The refractive index of seawater is one of the essential parameters in ocean observation, so it is necessary to achieve high-precision seawater refractive index measurements. A seawater refractive index measurement system based on position-sensitive detectors is an effective means to achieve high-precision measurement of seawater refractive index. However, in the actual seawater refractive index measurement process, the impact of the measurement error caused by the jitter signal obtained from the measurement on the measurement results cannot be ignored. In this paper, we theoretically analyse the causes of the long jitter signals during seawater refractive index measurement and quantify the influencing factors. Through the analysis, it can be seen that the angle between the two windows in the refractive index measurement area of seawater causes a great error on the measurement results, so the angle between the two windows should be controlled within 2.06° in order to effectively reduce the measurement error. At the same time, the factors affecting the measurement accuracy of the position-sensitive detector are analysed, and the factors leading to the reduction of the measurement accuracy of the position-sensitive detector are mainly the background light, the size of the photosensitive surface, and the change of the temperature of the working environment. For the above factors, we add a narrow-band filter with a bandwidth of 0.9nm in front of the photosensitive surface of the position-sensitive detector during the construction of the measurement system, so as to filter out stray light other than the signal light source. On the premise of ensuring the measuring range of the refractive index of seawater, a position-sensitive detector with a photosensitive surface size of 4 mm×4 mm was selected. In order to reduce the temperature variation of the working environment of the position-sensitive detector, the measurement system was partitioned. In order to validate the testing error range of the optimised test system, standard seawater samples under the same conditions were measured. It can be seen that the jitter signal of the measurement system has been reduced from 0.0022mm before the optimisation to 0.0011mm after the optimisation, and the deviation of the refractive index has been reduced by 1.09×10−8RIU. The experimental results show that the refractive index of seawater has been effectively reduced by adjusting the optical path and the structure of the seawater refractive index measurement system.

Wireless sensor networks can provide a cheap and flexible infrastructure to support the measurement of noise pollution. However, the processing of the gathered data is challenging to implement on resource-constrained nodes, because each node has its own limited power supply, low-performance and low-power micro-controller unit and other limited processing resources, as well as limited amount of memory. We propose a sensor node for monitoring of indoor ambient noise. The sensor node is based on a hardware platform with limited computational resources and utilizes a number of simplifications to approximate more complex and costly signal processing stage. Furthermore, to reduce the communication between the sensor node and a sink node, as well as the power consumed by the IEEE 802.15.4 (ZigBee) transceiver, we perform digital A-weighting filtering and non-calibrated calculation of the sound pressure level on the node. According to experimental results, the proposed sound level meter can accurately measure the noise levels of up to 100~dB, with the mean difference of less than 2~dB compared to Class 1 sound level meter. The proposed device can continuously monitor indoor noise for several days. Despite the limitations of the used hardware platform, the presented node is a promising low-cost and low-power solution for indoor ambient noise monitoring.

The use of low cost sensors has dramatically increased in recent years in all the engineering sectors. In the field of buildings and automotive, low cost sensors open very interesting perspectives, because they allow to monitor temperature and humidity distributions together with air quality, in a widespread and punctual way and allow control of all energy parameters. The main issue still open remains the validation of the measurements. In this work, we propose an innovative approach to verify the measurements given by some low-cost systems built ad hoc for automotive applications. Two independent low-cost measurement systems have been set to measure Particulate air Matter (PM) concentration, TVOC concentration, CO2 concentration, formaldehyde concentration, air temperature, relative humidity, pressure, air flow velocity and GPS position. These systems have been calibrated by the comparison with standard and certified sensors used by the regional authority of Emilia-Romagna region (ARPAE, Italy) for characterising the air-quality. The innovation of this approach lies in both the in-field comparison of low-cost and high quality sensors and the use of proper conversion approaches for mass concentration measurements. A quantitative analysis of the sensors performance is given, with a focus on effects of time-granularity, relative humidity, mass conversion from particle counts and size detection response. The results show that the low-cost sensors measurements of air temperature, relative humidity and particle number concentration are in a good agreement with high quality sensors measurements, with a strong impact of relative humidity on performance indicators. Overall, a good quality and consistency of the data among the sensors has been achieved.

This paper describes how to construct a low-cost magnetic levitation system (MagLev). The MagLev has been intensively used in the engineering education, allowing instructors and students to learn through hands-on experiments essential concepts, such as electronics, electromagnetism, and control systems. Built from scratch, the proposed MagLev depends only on simple, low-cost components readily available on the market. In addition to showing how to construct the MagLev, this paper presents a semi-active control strategy which seems novel when applied in the MagLev. Experiments performed in the laboratory have compared the proposed control scheme with the classical PID control. The corresponding real-time experiments illustrate both the effectiveness of the approach and the potential of the MagLev for education.

The present work aimed to validate a low-cost passive monitoring procedure. For its validation, the monitoring of atmospheric organic pollutants - polycyclic aromatic hydrocarbons (PAH) was carried out in a capital of the central-western region of Brazil. The sensors were fixed on poles intended for electrification during the dry season. After 15 days, samples were extracted by solvent extraction and analysed by High-Resolution Gas Chromatography with Flame Ionization Detector (HRGC-FID). For the validation of the procedure, PAHs monitored and standardised by the American Environmental Agency (EPA), a benchmark for environmental monitoring of air quality by several countries, were analysed. The results demonstrated that the low-cost passive monitoring method was effective in the quantification of PAH in the environment-air, capable of being used by countries that do not have many resources for monitoring air quality.

The commercial equipment that carries out the measurement of temperature has a high cost. Therefore, this article describes the development of a temperature measurement equipment, which uses a microcontrolled platform, responsible for managing the data of the collected temperature signals and making available the acquired information, so that they can be verified in real time at the measurement site, or remotely. The construction of the temperature measurement equipment was performed using open platform hardware / software, where performance tests were carried out with the objective of developing a temperature measurement equipment that has measurement quality and low cost.

The paper presents an error model of a measurement chain containing link performing discrete wavelet transform algorithm which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.

Herein, we present the design technique of a resonant rectifier for piezoelectric (PE) energy harvesting. We propose two diode equivalents to reduce the voltage drop in the rectifier operation, a minuscule-drop-diode equivalent (MDDE) and a low-drop-diode equivalent (LDDE). The diode equivalents are embedded in resonant rectifier integrated circuits (ICs), which use symmetric bias-flip to reduce the power wasted for charging and discharging the internal capacitance of a PE transducer. The self-startup function is supported by synchronously generating control pulses for the bias-flip from the PE transducer. Two resonant rectifier ICs, using both MDDE and LDDE, are fabricated in a 0.18 μm CMOS process and their performances are characterized under external and self-power conditions. Under the external-power condition, the rectifier using LDDE delivers an output power POUT of 564 μW and a rectifier output voltage VRECT of 3.36 V with a power conversion efficiency (PCE) of 90.1%. Under self-power conditions, the rectifier using MDDE delivers a POUT of 288 μW and a VRECT of 2.4 V with a corresponding PCE of 74.6%. The result shows that the power extraction capability of the proposed rectifier is 5.9 and 3.0 times higher than that of a conventional full-bridge rectifier.

In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in in-dustrial item sorting systems and proposes an object recognition and sorting system for auton-omous driving. In industrial sorting lines, goods sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot's real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consump-tion. These issues lead to the fact that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array) based item sorting system that achieves similar perfor-mance to current mainstream sorting systems at a lower cost and consumption than existing sorting systems. The recognition part employs a morphological recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degree-of-freedom robotic arm is introduced in the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.

This paper proposes a position control method for low cost EGR valve system in automotive application. Generally, position control system using in automotive application has many restrictions such as cost and space, the mechanical structure of actuator implies high friction and large difference between static friction and coulomb friction. This large friction difference occurs the vibrated position control result when the controller uses conventional linear controller such as P, PI. In this paper, low cost position control method which can apply under the condition of high difference friction mechanical system. Proposed method is verified by comparing conventional control result of experiments.

This paper introduces both a hardware and a software system designed to allow low-cost electronic monitoring of social insects using RFID tags. Data formats for individual insect identification and their associated experiment are proposed to facilitate data sharing from experiments conducted with this system. The antenna's configuration and their duty cycle ensure a high degree of detection rates. Other advantages and limitations of this system are discussed in detail in the paper.

The paper presents an analysis of the latest developments in the field of stereo vision in the low-cost segment, both for prototypes and for industrial designs. We described the theory of stereo vision and presented information about cameras and data transfer protocols and their compatibility with various devices. The theory in the field of image processing for stereo vision processes is considered and the calibration process is described in detail. Ultimately, we presented the developed stereo vision system and provided the main points that need to be considered when developing such systems. The final, we presented software for adjusting stereo vision parameters in real-time in the python language in the Windows operating system.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Developing a low-cost wireless energy meter with power quality measurement for smart grid applications represents a significant advancement in efficient and accurate electrical energy monitoring.In increasingly complex and interconnected electrical systems, this device is essential in various applications, such as smart grids, enabling real-time energy usage monitoring. In such a context, smart meters expand the possibilities for sustainable and efficient energy use and promote better utilization of energy sources, especially non-renewable ones. According to the 2020 International Energy Agency (IEA) research, non-renewable energy sources represent 65% of the global total. The smart meter developed in this work is built based on the ESP32 microcontroller and easily accessible components, using a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of the smart meter development was to enhance the software and simplify the hardware. Unlike traditional meters that perform electrical parameter calculations using complex hardware circuits, this project performed the calculations directly on the microcontroller. This approach reduced the complexity of the hardware, simplifying the meter’s design. With the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters were achieved without the need for additional circuits. This software-driven approach with simplified hardware brought benefits such as reduced production costs, lower energy consumption, and increased meter development and updates flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the transmission of collected data to remote monitoring systems for later analysis. The developed smart meter offers cost, energy efficiency, and flexibility benefits. It represents an innovative, efficient, and versatile solution for smart grid applications.

The transmission and analysis of data is one of the challenges of the 21st century. In the field of environmental measurement technology, existing broadband and wireless technologies have not been able to transmit data reliably and cost-effectively over long distances and in hard-to-reach places. LoRaWAN, an IoT technology, could be an energy-efficient, cost-effective and secure alternative as a narrowband technology in combination with battery-powered sensors and thus make an important contribution to the intelligent, largely wireless networking of objects, plants and machines (IoT), for example in the municipal sector. In addition to ecological and economic benefits, the quality of life in modern, intelligently networked cities can be enhanced by real time data acquisition. However, the prerequisite is that the quality of the data acquired via this method is sufficiently good. This paper therefore addresses the question of the quality of particulate matter data collected by low-cost sensors. To determine this, an SDS011 particulate matter sensor from Nova Fitness was ported to LoRaWAN. The sensor was installed next to a governmental measurement station. In a test that lasted five weeks, data from the SDS011 sensor were compared with those from the governmental station. Differences were identified and a correction approach was developed and applied. The efficiency of the approach was verified. Based on the results, it can be seen that the use of the low cost sensors has weaknesses. Problems can only be partially reduced. Nevertheless, the use of the low-cost sensors can be helpful for a flexible and cost effective collection of environmental data.

In small data set learning, obtaining a large number of training samples from the source class for transfer learning is a challenge for fine-grained visual classification. Based on the fact that fine-grained concepts can be learned with very few samples, we use very few labeled samples (for example, three) in each category. However, due to the difficulty in distinguishing the subtle differences between fine-grained images, we propose a local structure information extraction method of SDFGVC based on spatial frequency features. The proposed learning module enhances the ability of the network to find significant regions and shows excellent performance in experiments on six datasets.

Water pollution, intensified by the release of hard-to-degrade pollutants, poses severe threats to ecosystems, human health, and economic development. The existing advanced oxidation processes often involve high operational costs and may result in secondary pollution, highlighting the necessity for innovative and more sustainable solutions. To address these challenges, our study introduces a cost-effective and eco-friendly corona discharge barrier plasma discharger for wastewater treatment. Through the central composite design/response surface methodology, a high decolorization rate of 98% of methylene blue (MB) within 10 minutes was achieved by optimizing parameters such as pH and voltage. Furthermore, the mechanisms of the generation of reactive oxygen species via this device was discussed in detail and the degradation pathways of MB were described. Moreover, this device is also very energy-efficient, with a low energy density and electrical energy per order of 0.15 watt/mL and 5.79 kWh/m³/order, respectively. In conclusion, the plasma discharger developed in this study provides a cost-effective and environmentally sustainable solution for dye wastewater treatment. This research contributes significantly to the advancement of sustainable dye wastewater management practices, offering an innovative method that meets both environmental and economic objectives.

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SAR) and Ground Penetrating Radars (GPR) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency Modulated Continuous Wave operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device.

In this paper we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over a city of Sarajevo, Bosnia and Herzegovina. The system collects, calibrates and displays particulate metal concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including: higher resolution in space and time, lower costs, both horizontal and vertical measurements and many more. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope (cable car), we receive vertical measurements of temperature, humidity and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer.

Enclosed public spaces are the hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of Particulate Matter Sensors has been developed and named as Dynamic Aerosol Transport for Indoor Ventilation or DATIV system. This system can use multiple Particulate Matter Sensors (PMS) simultaneously and can be remotely controlled using a Raspberry Pie based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or Smartphone with corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

Livestock farming plays a crucial role in agriculture and is important in meeting people's needs for a better life in the new era. However, due to China's "double carbon" target, the development of animal husbandry has been affected by the level of carbon emissions. To study the spatial dis-tribution and dynamic evolution of livestock carbon emissions in Shandong Province, we used panel data from 16 prefecture-level cities in Shandong Province from 2001 to 2022 to determine the spatial differentiation of livestock carbon emissions in Shandong Province and their dynamic evolution by combining spatial and non-parametric estimation methods based on livestock carbon emissions and livestock emission intensity measured by city. The results of the study show that the distribution of carbon emissions from animal husbandry in Shandong Province is in a "dis-persed-aggregated" pattern, and the intensity of carbon emissions shows a gradual downward trend, with a ladder-like distribution characterized by high in the west and low in the east. Through the analysis of the dynamic evolution of nuclear density, we observe that the regional differences of animal husbandry carbon emissions in Shandong Province are expanding, showing the characteristics of polarization. The difference of carbon emissions in the peninsula is gradually narrowing, and the phenomenon of polarization is weakening. The carbon emissions of animal husbandry in the central and western regions of Shandong are polarized.

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

Background: Gait analysis systems offer invaluable insights for rehabilitation, yet their expense limits clinician access. We developed GAITWell®, a low-cost modular system for capturing spatiotemporal gait variables, and evaluated its measurement properties. Methods: The GAITWell® uses discreet binary sensors on interconnected boards to collect gait data, which is then analyzed using the DBSCAN algorithm on the Cartesian points from sensor outputs. Reliability was assessed using ICC2,1, standard error of the mean (SEM) and Bland-Altman plots. Validity was determined by comparing GAITWell® with the Qualisys Pro-Reflex system. Results: Participated 38 healthy adults, with an average age of 33.2 years (SD 13.0). Correlations between GAITWell® and the Qualisys system ranged from moderate to very high for most gait variables, with the exception of stride length, which demonstrated a low but significant correlation (r = .360, p < .05). The ICC2,1 indicated moderate to good agreement for most gait variables. Stance and double support times, cadence, and base of support exhibited poor reliability, characterized by larger SEM and limits of agreement. Conclusions: Our findings indicate that the GAITWell® system is a promising tool for gait analysis. Future research will enhance sensor accuracy and refine algorithms to improve reliability.

Home Security Systems are very important features of modern residential and office setups. This system is used to keep home safe from harmful gases, fire and intruders. It must be affordable, reliable and effective. In this paper, a GSM based home security system has been designed and implemented. The system consists of a GSM Module, MQ-2 smoke detector, Ultrasonic sonar sensor, LM35 temperature sensor, Arduino Uno and relay module. The MQ-2 smoke detector is used to detect harmful gases i.e. LPG, CH4, CO, Alcohol, Smoke or Propane. Any of the harmful gases stated above is detected it will warn the owner by activating the buzzer and displaying a warning message on the LCD display. It will also send a SMS to the owner. Our system can also measure temperate and if the temperature is increased above a certain range it will compensate it by activating Air Conditioner through the relay module. The system will also send a SMS to the owner. It can also detect intruders using Ultrasonic sensor and warn us through buzzer, SMS and displaying message on the LCD. Our system is reliable as it has very fast response and cost effective and it has been developed using our own technologies.

Urban cities in sub-Saharan Africa (SSA) are faced with ambient air pollution. This is an important public health problem with models and limited monitoring data indicating high concentrations of pollutants such as fine particulate matter (PM_2.5). Going through most global air quality index maps, however, information about ambient pollution from SSA is scarce. We evaluated the feasibility and practicality of longitudinal measurements of ambient PM_2.5 using low-cost air quality sensors (Purple Air-II-SD) across thirteen locations in seven countries in SSA. Devices were used to gather data over a 30-day period with the aim of assessing the efficiency of its data recovery rate and identifying challenges experienced by users in each location. The median data recovery rate was 94% (range: 72% to 100%). The mean 24-hour concentration measured across all sites was 38 µg/m³ with the highest PM_2.5 period average concentration of 91 µg/m³ measured in Kampala, Uganda and lowest concentrations of 15 µg/m³ measured in Faraja, The Gambia. Kampala-Uganda and Nnewi-Nigeria recorded the longest periods with concentrations>250µg/m³. Power outages, SD memory card issues, internet connectivity problems and device safety concerns were important challenges experienced when using Purple Air-II-SD sensors. Despite some operational challenges, this study demonstrated that it is reasonably practicable and feasible to establish a network of low-cost devices to provide data on local PM_2.5 concentrations in SSA countries. Such data are crucially needed to raise public-, societal and policymaker awareness about air pollution across SSA.

A well known technique to enhance the signal to noise ratio (SNR) of repetitive signals is to average them. The coherent parts of the signal add up constructively while the incoherent parts are averaged out. The prerequisite is that the signals are acquired under conditions of high repeatability, i.e.\ the signals must be sufficiently similar. In the present technical note we describe an efficient method for maintaining signal similarity by ensuring spatial and temporal proximity of laser waveform signals obtained by a sensor operated from an airborne platform. The method makes use of a few auxiliary parameters such as laser pulse repetition rate, mirror rotation rate, platform altitude, and flight speed. The method can be extended to be operated in real time.

Distracted driving is the leading cause of accidents, the cause of these distractions may include drowsiness, using cell phone, smoking a cigarette, etc. The proposed paper aims at monitoring the attentiveness of the driver and alerting the driver when his/her alertness goes below a certain threshold. It uses image processing using OpenCV (Computer Vision) and You Look Only Once (YOLO) algorithm to detect the eyes and object respectively and sounds an alarm when the driver is either drowsy or using his cell phone or smoking a cigarette.

Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierachical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot's base and foot workspace are delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm is employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trains foot points to facilitate robot movement along predefined paths. Experimental results demonstrate the method's adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieves a 100% success rate, enhances average overall scores by 6.27%, reduces average stride lengths by 39.04%, and attains a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection.

Density is a crucial parameter for quantitatively describing the physical properties of liquids. It serves as an important indicator for scientific research, production process control, pipeline transportation, and other aspects. In oil pipeline transportation and raw material processing, real-time online measurement of liquid density is of great significance. This paper analyzes the working principle of an on-line vibrating tube densitometer and derives the fitting equation for temperature, pressure, and density；it also conducts experiments with an on-line vibrating tube liquid densitometer and establishes a traceability chain for the experimental device. The experimental setup includes a desktop densitometer system, a multi-temperature field constant temperature stirring system, a walk-in constant temperature box, an automatic blowing system, and a frequency acquisition and calculation system. The uncertainty of the device's evaluation is U= 0.08kg/m3, k=2.The experimental research on the on-line vibrating tube liquid densitometer can be carried out within the range of (650~1400) kg/m3, (10~70)℃, (0~50)bar the metrological performance of the vibrating tube densitometer can be researched by utilizing the straight tube type and the elbow type on-line vibrating tube densitometer. A preliminary study was conducted on the effects of different densities, temperatures, and pressures on the vibrating tube system's vibration cycle. The fit coefficient and error were calculated, and the experimental results were compared to the theoretical analysis to confirm the device's conformity. The study verified the device's scientific and reasonable design, and demonstrated that it is feasible to use the device for follow-up research.

Microsystems are key enabling technologies, with applications found in almost every industrial field, including in-vitro diagnostic, energy harvesting, automotive, telecommunication, drug screening, etc. Microsystems, such as microsensors and actuators, are typically made up of components below 1000 microns in size that can be manufactured at low-unit cost through mass-production. Yet, the development of microsystems for commercial or educational purposes, has typically been limited to upper income countries due to the initial investment costs associated with the microfabrication equipment and processes. However, recent technological advances have enabled the development of low-cost microfabrication tools. In this paper, we describe a range of low-cost approaches and equipment (below £1000), developed or adapted and implemented in our laboratories. We describe processes including photolithography, micromilling, 3D printing, xurography and screen-printing used for the microfabrication of structural and functional materials. The processes that can be used to shape a range of materials with sub-millimetre feature sizes are demonstrated here in the context of Lab-on-Chips, but they can be adapted for other applications. We anticipate that this paper, which will enable researchers to build a low-cost microfabrication toolbox in a wide range of settings, will spark a new interest in microsystems.

There is a growing interest in the endocannabinoid system and the gut microbiota. Both areas have been suggested to play a role in endometriosis, however this is yet to be studied comprehensively. The purpose of this narrative review is to identify the gaps in endometriosis research in the context of these two areas. It underscores the limited knowledge regarding the involvement of the endocannabinoid system and gut microbiota in endometriosis. Further research in these areas is essential to unlock the therapeutic potential of cannabis and enhance the understanding of endometriosis pathogenesis.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

Air pollution has created a lot of problems in the developed and developing countries. To avoid or reduce these problems, constant monitoring of the air should be ensured. The conventional techniques is costly because it requires a lot of money and time consuming. Biomonitoring has been the alternative method. Moss, lichens and plants are biomonitors available to entrap air pollutants. The aim of this paper is to discuss one of the ways of monitoring air pollution – Moss bag technique. To do this, types, choice, preparation, handling of bags after preparation of moss were discussed. From the literatures consulted, it was discovered that there were differences in the techniques used by the researchers. In all, the use of mosses with emphasis on the employment of moss bag have proved to be a powerful tool in airborne particulate and toxic elements. To conclude, developing countries should focus more on this technique because it will reduce cost of air monitoring.

In recent years, the application of Indoor Positioning Systems (IPS) has experienced a significant increase in demand, with the aging of the world’s population and their changing lifestyles. While outdoor positioning systems, such as the Global Positioning System (GPS), have significantly advanced over the years, indoor positioning has been restrained by the limitations of the employed technologies. This paper presents a hybrid wireless positioning system able to locate wearables indoor accurately. It is based on the Wi-Fi and Bluetooth technologies, by using trilateration to determine the position of Bluetooth low energy (BLE) wearable devices with an accuracy of up to 1.8 meters. A graphical user interface (GUI) was used to illustrate the performance of the proposed systems, by allowing users to visualize the captured data in two dimensions and three- dimensions in real-time.

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating Energy Storage Systems (ESS) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This paper compares the performance of hybrid energy storage systems (HESS) to single battery, evaluating their energy supply cost and environmental impact through optimization problems. The model generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load while considering battery degradation. Seven technologies are assessed, with an example applied to an industrial site contrasting vanadium redox flow (VRFB) and lithium batteries considering the demand of a local load (building). The results also indicates that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

The LTTD process for highly concentrated crude oil-contaminated soil was studied to evaluate the remediation efficiency. In addition, the environmental impact difference between HTTD and LTTD was studied. OpenLCA software obtained from PE International was used to perform the analyses. The total ADP environmental impact of the HTTD process was 1.63E-04 MJ, whereas the total GWP impact was 4.14E+02 kg CO2-eq. In contrast, the LTTD process exhibits total ADP and GWP environmental impacts of 1.29E-04 MJ and 2.78E+02 kg CO2 eq, respectively. Analysis of the overall environmental impact reduction revealed that replacing the HTTD process with the LTTD process resulted in a total ADP reduction of 3.40E-05 MJ (20.5%) and a GWP reduction of 1.36E+02 kg CO2 -eq (32.9%). It is inferred that the LTTD treatment of contaminated soil can significantly reduce the environmental impact compared with conventional incineration and HTTD processes. Additionally, the LTTD-treated soil with coke or carbonized residue could act like biochar as a soil amendment. Approximately 50% of organic hydrocarbon contaminants are sequestered into coke or carbonized residues on the soil surface. With additional analyses of various oil-contaminated soils, the potential utility of LTTD-treated soils is expected to be maximized.

A low-cost bio-mass-derived carbon substrate has been employed to synthesize MoS₂@carbon composites through a hydrothermal method. Carbon fibers derived from natural cotton provide a three-dimensional and open framework for the uniform growth of MoS₂ nanosheets, thus constructing hierarchically coaxial architecture. The unique structure could synergistically benefit fast Li-ion and electron transport from the conductive carbon scaffold and porous MoS₂ nanostructures. As a result, the MoS₂@carbon composites, when served as anodes for Li-ion batteries, exhibit a high reversible specific capacity of 820 mAh g^-1, high-rate capability (457 mAh g^-1 at 2 A g^-1), and excellent cycling stability. The superior electrochemical performance makes the MoS₂@carbon composites to be low-cost and promising anode materials for Li-ion batteries.

This paper present a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source [1], is designed for researchers, students and makers for a broad range of their exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system are illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperatures in a lake.

This study presents a cost-effective IoT-based remote monitoring system for solar PV energy systems, along with a machine learning-based PV power estimator. Remote access is crucial for tracking PV systems installed in remote areas. In this system, an open-source and IoT-compatible data logger is employed. The data logger collects important performance data of the PV system and transfers it to a server. Real-time visualization of this data is displayed in the designed web and mobile monitoring interfaces. The measured data includes the current, voltage, and temperature information of the PV generator and battery, as well as environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, this data is used for PV power estimation using machine learning methods. This enables the identification of maintenance requirements and the prediction of potential issues in the PV system. When a problem occurs in the PV system, the user is alerted through the mobile application. Early detection and intervention prevent power loss and damage to system components. When comparing the results of linear regression, SVM, decision trees, random forests, and KNN machine learning methods for power estimation based on performance evaluation criteria, it was observed that the random forests algorithm provided the best results. In conclusion, the developed monitoring and estimation system, along with web and mobile interfaces, is suitable for large-scale PV energy systems.

This paper proposes a high-performance and low-cost in-flight collision avoidance system based on background subtraction for unmanned aerial vehicles (UAVs). The pipeline of proposed in-flight collision avoidance system is as follows: (i) dynamic background subtraction to remove the background and to detect moving objects, (ii) denoise using morphology and binarization methods, (iii) Euclidean clustering to cluster the moving object and to remove noise blobs, (iv) distinguish independent objects and track the movement using Kalman filter, and (v) collision avoidance using proposed decision-making techniques. This work focuses on the design and the demonstration of a vision-based fast moving object detection and tracking system with decision-making capabilities to perform evasive maneuvers to replace high vision system such as event camera. The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. The novelty of our method lies in the motion-compensating moving object detection framework, which accomplishes the task with background subtraction via 2-D transformation approximation. Clustering and tracking algorithms process detection data to track independent objects, and stereo-camera-based distance estimation is conducted to estimate the 3-D trajectory, which is then used during decision-making procedures. The examination of the system is conducted with a quadrotor UAV test vehicle, and appropriate algorithm parameters for various requirements are deduced.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

Background: Stoma closure is a widely performed surgical procedure, with 6295 undertaken in England in 2018 alone. This procedure is associated with significant complications; incisional hernias are the most severe, occurring in 30% of patients. Complications place considerable financial burden on the NHS; hernia costs are estimated at GBP 114 million annually. As recent evidence (ROCSS, 2020) found that prophylactic meshes significantly reduce rates of incisional hernias following stoma closure surgery, an evaluation of this intervention vs. standard procedure is essential. Methods: A cost-utility analysis (CUA) was conducted using data from the ROCSS prospective multi-centre trial, which followed 790 patients, randomly assigned to mesh closure (n=394) and standard closure (n=396). Quality of life was assessed using mean EQ-5D-5L scores from the trial, and costs in GBP using UK-based sources over a 2-year time horizon. Results: The CUA yielded an incremental cost-effectiveness ratio (ICER) of GBP 128,356.25 per QALY. Additionally, two univariate sensitivity analyses were performed to test the robustness of the model. Conclusion: The results demonstrate an increased benefit with mesh prophylaxis, but at an increased cost. Although the intervention is cost-ineffective and greater than the ICER threshold of GBP 30,000/QALY (NICE), further investigation into mesh prophylaxis for at risk population groups is needed.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

In order to design efficient and sustainable office spaces and to automate lighting, heating and air circulation in these facilities, solving the challenge of occupancy prediction is crucial. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors, however, they have not addressed nor compensate for such heat artifacts. Therefore, in this paper, we present a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We use a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We propose two novel workflows to predict the occupancy; one based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we use several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyze noise resources which affect the heat sensor data. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

The increasing regulatory pressure to monitor and reduce GHG emissions and air pollutants requires cost-effective methods for their surveillance. The most common technique used for scientific investigations on gas concentration monitoring in barns are accurate but expensive and with a complex maintenance. This research study analysed the potential use of a low-cost portable measurement devices for the measurement of ammonia (NH3) and carbon dioxide (CO2) concentrations in an open dairy barn. A comparison between gas concentrations acquired at different heights from the floor by using portable devices and those acquired by a photoacoustic infrared multigas spectroscope (i.e., reference measurement) in the same sampling locations was carried out to determine the precision of the low-cost portable devices. The performance of low-cost portable devices was statistically analysed by the application of the one-way analysis of variance, correlation analysis and regression analysis. The results showed a significant difference between gas concentration values at various heights from the floor for both NH3 and CO2. The correlations between concentrations acquired by the low-cost portable device and the INNOVA were statistically significant (r=0.83; P<0.001) for gas concentrations monitored at 0.4 m from the floor. Compared with the reference measurement device, the low-cost devices were effective in the monitoring of NH3 concentrations at 0.40 m from the floor though it underestimated them in the barn at increasing of the height from the floor, whereas the device was not adequate for CO2 concentrations. In detail, the relative measurement error of the low-cost devices compared to INNOVA was reduced close to the floor during NH3 concentration measurements. Within these limitations, this device could be useful for monitoring NH3 concentration in the barn and to assess variation in NH3 concentrations mainly related to the animal occupied zone. Further efforts are needed in this field of research to identify low-cost device that could simplify emission estimation from open dairy barns.

Nitrates can cause severe ecological imbalances in aquatic ecosystems, with considerable consequences for human health. Therefore, monitoring this inorganic form of nitrogen becomes essential for any water quality management structure. This research aimed at developing a novel Nitrate Portable Measurement System (NPMS) for monitoring nitrate concentrations in water samples. NPMS is a reagent-free ultraviolet system developed using low-cost electronic components. Its operation principle is based on Beer-Lambert law for measuring Nitrate concentrations in water samples through light absorption in the spectral range 295-315 nm. The system is equipped with ready to use ultraviolet sensor, Light Emission Diode (LED), op-amp, microcontroller, liquid crystal display, quartz cuvette, temperature sensor, and battery. All the components are assembled in a 3D-printed enclosure box which allows a very compact self-contained equipment, with high portability, enabling field and near real time measurements. The proposed methodology and the developed instrument were used to analyse multiple nitrate standard solutions. The performance was evaluated in comparison with the Nicolet Evolution 300, a classical UV-Vis spectrophotometer. The results demonstrate a strong correlation between the retrieved measurements by both instruments within the investigated spectral band and for concentrations above 5 mg NO3- /l.

In the realm of physical activity and sports science, motion tracking emerges as an important tool for specialists aiming to analyze movement data, whether for daily life activities or enhancing athletic performance. This study introduces an innovative approach to motion tracking through the development of a fully embedded smart shoe sensor system, which employs an inertial measurement unit (IMU) affixed to the shoe sole, coupled with six pressure sensors, to provide real-time calculation of individual stride lengths along with other vital movement statistics. Traditional motion tracking methods, which typically rely on various body-attached sensors, often necessitate substantial post-processing, delaying the feedback loop. In contrast, our system is designed to overcome these limitations by offering high-accuracy, real-time feedback with a minimal error rate of 1.88% from our experiments. Such prompt data acquisition facilitates immediate interventions by sports performance coaches or healthcare professionals, significantly contributing to injury prevention and performance enhancement. The experimental outcomes of this study underscore the system’s capability to deliver precise stride length measurements instantaneously, marking a significant advancement in motion tracking technology and its application in sports science and healthcare.

A sustainable low cost activated carbon substitute was produced based on pretreated lignocellulosic biomass, especially spruce sawdust. A harmful liquid waste, desalination brine was used for the treatment of a solid wood industry waste, spruce sawdust. This approach is in the circular economy theory and aims to the decarbonization of the economy. The pretreated sawdust was tested as an adsorbent appropriate for the removal of a commonly used pollutant, methylene blue, from industrial wastewater. The adsorption capacity of the pretreated material was found to increased four times comparing to the untreated one in the case that the Freundlich equation was fitted to the isotherms’ data. The treatment experimental conditions with desalination brine that gave maximum adsorption capacity correspond to 1.97 combined severity factor in logarithmic form value. The same adsorption capacity was found to increase five to six times comparing to the untreated spruce sawdust in the case that the Langmuir isotherm model was applied at the same data. Moreover, kinetic experiment was cried out as regards the methylene blue adsorption process. The desalination brine pretreated sawdust adsorption capacity increased 1.4 times comparing to the untreated one, in the case of the Lagergren kinetic equation fitting. Furthermore, the adsorption capacity of the same material was increased 1.6 times when the second order kinetic model was applied to the same data. Industrial scale applications can be based on the kinetic data findings as they have to do with a very shorter adsorption period comparing to the isotherm data.

Laser Photoacoustic Spectrometers (LPAS), which provide photoacoustic spectra for detecting food frauds or adulteration, and fluorescence-based systems for recognition of contamination by heavy metal ions in water are two important families of sensors for health and environmental purposes. Both sensor families require one or more low-frequency Lock-In Amplifiers (LIAs) to extract the signal of interest from background noise. In the cited applications, the required LIA frequency is quite low (up to 1kHz), and this leads to a simplification of the hardware with consequent good results in portability, economy, reduced size, weight and low-cost characteristics. The present system, called ENEA DSP Box Due, is based on a very inexpensive microcontroller proto-board and can replace four commercial LIAs, resulting in significant savings in both cost and space. Furthermore, it incorporates a dual-channel oscilloscope and a sinusoidal function generator. This article outlines the architecture of the ENEA DSP Box Due, its electrical characterization and its applications within a project concerning laser techniques for food and water safety.

Tackling the intricacies of network dismantling in complex systems poses significant challenges. This task has relevance across various practical domains, yet traditional approaches focus primarily on singular metrics, such as the number of nodes in the Giant Connected Component (GCC) or average pairwise connectivity. In contrast, we propose a unique metric that concurrently targets nodes with the highest degree and reduces the GCC size. Given the NP-hard nature of optimizing this metric, we introduce MaxShot, an innovative end-to-end solution that leverages graph representation learning and reinforcement learning. Through comprehensive evaluations on both synthetic and real-world datasets, our method consistently outperforms leading benchmarks in accuracy and efficiency. These results highlight MaxShot’s potential as a superior approach to address the network dismantling problem effectively.

Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target the risker samples. We first apply a Naive Bayes classifier to classify data into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring the representativeness of audit evidence. The user-based approach denotes the selection of samples between two percentiles in a class as audit evidence. It may be equivalent to a combination of monetary and variable sampling methods. The item-based approach represents the choice of risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary sampling methods. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples, handling complex patterns, correlations, and unstructured data, and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.

Scene classification plays an important role in the intelligent processing of high-resolution satellite (HRS) remotely sensed image. In HRS image classification, multiple features, e.g. shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS image. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structure across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

Neutron computed tomography (nCT) has been established at many major neutron sources worldwide, using high-end equipment requiring major investment and development. Many older and smaller reactors would be capable of doing nCT as well, but cannot afford the investment before feasibility is proven. We have developed a compact low-cost but high-quality detection system using a new cooled CMOS camera that can either be fully integrated into a sophisticated setup, or used with a rudimentary CT control and motion system to quickly evaluate feasibility of neutron CT at a given beam line facility. Exchanging the scintillation screen makes it feasible for X-rays as well, even for visible light (and transparent samples) using a matte screen. The control system uses a hack to combine motion control with existing imaging software so it can be used to test several dozen different cameras without writing specific drivers. Freeware software can do reconstruction and 3D imaging.

An analysis of the processes of plastic deformation and acoustic relaxation in a high-entropy alloy Al0.5CoCrCuFeNi was carried out. The following have been established: dominant dislocation defects; types of barriers that prevent the movement of dislocations; mechanisms of thermally activated movement of various elements of dislocation lines through barriers at room and low temperatures. Based on modern dislocation theory, quantitative estimates have been obtained for the most important characteristics of dislocations and their interaction with barriers.

In higher education, the cultivation of research competency is pivotal for students’ critical thinking development and their subsequent transition into the professional workforce. While statistics plays a fundamental role in supporting the completion of a research project, it is often perceived as challenging, particularly by students in majors outside mathematics or statistics. The connection between students’ statistical proficiency and their research competency remains unexplored despite its significance. To address this gap, we utilized the supervised machine learning approach to predict students’ research competency, as represented by their performance in a research methods class, with predictors of students’ proficiency in statistical topics. Predictors relating to students’ learning behavior in a statistics course such as assignment completion and academic dishonesty are also included as auxiliary variables. Results indicate that the three primary categories of statistical skills—namely, understanding of statistical concepts, proficiency in selecting appropriate statistical methods, and statistics interpretation skills— can be used to predict students’ research competency, as demonstrated by their final course scores and letter grades. This study advocates for strategic emphasis on the identified influential topics to enhance efficiency in developing students’ research competency. Findings could inform instructors in adopting a strategic approach to teaching the statistical component of research for enhanced efficiency.

Purpose: The ongoing COVID-19 crisis has created the need for social distancing between medical staff and patients, therefore, healthcare robotics is increasingly important as regards infectious control. The purpose of this study was to design and fabricate a remote controlled transport robot that could provide an optimum technology base for developing countries, having dual functions of telepresence and delivery operations inside or outside buildings, is easy to assemble and also easily able to be cleaned using standard chemical agents.Methods: This machine was designed and fabricated to incorporate two main functions; delivery and telepresence. The robot consists of a base frame, DC motors for driving and steering systems, actuator motor for driving wheel elevation, Wi-Fi AP/Repeater, network camera, and a necessary a back-up power system.Results: This robot can be used both indoors and outdoors. It can carry loads of more than 80 kg. The machine can cross obstacles greater than 3 cm. It can be cleaned by disinfectant agents. An actuator motor can lift the driving wheels above the surface to prevent gear damage. Conclusion: This article demonstrates the concept of a healthcare telepresence and delivery robot that can provide distancing during the present COVID-19 crisis, using optimum technology for low and middle-income countries. In the future, the researcher should consider development of the delivery controls, combined with the telepresence system using the internet of things, to improve the navigation system, the manipulator system for supporting multi-floor transportation, and also other multi-functional purposes.

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

We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 minutes allows us to form a multivariable timeseries that serves two-ways: a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and b) based on historical data predict future levels that are correlated with hive’s health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers.

One of the most popular techniques for determining electrical properties of material is the free-space measurement technique. With the potential for new applications requiring efficient, accurate and broadband material measurement systems using higher millimeter-wave and terahertz frequencies, the free-space measurement method has received renewed interest. This is mainly due to its simplicity, favorable properties as a non-destructive method, and it allows transmission and reflection measurements without any physical contact with the sample. This paper reviews and discusses state- of-the-art free space material measurement systems, starting with the different measurement techniques available for material characterization, their important concepts, post-processing in determining their properties, and progress towards expanding their use at higher frequencies. Also, this paper reviews dielectric material characterization using the free space method focusing on their application extension towards the higher frequencies. Besides, the algorithms for conversion methods and usage of lens in free space material measurement system was discussed in this paper. Finally, a future perspective on the outlook of this free space method is presented prior to the conclusion.

Metabolic pathways provide key information to achieve a better understanding of life and all its processes; this is useful information for the improvement of medicine, agronomy, pharmacy, and other similar areas. The main analysis tool used to study these pathways is based on the idea of pathway comparison, using graph data structures. Metabolic pathway comparison has been defined as a computationally complex task \cite{ay2011submap,abaka2013campways}. In previous work from 2017, two different approaches that simplify the problem of comparing pathways represented as graphs were introduced. The first algorithm consists of the transformation of a two-dimensional graph structure, representing a metabolic pathway, to a one-dimensional structure and thus aligning the corresponding data using a reduced 1 dimension string. The second algorithm consists of performing a paired analysis between reactions in pathways and thus eliminating all similarities, finally, showing these differences to the user. The suggestion is to use the information provided by these algorithms as a previous analysis to a deeper, more expensive, comparison tool use. Here we provide an extension of this work with more data and deeper analysis. These methods have shown to be an effective way to treat the problem of metabolic pathway comparison as listed in the discussion and results section. Our results show evidence of a quick, simple and effective way to resolve the described problem.

This paper proposes a novel single-stage buck-boost three-Level neutral-point-clamped (NPC) inverter with two independent dc sources coupled for the grid-tied photovoltaic (PV) application, which can effectively solve the unbalanced operational conditions generally appeared between two coupled independent PV sources induced by the unequal irradiation and temperature distribution. The proposed control scheme can simultaneously guarantee the maximum power point (MPP) operation of both PV sources and maintain the output waveform quality. Compared to the traditional two-stage PV inverter, the proposed NPC inverter could reduce the PV array voltage requirement and dc-link capacitors’ voltage rating, meanwhile show the advantage in operational efficiency. MATLAB simulations and the captured experimental results are presented to show the performance of the proposed three-level inverter.

The deposition of dust particles on solar panel modules constitutes a significant challenge, leading to a potential reduction in solar cell performance. This work tackles this issue by investigating the surface modification of a layer of transparent indium tin oxide (ITO), with a detailed analysis of the influence of its characteristics via different annealing conditions/processing. The ITO layer has been studied to enhance its characteristics for dust removal applications. Of particular note is that the developed ITO layer can be applied to dust removal using electrostatic or magnetic-based approaches. Thus, the correlation between annealing gas (using Argon, Oxygen, and Nitrogen), annealing temperature, and annealing ramping rate is explored with the obtained surface topography, as well as electrical and optical properties. The developed ITO layers annealed in the Argon medium at 600°C with an annealing ramping rate of 50°C/s showed smooth surface roughness, the highest electrical conductivity, and a relatively low refractive index. Such findings can play a crucial role in the future implementation of using the enhanced ITO layer for dust mitigation in solar panels, thus improving their long-term performance.

In terahertz communication systems, lens antennas used in transceivers are basically plano-convex dielectric lenses. As the aperture increases and the focal length decreases, the thickness and mass of the plano-convex lens increase rapidly. Through theory and simulation, we designed a Fresnel lens suitable for the terahertz band to meet the requirements of large aperture and short focal length, and simulated the performance, advantages and disadvantages of the terahertz Fresnel lens. A 300GHz terahertz wireless communication system was built to verify the gain effect of the Fresnel lens antenna. The experimental results show that the Fresnel lens can amplify the signal in the terahertz band. The theoretical gain of a 30 cm Fresnel lens is 48.83dB, while the actual measured gain is approximately 45dB.

This paper presents a low-cost C-band frequency-modulated continuous wave (FMCW) radar system for use in indoor through-wall metal detection. Indoor remote-sensing applications, such as through-wall detection and positioning, are essential for the comprehensive realization of the internet of things or super-connected societies. The proposed system comprises a two-stage radio-frequency power amplifier, a voltage-controlled oscillator, circuits for frequency modulation and system synchronization, a mixer, a 3-dB power divider, a low-noise amplifier, and two cylindrical horn antennas (Tx/Rx antennas). The antenna yields gain values in the 6.8 ~ 7.8 range when operating in the 5.83 ~ 5.94 GHz frequency band. The backscattered Tx signal is sampled at 4.5 kHz using the Arduino UNO analog-to-digital converter. Thereafter, the sampled signal is transferred to the MATLAB platform and analyzed using a customized FMCW radar algorithm. The proposed system is built using commercial off-the-shelf components, and it can detect targets within a 56.3 m radius in indoor environments. In this study, the system could successfully detected targets through a 4-cm-thick ply board with a measurement accuracy of less than 10 cm.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

The article presents several innovative systems developed through student laboratory projects, including two autonomous vehicles, a four-legged walking robot, an active ankle-foot orthosis, a ball on beam balancing mechanism, a ball on plate system, and a manipulator arm—all powered by pneumatic artificial muscles (PAMs). Fluidic muscles offer significant potential in various mechatronic systems, robots, and manipulators due to their flexible, lightweight nature and compliance. Their ability to produce smooth and adaptable motions is particularly beneficial in applications requiring natural and human-like movement, such as rehabilitation and assistive devices. Although PAMs powered control systems face challenges with nonlinear phenomena, their biologically inspired design holds promise for future applications across various industries. These may include innovative applicability in industrial, automotive, and aerospace sectors, as well as use in sports, medical aids, entertainment, and animatronics. Incorporating self-made laboratory systems actuated by PAMs into control systems education offers a comprehensive learning opportunity that combines theoretical concepts with hands-on experience. This approach develops the necessary skills of future engineers, expanding their understanding of technical principles and preparing them for future challenges in engineering practice.

An algorithm for the treatment of images affected by both blurring and salt&pepper noise is proposed with a cost only proportional to the number of pixels. The methodology uses a discretization scheme for the Laplace operator multiplied by a suitable nonlinear term depending on the gradient. Even if this approach resembles a diffusion type algorithm, only one step of the procedure is applied, leading to significant time savings. The procedure is successfully tested on some standard black&white natural images.

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.

Low-energy cosmic rays (LECRs) play a crucial role in the formation of planetary systems, and detecting and reconstructing the properties of early LECRs is essential for understanding the mechanisms of planetary system formation. Given that LECRs interact with the surrounding medium to produce nuclear de-excitation line emissions with energy mainly within 0.1–10 MeV, which are unaffected by stellar wind modulation, these gamma-ray emissions can accurately reflect the properties of LECRs. This study introduces an innovative method for using gamma-ray emissions to infer LECR properties. We employed the Parker transport equation to simulate the propagation and spectral evolution of LECRs in a protoplanetary disk and calculated the characteristic gamma-ray emissions resulting from interactions between LECRs and disk material. These gamma-ray emissions encapsulate the spectral information of LECRs, providing a powerful tool to reconstruct the cosmic ray environment at that time. This method, supported by further theoretical developments and observations, will fundamentally enhance our understanding of the impact of CRs on the origin and evolution of planetary systems and address significant scientific questions regarding the cosmic ray environment at the origin of life.

This paper aims at trying to answer the question of whether a low cost drone equipped with a regular camera can serve as a simple device to estimate the size of objects. Ad-ditionally, it assesses if the accuracy of measurement is good enough for using a drone for measurements, relying solely on GPS position and camera. A review of the literature pertaining to ground object measurements was conducted. The availability of such in-struments was evaluated in terms of purchase and rental costs, the number of instru-ments on the market, and the time to obtain them. The most common measurement methods were considered and their unit costs were reviewed. The cost of professional surveying and measuring equipment was compared to the possibility of using low-cost, non-commercial drones to measure objects and surfaces. The quality of the images ob-tained was compared with high-resolution satellite systems. Is an ordinary drone with a standard camera suitable for length measurement without professional measuring equipment? Should the results be considered only in the category of size estimation?

Nitrogen dioxide (NO₂) contributes to several impacts both for human health and the environment. Periodical measurement of NO₂ at industrial and residential areas needs comprehensive and reliable instrumentation; long-interference-free measures with minimum maintenance and re-calibration. DOAS can be used as a direct measurement technique based on specific absorption characteristics of NO₂ follow Beer-Lambert law. This research applies a low-cost folded path photometer for measurement NO₂ in air. Cheap tubular acrylic used as a detection cell with a 3D printed framework makes it compact, modular, and flexible. Evaluation of the DOAS conducted by instrument test responses using NO₂ gas. The estimated LOD was ~ 1263 pb using 2 nm resolution of the spectrometer and 6-meter detection cell length. Deviation of the DOAS estimated to be 0.8% at high concentration and 2.85% at a low concentration based on DOAS calibration. An intercomparison between methods used to evaluate instrument performance to measure NO₂ using emission from a motorcycle with coefficient correlation (R) 0.649 for paired DOAS-ASTM D1607 Griesz Saltzmann Method 0.846 for paired DOAS-Electrochemical Gas Analyzer. This significant correlation is caused by different respond time between paired methods, while it is still comparable for NO₂ measurement.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

Celiac disease is a chronic small intestinal immune-mediated enteropathy precipitated by exposure to dietary gluten, affecting approximately 1% of the global population and two million Americans. An increasing number of studies have identified a link between celiac disease and adverse maternal and fetal outcomes during pregnancy and after birth. Additionally, both celiac disease and pregnancy are associated with an increased risk for nutrient deficiencies, specifically vitamin B12 and folate. Deficiencies in these key nutrients are linked with several negative maternal and fetal health outcomes including preeclampsia, gestational diabetes, spontaneous abortion/miscarriage, preterm birth, NTDs, Intrauterine Growth Restriction, and low gestational age and birthweight. This review examines the current literature related to the folate trap and vitamin B12 deficiency in patients with celiac disease and pregnant women independently, and to provide rationale for future research to explore the correlation between the folate to B12 ratio in pregnant woman with celiac disease.

The issue of measurement reliability is certainly undervalued in the local agricultural marketplace organization. Besides, there was nothing identified concerning the extent of cereal commodity trade measurement cost. Hence, this paper aimed to estimate the magnitude of cereals trade quantity measurement cost caused by instrument error and unreliability; mainly by targeting the local marketplace in Ethiopia. The survey was conducted in six different districts’ marketplaces (n = 602) of Oromia region by employing administered structured questionnaires and site mass measurement calibration. The gathered data were analyzed using independent samples t-test, one sample t-test, and analysis of variance. According to the findings, the actual value measurement means of the quantity of most local units of the farmers were greater than small traders. The independent t-test result indicated that the average values of the quantity of the majority of units of measurement between farmers and small traders were varied significantly. Estimated measuring instruments’ error and unreliability associated average measurement cost of one-day market transaction over total cereals marketed amount were higher comparatively for a sack, bowl, and glass units. This study demonstrates that homogeneity in measurement, policy, and institutions that aid cereals trade would have an indispensable role to reduce measurement costs and ensure equitable exchange.

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.

The success of deep learning (DL) in offline brain-computer interfaces (BCIs) has not yet translated into efficient online applications. This is due to three primary challenges. First, most DL solutions are designed for offline decoding, making the transition to online decoding complex and unclear. Second, the use of sliding windows in online decoding increases the computational complexity of DL training. Third, DL models typically require large amounts of training data, which are often scarce in BCI applications. To address these issues and enable real-time decoding, even across different subjects without calibration data, we first introduce a novel approach called real-time adaptive pooling (RAP). RAP modifies the pooling layers of existing offline DL models towards the online decoding requirements. Additionally, it significantly reduces the computational demand during training by jointly decoding consecutive windows. To reduce the amount of training data required, our approach leverages different levels of domain adaptation. We show how different settings enable different adaptation solutions. The results demonstrate that our approach is both powerful and can be calibration-free, providing a robust and practical solution for real-time BCI applications. These findings pave the way for the development of co-adaptive and highly efficient DL-based online BCI systems.

Although commercially-available low-cost air quality sensors have low accuracy, the sensor system are being used to collect the data for the regulation of PM2.5 emission caused by industrial activities or to estimate the personal exposure for PM2.5. In this work, to solve the accuracy problem of low-cost PM sensor, we developed a new PM2.5 calibration model by combining the deep neural network (DNN) optimized in calibration problem and a LSTM optimized in time-dependent characteristics. First, two datasets were generated to test the accuracy performance and generalization performance of the PM2.5 calibration machine learning (ML) model. The PM2.5 concentrations, temperature and humidity by low-cost sensor and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmark (multiple linear regression model) and low-cost sensor results. For root mean square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41-60% of error compared to the raw data of low-cost sensor, and reduced 30-51% of error compared to the benchmark model. R2 of ML model, MLR and raw data were 93, 80 and 59 %. Also, the developed model still showed consistent calibration performance when calibrated with new sensors in different locations. Low-cost sensors combined with ML model not only can improve the calibration performance of benchmark, but also can be applied to the sensor monitoring systems for various epidemiologic investigations and regulatory decisions.

This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER) rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1) formulation of risk assessment hierarchy, (2) representation of both qualitative and quantitative information, (3) elicitation of attribute weights and information reliabilities, (4) aggregation of assessment information using the ER rule and (5) quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

There is an urgent need to advance safe and affordable COVID-19 vaccines for low- and middle-income countries of Asia, Africa and Latin America. Such vaccines rely on proven technologies such as recombinant protein-based vaccines to facilitate its transfer for emerging market vaccine manufacturers. Our group is developing a two-pronged approach to advance recombinant protein-based vaccines to prevent COVID-19 caused by SARS CoV2 and other coronavirus infections. One vaccine is based on a yeast-derived (Pichia pastoris) recombinant protein comprised of the receptor binding domain (RBD) of the SARS-CoV formulated on alum and referred to as the CoV RBD219-N1 Vaccine. Potentially this vaccine could be used as a heterologous vaccine against COVID-19. A second vaccine specific for COVID-19 is also being advanced using the corresponding RBD of SARS-CoV-2. The first antigen has already undergone cGMP manufacture and is therefore “shovel ready” for advancing into clinical trials, following vialing and required GLP toxicology testing. Evidence for its potential efficacy to cross-protect against SARS-CoV-2 includes cross-neutralization and binding studies using polyclonal and monoclonal antibodies. Evidence in support of its safety profile include our internal assessments in a mouse challenge model using a lethal mouse adapted SARS strain, which show that SARS-CoV RBD 291N1 (when adsorbed to Alhydrogel®) does not elicit eosinophilic lung pathology. Together these findings suggest that recombinant protein-based vaccines based on the RBD warrant further development to prevent SARS, COVID-19 or other coronaviruses of pandemic potential.

In France, the regional airport’s demand for services is facing challenges due to the continuous expansion of the high-speed train, high-speed line, and highway networks. This study focuses on the viability of regional airports in France through technical efficiency using data envelopment, principle component analysis, Malmquist productivity index, and regression analysis using bootstrapping. To face the current competitive environment, the regional airports in France adopted strategies, such as the construction of low-cost carrier (LCC) dedicated terminals (LCCTs) with lower expenses to attract more LCCs, increasing non-aeronautical revenue, and hosting regional hubs of LCCs. This is the first study that analyzes all of the French regional airports. The findings indicate that the existence of LCCTs positively affects technical efficiency on the airport’s performance, and share of LCCs at a regional airport leads to neither the efficiency nor the profit level.

This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from IoT sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R2 score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO2, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health.

Natural polluted insulator surfaces are always coated with various kinds of soluble constituents, and those constituents affect flashover performance differentially. Currently this fact is not considered either in laboratory experiments or field pollution degree measurements, causing the existing insulation selection method to be deficient. In this paper a systematical research on insulator flashover voltage gradient correction involving types of soluble pollution constituents was presented. Taking typical type glass insulator as the sample, its flashover tests polluted by typical soluble chemicals NaCl, NaNO₃, KNO₃, NH₄NO₃, MgSO₄, Ca(NO₃)₂ and CaSO₄ were carried out. Then the flashover gradient correction was made combining the flashover performance of each soluble constituent, the ESDD contribution of the seven constituents, as well as the saturation performance of CaSO₄. The correction was well verified with the flashover test results of insulator polluted by three types of soluble mixture. Research results indicate that the flashover gradient correction method proposed by this paper performs well in reducing the calculating error. It is recommended to carry out component measurements and flashover gradient correction to better select outdoor insulation configuration.

The aim of this study was to investigate the potential of converting potato peel waste into bio ethanol using an in-house produced multi-enzyme preparation. To achieve this, a variety of pre-treatment strategies, including thermal, chemical, and thermo-chemical methods, were applied to the potato peels. These included boiling for 30 and 60 min, steam at varying pressures and times, and different concentrations of chemicals such as H2SO4, HNO3, CH3COOH, HCl, NaOH, Ca(OH)2, KOH, NH3, and H2O2, either alone or in combination with steam. The pre-treated potato peels were then enzymatically hydrolyzed using a crude multi-enzyme cocktail derived from solid-state fermentation of wheat bran by a natural variety of Aspergillus niger P-19, containing cellulases, hemicellulases, pectinase, and amylases. The most effective pre-treatment combination was found to be 3% H2SO4 followed by steam under pressure and enzymatic hydrolysis using the crude multi-enzyme preparation. This combination resulted in the highest yield of reducing sugars (141.04 ±12.31 g/l) with a 98.49% carbohydrate conversion at a 20% substrate loading, giving an ethanol yield of 43.2 g/l. Furthermore, supplementing the medium with peptone, (NH4)(H2PO4), and ZnSO4 at 0.1%w/v each with 22% and 24% solid, respectively, resulted in appreciable yields of 51.67 and 54.75 g/l, further enhancing the ethanol yield.

The first exposure to influenza is thought to impact subsequent immune responses later in life. The consequences of this can be seen during influenza epidemics and pandemics with differences in morbidity and mortality for different birth cohorts. There is a need for better understanding of how vaccine responses are affected by early exposures to influenza viruses. In this analysis of hemagglutination inhibition (HI) antibody responses in two cohorts of military personnel we noticed differences related to age, sex, prior vaccination, deployment and birth year. These data suggest that HI antibody production, in response to influenza vaccination, is affected by these factors. The magnitude of this antibody response is associated with, among other factors, the influenza strain that circulated following birth.

The stepwise hydrogenation of the C=C bond and C=O group of acrolein on Au₃ and Au₅ model systems is investigated using the density functional theory(DFT) PW91 functional. Our results show that the C=C hydrogenation is more favorable than that of C=O bond on Au₃ with the barriers of the rate-determining step being 0.35 and 0.62 eV respectively. On the other hand, the C=O reduction is preferred over the hydrogenation of the C=C bond on Au₅. The corresponding barriers of the rate-determining steps are 0.45 and 0.54 eV, respectively. This demonstrated that the second hydrogenation step controls the reaction on both Au₃ and Au₅ for C=O and C=C hydrogenation and the C=O hydrogenation on Au₅ is preferred over the hydrogenation of the C=C bond, which is helpful to address the reactivity of small size-selected supported gold clusters.

This paper presents geometric analyses of welded frames after free relaxing and vibratory stress relief (VSR). Respected frames were tested components of a prototype packaging machine. Two types of relaxation were carried out to remove stresses introduced as a result of welding process. One of the frame was subjected to free relaxing, while the other one to accelerated vibration relaxing. Detection of the frame geometry changes was performed using a photogrammetric system. In addition, an evaluation of the geometry change was conducted for fifteen variants of steel frame support. A comparative analysis of the geometric deviations of the frames after free and vibratory stress relief confirmed the assumption that the frame after vibration stress relief better reproduces the nominal dimensions. Nevertheless, it should be emphasized that after vibratory stress relief the frame is not subject to further deformation, which is a desirable effect. In the case of free relaxing, the frame undergoes dimensional changes in a random manner. In summary, carrying out accelerated vibratory stress relief allows control of spontaneous dimensional changes in the designed frame of a packaging machine resulting from spontaneous relaxation of stresses arising from the welding process. The shortening of the relaxation process of the welded frame is also an unquestionable advantage.

Quantifying methane emissions is essential for meeting near-term climate goals and typically done using methane concentrations measured downwind of the source. One major source of methane that is important to observe and promptly remediate is fugitive emissions from oil and gas productions sites but installing methane sensors at the thousands of sites within a production basin is expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using machine learning (ML) to generate a model that converts metal oxide sensor output to methane mixing ratios. To generate test data, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. Over the duration of the measurements, the trace gas analyzer’s average methane mixing ratio was 2.40 ppm with a maximum of 147.6 ppm. The average calculated methane mixing ratios for the TGS2600 and TGS2611 using the ML algorithm were 2.42 ppm and 2.40 ppm with maximum values of 105.9 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios show overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was a good agreement between the ML derived metal oxide sensors’ mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with precision and accuracy comparable to higher price trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deploying, and allow for timely and accurate tracking of methane emissions.

Dry Fish is a rich source of proteins and nutrients and can be eaten in many forms. Oman is one of the important fish producers in the world and having high demand in neighboring countries for Oman’s fish. Fishery sector is considered as one of the important economic development sector in sultanate of Oman. In the fishery sector, use of technology is not up to the mark because of less awareness of technology application among Omani fishermen. Also, there is a demand for a greater number of cold storages in different regions to store fresh fishes which is not materialized yet. Similarly, there is a demand of perfect and cheap technology in Oman to dry fishes to improve the profit to the fishermen. Solar energy is abundantly available in Oman and there are high possibilities in Oman to use solar energy in domestic and industrial applications. Since the sunlight is available throughout the year, it is possible to convert in to thermal energy or electrical energy. Electricity can be produced from photovoltaic principle (PV) or indirectly with concentrated solar power. Similarly, we can use solar concentrators for many thermal applications. The solar dryer is a device which is used to dry fruits, vegetables, leaves, clothes etc. If any of the items are dried with the help of solar energy, then we can save lot of non-renewable source of energy and can help to improve economy of the country. The sun can, at least seasonally at Oman’s latitudes, provide a substantial part of the energy needed for various applications. In this work, we used solar dryer to make dry fish with high quality. Use of parabolic type concentrator to dry the fish is an effective solution which is an innovative idea to the fishermen. The temperature variation in dryer is directly proportional to the intensity of solar. The tracking mechanism is a combination of electronic and mechanical design which tracks the sun automatically. In this work, a parabolic concentrator with automatic tracking mechanism was designed and the performance analysis have been done. In the design, copper tubes are used for heat transfer and then the hot fluid is circulated in the box which is used to dry the fishes. As per the experiment, fish drying efficiency obtained from solar dryer is 77.9% and it took 3 days to dry king fish of 20kg. There are many advantages in parabolic type solar concentrators relate to the quality of dryness in the fishes and time taken to dry the fishes which are elaborately discussed in this paper. This solar dryer technology will improve green environment and also will save non-renewable sources of sultanate of Oman for longer period of time.

Magnetoelectric (ME) sensor is a new type magnetic sensor with ultrahigh sensitivity, low power consumption, and suitable for the measurement of low frequency weak magnetic field. In this study, metglas/PZT-5B ME sensor with a mechanical resonance frequency f_res of 60.041 kHz was prepared. It is interesting to note that its magnetic field resolution reaches 0.20 nT at f_res and 0.34 nT for DC field, respectively. In order to measure the ultralow frequency AC magnetic fields, a frequency upconversion technique was employed. Under this technique, a limit of detection (LOD) of AC magnetic field lower than 1 nT at 8 Hz is obtained, and the minimum LOD of 0.51 nT is achieved at 20 Hz. The high resolution ME sensor with sub-pT level is promising in the field of low frequency weak magnetic field measurement technology.

Abstract: High step-up voltage gain nonisolated DC-DC converter have attracted much attention in photovoltaic, fuel cells and other renewable energy system applications. In this paper, by combining input current ripple-free boost cell with coupled-inductor voltage-doubler cell, an input current ripple-free high voltage gain nonisolated converter is proposed. In addition, passive lossless clamp circuit is adopted to recycle the leakage inductor energy and to reduce the voltage spike across the power switch. By utilizing voltage-doubler cell consisting of diode and capacitor, the voltage stress of switch is further reduced and the resonance between the leakage inductor and the stray capacitor of the output diode is eliminated. A low switch-on-resistance low-voltage-rated MOSFET can therefore be employed to reduce the conduction loss and cost. The reverse recovery loss of output diode is reduced, and the efficiency of converter can be improved. Furthermore, the proposed converter can achieve nearly zero input current-ripple and make the design of electromagnetic interference (EMI) filter easy. Steady state analysis and operation mode of the converter is performed. Finally, experimental results are presented to verify the analysis results of the proposed converter.

This article is about a framework for determining the degree of realism of any given 1 passenger ride motion discomfort measurement formula. After providing some context and reviewing 2 evidence of deficiency in currently popular ride motion discomfort measurement formulas, the article 3 outlines the research program that needs to be carried out in order to establish such a framework. 4 The research begins with gathering recordings of uncomfortable ride motion episodes encountered in 5 a chosen type of passenger transport service. It then has test subjects compare the episodes via a ride 6 motion simulator and adjust their amplitudes pair wise until they cause equal discomfort. It explains 7 how to take the pair wise amplitude adjustments and determine amplitude adjustments that bring all 8 of the motion episode recordings to a common level of discomfort so that they form a normalized set. 9 Then, the lower the scatter of the scores assigned by any given discomfort measurement formula to 10 the members of that set, the more realistic that formula will be for the chosen service.

Utilization of wasted heat instead of fuel combustion is effective to reduce primary energy consumption for mitigating global warming problem. Because wasted heat sources are not necessarily located close to areas of heat demand, one of the difficulties is that wasted heat has to be transferred from heat source side to heat demand side, which may require heat transportation over long distance. From this point we proposed and have examined new idea of heat transportation using ammonia-water as the working fluid which system is named Solution Transportation Absorption chiller, in short STA. Our previous studies of STA were mainly the experimental investigation with STA facility which cooling power was 25RT (90kW). As a result, the COP of STA was found almost same value 0.65 with the conventional absorption chiller without depending on the transportation distances. The simulation using AspenHYSYS also examined with same experimental condition. The experimental data showed good agreement with the simulation calculation. In this study, we examined the large-scale cooling power STA on simulation. The examination cooling powers were from 90 kW(25RT) to 3517 kW(1000RT). All cooling power achieved around COP 0.64 including pump power consumptions. In addition, we performed the dynamic simulation. As the results, there was no effect of pipeline size on the cooling capacities and mass flow rates. Furthermore, the stability time of the cooling capacities and mass flow rates were almost same regardless of the pipeline size and cooling capacity. In other words, STA may be achieved the same COP even though having various complex conditions compared with the conventional absorption chiller.

The goal of this paper is to present a low-cost, low-power prototype of a pulsed Ultra Wide Band UWB) oscillator and an UWB elliptical dipole antenna integrated on the same Radio Frequency (RF) Printed Circuit Board (PCB) and its digital control board for Real Time Locating System (RTLS) applications. The design is compatible with IEEE 802.15.4 high rate pulse repetition UWB standard being able to work between 6 GHz and 8.5 GHz with 500 MHz bandwidth and with a pulse duration of 2 ns. The UWB system has been designed using the CST Microwave Studio transient Electro-Magnetic (EM) circuit co-simulation method. This method integrates the functional circuit simulation together with the full wave (EM) simulation of the PCB’s 3D model allowing fast parameter tuning. The PCB has been manufactured and the entire system has been assembled and measured. Simulated and measured results are in excellent agreement with respect to the radiation performances as well as the power consumptions.

The vast majority of autonomous driving systems are limited to applications on roads with clear lane markings and are implemented using commercial grade sensing systems coupled with specialized graphic accelerator hardware. This research reviews an alternative approach for autonomously steering vehicles that eliminates the dependency on road markings and specialized hardware. A combination of machine vision, machine learning and artificial intelligence based on popular pre-trained Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) was used to drive a vehicle along roads lacking lane markings (unmarked roads). The team developed and tested this approach on the ACTor (Autonomous Camus TranspORt) vehicle - an autonomous vehicle development and research platform coupled with a low-cost webcam based sensing system and minimal computational resources. The proposed solution was evaluated on real-world roads and varying environmental conditions. It was found that this solution may be used to successfully navigate unmarked roads autonomously with acceptable road following behavior.

Objective: Design and validate a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90\% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An accelerometer provides monitoring of head movements. The EEG amplifier operates with 24 bits resolution up to 500 Hz sampling frequency, and can communicate with other devices using 802.11 {b/g/n/WiFi}. It has high SNR and CMRR (121 dB and 110 dB, respectively), and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as backend coding language and JS, CSS, HTML as front-end coding languages, and includes training and optimization of Support Vector Machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation provides proof-of-concept validation for device use at both the clinic and at home. Significance: The low-cost, accessibility, usability, interoperability, and programmability of the proposed closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.

Over the last two decades, microfluidics has received significant attention from both academia and industry, and researchers report thousands of new prototype devices each year for use in a broad range of environmental, pharmaceutical, and biomedical engineering applications. While lab-on-a-chip fabrication costs have continued to decrease, the hardware required for monitoring fluid flows within microfluidic devices themselves remains expensive and often cost prohibitive for researchers interested in starting a microfluidics project. As microfluidic devices become capable of handling complex fluidic systems, low-cost, precise and real time pressure and flow rate measurement capabilities has become increasingly important. While many labs use commercial platforms and sensor, these solutions can often cost thousands of dollars and can be too bulky for on-chip use. Here we present a new inexpensive and easy -to-use piezoresistive pressure and flow sensor that can be easily integrated into existing on-chip microfluidic channels. The sensor consists of PDMS-Carbon black conductive membranes and uses an impedance analyzer to measure impedance change due fluid pressure. The sensor costs several orders of magnitude less than existing commercial platforms and can monitor local fluid pressures and calculate flow rates based on pressure gradient.

Background: Simulation plays a crucial role in health studies, as it helps medical students apply their theoretical knowledge in real-life situations. Moulage is one of the techniques that helps in making simulation more realistic or high-fidelity. It uses special effects to emulate wounds for a better understanding of what the wound is like visually. Still, moulage is expensive, time-consuming, resource-intensive, and requires the training of staff, which is why we need to find low-cost substitutes for moulage materials. Method: When searching the database “PubMed” for the terms “Low-cost and Medical moulage”, we retrieved 222 studies, out of which when excluding results not related to low-cost, we obtained 62 studies, from which when removing studies that do not contain information regarding moulage, we found two papers, after referring to citations and cited articles of those papers, we ended up with six studies. Based on the selected articles and additional articles sourced from their reference list, a total of 11 studies were included in the review. Results: We understand that moulage is a technique that helps make simulations come alive, but the resources required to use it are at times, expensive, which is why we need to find methods to do low-cost moulage, and many studies address that it can be as simple as using homemade ingredients. Students from a previous study have talked about their opinions regarding the realistic component of moulage and whether if it is any different from other moulages. Most of the students agreed that the moulage ranked well in face and content validity. However, further innovations must be introduced in the field to be widely spread and lead to newer opportunities. Conclusion: Although the research done under moulage is limited, it is accepted that moulage is helpful for simulation-based studies and that low-cost moulage can help make medical studies a better experience for students studying it. Students have a favorable opinion on the realistic aspect of the low-cost moulage applied to them. Newer methods can be introduced to moulage, and it can be implemented in low-income countries.

Background: Simulations have historically aided training programs by providing a realistic and holistic replication of professional scenarios and procedures. Simulations have developed over the past 40 years to include varying fidelities and modalities of simulation. Learning in a simulation-centered environment has benefits, ranging from improved patient care to specific skills acquisition while catering to students’ numerous and varied learning approaches. Simulation is a multifaceted field that benefits all parties, the teachers, the learners, and the patients. The application of simulation to medical education and its amalgamation with other modes and substitutes allows for a more integrated learning and testing curriculum that advances the current trajectory of medical education. Such developments, however, are limited to resource rich areas, leaving behind low-middle income countries to use traditional, less evolved methodologies and practices. This review aimed to explore different aspects of simulation and focus specifically on low-cost task trainers and their accessibility. Method: The purpose of the study was to assess the accessibility of low-cost task trainers in terms of cost-effectiveness, distribution, validation, and frequency within specific specialties. To do so, 84 PubMed publications have been screened, and 39 filtered research studies have collected the necessary data. After analyzing the papers, we classified the following information – process, specialization, validation (y/n), costs, development location, and year of publication. Results: After carefully analyzing the accumulated data from the selected 39 publications, we found that most studies (i.e., 6 out of 39) were published in 2020. Emergency Medicine was the most common specialty for which low-cost trainers were developed (9 out of 39 procedural simulators); Otolaryngology followed this with 8 out of 39 trainers and general surgery with 7/39 of the task-trainers. The price ranges fluctuated and fell within the price bracket of USD 0 to USD 400 collectively. Our review also uncovered the concentration of development of such innovations solely in high income countries (HICs). Conclusion: Simulation is an invaluable tool applicable to a diverse range of phases of medical education. Future conjunction of simulation with low-cost substitutes along with increased encouragement and enthusiasm towards developing cost effective simulation-based learning environments (SBLEs) with the reserves and requirements of these areas in mind may prove to be a reliable option for low and middle resource settings

Congestion pricing has received lots of attention in the scientific discussion. Congestion pricing means that the operator increases prices at the time of congestion and the traffic demand is expected to decrease. In a certain sense, shadow prices are an optimal way of congestion pricing: users are charged shadow prices, i.e., the expectations of future losses because of blocked connections. The shadow prices can be calculated exactly from Howard’s equation, but this method is difficult. The paper presents simple approximations to the solution of Howard’s equation and a way to derive more exact approximations. If users do not react by lowering their demand, they will receive higher bills to pay. Many users do not react to increased prices but would want to know how the congestion pricing mechanism affects the bills. The distribution of the price of a connection follows from knowing the shadow prices and the probability of a congestion state. There is another interesting distribution. The network produces profit to the operator, or equivalently, blocked connections produce a cost to the operator. The average cost rate can be calculated from Howard’s equation, but the costs have some distribution. The distribution gives the risk that the actual costs exceed the average costs, and the operator should include this risk to the prices. The main result of this paper shows how to calculate the distribution of the costs in the future for congestion pricing by shadow prices and for congestion pricing with a more simple pricing scheme that produces the same average costs.