 0.084. There is no significant jump or drift. Through the tightly coupled processing of binocular vision SLAM and UWB positioning, more reliable and accurate mobile robot positioning can be achieved, providing a useful reference for the autonomous navigation of mobile robots in complex environments.

Graphene materials synthesized using direct laser writing (laser-induced graphene; LIG) are fa-vorable sensor materials because of their large surface area, ease of fabrication, and cost effec-tiveness. In particular, LIG decorated with metal nanoparticles (NPs) has been used in various sensors, including chemical sensors and electronic and electrochemical biosensors. However, the effect of metal decoration on LIG sensors remains controversial; hypotheses based on computa-tional simulations do not always match experimental results, and even experimental results re-ported by different researchers have not been consistent. In the present study, we explored the effects of metal decorations on LIG gas sensors, with NO2 and NH3 gases as representative oxi-dizing and reducing agents, respectively. To eliminate unwanted side effects arising from metal salt residues, metal NPs were directly deposited by vacuum evaporation. Although the gas sen-sitivities of the sensors deteriorate upon metal decoration irrespective of the metal work function in the case of NO2 gas, they improve upon metal decoration in the case of NH3 exposure. A careful investigation of the chemical structure and morphology of the metal NPs in the LIG sensors shows that the spontaneous oxidation of metal NPs with a low work function changes the be-havior of the LIG gas sensors and that the sensor behaviors under NO2 and NH3 gases follow different principles.

In this study, we introduce the concept and construction of an innovative Digital Miniature Cathode Ray Magnetometer designed for the precise detection of magnetic fields. This device addresses several limitations inherent to magnetic probes such as D.C. offset, nonlinearity, temperature drift, sensor aging, and the need for frequent recalibration, while capable of oper-ating in a wide range of magnetic fields. The core principle of this device involves the utilization of a charged particle beam as the sensitivity medium. The system leverages the interaction of an electron beam with a scintillator material, which, then, emits visible light that is captured by an imager. The emitted scintillation light is captured by a CMOS sensor. This sensor not only rec-ords the scintillation light but also accurately determines the position of the electron beam, providing invaluable spatial information crucial for magnetic field mapping. The key innova-tion lies in the combination of electron beam projection, CMOS imager scintillation-based de-tection and digital image signal processing. By employing this synergy, the magnetometer achieves remarkable accuracy, sensitivity and dynamic range. The precise position registration enabled by the CMOS sensor further enhances the device's utility in capturing complex magnetic field patterns, allowing for 2D field mapping. In this work, the optimization of the probe's per-formance is tailored for applications related to the characterization of insertion devices in light sources, including undulators.

This paper introduces the AltumView Sentinare smart activity sensor for senior care and patient monitoring. The sensor uses an AI chip and deep learning algorithms to monitor the activity of people, collect activity statistics, and notify caregivers when emergencies such as falls are detected. To protect privacy, only skeleton (stick figure) animations are transmitted instead of videos. The sensor is highly affordable, accessible, and versatile. It was a CES 2021 Innovation Award Honoree, and has been selected by Amazon as one of only three fall detection devices integrated into its Alexa Together urgent response service, and has received very positive reviews from Amazon customers. It has also been used in different senior care settings in about ten different countries. The paper presents the main features of the system, the evidences and lessons learned from its practical applications, and future directions.

Controlling robots in space with necessarily low material and structural stiffness is quite challenging at least in part due to the resulting very low structural resonant frequencies. The frequencies are sometimes so low, the vary act of controlling the robot with medium or high bandwidth controllers lead to excitation of resonant vibrations. Biomimetics or biomimicry emulates models, systems, and elements of nature for solving such complex problems. Recent seminal publications have re-introduced the viability of optimal command shaping, and one recent instantiation mimics baseball pitching to propose control of highly flexible space robots. The readership will find a perhaps dizzying array of thirteen decently performing alternatives in the literature but could be left bereft selecting a method(s) deemed to be best suited for a particular application. Bio–inspired control of space robotics is presented in a quite substantial (perhaps not comprehensive) comparison, and the conclusions of the study indicate the top three performing methods based on minimizing control effort (i.e., fuel) usage, tracking error mean, and tracking error deviation include.

In this work, a ZIF-67/MWCNTs/Nafion sensor platform was constructed based on the good adsorption capacity of ZIF-67, the electrical conductivity of multiwalled carbon nanotubes (MWCNTs) and the excellent chemical stability of Nafion for the detection of Cu2+ in water. Meanwhile, the modified materials were characterized by scanning electron microscopy (SEM), Transmission electron microscopy (TEM), BET specific surface area test, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) and Fourier transform infrared spectrometry (FT-IR). Cyclic voltammetry (CV), electrochemical impedance (EIS), and square wave solvation voltammetry (SWSV) electrochemical methods were used to perform applied test studies on ZIF-67/MWCNTs/Nafion/GCE. The results show that ZIF-67/MWCNTs/Nafion/GCE has high sensitivity (57.5 μA/μM) and a low limit of detection (15.0 nM) for the electrochemical detection of Cu2+ ions in an electrochemical sensing system. It has high adsorption selectivity for Cu2+, and the recovery of Cu2+ in real water reached 98.6%-103%. The modified electrode has good repeatability, reproducibility, anti-interference, and stability, which makes this sensing platform can be practically applied to the detection of domestic water.

ABSTRACT Heavy metals have been classified as dangerous environmental pollutants. Interest in feasible ways to reduce heavy metals pollution is a noble goal. Adsorption is an effective method based on its effectiveness and low- cost. Nowadays, low-cost bio-sorbents have been utilized to replace costly expensive adsorbents. Rich cellulose materials are effective heavy metal adsorbents, and their performance can be enhanced via chemical modification. The present work reviews the steady progress in the utilization of agriculture by-product wastes as heavy metal bio-sorbent. The unique advantages of these by-products such as renewability, abundance, simple processing, and low cost, justifier this work. The results confirm that the chemically treated agro-wastes exhibit high- removal efficiency (reaches 95% in most cases). The adsorption isotherm studies indicate that the concentration of the metal ions has a significant effect on the adsorption process. The Freundlich model provides a physical adsorption model of the metal ions on the bio-sorbent surface. Langmuir isotherm offers an estimate of the effect of different factors on the efficiency of the process. The adsorption kinetics studies offer an idea of reaction pathways including the adsorption mechanism and the efficiency of the metal ion removal. Applying the pseudo-first-order and pseudo-second-order models describes the adsorption kinetics. This information can be utilized in developing effective strategies to remove heavy metal ions using chemically treated agro-waste. The data confirmed that chemical modification is a significant parameter in the efficiency of metal ion removal. The feasibility and effectiveness of using these cellulosic by-products as bio-sorbent should be given attention and encouraged.

The biofibres wide application as mortars enhancement have been so far restricted by factors related to their chemical composition and hygroscopic nature. Their hydrophilic behavior increases the water demand of mortar-mixtures and diminishes their adhesion to the matrix, while further moisture-related fibre degradation issues may arise. Additionally, the natural fibres seem to be susceptible to degradation caused during the exposure to alkaline environments conditions, such as those of cement mortars, restricting their utilization in construction industry. Therefore, the current study focusses on the potential of fibre modification through treatments that would permanently alter their structure and chemical composition in the whole mass or surface towards their performance improvement. In this study, mild hydrothermally treated wood fibres of black pine and beech species were prepared and incorporated in cement mortars to assess their performance, applying the proportion of 1.5% v/v in the mortar. The mortars workability (at fresh state), physical, hygroscopic, chemical, thermal, and mechanical characteristics at the age of 28, 90 and 365 days and weathering performance by subjecting them at different artificial ageing environments (freeze-thaw cycles or outdoor exposure) were examined. The results indicated the beneficial role of the treated fibres in the dimensional stability, flexural strength, thermal insulation properties and capillary absorption of the mortar specimens, especially during ageing process, with the black pine fibres to reveal the highest improvement. The hydrothermally treated wood fibres seem to ensure the integrity maintenance of cement mortars under all ageing conditions, proving that the specific bio-additives could provide low-cost and eco-friendly mortar enhancement pathways.

In this paper, an ultracompact combined sensor for displacement and angle synchronous measurement is proposed based on self-imaging effect of optical microgratings. Using a two-grating structure, linear and angular displacement can be measured by detecting the change of phase and amplitude of the optical transmission respectively within one single structure in meantime. The optical transmitted properties of the two-grating structure are investigated in both theory and simulation. Simulated results indicate that, the optical transmission changes in a sinusoidal relationship to the input linear displacement. Meanwhile, the amplitude of the curve decreases with an input pitch angle, indicating the ability for synchronous measurement within one single compact structure. The synchronous measurement of the linear displacement and the angle is also demonstrated experimentally. The results show a resolution down to 4nm for linear displacement measurement, and a sensitivity of 0.26mV/arcsec within a range of ±1° for angle measurement. Benefitting from a simple common-path structure without using optical components including reflectors and polarizers, the sensor shows an ultrahigh compactness for multiple-degrees-of-freedom measuring, indicating the great potential for this sensor in the fields such as integrated mechanical positioning and semiconductor fabrication.

Abstract: The expansion of global industries results in the release of harmful volatile acid vapors into the environment, posing a threat to various life forms. Hence, it's crucial to prioritize the development of swift sensing systems capable of monitoring these volatile acid vapors. This initiative holds great importance in safeguarding a clean and safe environment. This paper presents the synthesis and characterization of pyrene-based covalent organic frameworks (COFs) demonstrating exceptional crystallinity, thermal stability, and intense fluorescence. Three COFs—PP-COF, PT-COF, and PE-COF—were synthesized, showcasing large surface areas and robust thermal stability up to 400 °C. The fluorescence properties and intramolecular charge transfer within these COFs were significantly influenced by their Schiff base bonding types and π-stacking degrees between COF layers. Notably, PE-COF emerged as the most fluorescent among the three COFs and exhibited exceptional sensitivity and rapid response as a fluorescent chemosensor for detecting HCl in solution. The reversible protonation of imine bonds in these COFs allowed for the creation of highly sensitive acid vapor sensors, showcasing a shift in spectral absorption while maintaining structural integrity. This research highlights the potential of COFs as reliable and reusable sensors for detecting harmful acid vapors, addressing environmental concerns arising from industrial activities.

We simulated the glass-blowing process to model a shell resonator, which is a key component in MRIG (Micro Rate Integrating Gyroscope), using the finite elements method to solve a non-isothermal problem. We also calculated the mechanical characteristics of the modeled resonator, including effective mass, eigenfrequencies, angular gain, and energy loss, to predict the resonator's performance as a gyroscope. One key aspect we addressed is the bending of the resonator's rim toward the stem during glass-blowing process. This phenomenon occurs due to increased heat loss at the mold's edge and reduced heat flux at the side of the Gaussian heat source. To mitigate rim bending and the subsequent reduction in capacitance between the rim and 3D electrodes, which can degrade performance in micro glass-blown shell resonators for gyroscopes, we propose two potential mold solutions. The first is using a short-stem mold, which requires extensive polishing to eliminate the bent rim. The second solution is using a mold with a suitable chamfer at the edge, resulting in a straight rim. To quantitatively compare resonators created by these two types of molds, we calculated the noise factor for predicting its gyroscope performance. From these calculations, we find out that using the chamfered mold enables us to increase in the resonator's effective mass, eigenfrequency, angular gain, and quality factor, consequently reducing the noise factor compared to using the short-stem mold. This simulation process will assist us in getting insight into understanding a shell resonator as a gyroscope, designing a mold and determining experimental conditions.

This study aimed to simulate dynamic irrigation management zones (MZs) in two maize fields for a variable rate hose-reel fertigation machine (VRFM) with a 4-section boom control. Soil moisture content was measured from nine and four soil moisture sensors in Field 1 (8.2 ha) and Field 2 (2.5 ha), respectively in different dates during the 2022 crop season. Three- and five-MZs scenarios were simulated per irrigation and the theoretical maps were processed for implementation. The application maps fitted to the VRFM showed significant spatiotemporal variations in irrigation requirements. For instance, in Field 1, 3-MZs modelling showed that the areas requiring High (H), Medium (M), and Low (L) level irrigation on July 21 were 1.60, 4.84, and 1.85 ha, respectively, whereas the farmer applied uniform rate over the whole field. H level sub-areas ranged between 1.22 ha (July 25) and 3.25 ha (July 7), showing a coefficient of variation (CV) of 43.32% for the 3-MZs, whereas H level sub-areas for 5-MZs varied from 0.41 ha (July 2) to 1.49 ha (July 7) with a CV value of 48.84%. High levels of within-field variability can be addressed using precise and dynamic irrigation MZs fitted to the irrigation technology used.

Millions of people worldwide experience visual problems nowadays. According to the recent WHO survey, approximately…… million people are blind. They become reliant on others because of this issue. According to recent studies, 108 various techniques are utilized to help the moment of people who are visually impaired. In this research we will employ infrasonic sound to improve the ability of blind persons to identify obstacles that comes in its way at the distance ranging from 2 to 3.5 maters. The concept was created by drawing inspiration from a mammal termed certain animals inherent navigational system. The infrasonic detection for the visually impaired people is a complicated procedures ,now we will simply discuss the obstacle finding mechanism in this article. The project uses an Arduino UNO microcontroller, infrasonic sensors, a buzzer powered by 5 volts , a vibrator, burning Arduino cable and a few connecting wires. On a cap that the blind people will wear all of this arrangement will be placed. High frequency sound is emitted by the infrasonic emitter picked up by the infrasonic receiver after reflecting off of the obstruction and then transmitted to the processor for additional required activity in the form of sound and vibration.