The up-to-date challenge towards clean road transport are non-exhaust emissions. Important advances regarding measurement systems, including well defined characterisation techniques, as well as regulation, will happen in the next few years. In this work we present detailed results of particle emissions from a test bed for wheel suspension and brakes, consisting of aerosol (size distribution, particle number (PN) and mass (PM)) and electron microscopy measurements (EM) under different load conditions. Standard tyres and brakes from series production were tested with a high-load driving cycle while particle measurements were conducted by gravimetric measurements and with a TSI SMPS, a TSI APS and a GRIMM OPS. Furthermore, samples were analyzed by electron microscopy. A bimodal particle size distribution (PSD) was obtained with the SMPS, with peaks at 20nm and around 400nm. EM analysis of >1400 single particles from the electrostatic sampler matches the PSD results. EM analysis also showed ultrafine particles, mainly from O, Fe, Si, Ba, Mg and S and also fractal particles with high C fractions. Our results suggest, in agreement with previously published literature, that particulate emissions are related to brake disc temperature and occur in significant amounts above a threshold temperature.

This paper presents a piezoresistive pressure sensor with a shield layer for improved stability. Compared with the conventional piezoresistive pressure sensors, p-type piezoresistors are covered by an n-type shield layer, which is formed by ion implantation. The proposed pressure sensors have been successfully fabricated by bulk micromachining techniques. The impact of electrical field on piezoresistors is studied by simulation. The temperature drift of the pressure sensor has been investigated by both simulation and experimental measurement. Characteristics of developed pressure sensors are tested from -40 C to 125 C. A sensitivity of 0.022 mV/V/KPa and a maximum non-linearity of 0.085% FS are measured for the fabricated sensor in a pressure range of 1 MPa. The temperature coefficients of resistance of shielded piezoresistors are found to be smaller than those of un-shielded ones. It is demonstrated that the shield layer is able to reduce the drift caused by electrical field and ambient temperature variation.

Wearable technology has been proposed as a potential tool to change the way of human life, such as the smart bracelet and the Google Glass. In the wearable technology, the inertial sensor has great significance in tracking the object movements. The paper focused on detecting the movements of user’s finger based on the inertial sensor to give the control signals. Firstly, the attitude matrix, which represented the transformation relation of carrier coordinate system and the navigation coordinate system, was obtained. Secondly, the attitude matrix was expressed based on the quaternions. Thirdly, the finger gesture was processed by the attitude matrix to get the attitude angle. Finally, the robot was controlled by attitude angle to make the moving action. The experimental results showed the detection of the finger movement is effective.

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.

A portable laser photoacoustic sensor based on a Field-Programmable Gate Array (FPGA) is reported for methane detection. A tunable DFB diode laser in the 1654 nm wavelength range is used as an excitation source. The photoacoustic signal processing was implemented by a FPGA device. A small resonant photoacoustic cell is designed. The minimum detection limit (1σ) of 10 ppm for methane (CH₄) is demonstrated.

The design and usage of the addressed combined fiber-optic sensors (ACFOS) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated. The main development trend of the combined fiber-optic sensors (CFOS), consisting of the fiber Bragg grating (FBG) and the Fabry-Perot resonator (FPR), which are successively formed at the optical fiber end, is highlighted. The addressed fiber Bragg structures (AFBS) usage instead of the FBG in the CFOS leads not only to significant cheapening of the sensor system due to microwave photonics interrogating methods, but also to increasing its metrological characteristics. The structural scheme of the multisensory gas concentration monitoring system is suggested. The suggested scheme allows detecting four types of the greenhouse gases (СО2, NO2, CH4, OX) depending on the material and thickness of the polymer film, which is the FPR sensitive element. The usage of Karunen-Loeff transform (KLT), which allows separating each component contribution to the reflected spectrum according to its efficiency, is proposed. In the future, it allows determining the gas concentration at the AFBS address frequencies. The estimations have shown that the ACFOS design in the multisensory system allows measuring the environment temperature in the range of −60…+300 °C with an accuracy of 0.1–0.01 °C, and the gas concentration in the range of 10…90% with the accuracy of 0.1–0.5%.

In this project, four of fibers Bragg gratings were fabricated by injecting different volumes of liquids (star line Glass Mechanix optical adhesive material, olive oil diluted with ethanol) into the hollow core fiber. The amplitude splitting interferometric technique with a high resolution specially designed translation stage was used for the fabrication process. The fabrication was done using ultraviolet laser operated at wavelength 405nm. The fabricated Bragg length of the four fibers is equal to 3.8 cm. The results presented fiber Bragg grating (FBG) with successful fabrication at 653.3 nm Bragg reflected wavelength.

This paper investigates an Energy Neutral System using Micro Energy Harvesters for a Health Monitoring Wireless Sensor Node (HM-WSN). We have implemented HM-WSN consisting of a solar cell energy harvester, a Power Management Unit (PMU) and the ucontroller, sensor and transmitter acting as load. A battery extender and a switching logic circuit for sensor node is designed to evaluate the efficiency of the proposed system. The HM-WSN senses body temperature with improvement in lifetime of coincell. The HM-WSN is built using commercially available off the shelf (COTS components and consumes 47.2uW for data being updated every 134s. It can be operated for approximately 1.32 years only on a coincell. Furthermore, the HM-WSN can be operated with solar energy at 200lux, achieving 96.36 years with a combination of a coincell with an additional capacitor of 2.2mF. It attains 4.07 years to 16.87 years lifetime with the load capacitor varying from 1mF to 1.5mF at 350lux lighting condition. With the same ambient condition and a 2mF load capacitor, HM-WSN attains complete energy autonomy.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

The existence of low-cost and accurate tools that can equip the First Responders (FRs) to detect trapped victims and provide insights about their health condition in the aftermath of a disaster, is imperative. To address the problem of detecting trapped victims behind walls or debris, we have developed a tool that exploits an Ultra-Wide Band (UWB) radar sensor for data collection and Machine Learning (ML) algorithms for data analysis. To evaluate the efficacy of our approach, we collected data from nine humans, both in standing and lying down positions. Next, we applied various ML algorithms to the collected dataset for two discrete sub-tasks that are of interest from an FR’s perspective. The first task is the detection of the victim’s presence, where the algorithms attained more than 95% accuracy and F1-Score. The second task is the estimation of the distance between the radar sensor and the victim, where the tool showed an average error of less than 40 cm.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

In this paper, a method is developed for presenting a novel virtual torque sensor based on precise model and position measurements avoids the need of traditional strain gauges and amplifiers. More specifically, the harmonic drive compliance model and the Gaussian process regression (GPR) technique are used together to achieve virtual torque sensor measurement. While the harmonic drive compliance model provides the analytic part, the Gaussian process regression method is used to reconstruct the unmolded part based on motor-side and link-side joint angles as well as motor current. After an automatic offline calibration, the method allows for a lean online implementation. The virtual torque sensor measurement is compared with measurements of a commercial torque sensor, and the results have attested the effectiveness of the proposed method.

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.

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.

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.

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.

An easy to implement and cost-effective Fiber Bragg Grating (FBG) sensor interrogation technique based on a ring Erbium-Doped Fiber Laser (EDFL) topology is proposed and experimentally assessed. The FBG sensor is part of the EDFL cavity and must have a central wavelength located within the linear region of the EDF’s amplified spontaneous emission (ASE) spectrum which occurs between 1530 and 1540nm. In this manner, the wavelength-encoded response of the FBG under strain is converted to a linear variation of the laser output power, removing the need for spectrum analysis as well as any limitations from the use of external edge-filtering components. In addition, the laser linewidth is significantly reduced with respect to the FBG bandwidth thus improving the resolution of the system, whereas its sensitivity can be controlled through the pumping power. The performance of the system has been characterized by modeling and experiments for EDF's with different lengths, doping concentrations and pumping power levels. The influence of mode-hopping in the laser cavity on the resolution and accuracy of the system has also been investigated.

A reliable and efficient rail track defect detection system is essential for maintaining rail track integrity and avoiding safety hazards and financial losses. Eddy current (EC) testing is a non-destructive technique that can be employed for this purpose. The trade-off between spatial resolution and lift-off should be carefully considered in practical applications to distinguish closely spaced cracks such as those caused by rolling contact fatigue (RCF). A multi-channel eddy current sensor array has been developed to detect defects on rails. Based on the sensor scanning data, defect reconstruction along the rails is achieved using an inverse algorithm that includes both direct and iterative approaches. In experimental evaluations, the EC system with the developed sensor is used to measure defects on a standard test piece of rail with a probe lift-off of 4-6 mm. The reconstruction results clearly reveal cracks at various depths and spacings on the test piece.

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.

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.

This paper presents a two-stage ADC based on pseudo-differential operational transconductance amplifier (OTA), which is designed for the readout circuit of X-ray linear array sensor. This hybrid ADC employs an incremental sigma-delta ADC and a cyclic ADC, achieving a good trade-off between accuracy and conversion speed. The two stages share the same hardware to reduce power consumption and die area. A common-mood feedback module is used to suppress the influence of charge injection, and the effectiveness is demonstrated by detailed theoretical analysis. A test chip of 14-bit ADC is fabricated in 0.35μm CMOS technology. The measured root mean square (RMS) value of DNL is 0.254 LSB, and the maximum value of INL is -0.776/+1.56 LSB. The measured effective number of bits (ENOB) is 13.43 bits.

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Sixteen features are extracted and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and that collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values being greater than 95%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

The Predicted Mean Vote (PMV) and the Predicted Percentage of Dissatisfied (PPD) indices are used to assess the indoor environment in terms of human thermal comfort-discomfort. In this study, an experimental combined objective and subjective investigation of thermal comfort perception has been performed in students between 16-18 years old, in a non-air-conditioned school building. The objective approach included instrumentation measurements and data processing according to ISO 7730, whereas, the subjective one was based on answers collection following ISO 10551. The study is mainly devoted to the verification of Fanger’s approach in a building, in free running conditions, under a mild (moderate) climate.The comparison between instrumentation data and questionnaire results presented an underestimation of the mean vote, predicting a cooler sensation than the actual one.

Myocardial hypertrophy is the most common condition that accompanies heart changings in children. Transcriptional pathways regulating gene expression playing a critical role both in cardiac embryogenesis and in the pathogenesis of congenital hypertrophic cardiomyopathy, neonatal posthypoxic myocardial hypertrophy and congenital heart diseases. This paper describes the state of cardiac gene expression and potential pharmacological modulators at different transcriptional levels. An experimental model of perinatal cardiac hypoxia showed downregulated expression of genes responsible for cardiac muscle integrity and overexpressed genes associated with energy metabolism and apoptosis, which may provide a basis for a therapeutic approach. Current evidence suggests that RNA drugs, theaflavin, neuraminidase, proton pump and histone deacetylase inhibitors are promising pharmacological agents in progressive cardiac hypertrophy. The different points of application of the above drugs makes combined use possible, potentiating the effects of inhibition in specific signaling pathways. The special role of N-acetyl cysteine in both the inhibition of several signaling pathways and the reduction of oxidative stress was emphasized.

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.

In the context of improving aircraft safety, this study focuses on the development and evaluation of a graphene-based ice detection system using an environmental chamber. The research is driven by the need for more accurate and efficient ice detection methods, crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors’ heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors, tested under varied and controlled atmospheric conditions, exhibit a remarkable potential in enhancing ice detection systems for aircraft. Their lightweight, efficient and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.

Wireless Powered Communication Networks (WPCN), which has attracted much attention of researchers, also been recently recommended in 5^th generation (5G) wireless networks. With the help of the WPCN, the reliability and battery life of wireless low-power devices can be improved. In this paper, we investigate throughput and ergodic capacity in WPCN-assisted amplify-and-forward (AF) relaying system, considering two transmission modes including delay-tolerant and delay-limited. As important achievement, we propose symmetric energy harvesting protocol, namely time power switching relaying (TPSR) in order to find maximal throughput. In particular, both time switching and power switching coefficients in this schemes are considered. Unlike most of the previous works, we further focus on impact of outdated channel state information (CSI) in this WPCN. In order to evaluate information processing efficiency, the performance can be substantially improved by optimally harvesting time and power coefficients of the received signal at relay node for energy and information extraction, and by deploying several scenarios. By deploying Monte Carlo simulation, it is confirmed that the system performance is more sensitive to CSI estimation error, noise variance, signal-to-noise ratio (SNR) and resulting in other reasonable computations of TPSR need be deployed to obtain QoS requirement.

In this paper, the integrated performance of a modular biomass boiler with an existing industrial Rankine steam heat and power cycle and a supplementary supercritical-CO2 (sCO2) Brayton cycle is analyzed. The aim is to leverage the high efficiency supplementary sCO2 cycle to increase net generation and energy efficiency from the existing biomass boiler. Two sCO2 heater configurations situated within the flue gas flow path are investigated, namely a single convective-dominant heater, and a dual heater configuration with a radiative and a convective heater. A quasi-steady-state 1D model was developed to simulate the integrated cycle, including detailed component characteristics for the Rankine and Brayton cycles. The model solves the mass, energy, momentum, and species balance equations. The system is analyzed for three cases: (i) the existing Rankine cycle without the sCO2 integration, (ii) with the single convective-dominant sCO2 heater configuration, and (iii) the dual sCO2 heater configuration. The results show the required rate of overfiring for the sCO2 configurations, with a 15.3% increase in fuel flowrate resulting in an additional 21.2% in net power output. The model quantifies the impact of the sCO2 heaters, with reduced heat uptakes for downstream boiler heat exchangers. Furnace water wall heat uptake increased due to overfiring, offsetting the reduced heat uptakes at downstream evaporative heat exchangers. The dual configuration has more impact on Rankine cycle operation due to the radiative sCO2 heater placement in front of the second superheater, absorbing some of the direct radiation from the furnace.

Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers BERT and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK(National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included resumes collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.

The market for autonomous vehicles (AV) is expected to experience significant growth over the coming decades and to revolutionize the future of transportation and mobility. The AV is a vehicle that is capable of perceiving its environment and perform driving tasks safely and efficiently with little or no human intervention and is anticipated to eventually replace conventional vehicles. Self-driving vehicles employ various sensors to sense and perceive their surroundings and, also rely on advances in 5G communication technology to achieve this objective. Sensors are fundamental to the perception of surroundings and the development of sensor technologies associated with AVs has advanced at a significant pace in recent years. Despite remarkable advancements, sensors can still fail to operate as required, due to for example, hardware defects, noise and environment conditions. Hence, it is not desirable to rely on a single sensor for any autonomous driving task. The practical approaches shown in recent research is to incorporate multiple, complementary sensors to overcome the shortcomings of individual sensors operating independently. This article reviews the technical performance and capabilities of sensors applicable to autonomous vehicles, mainly focusing on vision cameras, LiDAR and Radar sensors. The review also considers the compatibility of sensors with various software systems enabling the multi-sensor fusion approach for obstacle detection. This review article concludes by highlighting some of the challenges and possible future research directions.

A compact Mach-zehnder interferometer with a novel design of directional couplers and a phase shifter has been presented as a thermo-optical sensor. With the aim of reducing device size to micro and nano dimension silicon-on-insulator technology was employed. That allowed miniaturization of device size through the reduction of its cross sectional area to 0.066 µm² and the radius of curvature of both the arms of the directional coupler and S-bends of the phase shifter to 5 µm and C-bends to 3 µm. These nano size device dimensions made it possible to reduce the coupling gap to 0.2 µm, which resulted in a significant reduction in the coupling length. The device geometry and its performance characteristics were analyzed and optimized using coupled mode analysis and finite difference time domain simulation tools, respectively. The wavelength dependent transmission loss of the device was measured at different temperature to verify and validate its performance characteristics. Tested devices showed a remarkable temperature dependent transmission characteristic offering significant changes in transmission loss band – with as low as 0.45 ⁰C change in substrate temperature. The extinction ratio and the free spectral range of the device were 26 dB and 0.26 nm respectively in the wavelength range of 1549.5 nm – 1550.5 nm. These results imply that the devices presented here can be used as compact and highly sensitive thermal sensors and optical switches.

A space charge density wireless measurement system based on the idea of distributed measurement is proposed for collecting and monitoring the space charge density in an ultra-high-voltage direct-current (UHVDC) environment. The proposed system architecture is composed of a number of wireless nodes connected with space charge density sensors and a base station. The space charge density sensor based on atmospheric ion counter method is elaborated and developed, and the ARM microprocessor and Zigbee radio frequency module are applied. The wireless network communication quality and the relationship between energy consumption and transmission distance in the complicated electromagnetic environment is tested. Based on the experimental results, the proposed measurement system demonstrates that it can adapt to the complex electromagnetic environment under the UHVDC transmission lines and can accurately measure the space charge density.

Acoustic Radiation Forced Impulse (ARFI) is an ultrasound integrated method, measuring stiff-ness by point shear wave elastography. To evaluate the diagnostic performance of ARFI of the liver and of the spleen, combined with spleen dimension and platelet count, in predicting clinical events in cirrhosis, a prospective and cross-sectional study was conducted between February 2017 and February 2021. The following ratio scores with ARFI measurements were performed: ALSDP (ARFI Liver-Spleen Diameter-to-Platelets ratio score), ASSDP (ARFI Spleen-Spleen Diam-eter-to-Platelets ratio score), ASSAP (ARFI Spleen-Spleen Area-to-Platelets ratio score), ALSAP (ARFI Liver-Spleen Area-to-Platelets ratio score). In 100 enrolled subjects, spleen ARFI, ASSDP, ASSAP, were significantly associated with high-risk oesophageal varices (HRVs) in the prospec-tive short- and long-term follow-up and in the cross-sectional study (p 0.7). ARFI-ratio scores can predict, in cirrhotic patients, the risk of developing HVRs in short and long-term period.

Separase is a key cysteine protease to separate sister chromatids through digestion of cohesion ring that inhibits chromosome segregation as trigger of metaphase–anaphase transition in eukaryotes and highly regulated its activity by binding with securin and cyclinB-CDK1 complex. Those bindings prevent proteolytic activity of separase until the onset of anaphase. Chromosome missegregation and aneuploidy are frequently observed in malignancies. However, there are some difficulties for biochemical examinations due to instability of separase in vitro and a few spatiotemporal resolution approaches monitoring live separase activity throughout mitotic processes. Here we have developed FRET-based molecular sensors including GFP variants with separase cleavable sequences as donor and covalently attached fluorescent dye as acceptor molecules applicable to conventional live cell imaging and flow cytometric analysis because of efficient live cell uptake. We investigated performance of equivalent molecular sensors localized or not localized inside nucleus under cell cycle control using flow cytometry. Synchronized cell cycle progression rendered significant separase activity detections in both molecular sensors. We obtained consistent outcomes with localized molecular sensor introduction and cell cycle control by fluorescent microscopic observations. We thus established live cell separase activity monitoring systems specifically or statistically that could lead to elucidate separase properties in detail.

Severe combined immunodeficiency (SCID) is a primary inherited immunodeficiency that could be fatal, usually due to opportunistic infections caused by bacteria, viruses, fungi, and protozoa if left untreated. Opportunistic infections are the most common presentation of SCID these include viral, fungal, or intracellular bacteria, and infection should prompt immunological investigation. This review aimed to provide a comprehensive approach to the microrganisms associated with severe combined immunodeficiency (SCID) and its management. The authors described SCID as a syndrome and summarized the different microorganisms that affect children and how they are managed.

The effectiveness of combining strength-specific training with endurance training to enhance the performance of endurance runners remains uncertain. This study aimed to analyze effect of practising maximum-strength (MAX), explosive strength (EXP) or both combined (COMB) on seven runners’ performance-indicators: vertical jump (VJ), one-repetition-maximum-squat (1RM), peak-velocity/peak-running-speed (PV), lactate-threshold (over incremental-test-protocols, LT), middle-distance-time-trial (TT), maximum-oxygen-consumption (VO2max), running-economy (RE). Systematic-review (Scopus, Wed of Science, Sports Discuss, PubMed) with meta-analysis was conducted following PRISMA standards. Inclusion-criteria (PICOS) were: recreational or well-trained athletes aged 18-45 performing concurrent-training ≥five-week. Used search-terms were related to different types of strength/endurance, participants’ age, sport-modality. 20 manuscripts were selected, quality-assessed with PEDro. MAX training is more effective than EXP, COMB in improving VJ, 1RM, PV, while COMB is more effective than MAX, EXP to enhance TT. MAX is more effective than EXP in improving LT. Concurrent-workouts do not provide additional benefits to VO2max. It is unknown which strength-modality (MAX, EXP or COMB) is more effective in improving RE. Concurrent-training is more effective than single-mode-endurance-training for enhancing specific performance-variables in adult-endurance-runners. Middle-, long-distance-runners may consider incorporating MAX-training to target specific goals, e.g., improving VJ, 1RM, LT, PV while utilizing COMB-training to enhance TT. Certain variables may benefit from EXP. New randomized-controlled-trials are required to confirm these findings.

Shoulder tendinopathies produce pain and reduce functionality. The aim of this randomized clinical trial was to analyze the effects of Percutaneous electrolysis (PE), Percutaneous peripheral Nerve Stimulation (PNS) and eccentric exercise (EE), on pain (NPRS), strength, electromyographic activity, ultrasound characteristics of the tendon (echogenicity, thickness and hypervascularization) and functionality (DASH and SPADI) on individuals with supraspinatus tendinopathy. Participants (n=50) were divided into two groups, they received 4 treatment sessions, one per week, of PE and PNS (n=25) or 10 treatment sessions of TENS and US (n=25). Both groups performed EE program consisted of 3 sets of 10 repetitions of each of the 3 exercises, twice a day, during the 4 weeks. Follow-up was carried out at 4, 12 and 24 weeks after the start of the intervention. There are statistically significant differences in the analysis between groups (p<0.001) in the post-treatment and follow-up measurements favorable to the PE+PNS+EE treatment on pain (NPRS), strength, supraspinatus electromyographic amplitude, ultrasound characteristics of the tendon (echogenicity, thickness and hypervascularization) and DASH and SPADI questionnaires. The combined treatment PE, PNS and EE is an effective option in the clinical management of tendinopathies, with positive results in the short and long term on the variables studied.

Brushless Synchronous Machines (BSM) are replacing conventional synchronous machines with static excitation in generation facilities due to the absence of sparking and lower maintenance. However, this excitation system makes challenging measuring electric parameters in the rotor. This fact highly difficult the detection of ground faults, which are the most common type of electrical fault in electric machines. In this paper, a ground fault detection method for BSM is proposed. It is based on an inductive AC/DC rotating current sensor installed in the shaft. In case of a ground fault in the rotating parts of the BSM, a fault current will flow through the rotor’s sensor, inducing voltage in its stator. Analyzing the frequency components of the induced voltage the detection of a ground fault in the rotating elements is possible. The ground faults detection method proposed covers the whole rotor and discern between DC and AC sides. To corroborate the detection method, experimental tests have been performed using a prototype of this sensor connected to laboratory synchronous machines achieving satisfactory results.

The aim of this study is to solve the problems of complicated pretreatment and high analytical cost in the detection technology of trace drug toxicants in municipal wastewater. A high-performance magnetic sorbent was synthesized for enrichment of trace drug substances in wastewater to develop magnetic solid-phase extraction pretreatment combined with the acoustic ejection mass spectrometry (AEMS) analytical methods for the molecules in wastewater drug toxicants. The magnetic nanospheres was successfully prepared by magnetic nanoparticles modified with divinylbenzene and vinyl pyrrolidone.The results showed that the linear dynamic range of 17 drugs was 1-500 ng/L, the recovery was 43%-105%, the matrix effect was more than 67%, the limit of quantitative detection was 1-2 ng/L, and the detection time of each sample was 2.98s. It can be seen that the developed magnetic solid-phase extraction (MSPE) method is a good solution to the problem of complicated pre-treatment and analytical cost in the detection of drugs in wastewater. The developed magnetic material and acoustic excitation coupled mass spectrometry analysis method can realize the low-cost, efficient enrichment and fast analysis of different kinds of drug molecules in urban sewage.

in our increasingly interconnected world, sensor networks are critical in gathering and sending data for various applications, from environmental monitoring and industrial automation to healthcare and smart cities. However, as sensor networks expand in importance, so does the need to solve the multidimensional concerns of security, privacy, and forensics. This article explores the complex world of sensor network security, the delicate balance between data privacy and utility, and the emerging area of sensor network forensics. This article focuses on risk assessment of a network.

The combined heat and power system is based on the principle of "cascade utilization of thermal energy" for system integration and optimization. The rational utilization of energy, as well as the rational arrangement of energy flow and the process optimization combination of the overall system are taken into account in order to achieve multifunctional goals in the field of thermal engineering. The present paper presents a detailed assessment of combined heat and power systems from above standpoints. This article is divided into two parts “CHP based on thermo-dynamic cycles” and “CHP based on non-thermodynamic cycles”. Each part is then classified based on power subsystem energy consumption efficiency (high or low), including Rankine cycle, ORC, Stirling cycle, gas turbine, reciprocating engine, PVT and fuel cell. The present paper also helps identify the research gaps in this area and provides direction on future studies on combined heat and power systems.

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.

Preventing the rebound of black and odorous water bodies is critical for improving the ecological environment of water bodies. This study investigated the effect and mechanism of in-situ improvement of sediment microenvironment by nitrate in tailwater of wastewater treatment plants combined with aerobic denitrifying bacteria under low-DO regulation (TailN+CFM+LDO). On the 60th day of remediation, the dissolved oxygen and oxidation-reduction potential in the overlying water increased to 5.6 mg/L and 300 mV, respectively, the acid volatile sulfide in the sediment decreased by 70.4%, and the organic matter in the sediment was reduced by 62.7%, in which the HFOM was degraded from 105 g/kg to 56 g/kg, and the potential risk of water reverting to black and odorous conditions significantly decreased. Amplicon sequencing analysis revealed that the relative abundance of electroactive bacteria Thiobacillus and Pseudomonas with denitrification capacity was found to be significantly higher in the TailN+CFM+LDO group than in the other remediation groups. Functional prediction of 16S sequencing results indicated that both the quantity and activity of critical microbial enzymes involved in nitrification and denitrification processes could enhance in the TailN+CFM+LDO group. These results improved our understanding of the improvement of the sediment microenvironment and could thus facilitate its application.

Neuroblastoma (NB) is a childhood cancer with heterogeneous characteristics, posing challenges to effective treatment. NBs express somatostatin receptors that facilitates the use of somatostatin analogs (SSTAs) as tumor-seeking agents for diagnosis and therapy. High-risk (HR) NBs often have gain-of-function mutations in the receptor tyrosine kinase anaplastic lymphoma kinase (ALK). Despite intensive multimodal treatment, survival rates remain below 40% for children with HR-NB. The aim of this work was to investigate the combined effect of the SSTA 177Lu-octreotide with the ALK inhibitor lorlatinib. Mice bearing human HR-NB CLB-BAR tumors were treated with lorlatinib, 177Lu-octreotide, combination of these pharmaceuticals or saline (control). Tumor volume was monitored and tumor samples were evaluated for cleaved caspase-3 and expression of 84 human genes involved in apoptosis. Combination treatment with 177Lu-octreotide and lorlatinib demonstrated synergistic antitumor effects. Increased number of cleaved caspase 3-positive cells was observed in tumors from mice treated with 177Lu-octreotide alone and in combination with lorlatinib. Modulation of Bcl-2 family gene expression was observed only in the presence of both 177Lu-octreotide and lorlatinib, with BID downregulated and HRK upregulated on days 2 and 7, respectively. The data suggests that ALK signaling pathway inhibition may contribute to radiosensitization in radionuclide therapy with 177Lu-octreotide.

Bare titania and metal promoted TiO2 catalysts were employed in the treatment of nitrates, which are ubiquitous pollutants of wastewater. The results show that the process can be carried out under visible light (from white light LED lamp) and, in the best case, 23.5% conversion of nitrate was obtained over 4 hours with full selectivity towards N2 by employing 0.1 mol% Ag/TiO2 prepared by flame spray pyrolysis. Moreover, the performance was worse when testing the same catalysts with tap water (11.3% conversion), due to the more complex composition of the matrix. At last, it was found that the photoreduction of nitrate can be effectively performed in combina-tion with the photo-oxidation of ammonium without loss in the activity, opening to the possi-bility to treat highly polluted wastewater with a single process. The latter treatment employs the two contaminants simultaneously as electron and holes scavengers, with very good selectiv-ity, in a completely new process that we may call Photo-Selective Catalytic Reduction (Pho-to-SCR).

Plants are continually exposed to multiple stresses, which co-occur in nature and the net effects are frequently more non-additive (i.e., synergistic or antagonistic) suggesting ‘unique’ responses respect to that of the individual stress. Further, plant stress responses are not uniforms showing a high spatial and temporal variability among and along the different organs. In this respect, the present work investigated the morphological responses of different root types (seminal, seminal lateral, primary, primary lateral) of maize plants exposed to single (drought and heat) and combined stress (drought + heat). Data were evaluated by a specific root image analysis system (WinRHIZO) and analyzed by uni- and multi-variate statistical analysis. The results indicated that primary root and their laterals were the types more sensitive to the single and combined stresses while the seminal laterals specifically responded to the combined only. Further, antagonistic and synergistic effects were observed for the specific traits in the primary and their laterals and in the seminal lateral roots in response to the combined stress. These results suggested that maize root system modified specific root types and traits to face with different stressful environmental conditions highlighting that the adaptation strategy to the combined stress may be different from that of the individual ones. The knowledge of “unique or shared” responses of plant to multiple stress can be utilized to develop varieties with broad spectrum stress tolerance.

The diffraction-like process displayed by a spatially localized matter wave is here analyzed in a case where the free evolution is frustrated by the presence of hard-wall-type boundaries (beyond the initial localization region). The phenomenon is investigated in the context of a nonrelativistic, spinless particle with mass m confined in a one-dimensional box, combining the spectral decomposition of the initially localized wave function (treated as a coherent superposition of energy eigenfunctions) with a dynamical analysis based on the hydrodynamic or Bohmian formulation of quantum mechanics. Actually, such a decomposition has been used to devise a simple and efficient analytical algorithm that simplifies the computation of velocity fields (flows) and trajectories. As it is shown, the development of space-time patters inside the cavity depends on three key elements: the shape of the initial wave function, the mass of the particle considered, and the relative extension of the initial state with respect to the total length spanned by the cavity. From the spectral decomposition it is possible to identify how each one of these elements contribute to the localized matter wave and its evolution; the Bohmian analysis, on the other hand, reveals aspects connected to the diffraction dynamics and the subsequent appearance of interference traits, particularly recurrences and full revivals of the initial state, which constitute the source of the characteristic symmetries displayed by these patterns. It is also found that, because of the presence of confining boundaries, even in cases of increasingly large box lengths, no Fraunhofer-like diffraction features can be observed, as happens when the same wave evolves in free space. Although the analysis here is applied to matter waves, its methodology and conclusions are also applicable to confined modes of electromagnetic radiation (e.g., light propagating through optical fibers).

The integration of Wireless Sensor Networks (WSNs) into agricultural areas has had a significant impact and has provided new, more complex, efficient, and structured solutions for enhancing crop production. This research reviews the role of Wireless Sensor Networks (WSNs) in monitoring the macro-nutrient content of plants. The review study focuses on identifying the types of sensors used to measure macro-nutrients, determining sensor placement within agricultural areas, implementing wireless technology for sensor communication, and selecting device transmission intervals and ratings. The study of NPK (Nitrogen, Phosphorus, Potassium) monitoring using sensor technology in precision agriculture is of high significance in efforts to improve agricultural productivity and efficiency. In addition to fostering technological innovations and precision farming solutions, in future this research aims to increase agricultural yields, particularly by enabling the cultivation of certain crops in locations different from their original ones.

The individual and combined effects of extracellular products (ECP) from Arthrobotrys oligospora (Ao) and A. musiformis (Am) growth in liquid media against Haemonchus contortus L3 (HcL3) were assessed. The following methodological steps were established: isolation, morphological (MI) and molecular identification (Mol-I), assessment of nematocidal activity (NA) of fungal liq-uid culture filtrates (LCF) produced in two liquid media alone and in combination and finally, the myco-compound profile identification (MCP). The MI of both isolates suggested that the fungi corresponded to the species Ao and Am. This result was confirmed by PCR analysis followed by sequencing, alignment and obtaining coverage and similarity with respect to isolates reported in the NCBI database. Likewise, the highest Hc mortalities were 91.4% with individual LCF of Am and 86.2% with those of Ao at the highest concentration (100 mg/mL) in Czapek-Dox Broth. The combination of both LCF resulted in a similarly high larval mortality with no statistical differ-ences in relation to individual activity (P>0.05). The MCP showed the presence of alkaloids in both fungi. Coumarins, triterpenes and saponins were found only in Ao. Both fungi produced ECP with a high NA that could be identified and assessed in future studies as potential natural an-thelmintic compounds.

The monitoring of maternal and child health, using wearable devices made with wireless sensor technologies, is expected to reduce maternal and child death rates. Wireless sensor technologies have been used in wireless sensor networks to enable the acquisition of data for monitoring machines, smart cities, transportation, asset tracking, and tracking of human activity. Applications based on wireless body area network (WBAN) have been used in healthcare for measuring and monitoring of patient health and activity through integration with wearable devices. Wireless sensors used in WBAN can be cost-effective, enable remote availability, and can be integrated with electronic health record (EHR) management systems. Interoperability of WBAN sensor data with other linked data has the potential to improve health for all, including maternal and child health through the improvement of data access, data quality and healthcare access. This paper presents a survey of the state-of-the-art techniques for managing WBAN sensor data interoperability. The findings in this study will provide reliable support to enable policymakers and health care providers to take action to enhance the use of e-health to improve maternal-child health and reduce the mortality rates of women and children.

The aim of this review is to provide a survey of the recent advances and the main remaining challenges related to the ultrananocrystalline diamond (UNCD) nanowires and other nanostructures which exhibit excellent capability as the core components for many diverse novel sensing devices, due to the unique material properties and geometry advantages. The doping introduced in the gas phase during deposition promotes p-type or n-type conductivity. With the establishment of the UNCD nanofabrication techniques, more and more nanostructure based devices are being explored in measuring basic physical and chemical parameters via classic and quantum methods, as exemplified by gas sensors, ultraviolet photodetectors, piezoresistance effect based devices, biological applications, and nitrogen-vacancy color center based magnetic field quantum sensors. Highlighted finally are some of the remaining challenges and future outlook in this area.

Reconstructing ancestral species is a challenging endeavour: fossils are often scarce or enigmatic, and inferring ancestral characters based on novel molecular approaches (e.g. comparative genomics or developmental genetics) has long been controversial. A key philosophical challenge pertinent at present is the lack of a theoretical framework capable of evaluating inferences of homology made through integration of multiple kinds of evidence (e.g. molecular, developmental, or morphological). Here, I present just such a framework. I start with a brief history and critical assessment of attempts at inferring morphological homology through developmental genetics. I then bring attention to a recent model of homology, namely Character Identity Mechanisms (DiFrisco, Love, & Wagner, 2020), intended partly to elucidate the relationships between morphological characters, developmental genetics, and homology. I utilise and build on this model to construct the evaluative framework mentioned above, which judges the epistemic value of evidence of each kind in each particular case based on three proposed criteria: effectiveness, admissibility, and informativity, as well as providing a generalised guideline on how it can be scientifically operationalised. I then point out the evolution of the eumetazoan body plan as a case in point where the application of this framework can yield satisfactory results, both empirically and conceptually. I will conclude with a discussion on some potential implications for more general philosophy of biology and philosophy of science, especially surrounding evidential integration, models and explanation, and reductionism.

The combined muscle-skeletal traumas (MST) are very frequent over the last few years. The emergency care try to save the life of the patient and to receive resuscitation treatment. The role of Trauma surgeon is to choose the right moment for surgery. Aim: To present the management of patient with combined height injury, fracture stabilization, partial foot amputation, local flaps, skin grafting and HIFU – thermotherapy as scar treatment. Materials and methods: 40 years old mail patient, injured after a fall from 8 meters in an elevator shaft. After resuscitation treatment to control the shock the Imaging results are: burst fracture of L3 with spinal canal stenosis, fracture of the sacral bone Denis I, fracture of the right humerus in the proximal third, Monteggia fracture of the left arm, fracture of the proximal third of the right tibia as well as comminuted fractures of both calves and of both heel bones. Results: After a few operations his final result was well healed bone fractures and a good quality soft tissue coverage allowing the patient a satisfactory quality of life. Conclusions: Combined traumas of the muscle- skeletal system are severe, serious and difficult to manage. The good outcome in their treatment requires a team approach between the different medical specialists.

This work introduces a neuromorphic sensor based on force-sensing resistors (FSR) and spiking neurons for robotic systems. The proposed sensor integrates the FSR in the schematic of spiking neuron in order to make the sensor to generate spikes with frequency that depends on the applied force. The performance of the proposed sensor is evaluated for the control of a SMA-actuated ro-botic finger by monitoring the force during steady state when the finger pushes on a tweezers. For comparison purposes, we performed similar evaluation when the SNN receives input from a widely used compression load cell (CLC). The results show that the proposed FSR based neuro-morphic sensor has very good sensitivity to low forces and the function between the spiking rate and the applied force is continuous with good variation range. However, when compared to the CLC, the proposed sensor has lower linearity of the response, but it benefits from two orders of magnitude lower power consumption. These aspects imply that the neuromorphic sensor based on FSR are useful for bioinspired control of humanoid robotics representing a low power and low cost alternative the widely used sensors.

Classical and optimal control architectures for motion mechanics with fusion of noisy sensors use different algorithms and calculations to perform and control any number of physical demands, to varying degrees of accuracy, precision, and cost. Their performances are tested for the purpose of comparison through the means of a Monte Carlo simulation that simulates how different parameters might vary under noise, representing real-world imperfect sensors. We find that improvements in one figure of merit often come at a cost in the performance in the others, especially depending on the presence of noise in the system sensors. If sensor noise is negligible, open-loop optimal control performs the best. However, in the overpowering presence of sensor noise, using a control law inversion patching filter performs as the best replacement, but has significant computational strain.

To analyze the combined response of ocular cyclotorsion and head tilt during body tilt and to interpret the physiologic role of those movements, each of the three subjects completed six different experiments of body tilt not fixating their head movements using a flight simulator, which consisted of combinations of three tilt angles (30, 45, and 60) and two visual conditions. Head tilt and ocular cyclotorsion of the right eye before, during, and after the tilt maneuver were recorded and analyzed by time sequence. In the active tilting phase, the right eye showed a stepladder pattern of multiple repeats of fast and slow cyclotorsion along with head tilt to obtain maximum visual information. During the stationary tilting phase, there occurred a small amount (mean, 2.3) of ocular cyclotorsion which was reciprocal to that of head tilt. The visual cue of body tilt through the central visual field induced a larger amount of head tilt but affected little ocular cyclotorsion. The maximum degree of ocular cyclotorsion during each experiment was 10.2 on average. In conclusion, the combined response of ocular cyclotorsion and head tilt develops during body tilt and it compromises the conflicting needs between binocular vision and gravitational orientation.

The fusion of penetrating peptides (PPs), e.g., cell penetration peptides (CPPs), or antimicrobial peptides (AMPs) together with antimicrobial agents is an expanding research field. Specific AMPs, such as lactoferricin B (LfcinB), have demonstrated strong antibacterial, antifungal, and antiparasitic activity, as well as valuable anticancer activity, proving beneficial in the development of anticancer conjugates. The resulting conjugates offer potential dual functionality, acting as both an anticancer and an antimicrobial agent. This is especially necessary in cancer treatment where microbial infections pose a critical risk. Leukemic cells frequently exhibit altered outer lipid membranes compared to healthy cells, making them more sensitive to compounds that interfere with their membrane. In this communication, we revisited and reanalyzed our earlier research on LfcinB and its conjugates. Furthermore, we carried out new experiments with a specific focus on cell proliferation, changes in membrane asymmetric phosphatidylserine location, intracellular reactive oxygen species (ROS) generation, mitochondrial functions, and in vitro bacterial topoisomerase inhibition.

Microsoft has implemented several measures to defend against macro viruses, including the use of Anti-malware Scan Interface (AMSI) and automatic macro blocking. Nevertheless, evidence shows that threat actors have found ways to bypass these mechanisms. As a result, phishing emails continue to apply malicious macros as their essential attack vector. In this paper, we analyze 77 obfuscation features from the attacker’s point of view and extract 46 suspicious keywords in macro. We first combine the above two types of features to train machine learning models on a public dataset. Then, we carry out the same experiment on self-constructed dataset, a collection of newly discovered samples, to see if our proposed method could discover the unseen malicious macros. Experimental results demonstrate that, comparing with the existing researches, our proposed method has a higher detection rate and better consistency. Furthermore, ensemble multi-classifiers with distinct feature selection further improve the detection performance.

The ketogenic diet and walking exercise training activity are two key public health lifestyle factors. The potential of combined lifestyle factors interventions focused on getting to compliance in forced exercise. A balanced ketogenic diet and regular exercise activity is a key modifiable factor to the prevention and management of chronic diseases. Influence health across the lifespan and reduction of the risk of premature death through several biological mechanisms. Community older group’s lifestyle factors interventions contribute identity in their natural living environment. While the older health benefits of walking exercise training strategies are commonly to study, combined ketogenic diet and walking exercise interventions have induced greater benefits in community older groups.

This article uses machine learning analysis of wearable sensor data to explore the topic of driver differentiation. The study focuses on using detailed analyzes of data collected from wearable sensors to identify patterns that differentiate skilled drivers from inexperienced ones. On a predetermined driving route, participants experienced a variety of driving conditions, including parking, navigating cities, driving on highways and driving through residential neighborhoods. The gathered data was analyzed using machine learning techniques, yielding a classification accuracy of 94% overall. The results highlight important differences in sensor data between inexperienced and experienced drivers, providing insight into possible uses for improving safety protocols, driver education, and personalized feedback systems. The article fosters improvements in traffic safety and driver education by offering insightful information on the link between driver behavior analysis and machine learning.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

Sulfur, peroxide and combined sulfur and peroxide curing systems were applied for cross-linking of unfilled and carbon black filled rubber compounds based on ethylene-propylene-diene-monomer rubber. The results revealed that the type of curing system influenced the course and shape of curing isotherms. This resulted in the change of curing kinetics of rubber compounds. The cross-link density of vulcanizates cured with combined vulcanization systems was lower than that for vulcanizates cured only in the presence of sulfur or peroxide system. The results revealed good correlation between the cross-link density as well as the structure of the formed cross-links and physical-mechanical characteristics of the materials. It can be stated that by proper combination of vulcanization systems it is possible to modify tensile behavior of vulcanizates in targeted manner. On the other side, dynamical-mechanical properties were found not be significantly influenced by the composition of curing system.

Covid-19 fatality rates of 0.37% (Gangelt, Germany) and 0.17% (Santa Clara, USA) have been reported, estimated from serological studies. We show that the two confidence intervals strongly overlap when the uncertainty in the number of deaths is taken into account, so that the two investigations may be regarded as representative of the same population (tentatively: “Western society with no overload of the Health Care System during the pandemic”). Combining the results, the Covid-19 fatality rate is estimated to be found with 95% confidence in the range [0.1%; 0.3%].

Terms related to gradients of scalar fields are introduced as scalar products into the formulation of entropy. A Lagrange density is then formulated by adding constraints based on known conservation laws. Applying the Lagrange formalism to the resulting Lagrange density leads to the Poisson equation of gravitation and also includes terms being related to curvature of space. The formalism further leads to terms possibly explaining nonlinear extensions known from modified Newtonian dynamics approaches. The article concludes with a short discussion of the presented methodology and provides an outlook on other phenomena, which might be tackled using this new approach.

The recently explored synergistic combination of graphene-based materials and deep eutectic solvents (DESs) is opening novel and effective avenues for developing sensing devices with optimized features. In more detail, remarkable potential in terms of simplicity, sustainability, and cost-effectiveness have been demonstrated for sensors, resulting in the creation of hybrid devices with enhanced signal-to-noise ratio, linearity, and selectivity. Therefore, this review aims to provide a comprehensive overview of the currently available scientific literature discussing investigations and applications of sensors that integrate graphene-based materials and deep eutectic solvents, with an outlook for the most promising developments of this approach.

Background: Intelligence quotient (IQ) is a measure of intellectual ability of performing, comprehension, and learning. Previous studies reported that intelligence measures predict various measures of job performance and income. Emotional awareness (EA) is the measure of emotional capacity to recognize and make sense of one’s emotions, as well as those of others. A high level of emotional awareness (EA) indicates one can learn from expressed emotions quickly. Both IQ and EA are important for personal and professional success.Objective: This study tests the hypothesis that the average national income rank is best predicted by the combined effect of the population's average intelligence quotient (one's ability to perform and learn) and emotional awareness (ability to recognize and make sense of emotions).Method: The population’s average intelligence quotient (IQ), emotional awareness (EA), and indices of good governance, which include corruption perception index and educational expenses for each country, were obtained from public data sources. The outcome variable was per capita gross national income. All the variables that are statistically significant in univariate analysis were included in the multivariate regression analyses but excluded from the final model if not statistically significant.Result: The total number of countries included in the final analysis was 81 because of missing values in different variables. Intelligence quotient (IQ) and emotional awareness (EA) were found to be highly correlated, at 0.77 and 0.32 respectively, with the per capita gross national income. The independent effects of intelligence quotient (IQ) and emotional awareness (EA) were found significant in the multivariate model after adjusting for measures of good governance. The R square value for the final multivariate model was 0.82. The corruption perception index (CPI) and educational expenses were strongly correlated with other measures of good governance such as democracy index, functioning of government, electoral process and pluralism, political participation, and civil liberty, but these variables were found not significant in the multivariate model. Conclusion: The study concludes the effect of intelligence quotient (IQ) and emotional awareness (EA) are complementary to each other but intelligence quotient (IQ) is a stronger predictor than emotional awareness (EA) for gross national income or wealth. We also find that indicators of good governance, including corruption perception index and educational expenses, have important associations with per capita gross national income. This study implies a nation may build more wealth if the educational system focuses on developing emotional awareness in addition to intelligence. The study concludes the effect of intelligence quotient (IQ) and emotional awareness (EA) are complementary to each other but intelligence quotient (IQ) is a stronger predictor than emotional awareness (EA) for gross national income or wealth. We also find that indicators of good governance, including corruption perception index and educational expenses, have important associations with per capita gross national income. This study implies a nation may build more wealth if the educational system focuses on developing emotional awareness in addition to intelligence.

Objective: The combination of docetaxel (DTX) with Laser-Activated NanoTherapy (LANT), as a treatment for head and neck cancer (HNC) may enhance the therapeutic efficacy of lower doses of DTX, thereby minimizing the effective dosage, side effects and treatment times. Material and methods: Three HNSCC cell lines, Detroit 562, FaDu, and CAL 27, were treated with four combinations of DTX + LANT to evaluate DTX dose reduction and cell viability. Results: The 1 nM DTX + 5 nM LANT combination was the most effective treatment, increasing cell death over its corresponding DTX monotreatment with approximately 86.6%, 80.7%, and 92.1% cell death for Detroit 562, FaDu, and CAL 27, respectively. In Detroit 562, the 1 nM DTX + 5 nM LANT combination treatment resulted in the highest percentage of DTX dose reduction at 84.6%; in FaDu and CAL 27, the 0.5 nM DTX + 5 nM LANT combination treatment resulted in the highest percentage of DTX dose reduction at 78.2% and 82.4%, respectively. Conclusion: LANT may increase the therapeutic efficacy of DTX at significantly lower doses, which could improve patient outcomes.

Millions of people worldwide have an inadequate intake of selenium (Se) and zinc (Zn), and agronomic biofortification may minimise these problems. To evaluate the efficacy of combined foliar Se and Zn fertilisation in bread making wheat (Triticum aestivum L.), foliar Se (as NaSeO4) and soil and foliar Zn applications (as ZnSO4·7H2O) were tested individually and in all combinations. A 2-year field experiment was established in southern Spain under semiarid Mediterranean conditions, by following a split-split-plot design. The study year (2017/2018, 2018/2019) was considered as the main-plot factor, soil Zn application (50 kg Zn ha-1, nil Zn) as a subplot factor, and foliar application (nil, 10 g Se ha-1, 8 kg Zn ha-1, 10 g Se ha-1 + 8 kg Zn ha-1) as a sub-subplot factor. The best treatment to increase both Zn and Se concentration in both straw, 12.3- and 2.7-fold respectively, and grain, 1.2- and 4.1-fold respectively was the combined foliar application of Zn and Se. This combined Zn and Se application also increased on average the yield of grain, main product of this crop, by almost 26%. Therefore, bread-making wheat seems to be a very suitable crop to be used in biofortification programs with Zn and Se to alleviate their deficiency in both, people when using its grain and livestock when using its straw.

Triple negative breast cancer (TNBC) is an aggressive subtype of the disease with poor clinical outcomes and limited therapeutic options. Immune checkpoint blockade (CP) has surged to the forefront of cancer therapies with widespread clinical success in a variety of cancer types. However, the percentage of TNBC patients that benefit from CP as a monotherapy is low and clinical trials have shown the need for combined therapeutic modalities. Specifically, there has been interest in combining CP therapy with radiation therapy where clinical studies primarily with external beam have suggested their therapeutic synergy, contributing to the development of anti-tumor immunity. Here, we have developed a therapeutic platform combining radionuclide therapy (RT) and immunotherapy utilizing a radiolabelled biomolecule and CP in an E0771 murine TNBC tumor model. Survival studies show that while neither monotherapy is able to improve therapeutic outcomes, the combination of RT + CP extended overall survival. Histologic analysis showed that RT + CP increased necrotic tissue within the tumor and decreased levels of F4/80+ macrophages. Flow cytometry analysis of the peripheral blood also showed that RT + CP suppressed macrophages and myeloid-derived suppressive cells, both of which actively contribute to immune escape and tumor relapse.

BACKGROUND Studies demonstrate that T cells secrete interleukin and other cytokines to promote the growth and activation of B cells, and B lymphocytes release antibodies that attach to the PLA2R and THSD7A surface antigens on podocytes to create immunological complexes that are deposited beneath the glomerular epithelium, which damage the filtration barrier and trigger proteinuria.The combination regimen of Calcineurin inhibitors（CNIs）and rituximab(RTX) could simultaneously inhibit the proliferation of T and B lymphocytes and reduced the production of immune complexes.This kind of therapeutic method for idiopathic membranous nephropathy may achieve better clinical results and less complications. METHODS Fifty-eight patients who were diagnosed as idiopathic membranous nephropathy（IMN）through a biopsy suffering from nephrotic syndrome (NS) were recruited, and were divided into RTX treatment group(RTX group), CNIs combined with RTX treatment group(CNIs+RTX group) and CNIs combined with glucocorticoids(GC) treatment group(CNIs+CG group) according to their treatment background. All patients in 3 groups were followed up for at least 12 months. Clinical composite remission(including complete or partial remission) at 12 months was the main result; safety and the incidence of adverse events were the secondary outcomes. RESULTS The PLA2R antibody level of RTX+CNIs group at the baseline much higher than RTX and CNIs+GC groups (240.30(84.56,386.99)RU/ml vs. 44.94(11.48,143.94)RU/ml, 7.72(2.00,102.68) RU/ml, respectively, p=0.006). The PLA2R antibody concentrations among the three groups become comparable at the end of 3 months(36.80(2.10,88.05) RU/ml vs.14.32(2.00,47.98)RU/ml, 2.60(2.00,24.50) RU/ml, respectively, p>0.05). At 12 months, all patients in the CNIs+GC group achieved immune remission, and the PLA2R-ab concentration was less than 14RU/ml. At 3 months, 33of 58 patients(56.90%) had a composite remission(complete or partial remission), including 4 of 14 patients (27.85%) in the RTX group, 11 of 17 patients(64.71%) in the CNIs+RTX group and 18 of 27 patients (66.67%) in the CNIs+GC group (p=0.048). At 12 months, 51 of 58 patients(87.93%) had a composite remission, including 12 of 14 patients (85.71%) in the RTX group, 15 of 17 patients(88.24%) in the CNIs+RTX group and 24 of 27 patients (88.89%) in the CNIs+GC group (p=0.957). During the 12-month study, 7 patients did not response for the treatment. 1 patient relapsed during the 12-month treatment with a re-elevation of antibodies. Adverse events occurred in 18(31.03%)patients in the whole study, including 3 patients(5.17%) in the RTX group, 2 patients(3.45%) in the CNIs+RTX and 13patients(22.41%) in the CNIs+GC group(p< 0.05). CONCLUSIONS The combination of CNIs and RTX can effectively alleviate idiopathic membranous nephropathy with nephrotic syndrome in the first 3 months，as well as the combination of CNIs and GC, and the efficacy of both treatment methods for IMN was superior to that of RTX alone at the first 3 months. Furthermore, the RTX+CNIs group had less side effects compared with GC+CNIs group. So CNIs combined with RTX could be recommended as a superior therapeutic method for IMN treatment to shorten remission durion with less complications.

Wireless Sensor Networks (WSN) is designed to collect information across a large number of limited battery sensor nodes. Therefore, it is important to minimize the energy consumption of each node, which leads to the extension of the network life. Our goal is to design an intelligent WSN that collects as much information as possible to process it intelligently. To achieve this goal, an agent has been migrated to each node in order to process the information and to cooperate with these neighboring nodes while Mobile Agents (MA) can be used to reduce information between nodes and send those to the base station (Sink). This work proposes to use communication architecture for wireless sensor networks based on the Multi Agent System (MAS) to ensure optimal information collection. The collaboration of these agents generates a simple message that summarizes the important information in order to transmit it by a mobile agent. To reduce the size of the MA, the nodes of the network have been grouped into sector. As for each MA, we have established an optimal itinerary, consuming a minimum amount of energy with the data aggregation efficiency in a minimum time. Successive simulations in large scale wireless sensor networks through the SINALGO simulator show the performance of our proposal, in terms of energy consumption and package delivery rate.

This paper address the development of a new technic in the sleep analysis domain. Sleep is defined as a periodic physiological state during which vigilance is suspended and reactivity to external stimulations diminished. We sleep on average between six and nine hours per night and our sleep is composed of four to six cycles of about 90-minutes each. Each of these cycles is composed of a succession of several stages of sleep, more or less deep. The analysis of sleep is usually done using a polysomnography. This examination consists of recording, among other things, electrical cerebral activity by electroencephalography (EEG), ocular movements by electrooculography (EOG) and chin muscle tone by electromyography (EMG). The recording is done mostly in a hospital, more specifically in a service for monitoring the pathologies related to sleep. The readings are then interpreted manually by an expert to generate a hypnogram, a curve showing the succession of sleep stages during the night in 30-second epochs. The proposed method is based on the follow-up of the thermal signature that makes it possible to classify the activity into three classes: "awakening", "calm sleep" and "agitated sleep". The contribution of this non-invasive method is part of the screening of sleep disorders, to be validated by a more complete analysis of the sleep. The measure provided by this new system, based on temperature monitoring (patient and ambient), aims to be integrated into the tele-medicine platform developed within the framework of the Smart-EEG project by the SYEL - SYstèmes ELectroniques team. Analysis of the data collected during the first surveys carried out with this method showed a correlation between thermal signature and activity during sleep. The advantage of this method lies in its simplicity and the possibility of carrying out measurements of activity during sleep and without direct contact with the patient at home or hospitals.

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 paper describes the optimal design of a miniature fiber-optic linear displacement sensor. It is characterized by its ability to measure displacements along a millimetric range with sub-micrometric resolution. The sensor consists of a triangular reflective grating and two fiber-optic probes. The measurement principle of the sensor is presented. The design of the sensor's triangular grating has been geometrically optimized by considering the step angle of the grating to enhance the sensor's resolution. The optimization method revealed a global optimum at which the highest resolution is obtained

Sensor enabled big data and Artificial Intelligence platforms has the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased interest in animal welfare, the likely reduction in the number of animals in relation to population growth in the coming decade and the growing demand for animal proteins pose an acute challenge to prioritizing animal welfare on the one hand while maximizing the efficiency of production systems on the other. To stimulate a sustainable, digital, and resilient recovery of the agricultural and livestock industrial sector, there is an urgent need for testing and develop new ideas and products such as wearable sensors. By validating and demonstrating a fully functional wearable sensor prototype within an operational environment on the livestock farm that includes a miniaturized animal-borne biosensor and an artificial intelligence (AI) based data acquisition and processing platform, the unmet current needs can be fulfilled. The expected quantifiable results from wearable biosensors will demonstrate that the digitization technology can perform acceptably within the performance parameters specified by the agricultural sector and under operational conditions, to measurably improve livestock productivity and health. There is a need for a multimodal, digitized, automated biosensor and health monitoring platform containing AI algorithms for optimized stress and disease prediction in farm animals. An experimental development of non-invasive wearable and wireless physiological sensor networks that can be deployed in agricultural environments would allow for real-time extraction and integration of physiological data and display on decision support dashboards for livestock workers. By testing the effectiveness of the system in generating meaningful longitudinal data for further research into physiological and behavioral characteristics of farm animals, novel insights about animal welfare can be developed. The successful implementation of the digital wearable sensor networks would provide actionable real-time information on animal health status and can be deployed directly on the livestock farm, which will strengthen the green and digital recovery of the economy due to the significant innovation potential. Continuous monitoring of animal health and physiological functioning promotes more environmentally and socially acceptable developmental pathways. Once demonstrated, the portable animal health monitoring platform will close a critical gap in digitized livestock farming and position the agricultural industry as a frontrunner in the sector of farm animal monitoring and measurement systems. The multimodal wearable sensor-driven AI approach is expected to significantly improve the effectiveness of livestock decision support systems and the selection of resilient animals for the next generation and provide predictive data for end-users in livestock farming.

Smartwatches provide technology-based assessments in Parkinson’s disease (PD). We present results for sensor validation and disease classification via Machine Learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches and within a 15-min examination. Symptoms and medical history were captured on the paired smartphone. A broad range of different ML classifiers were cross-validated. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. The most advanced task of distinguishing PD vs DD was evaluated with 74,1% balanced accuracy, 86,5% precision and 90,5% recall by Multilayer Perceptrons. Deep Learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle-tremor signs with low noise. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers but it remains challenging for distinguishing similar disorders.

In this work, an embroidered textile moisture sensor is presented. The sensor is based on a capacitive interdigitated structure embroidered on a cotton substrate with an embroidery conductor yarn composed by 99% pure silver plated nylon yarn 140/17 dtex. In order to evaluate the sensor sensitivity, the impedance of the sensor has been measured by means of a LCR meter from 20 Hz to 20 kHz on a climatic chamber with a sweep of the relative humidity from 25% to 65% at 20 ºC. The experimental results show a clear and controllable dependence of the sensor impedance with the relative humidity. Moreover, the reproducibility of the sensor performance subject to the manufacturing process variability and washing process is also evaluated. The results show that the manufacturing variability introduce a moisture measurement error up to 4%. The washing process impact on the sensor behavior after applying the first washing cycle implies a sensitivity reduction higher than 14%. Despite these effect, the textile sensor keeps its functionality and can be reused in standard conditions. Therefore, these properties point out the usefulness of the proposed sensor to develop wearable applications on health and fitness scope including the user needs to have a life cycle longer than one-time use

The Combined Theoretical Signal (CTS) model, with a 90% or higher validity rate, stands as the first validated universal model integrating gravity and quantum physics. This manuscript presents the final validation and enhancement of the CTS model using recent pulsar-sourced gravitational wave data (J2322+2057, J2234+0611, J2043+1711, and J2317+1439) and strain data from GW150914, GW151226, GW190521, and GW200105. Our findings demonstrate a significant alignment between theoretical predictions and observational data, positioning CTS as a groundbreaking framework with profound implications for theoretical physics and cosmology. The CTS model not only bridges the gap between gravity and quantum mechanics but also offers a new perspective on the formation of massive black holes, the Quantum Cosmic Fabric (QCF), and the structure of the universe as an interconnected hierarchical tree of universes.

One of the most important sources of energy is the sun. Taiwan is located at north 22-25° latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location which can obtain sustainable and stable solar resources. This study takes research on the forecast of solar radiation to maximize the benefits of solar power generation, and develops methods that can predict the future solar radiation pattern to help reduce the costs of solar power generation. This study builds supervised machine learning models, known as deep neural network (DNN) and long short-term memory neural network (LSTM). The hybrid supervised and unsupervised model, namely cluster-based artificial neural network (k-means clustering and fuzzy C-means clustering-based models), was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time updated solar radiation forecast system capable of predicting the next 12 hours. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models have their advantages and disadvantages in predicting different future times. Therefore, the hybrid prediction system can predict future solar radiation more accurately than a single model.

Accurate and efficient determination of excited-state polarizabilities (α) is an open problem both experimentally and computationally. Following our previous work, [Phys. Chem. Chem. Phys. 2023, 25, 2131−2141], where one can employ simple ground-state density-based functions from the information-theoretic approach (ITA) to accurately and efficiently predict the macromolecular polarizabilities, we aim to predict the lowest excited-state polarizabilities in this work. The philosophy is to use density-based functions to depict the excited-state polarizabilities. As a proof-of-principle application, employing 2-(2′-hydroxyphenyl)benzimidazole and its derivatives as model systems, we have verified that either with the ground-state (S0) or excited-state (S1) densities as input, ITA quantities can be strongly correlated with the excited-state polarizabilities. When the transition densities are considered, both S0 and S1 polarizabilities are in good relationships with some ITA quantities. Furthermore, excitation and emission energies can be predicted based on multivariant linear regression equations of ITA quantities.

Mineral surfaces have been demonstrated to play a central role in prebiotic reaction, which are supposed to be at the basis of the origin of life. Among the various molecules proposed as precursors for these reactions, one of the most interesting is formamide. Formamide has been shown to be a pluripotent molecule, generating a wide distribution of relevant pre-biotic products. In particular, the outcomes of its reactivity are strongly related to the presence of mineral phases, acting as catalysts toward specific reaction pathways. Even if the mineral-products relationship has been deeply studied for a large pool of materials, the fundamental description of the formamide reactivity over the mineral surface at a microscopic level is missing in the literature. In particular, a key step of formamide chemistry at surfaces is its adsorption on the available interaction sites. This report aims to investigate the adsorption of formamide over a well-defined amorphous silica, chosen as model mineral surface. An experimental IR investigation of formamide adsorption has been carried out and its outcomes have been interpreted on the basis of first principles simulation of the process adopting a realistic model of the amorphous silica.

The need for industrial and commercial machinery to maintain high torque while accurately following a variable angular speed is increasing. To meet this demand, induction motors (IMs) are commonly used with variable speed drives (VSDs) that employ a field-oriented control (FOC) scheme. Over the last thirty years, IMs have been replacing independent connection direct current motors due to their cost-effectiveness, reduced maintenance needs, and increased efficiency. However, IMs and VSDs exhibit nonlinear behavior, uncertainties, and disturbances. This paper proposes a robust combined adaptive passivity-based control (CAPBC) for this class of nonlinear systems that applies to angular rotor speed and stator current regulation inside an FOC scheme for IMs´ VSDs. It uses general Lyapunov-based design energy functions and adaptive laws with σ-modification to assure robustness after combining control and monitoring variables. Lyapunov’s second method and Barbalat Lemma prove that the control and identification error tends to be zero over time. Moreover, comparative experimental results with standard Proportional Integer controller (PIC) and direct APBC, show the proposed CAPBC effectiveness and robustness under normal and changing conditions

Combined sewer overflow (CSO) is a significant environmental concern and public health (e.g., water contamination, eutrophication, and beach closure). The Environmental Protection Agency (EPA) has introduced the National Pollutant Discharge Elimination System (NPDES) permitting program to regulate and address this matter. This program mandates the control of CSOs for more than 700 municipalities obligated to devise Long-term Control Plans (LTCPs) to curb combined sewer overflows and bring them down to safe levels. LTCP involves diverse strategies, including sewer separation, green infrastructure improvements, and conventional gray infrastructure upgrades. This study investigates several municipalities’ solutions for CSO problems in conventional methods and wireless sensor technology as real-time control. The investigation mainly focuses on a comparative analysis of two cities, Richmond, Virginia, and South Bend, Indiana, such as average rainfall, the frequency of overflows, and the capacity of treatment plants. The findings indicate that integrating sensor technology could significantly enhance modeling endeavors, bolster the capacity of existing structures, and substantially enhance preparedness for storm events. The EPA’s Storm Water Management Modeling (SWMM) software is utilized. Through an analysis of SWMM data, the study suggests the potential for leveraging wireless sensor technology to achieve more robust control over CSOs as a part of LTCPs.

Although chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD) have distinct clinical features, both diseases may coexist in a patient because they share similar risk factors such as smoking, male sex, and old age. Patients with both emphysema in upper lung fields and diffuse ILD are diagnosed with combined pulmonary fibrosis and emphysema (CPFE), which causes substantial clinical deterioration. Patients with CPFE have higher mortality compared with patients who have COPD alone, but results have been inconclusive compared with patients who have idiopathic pulmonary fibrosis (IPF). Poor prognostic factors for CPFE include exacerbation, lung cancer, and pulmonary hypertension. The presence of interstitial lung abnormalities, which may be an early or mild form of ILD, is notable among patients with COPD, and is associated with poor prognosis. Various theories have been proposed regarding the pathophysiology of CPFE. Biomarker analyses have implied that this pathophysiology may be more closely associated with IPF development, rather than COPD or emphysema. Patients with CPFE should be advised to quit smoking and undergo routine lung function tests, and pulmonary rehabilitation may be helpful. Various pharmacologic agents may be beneficial in patients with CPFE, but further studies are needed.

The principal minimum horizontal stress plays an important role in the study of reservoir characteristics, modeling of oil and gas reservoir, drilling, production and stimulating wells. However, it is currently not possible to measure the minimum horizontal stress along the wellbore as a geophysical parameter logging. Minimum horizontal stress is measured by leak-off test (LOT) at only several points in a well. In order to have the values all along the wellbore, experimental formulas were established to determine the minimum horizontal stresses for different fields. Then these formulas must be calibrated with LOT data whose number is usually limited, even sometimes unavailable. On the other hand, the empirical formulas of one field might not be accurate for another. This study presents a new approach to solve the problem of minimum horizontal stress estimation by a combination of artificial intelligence and geostatistics. The method consists of using artificial neural network (ANN) to build a model of minimum horizontal stress estimation from relevant parameters such as true vertical depth, pore pressure and vertical stress, then combined with Kriging interpolation to obtain the distribution in space of the minimum horizontal stress. Hence, this method can estimate the minimum horizontal stress with a limited amount of available data and therefore we do not need to drill new wells or to find empirical formulas for each survey area. The method was then applied in a case study involved real geomechanical dataset of Hai Thach - Moc Tinh field in Nam Con Son basin, Vietnam.

IoTs and WSNs utilize their connectivity to enable solutions supporting a spectrum of industries in different and volatile environments. To effectively enhance the security and quality of service of networks, empirical research should take into account a variety of factors and be structured in a replicable manner. This will not only ensure scalability but also enable verification of conclusions, leading to more constructive outcomes. Cooja offers limited performance analysis capabilities of simulations, which are often extracted and calculated manually. In this paper, we introduce a framework and a toolkit that enable researchers to conduct structured and conclusive experiments considering different factors and metrics, experiment design to results analysis. Besides, this tool summarizes multiple network metrics and allows multiple scenarios. As proof of concept, this paper studies the flooding attacks on IoT and illustrates their impact on the network, leveraging the proposed framework and tools.

A planar microwave sensor devoted to the detection of humidity in underwear and clothes in general is proposed. The ultimate goal of the sensor is to detect the presence of liquids in fabrics, of interest to aid patients that suffer from certain pathologies, such as hyperhidrosis and enuresis. The main target in the design of the sensor, considering the envisaged application, is simplicity. Thus, the sensor operates at a single frequency, and the working principle is the variation in the mag-nitude of the transmission coefficient of a matched line loaded with an open-ended quar-ter-wavelength sensing stub resonator. The stub, which must be in contact with the so-called fabric under test (FUT), generates a notch in the transmission coefficient with a resonance frequency that depends on the humidity level of the fabric. By designing the stub with a moderately high quality factor, the variation in the resonance frequency causes a significant change in the magnitude level at the operating frequency, the resonance frequency when the sensing stub is loaded with the dry fabric, and the presence of liquid can be detected by means of an amplitude detector. A prototype device is proposed and experimentally validated.

Use of a multi-sensor approach can provide citizens a holistic insight in the air quality in their immediate surroundings and assessment of personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from 7 European cities, discusses data fusion and harmonization on a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants, and offers possible solutions and improvements. Harmonizing the data streams identified issues with the used devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate the process of generating visualizations and reports and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization on a diverse set of multi-sensor data streams considerably improved the quality and quantity of data that a research participant receives. Though automatization accelerated the production of the reports considerably, manual structured double checks are strongly recommended.

Distributed fiber optic vibration sensors based on Φ-OTDR using Rayleigh backscattering of highly coherent light source pulses are very popular type in the area of sensors due to many attractive features, mainly due to its distributed manner of sensing, i.e. they can operate as thousands of local sensors simultaneously. The paper summarizes and describes several important modifications of the original sensor scheme to enhance its key performance parameters and to extend its application potential. A multi-fiber sensor form was proposed. Single- and two-fiber sensors were designed, constructed and tested both in the laboratory and in a real environment. Variants of two-fiber sensor utilizing optical switches for the sensor flexibility enhancement are proposed and their applications are described.

Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise of mobile event producers, location information has become a valuable filtering criterion as it not only offers extra information on the event described but also enhances trust on the producer. Implementing mechanisms that validate the quality of location information becomes then imperative. The lack of such strategies in cloud architectures compels the adoption of new communication schemes for IoT-based urban services. To serve the demand for location verification in urban event-based systems (DEBS), we have designed three different fog architectures that combine proximity and cloud communication. Moreover, we have successfully assessed their performance using network simulations with realistic urban traces.

Load balancing, energy efficiency and fault tolerance are among the most important data dissemination issues in Wireless Sensor Networks (WSNs). In order to successfully cope with the mentioned issues, two main approaches (namely, Data-centric Storage and Distributed Data Storage) have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the mentioned problems, the proposed solutions typically focus on one issue at a time. In this paper, we integrate the Data-centric Storage (DCS) features into Distributed Data Storage (DDS) mechanisms and present a novel approach, denoted as Collaborative Memory and Energy Management (CoMEM), to overcome both problems and bring memory and energy efficiency to the data loss mechanism of WSNs. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms existing approaches in various WSN scenarios.

Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.

Wireless Sensor Networks (WSNs) are being increasingly adopted in critical applications, where verifying the correct operation of sensor nodes is a major concern. Undesired events may undermine the mission of the WSNs. Hence their effects need to be properly assessed before deployment to obtain a good level of expected performance and during the operation in order to avoid dangerous unexpected results. In this paper we propose a methodology to support design and deployment of dependable WSNs by means of an event-based formal verification technique. The methodology includes a process to guide designers towards the realization of a dependable WSN and a tool ("ADVISES") to simplify its adoption. The tool allows to generate automatically formal specifications used to check correctness properties and evaluate dependability metrics at design time and at runtime. During the runtime we can check the behavior of theWSN accordingly to the results obtained at design time and we can detect sudden and unexpected failures, in order to trigger recovery procedures. The effectiveness of the methodology is shown in the context of two case studies, aiming to illustrate how the tool is helpful to drive design choices and to check the correctness properties of theWSN at runtime

Virtual validation of radar sensor models is becoming increasingly important for the safety validation of adf. Therefore, methods for quantitative comparison of radar measurements in the context of model validation need to be developed. This paper presents a novel methodology for accessing and quantifying validation measurements of radar sensor models. This method uses edf and the so-called dvm to effectively quantify deviations between distributions. By applying this metric, the study measures the consistency, reproducibility and repeatability of radar sensor measurements. Different interfaces and different levels of detail are investigated. By comparing the radar signals from real world experiments where different objects are present, valuable insights are gained into the performance of the sensor. In particular, the research extends to assessing the impact of varying rain intensities on the measurement results, providing a comprehensive understanding of the sensor’s behaviour under these conditions. This holistic approach significantly advances the evaluation of radar sensor capabilities and enables the quantification of the maximum required quality of radar simulation models.

Among the five human senses, light, sound, and force perceived by eye, ear, and skin, respectively are physical actions, and therefore can be easily measured and expressed as objective, univocal, and simple digital data with physical quantity. However, taste and odor molecules perceived by tongue and nose are chemical actions, it has been difficult to express them as objective and univocal digital data, since no reference chemicals can be defined. Therefore, while the recording, saving, transmitting to remote locations, and replaying human visual, auditory, and tactile information as digital data in digital devices has been realized (this series of data flow is defined as DX (digital transformation) in this review), the DX of human taste and odor information is not yet in the realization stage. Particularly, since there are at least 400,000 types of odor molecules and an infinite number of complex odors that are mixtures of these molecules, it has been considered extremely difficult to realize "human olfactory DX" by converting all odors perceived by the human olfaction into digital data. In this review, we discuss the current status and future prospects of the development of "human olfactory DX," which we believe can be realized by utilizing odor sensors that employ the olfactory receptors (ORs) that support human olfaction as sensing molecules (i.e., human OR sensor).

An antenna sensor is proposed to execute dual functions of antenna and sensor in the wireless sensor system, in order to reduce data loss and to increase transmission rate by omitting a certain interface. The as-made sensor was test at a center frequency of 46 MHz for measuring human finger postures using principle of dipole antenna. The antenna sensor was attached on a wearable glove. The results showed that the motion sensor can accurately identify finger angles at 0°, 20°, 40°, 60° and 80°.

Sorghum has become increasingly popular for diverse applications, including food, feed, and bioenergy. The present study aimed to perform a bibliometric and content analysis on sorghum research in the industrial-agricultural sector to evaluate global research patterns, emerging topics, and future trends. The bibliometric analysis of the Scopus bibliographic metadata was done using Bibliometrix and Vosviewer. The results of this investigation show the descriptive analysis underscored fluctuation in research output and citation impact over the years. The identified four patterns through co-occurrence analysis encompassed central themes of sorghum research, including genetic expression and plant stress cluster, cereal crop and yield cluster, biomass and microbial bioprocessing, and metabolism and antioxidant cluster. The thematic map portrays a comprehensive view of cereal crops and yields, biomass and microbial bioprocessing, metabolism and antioxidant research, environmental remediation and sustainable agriculture practices, and the declining theme of starch. The findings provide valuable insights into the global pattern of sorghum research and emerging trends, thus directing future multidisciplinary research efforts for enhanced knowledge and sustainable practices in the industrial-agricultural sector.

Chemical fertilizers are commonly used to meet the nutritional demands of the crops and boost their yield. However, their high costs and excessive application in soils increases the cost of production and have negative effects on the soil and environmental health. Vermicompost is an organic amendment that can potentially lessen the dependence on chemical fertilizers with additional advantages of sustainable nutrient supply to crops and maintaining soil health. To evaluate the potential of two diverse vermicomposts, sole and combined application of these vermicomposts with reduced rates of chemical fertilizers were used for tomato cultivation in a field study. The results indicated that vermicompost produced from cow dung combined with chemical fertilizers proved to be more effective in improving tomatoes' growth, physiology, yield and nutritional attributes. Combined application of vermicompost and chemical fertilizer significantly improved root length (21.6%), plant height (167%), SPAD value (13.5%),chlorophyll 'a' (96%), chlorophyll 'b' (161%), relative water content (16%), membrane stability index (18%), carotenoid (87%), yield (82%), photosynthetic rate (148%), and fruit diameter (83%) protein (89%), fat (27.5%), fiber (12%), vitamin C (52%), calcium (54%), magnesium (117%), phosphorus (38%), potassium (128%), as compared to control treatment (NP applied). In addition, significant improvements in different soil physico-chemical properties were also pragmatic. The results suggest that vermicompost application with reduced doses of chemical fertilizers can be used to improve crop yield and soil physico-chemical properties.

How do sensor research and technologies grow over time? This paper applies the network analysis with a new computational approach to map the structure and evolution of sensor research and technologies over a 30-year time frame (1990-2020).The goal of this study is to analyze the evolution of sensor research for forecasting emerging scientific and technological trajectories. Results show that the scientific interaction within ecosystem (represented with networks) of sensor generates a co-evolution of scientific fields supporting the accelerated growth of different technological tra-jectories, such as: wireless sensors, fiber optic and optical sensors, gas sensors and biosensors. These results suggest main theoretical implications that explain the evolution of sensor research with critical aspects of innovation management to support R&D investments towards new technological trajectories having a high potential of growth.

The electric vehicle (EV) industry challenges battery joining technologies by requiring higher energy density both by mass and volume. Improving the energy density via new battery chemistry would be the holy grail but is seriously hindered and progresses slowly. In the meantime, alternative ways, such as implementing more efficient cell packaging by minimizing the electrical resistance of joints are of primary focus. In this paper we discuss the challenges associated with the electrical characterisation of laser soldered joints in general, and the minimization of their resistive losses, in particular. In order to assess the impact of the joint resistance on the overall resistance of the sample, the alteration in resistance was monitored as a function of voltage probe distance and modelled by finite element simulation. The experimental measurements showed two different regimes: one far from of the joint area and another in its vicinity and within the joints cross section. The presented results confirm the importance of the thickness of the filler material, the effective and the total soldered area, and the area and the position of the voids within the total soldered area in determining the electrical resistance of joints.