bluuebunny/crossref_metadata_2025 huggingface dataset:
Strength of Coarse-Grained Soil Stabilized by Poly (Vinyl Alcohol) Solution and Silica Fume under Wet–Dry Cycles
Authors: Zhao Zhewei , Li Wenwei , Shi Haiping , Li Zhongyao , Li Jiahuan , Zhao Cheng , Wang Peiqing
Published: August 2022

To investigate an environmentally benign stabilizer for coarse-grained soil in southeast Tibet, poly (vinyl alcohol) (PVA) and silica fume were used to improve the geotechnical properties of coarse-grained soil. Unconfined compressive strength (UCS) and wet–dry cycle tests were conducted on prepared samples to evaluate the effect of the additive content and curing age on the strength and durability of coarse-grained soil. The results reveal that the UCS of the samples increased with the additive content of PVA solution and the curing age. The optimal value for the additive content of PVA solution and the curing age is 12% and 7 days, respectively. With the optimal PVA solution content, the PVA solution combined with silica fume stabilizer exhibited better reinforcement compared with pristine PVA. The UCS of the samples stabilized by PVA solution and silica fume increased depending on the curing age, and plateaued after 14 days. Samples with 12% PVA solution and 6% silica fume achieved a satisfactory UCS of 1543.17 kPa after curing for 28 days. As the number of wet–dry cycles increased, the UCS of the samples stabilized by the PVA solution and silica fume exhibited an upward trend during the first three wet–dry cycles, owing to the filling of pores by the gel produced by the silica fume, but began to decline as the number of wet–dry cycles increased. All samples retained a high UCS value after 10 wet-dry cycles compared with the samples that were not subjected to wet–dry cycles.
Customizable Collagen Vitrigel Membranes and Preliminary Results in Corneal Engineering
Authors: Montalvo-Parra María Dolores , Ortega-Lara Wendy , Loya-García Denise , Bustamante-Arias Andrés , Guerrero-Ramírez Guillermo-Isaac , Calzada-Rodríguez Cesar E. , Torres-Guerrero Guiomar Farid , Hernández-Sedas Betsabé , Cárdenas-Rodríguez Italia Tatnaí , Guevara-Quintanilla Sergio E. , Salán-Gomez Marcelo , Hernández-Delgado Miguel Ángel , Garza-González Salvador , Gamboa-Quintanilla Mayra G. , Villagómez-Valdez Luis Guillermo , Zavala Judith , Valdez-García Jorge E.
Published: August 2022

Corneal opacities are a leading cause of visual impairment that affect 4.2 million people annually. The current treatment is corneal transplantation, which is limited by tissue donor shortages. Corneal engineering aims to develop membranes that function as scaffolds in corneal cell transplantation. Here, we describe a method for producing transplantable corneal constructs based on a collagen vitrigel (CVM) membrane and corneal endothelial cells (CECs). The CVMs were produced using increasing volumes of collagen type I: 1X (2.8 μL/mm2), 2X, and 3X. The vitrification process was performed at 40% relative humidity (RH) and 40 °C using a matryoshka-like system consisting of a shaking-oven harboring a desiccator with a saturated K2CO3 solution. The CVMs were characterized via SEM microscopy, cell adherence, FTIR, and manipulation in an ex vivo model. A pilot transplantation of the CECs/CVM construct in rabbits was also carried out. The thickness of the CVMs was 3.65–7.2 µm. The transparency was superior to a human cornea (92.6% = 1X; 94% = 2X; 89.21% = 3X). SEM microscopy showed a homogenous surface and laminar organization. The cell concentration seeded over the CVM increased threefold with no significant difference between 1X, 2X, and 3X (p = 0.323). The 2X-CVM was suitable for surgical manipulation in the ex vivo model. Constructs using the CECs/2X-CVM promoted corneal transparency restoration.
An Emergy-Based Sustainability Method for Mechanical Production Process—A Case Study
Authors: Yang Yaliu , Zhang Cuixia , Wang Cui
Published: August 2022

To promote the sustainability of mechanical production process, this study proposes an emergy-based sustainability evaluation method to evaluate the resource utilization and environmental pollution of the mechanical production process. Firstly, this study constructs the sustainability evaluation index system of the mechanical production process from the perspectives of economy and environment. Secondly, an emergy analysis method-based sustainable evaluation of the mechanical production process is constructed. Finally, taking the gear manufacturing process as an example, the method is tested. The results show that this method can effectively quantify and identify the sustainability of the gear manufacturing process and provide effective feedback to improve the process. The resulting improvements can aid in reconsidering the economic and environmental factors, improving the production efficiency and the sustainability of the production process, and reducing the difficulty of operating the lathe in the production process. This study provides method support for the sustainability evaluation of mechanical production process and decision support for its improvement.
Carotenoid-Producing Yeasts: Selection of the Best-Performing Strain and the Total Carotenoid Extraction Procedure
Authors: Šovljanski Olja , Saveljić Anja , Tomić Ana , Šeregelj Vanja , Lončar Biljana , Cvetković Dragoljub , Ranitović Aleksandra , Pezo Lato , Ćetković Gordana , Markov Siniša , Čanadanović-Brunet Jasna
Published: August 2022

Yeasts are considered an extraordinary alternative source of natural carotenoids and pigmented terpenoids with multiple applications. Production of carotenoids by yeast fermentation technology has many benefits; it is cost-effective, easily scalable, and safe. The aim of this research is the isolation of yeasts from natural resources and selection of the most potent bioagent for carotenoid production. Additionally, an upgraded carotenoid extraction protocol we established, which implies the testing of four methods for cell lysis (hydrochloric acid treatment, ultrasound treatment, milling treatment, and osmotic pressure treatment), three extraction methods (conventional extraction, ultrasound extraction, and conventional + ultrasound extraction), and three extraction solvents (acetone, isopropanol/methanol (50:50), and ethanol). For the first time, the obtained results were further modeled by an artificial neural network (ANN). Based on the obtained maximal carotenoid yield (253.74 ± 9.74 mg/100 g d.w) for the best-performing Rhodotorula mucilaginosa, the optimized extraction procedure involving milling treatment (for cell lysis) and conventional extraction with acetone (for carotenoid extraction) convincingly stood out compared to the other 35 tested protocols. Therefore, the selected carotenoid extraction protocol was verified with respect to its universality for all other yeast isolates, demonstrating its simplicity and effectiveness.
Hindered Settling of Fiber Particles in Viscous Fluids
Authors: Jirout Tomáš , Jiroutová Dita
Published: August 2022

In the current literature, information can mainly be found about free and hindered settling of isometric particles in Newtonian and non-Newtonian fluids. These conclusions cannot be used to describe the sedimentation of non-isometric particle in non-Newtonian fluids. For this reason, we have carried out systematic experiments and calculated the correlation of the hindered settling velocity of a cloud of non-isometric particles in high-viscosity and pseudoplastic liquid. The experiments were performed in transparent model fluids, namely, glycerine (a Newtonian fluid) and an aqueous solution of carboxylmethylcelulose CMC (a non-Newtonian pseudo-plastic liquid). These fluids have similar rheological properties, for example, the fresh fine-grained cementitious composites HPC/UHPC. The experiments were carried out with steel fibers with a ratio of d/l = 0.3/20. The settling velocity was determined for fiber volumes from 1% to 5%. While it is known from previous studies that for spherical particles the hindered settling velocity is proportional to the porosity of a suspension cloud on exponent 4.8, which was confirmed by our verification experiment, for the studied fiber particles it is proportional to the porosity on exponent 22.1. This great increase in the exponent is an effect of both the shape of the particles and, in particular, a mutual influence that arises from their interweaving and connection in the suspension.
Techno-Economic Analysis and Feasibility of Industrial-Scale Activated Carbon Production from Agricultural Pea Waste Using Microwave-Assisted Pyrolysis: A Circular Economy Approach
Authors: Zein Sharif H. , Antony Ansu
Published: August 2022

This paper examines a novel approach to activated carbon (AC) production that uses pea waste (PW) and to what extent it is economically competitive with current production methods. Additionally, the outcome is to provide a detailed economic analysis to understand whether this process is viable. The focus of this production route and the economic analysis will be on a United Kingdom (UK) basis. The plant will be located within the north UK to minimise storage and transportation costs. It also has extensive links to other clusters of nearby industries that would produce from this process in air pollution control or wastewater treatments. The overall production process is detailed, and detailed equipment specifications, including the sizing of equipment and utility requirements, were also given. Material balance calculations are carried out to assess the performance and improve process design. An economic analysis is performed to study the potential of biomass-to-AC conversion costs and commercialisation viability. The project’s investment is about £100 million. The cost of the plant can be recovered from year 3 (mid) for the 20-year life of the plant. The Net Present Value (NPV) is based on cumulative cash flow. The NPV is calculated as GBP 4,476,137,297.79 for 2020, and the associated internal rate of return (IRR) and the return on investment (ROI) for the project are 55% and 52%, respectively.
Food Quality, Drug Safety, and Increasing Public Health Measures in Supply Chain Management
Authors: Haji Mona , Kerbache Laoucine , Al-Ansari Tareq
Published: August 2022

Over the last decade, there has been an increased interest in public health measures concerning food quality and drug safety in supply chains and logistics operations. Against this backdrop, this study systematically reviewed the extant literature to identify gaps in studying food quality and drug safety, the proposed solutions to these issues, and potential future research directions. This study utilized content analysis. The objectives of the review were to (1) identify the factors affecting food quality and possible solutions to improve results, (2) analyze the factors that affect drug safety and identify ways to mitigate them through proper management; and (3) establish integrated supply chains for food and drugs by implementing modern technologies, followed by one another to ensure a multi-layered cross-verification cascade and resource management at the different phases to ensure quality, safety, and sustainability for the benefit of public health. This review investigated and identified the most recent trends and technologies used for successfully integrated supply chains that can guarantee food quality and drug safety. Using appropriate keywords, 298 articles were identified, and 205 were shortlisted for the analysis. All analysis and conclusions are based on the available literature. The outcomes of this paper identify new research directions in public health and supply chain management.
SOC Estimation of E-Cell Combining BP Neural Network and EKF Algorithm
Authors: Gao Yun , Ji Wujun , Zhao Xin
Published: August 2022

Power lithium battery is an important core component of electric vehicles (EV), which provides the main power and energy for EV. In order to improve the estimation accuracy of the state of charge (SOC) of the electric vehicle battery (E-cell), the extended Kalman filter (EKF) algorithm, and backpropagation neural network (BPNN) are used to build the SOC estimation model of the E-cell, and the self-learning characteristic of BP neural network is used to correct the error and track the SOC of the E-cell. The results show that the average error of SOC estimation of BP-EKF model is 0.347%, 0.0231%, and 0.0749%, respectively, under the three working conditions of constant current discharge, pulse discharge, and urban dynamometer driving schedule (UDDS). Under the influence of different initial value errors, the average estimation errors of BP-EKF model are 0.2218%, 0.0976%, and 0.5226%. After the noise interference is introduced, the average estimation errors of BP-EKF model under the three working conditions are 1.2143%, 0.2259%, and 0.5104%, respectively, which proves that the model has strong robustness and stability. Using the BP-EKF model to estimate and track the SOC of E-cell can provide data reference for vehicle battery management and is of great significance to improve the battery performance and energy utilization of EV.
COVID-19 Pandemic Highlights the Importance of Inclusive Leadership in Egyptian Hospitals to Improve Nurses’ Psychological Distress
Authors: Taie Eman Salman , Zoromba Mohamed A.
Published: August 2022

Background: The pandemic has highlighted the crucial role of nurses in health services. Nurses work at the forefront of the healthcare system, provide infection control training, and help reduce the spread of misinformation about the pandemic. Inclusive leaders create psychological safety that improves motivation and boosts job performance. Aim: To explore the effect of nurse managers’ inclusive leadership style on nurses’ psychological distress during the COVID-19 pandemic in Egyptian hospitals. Method: This was a quasi-experimental study. The study subjects consisted of two groups: managers (171) and staff nurses (1573). The study was conducted in four hospitals (one university hospital, one private hospital, one therapeutic institution, and one health insurance hospital). Three tools were used for collecting data (Inclusive Leader Questionnaire, Nurse Managers’ Knowledge about Inclusive Leadership, and Kessler Psychological Distress Scale). Results: In total, 67.25% of staff nurses perceived their nurse managers as poor inclusive leaders, and only 12.86% perceived them as good inclusive leaders. Regarding nurse managers’ knowledge about inclusive leadership, 76.023% had unsatisfactory knowledge levels before awareness sessions, and only 7.017% had a satisfactory level compared to after awareness sessions, when the majority of them had satisfactory knowledge levels. Furthermore, before awareness sessions, staff nurses experienced mild to moderate psychological distress during the COVID-19 pandemic, and only 8.2% were severe. After awareness sessions, 58.55% of them experienced mild psychological distress, and others became well (25.8%). Conclusions: Nurse managers lack knowledge about inclusive leadership before conducting awareness sessions. The majority of staff nurses perceived their nurse managers as poor inclusive leaders. Satisfactory knowledge levels among nurse managers after awareness sessions improved nurses’ psychological distress. Finally, there were strong, statistically significant positive correlations between inclusive leadership and nurses’ psychological distress.
Avoiding the Trap of Parallelism: Interlocking Parallel Style in the Interpretation of Laozi 29
Authors: Fech Andrej
Published: August 2022

The present paper deals with a specific argumentative feature found in the Laozi, namely, “interlocking parallel style” or IPS. It shows how knowledge of this structure can be helpful for the understanding and interpretation of the text. At the same time, the paper demonstrates that, in some cases, rigorously imposing IPS can be counterproductive. To this end, the paper analyses Laozi 29, the commentary to it penned by Wang Bi, as well as a close parallel in the fifth chapter of the Wenzi.
Testing and Disrupting Ontologies: Using the Database of Religious History as a Pedagogical Tool
Authors: Danielson Andrew J. , Arbuckle MacLeod Caroline , Hamm Matthew J. , Canlas Gino , Randall Ian E. , Moreiras Reynaga Diana K. , Weideman Julian , Monroe M. Willis
Published: August 2022

In an age of “Big Data” the study of the history and archaeology of religion faces an exponentially increasing quantity and range of data and scholarly interpretation. For the student and scholar alike, new tools that allow for efficient and accurate inquiry are a necessity. Here, the open-access and digital Database of Religious History (DRH) is presented as one such tool that addresses this need and is well suited for use in the classroom. In this article, we present the basic structure of the database along with a demonstration of its potential use. Following a thematic inquiry into questions concerning “high gods”, individual disciplinary-specific case studies examine applications to particular contexts across time and space. These case studies demonstrate the ways in which the DRH can test and disrupt ontologies through its ability to efficiently cross traditional disciplinary boundaries.
Kant’s Moral Theism and the Concept of the Highest Good
Authors: Zhong Huanlin
Published: August 2022

The concept of the highest good plays a vital role in Kant’s moral theism. However, as many scholars suggest, Kant’s own texts about the very concept can be interpreted in different ways, some of which may imply that the practical postulate of God is not necessary for the realization of the highest good. In this paper, I argue, on the contrary, that within the framework of Kant’s philosophy, no matter how we interpretate the concept of the highest good, the practical postulate of God is necessary for the realization of it. The combination of virtue and happiness, let alone their perfect and exact proportionality, both for individuals and for the world as a whole, is not possible without such a postulate of God, since for Kant, virtue and happiness, as radically different elements of the highest good, are governed by different laws and true virtue can only be discerned by God. Nevertheless, this by no means entails that atheists, those who do not believe in God, cannot or should not strive for virtue and moral perfection anymore. As long as one is committed to the moral law and acts as the law commands, he can substantially contribute to realizing the highest good.
State and Church Relationships under the European Convention on Human Rights: A Value Framework for State Action
Authors: Chaibi Moncef
Published: August 2022

As they impact the condition of religious groups, and in fine that of the very individuals composing them, state-church relationships are an important dimension of religious freedom. The way states interact with religious groups, communities, and associations has a decisive impact on the religious condition of their members, in their effort to develop their religious beliefs or practices. Therefore, the purpose of this article is to explore the European Court of Human Rights’ regulation of state-church relationships. The research focuses on ECtHR’s judgments issued on article 9, either autonomously or in connection with article 11 of the European Convention on Human Rights. It also considers cases adjudicated upon article 2 of Protocol I to the Convention. For optimal consistency with Europe’s social evolution, however, special attention was given to those cases issued from early 2011. The article argues that behind the margin of appreciation granted for states to enact any system they see fit, the Court puts forward limits they cannot trespass. Indeed, the systems adopted have to abide by the values underlying the Convention as a whole. The article argues said values materialize a global framework that unifies states into one European global approach, thus giving a further illustration of the oligopolistic Pluralism that the Court develops in its regulation of individual religious freedom.
Investigation of Reciprocal Cross Effects in F2 Nellore–Angus Calves
Authors: Mickey Dana M. , Riley David G. , Sanders James O. , Herring Andy D.
Published: August 2022

The objectives of this study were to analyze calf performance traits and sex ratio frequencies in reciprocal Bos indicus–Bos taurus F2 Nellore–Angus calves (n = 539). The F2 calves were produced using reciprocal Angus-sired (AN) and Nellore-sired (NA) F1 sires and F1 dams, resulting in four F2 cross types. Calf weight traits were analyzed using mixed models. Deviations from 50% calf sex ratios were tested against expected binomial thresholds. Dam type influenced Julian birth date (p < 0.05) with calves from AN cows born 5 to 6 days earlier than calves from NA cows. Sire type and dam type affected birth weight (p < 0.05) with calves from NA parents approximately 2.5 kg heavier than those from AN parents; weaning weight did not differ for parent F1 type. However, an interaction between sire and dam types affected weaning weight (p = 0.038) where AN x AN calves were 5 to 8 kg lighter than other crosses. Sire type influenced calf sex distribution at birth (p = 0.036) and at weaning (p = 0.026) with NA sires producing over 66% male calves. These results suggest birth weight reciprocal differences due to parental line of descent typically observed among F1 Bos indicus–Bos taurus crosses can persist in subsequent generations.
A Hybrid Mayfly-Aquila Optimization Algorithm Based Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks
Authors: Natesan Gobi , Konda Srinivas , de Prado Rocío , Wozniak Marcin
Published: August 2022

In recent times, Wireless Sensor Networks (WSNs) are becoming more and more popular and are making significant advances in wireless communication thanks to low-cost and low-power sensors. However, since WSN nodes are battery-powered, they lose all of their autonomy after a certain time. This energy restriction impacts the network’s lifetime. Clustering can increase the lifetime of a network while also lowering energy use. Clustering will bring several similar sensors to one location for data collection and delivery to the Base Station (BS). The Cluster Head (CH) uses more energy when collecting and transferring data. The life of the WSNs can be extended, and efficient identification of CH can minimize energy consumption. Creating a routing algorithm that considers the key challenges of lowering energy usage and maximizing network lifetime is still challenging. This paper presents an energy-efficient clustering routing protocol based on a hybrid Mayfly-Aquila optimization (MFA-AOA) algorithm for solving these critical issues in WSNs. The Mayfly algorithm is employed to choose an optimal CH from a collection of nodes. The Aquila optimization algorithm identifies and selects the optimum route between CH and BS. The simulation results showed that the proposed methodology achieved better energy consumption by 10.22%, 11.26%, and 14.28%, and normalized energy by 9.56%, 11.78%, and 13.76% than the existing state-of-art approaches.
Attitude Solving Algorithm and FPGA Implementation of Four-Rotor UAV Based on Improved Mahony Complementary Filter
Authors: Zhu Yanping , Liu Jing , Yu Ran , Mu Zijian , Huang Lei , Chen Jinli , Chen Jianan
Published: August 2022

With the development of modern industry, small UAVs have been widely used in agriculture, mapping, meteorology, and other fields. There is an increasing demand for the core attitude-solving algorithm of UAV flight control. In this paper, at first, a novel attitude solving algorithm is proposed by using quaternions to represent the attitude matrix and using Allan variance to analyze the gyroscope error and to quantify the trend of the error over time, so as to improve the traditional Mahony complementary filtering. Simulation results show that the six-axis data from the initial sensors (gyroscope and accelerometer) agree well with the measured nine-axis data with an extra magnetometer, which reduces the complexity of the system hardware. Second, based on the hardware platform, the six-axis data collected from MPU6050 are sent to FPGA for floating-point operation, transcendental function operation, and attitude solution module for processing through IIC communication, which effectively validates the attitude solution by using the proposed method. Finally, the proposed algorithm is applied to a practical scenario of a quadrotor UAV, and the test results show that the RMSE does not exceed 2° compared with the extended Kalman filter method. The proposed system simplifies the hardware but keeps the accuracy and speed of the solution, which may result in application in UAV flight control.
Calibration Uncertainty of Non-Catching Precipitation Gauges
Authors: Baire Quentin , Dobre Miruna , Piette Anne-Sophie , Lanza Luca , Cauteruccio Arianna , Chinchella Enrico , Merlone Andrea , Kjeldsen Henrik , Nielsen Jan , Østergaard Peter Friis , Parrondo Marina , Garcia Izquierdo Carmen
Published: August 2022

Precipitation is among the most important meteorological variables for, e.g., meteorological, hydrological, water management and climate studies. In recent years, non-catching precipitation gauges are increasingly adopted in meteorological networks. Despite such growing diffusion, calibration procedures and associated uncertainty budget are not yet standardized or prescribed in best practice documents and standards. This paper reports a metrological study aimed at proposing calibration procedures and completing the uncertainty budgets, to make non-catching precipitation gauge measurements traceable to primary standards. The study is based on the preliminary characterization of different rain drop generators, specifically developed for the investigation. Characterization of different models of non-catching rain gauges is also included.
Enhanced Photoacoustic Visualisation of Clinical Needles by Combining Interstitial and Extracorporeal Illumination of Elastomeric Nanocomposite Coatings
Authors: Shi Mengjie , Bodian Semyon , West Simeon J. , Sathasivam Sanjayan , Gordon Ross J. , Collier Paul , Vercauteren Tom , Desjardins Adrien E. , Noimark Sacha , Xia Wenfeng
Published: August 2022

Ultrasound (US) image guidance is widely used for minimally invasive procedures, but the invasive medical devices (such as metallic needles), especially their tips, can be poorly visualised in US images, leading to significant complications. Photoacoustic (PA) imaging is promising for visualising invasive devices and peripheral tissue targets. Light-emitting diodes (LEDs) acting as PA excitation sources facilitate the clinical translation of PA imaging, but the image quality is degraded due to the low pulse energy leading to insufficient contrast with needles at deep locations. In this paper, photoacoustic visualisation of clinical needles was enhanced by elastomeric nanocomposite coatings with superficial and interstitial illumination. Candle soot nanoparticle-polydimethylsiloxane (CSNP-PDMS) composites with high optical absorption and large thermal expansion coefficients were applied onto the needle exterior and the end-face of an optical fibre placed in the needle lumen. The excitation light was delivered at the surface by LED arrays and through the embedded optical fibre by a pulsed diode laser to improve the visibility of the needle tip. The performance was validated using an ex-vivo tissue model. An LED-based PA/US imaging system was used for imaging the needle out-of-plane and in-plane insertions over approach angles of 20 deg to 55 deg. The CSNP-PDMS composite conferred substantial visual enhancements on both the needle shaft and the tip, with an average of 1.7- and 1.6-fold improvements in signal-to-noise ratios (SNRs), respectively. With the extended light field involving extracorporeal and interstitial illumination and the highly absorbing coatings, enhanced visualisation of the needle shaft and needle tip was achieved with PA imaging, which could be helpful in current US-guided minimally invasive surgeries.
Non-Contact Detection of Vital Signs Based on Improved Adaptive EEMD Algorithm (July 2022)
Authors: Xu Didi , Yu Weihua , Deng Changjiang , He Zhongxia Simon
Published: August 2022

Non-contact vital sign detection technology has brought a more comfortable experience to the detection process of human respiratory and heartbeat signals. Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method which can be used to decompose the echo data of frequency modulated continuous wave (FMCW) radar and extract the heartbeat and respiratory signals. The key of EEMD is to add Gaussian white noise into the signal to overcome the mode aliasing problem caused by original empirical mode decomposition (EMD). Based on the characteristics of clutter and noise distribution in public places, this paper proposed a static clutter filtering method for eliminating ambient clutter and an improved EEMD method based on stable alpha noise distribution. The symmetrical alpha stable distribution is used to replace Gaussian distribution, and the improved EEMD is used for the separation of respiratory and heartbeat signals. The experimental results show that the static clutter filtering technology can effectively filter the surrounding static clutter and highlight the periodic moving targets. Within the detection range of 0.5 m~2.5 m, the improved EEMD method can better distinguish the heartbeat, respiration, and their harmonics, and accurately estimate the heart rate.
A Study of Optimizing Lamb Wave Acoustic Mass Sensors’ Performance through Adjustment of the Transduction Electrode Metallization Ratio
Authors: Gholami Fatemeh , Shih Andy , Robichaud Alexandre , Cicek Paul-Vahe
Published: August 2022

This paper presents the design and simulation of a mass sensitive Lamb wave microsensor with CMOS technology provided by SilTerra. In this work, the effects of the metalization ratio variation on the transmission gain, total harmonic distortion (THD), and two different resonant modes (around 66 MHz and 86 MHz) are shown. It has been found that the metalization ratio can be adjusted in order to obtain a compromise between transmission gain and sensitivity, depending on the design criteria. By adding a Si3N4 layer on top of the device, a five-fold improvement in transmission gain is reached. It was also shown that the transmission of the input differential IDT configuration is 20% more efficient than a single terminal. With this combination, the mass sensitivity is about 114 [cm2/gr].
Less Vibrotactile Feedback Is Effective to Improve Human Balance Control during Sensory Cues Alteration
Authors: Anctil Noémie , Malenfant Zachary , Cyr Jean-Philippe , Turcot Katia , Simoneau Martin
Published: August 2022

For individuals with altered sensory cues, vibrotactile feedback improves their balance control. However, should vibrotactile feedback be provided every time balance control is compromised, or only one-third of the time their balance is compromised? We hypothesized that vibrotactile feedback would improve balance control more when provided every time their balance is compromised. Healthy young adults were randomly assigned to two groups: group 33% feedback (6 males and 6 females) and group 100% feedback (6 males and 6 females). Vibrotactile feedbacks related to the body’s sway angle amplitude and direction were provided, while participants stood upright on a foam surface with their eyes closed. Then, we assessed if balance control improvement lasted when the vibrotactile feedback was removed (i.e., post-vibration condition). Finally, we verified whether or not vibrotactile feedback unrelated to the body’s sway angle and direction (sham condition) altered balance control. The results revealed no significant group difference in balance control improvement during vibrotactile feedback. Immediately following vibrotactile feedback, both groups reduced their balance control commands; body sway velocity and the ground reaction forces variability decreased. For both groups, unrelated vibrotactile feedback worsened balance control. These results confirmed that participants processed and implemented vibrotactile feedback to control their body sways. Less vibrotactile feedback was effective in improving balance control.
An Improved Convolutional Capsule Network for Compound Fault Diagnosis of RV Reducers
Authors: Xu Qitong , Liu Chang , Yang Enshan , Wang Mengdi
Published: August 2022

In fault diagnosis research, compound faults are often regarded as an isolated fault mode, while the association between compound faults and single faults is ignored, resulting in the inability to make accurate and effective diagnoses of compound faults in the absence of compound fault training data. In an examination of the rotate vector (RV) reducer, a core component of industrial robots, this paper proposes a compound fault identification method that is based on an improved convolutional capsule network for compound fault diagnosis of RV reducers. First, one-dimensional convolutional neural networks are used as feature learners to deeply mine the feature information of a single fault from a one-dimensional time-domain signal. Then, a capsule network with a two-layer stack structure is designed and a dynamic routing algorithm is used to decouple and identify the single fault characteristics for compound faults to undertake the diagnosis of compound faults of RV reducers. The proposed method is verified on the RV reducer fault simulation experimental bench, the experimental results show that the method can not only diagnose a single fault, but it is also possible to diagnose the compound fault that is composed of two types of single faults through the learning of two types of single faults of the RV reducer when the training data of the compound faults of the RV reducer are missing. At the same time, the proposed method is used for compound fault diagnosis of bearings, and the experimental results confirm its applicability.
Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection
Authors: Neves Paulo Alexandre , Simões João , Costa Ricardo , Pimenta Luís , Gonçalves Norberto Jorge , Albuquerque Carlos , Cunha Carlos , Zdravevski Eftim , Lameski Petre , Garcia Nuno M. , Pires Ivan Miguel
Published: August 2022

Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer’s disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.
Spectral Efficiency Optimization of Uplink Millimeter Wave MIMO-NOMA Systems
Authors: Zhang Yinhao , Deng Honggui , He Jun , Zhu Zaoxing , Peng Chengzuo , Xiao Haoqi
Published: August 2022

In this paper, we considered uplink communication, focusing on the improvement of spectral efficiency (SE) for millimeter wave (mmWave) multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Firstly, we proposed an adaptive cluster head selection algorithm. Then, a channel-aligned analog beamforming scheme was designed based on the selected cluster heads. After that, the user grouping algorithm was designed based on the user-equivalent channel correlation. Subsequently, the power allocation problem was transformed from a nonconvex problem to a convex one using the quadratic transformation (QT) method considering all relevant constraints. Finally, the optimal user power allocation and digital beamforming design was obtained by iteratively optimizing the power and digital beamforming. Simulation results show that our proposed scheme can achieve a higher SE than existing methods.
Compact Slot Microring Resonator for Sensitive and Label-Free Optical Sensing
Authors: Shi Bingyao , Chen Xiao , Cai Yuanyuan , Zhang Shuai , Wang Tao , Wang Yiquan
Published: August 2022

A novel all-pass slot microring resonator (SMRR), intended for label-free optical biosensing based on silicon-on-insulator platforms, is proposed. The sensor consists of a bent asymmetric directional coupler and an asymmetric-slot microring waveguide. The appropriate slot width of 140 nm is identified by the three-dimensional finite-difference time-domain (3D-FDTD) method for better light–matter interaction in applications. According to numerical calculations, the SMRR sensor with a footprint of 10 µm × 10 µm has a concentration sensitivity of 725.71 pm/% for sodium chloride (NaCl) solutions. The corresponding refractive index sensitivity is 403 nm/RIU (refractive index unit), which is approximately six times greater than that of traditional microring resonator sensors. A low detection limit of 0.129% is also achieved. This SMRR is an excellent candidate for label-free optical biosensors due to its compact structure and excellent sensing capability.
Model-Based 3D Contact Geometry Perception for Visual Tactile Sensor
Authors: Ji Jingjing , Liu Yuting , Ma Huan
Published: August 2022

Tactile sensing plays an important role for robots’ perception, but the existing tactile technologies have multiple limitations. Visual-tactile sensor (VTS) is a newly developed tactile detector; it perceives the contacting surface shape, or even more refined texture, by way of the contact deformation image captured by a camera. A conventional visual perception is usually formulated as a data processing. It suffers issues of cumbersome training set and complicated calibration procedures. A novel model-based depth perceptual scheme is proposed where a mapping from the image intensity to the contact geometry is mathematically formulated with an associated tailored fast solver. The hardware calibration requires single image only, leading to an outstanding algorithmic robustness. The non-uniformity of the illumination condition is embodied by the stereo model, resulting in a robust depth perception precision. Compression tests on a prototype VTS showed the method’s capability in high-quality geometry reconstruction. Both contacting shape and texture were captured at a root-mean-square error down to a sub-millimeter level. The feasibility of the proposed in a pose estimation application is further experimentally validated. The associated tests yielded estimation errors that were all less than 3° in terms of spatial orientation and all less than 1mm in terms of translation.
Real-Time Prediction of Growth Characteristics for Individual Fruits Using Deep Learning
Authors: Hondo Takaya , Kobayashi Kazuki , Aoyagi Yuya
Published: August 2022

Understanding the growth status of fruits can enable precise growth management and improve the product quality. Previous studies have rarely used deep learning to observe changes over time, and manual annotation is required to detect hidden regions of fruit. Thus, additional research is required for automatic annotation and tracking fruit changes over time. We propose a system to record the growth characteristics of individual apples in real time using Mask R-CNN. To accurately detect fruit regions hidden behind leaves and other fruits, we developed a region detection model by automatically generating 3000 composite orchard images using cropped images of leaves and fruits. The effectiveness of the proposed method was verified on a total of 1417 orchard images obtained from the monitoring system, tracking the size of fruits in the images. The mean absolute percentage error between the true value manually annotated from the images and detection value provided by the proposed method was less than 0.079, suggesting that the proposed method could extract fruit sizes in real time with high accuracy. Moreover, each prediction could capture a relative growth curve that closely matched the actual curve after approximately 150 elapsed days, even if a target fruit was partially hidden.
High-Frequency Surface Acoustic Wave Resonator with Diamond/AlN/IDT/AlN/Diamond Multilayer Structure
Authors: Lei Liang , Dong Bo , Hu Yuxuan , Lei Yisong , Wang Zhizhong , Ruan Shuangchen
Published: August 2022

A high-frequency surface acoustic wave (SAW) resonator, based on sandwiched interdigital transducer (IDT), is presented. The resonator has the structure of diamond/AlN/IDT/AlN/diamond, with Si as the substrate. The results show that its phase velocity and electromechanical coupling coefficient are both significantly improved, compared with that of the traditional interdigital transduce-free surface structure. The M2 mode of the sandwiched structure can excite an operation frequency up to 6.15 GHz, with an electromechanical coupling coefficient of 5.53%, phase velocity of 12,470 m/s, and temperature coefficient of frequency of −6.3 ppm/°C. This structure provides a new ideal for the design of high-performance and high-frequency SAW devices.
A Comprehensive Collection and Analysis Model for the Drone Forensics Field
Authors: Alotaibi Fahad Mazaed , Al-Dhaqm Arafat , Al-Otaibi Yasser D. , Alsewari Abdulrahman A.
Published: August 2022

Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by drone-based evidence. Drone-based evidence collection and preservation entails accumulating and collecting digital evidence from the drone of the victim for subsequent analysis and presentation. Digital evidence must, however, be collected and analyzed in a forensically sound manner using the appropriate collection and analysis methodologies and tools to preserve the integrity of the evidence. For this purpose, various collection and analysis models have been proposed for drone forensics based on the existing literature; several models are inclined towards specific scenarios and drone systems. As a result, the literature lacks a suitable and standardized drone-based collection and analysis model devoid of commonalities, which can solve future problems that may arise in the drone forensics field. Therefore, this paper has three contributions: (a) studies the machine learning existing in the literature in the context of handling drone data to discover criminal actions, (b) highlights the existing forensic models proposed for drone forensics, and (c) proposes a novel comprehensive collection and analysis forensic model (CCAFM) applicable to the drone forensics field using the design science research approach. The proposed CCAFM consists of three main processes: (1) acquisition and preservation, (2) reconstruction and analysis, and (3) post-investigation process. CCAFM contextually leverages the initially proposed models herein incorporated in this study. CCAFM allows digital forensic investigators to collect, protect, rebuild, and examine volatile and nonvolatile items from the suspected drone based on scientific forensic techniques. Therefore, it enables sharing of knowledge on drone forensic investigation among practitioners working in the forensics domain.
Feedback Integrators for Mechanical Systems with Holonomic Constraints
Authors: Chang Dong Eui , Perlmutter Matthew , Vankerschaver Joris
Published: August 2022

The feedback integrators method is improved, via the celebrated Dirac formula, to integrate the equations of motion for mechanical systems with holonomic constraints so as to produce numerical trajectories that remain in the constraint set and preserve the values of quantities, such as energy, that are theoretically known to be conserved. A feedback integrator is concretely implemented in conjunction with the first-order Euler scheme on the spherical pendulum system and its excellent performance is demonstrated in comparison with the RATTLE method, the Lie–Trotter splitting method, and the Strang splitting method.
Single LED, Single PD-Based Adaptive Bayesian Tracking Method
Authors: Kim Duckyong , Park Jong Kang , Kim Jong Tae
Published: August 2022

Recently, with the growing interest in indoor location-based services, visible light positioning (VLP) systems have been extensively studied owing to their advantages of low cost, high energy efficiency, and no electromagnetic interference. However, due to structural limitations which lead to the requirement of multiple signal sources, it has been challenging to apply VLP in real-world scenarios. In this study, we propose a single LED, single PD-based tracking system that solves these problems by applying a new Bayesian method that can effectively reduce the computational burden of particle filters. The method of evaluating particle reliability developed in this work adjusts the number of particles on the fly. Using the absolute position of the single LED source, the long-term cumulative error of the inertial measurement unit can be continuously corrected. In this regard, the applicability of the VLP system can be enhanced in places where the multiple luminescent signals are hard to consistently detect. The proposed system was verified through experiments in a classroom and a corridor, and the results show an average error of less than 11 cm at travel distances of 80 to 100 m.
Methodology for LoRa Gateway Placement Based on Bio-Inspired Algorithmsfor a Smart Campus in Wooded Area
Authors: Cruz Hugo A. O. , Ferreira Sidnir C. B. , Araújo Jasmine P. L. , Barros Fabrício J. B. , Farias Fabrício S. , Neto Miércio C. A. , Tostes Maria E. L. , Nascimento Andréia A. , Cavalcante Gervásio P. S.
Published: August 2022

The Internet of Things (IoT) device scenario has several emerging technologies. Among them, Low-Power Wide-Area Networks (LPWANs) have proven to be efficient connections for smart devices. These devices communicate through gateways that exchange points with the central server. This study proposes an empirical and statistical methodology based on measurements carried out in a typical scenario of Amazonian cities composed of forests and buildings on the Campus of the Federal University of Pará (UFPA) to apply an adjustment to the coefficients in the UFPA propagation model. Furthermore, an Evolutionary Particle Swarm Optimization (EPSO) metaheuristic with multi-objective optimization was applied to maximize the coverage area and minimize the number of gateways to assist in the planning of a LoRa network. The results of simulations using the Monte Carlo method show that the EPSO-based gateway placement optimization methodology can be used to plan future LPWAN networks. As reception sensitivity is a decisive factor in the coverage area, with −108 dBm, the optimal solution determined the use of three gateways to cover the smart campus area.