bluuebunny/crossref_metadata_2025 huggingface dataset:
Protection against Paraquat-Induced Oxidative Stress by Curcuma longa Extract-Loaded Polymeric Nanoparticles in Zebrafish Embryos
Authors: Teixeira Ana , Sárria Marisa P. , Pinto Inês , Espiña Begoña , Gomes Andreia C. , Dias Alberto C. P.
Published: September 2022

The link between oxidative stress and environmental factors plays an important role in chronic degenerative diseases; therefore, exogenous antioxidants could be an effective alternative to combat disease progression and/or most significant symptoms. Curcuma longa L. (CL), commonly known as turmeric, is mostly composed of curcumin, a multivalent molecule described as having antioxidant, anti-inflammatory and neuroprotective properties. Poor chemical stability and low oral bioavailability and, consequently, poor absorption, rapid metabolism, and limited tissue distribution are major restrictions to its applicability. The advent of nanotechnology, by combining nanosacale with multi-functionality and bioavailability improvement, offers an opportunity to overcome these limitations. Therefore, in this work, poly-Ɛ-caprolactone (PCL) nanoparticles were developed to incorporate the methanolic extract of CL, and their bioactivity was assessed in comparison to free or encapsulated curcumin. Their toxicity was evaluated using zebrafish embryos by applying the Fish Embryo Acute Toxicity test, following recommended OECD guidelines. The protective effect against paraquat-induced oxidative damage of CL extract, free or encapsulated in PCL nanoparticles, was evaluated. This herbicide is known to cause oxidative damage and greatly affect neuromotor functions. The overall results indicate that CL-loaded PCL nanoparticles have an interesting protective capacity against paraquat-induced damage, particularly in neuromuscular development that goes well beyond that of CL extract itself and other known antioxidants.
Lignocellulosic-Based Materials from Bean and Pistachio Pod Wastes for Dye-Contaminated Water Treatment: Optimization and Modeling of Indigo Carmine Sorption
Authors: Nindjio Gaël Ferdinand Kazé , Tagne Rufis Fregue Tiegam , Jiokeng Sherman Lesly Zambou , Fotsop Cyrille Ghislain , Bopda Aurelien , Doungmo Giscard , Temgoua Ranil Clément Tonleu , Doench Ingo , Njoyim Estella Tamungang , Tamo Arnaud Kamdem , Osorio-Madrazo Anayancy , Tonle Ignas Kenfack
Published: September 2022

In this work, biomass lignocellulosic materials extracted via chemical and physical treatments from bean and pistachio pod waste were used for the optimized elimination of Indigo Carmine (IC) from aqueous medium, using a design of experiments methodology. The physicochemical properties of the studied materials (raw and treated counterparts) used for the sorption of IC were investigated by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM) coupled with EDX, and thermal analysis. Key variables influencing the adsorption of IC, namely the initial IC concentration, the pH of the solution, the stirring time and the mass of adsorbents, were optimized by the central composite design (CCD) with three center points, the measured response being the amount of IC adsorbed. The optimal conditions obtained from the statistical analysis for the removal of IC were as follows: maximum adsorbed amounts of IC: 1.81 mg/g, 2.05 mg/g, 3.56 mg/g; 7.42 mg/g, 8.95 mg/g, 15.35 mg/g, for raw bean pods (RBS), BST1 and BST2 (bean pods chemically treated), and for raw pistachio pods (RPS), PST1 and PST2 (pistachio pods chemically treated), respectively. The pseudo-second-order nonlinear kinetics model well described the IC adsorption kinetics for RBS, BST1 and BST2, while the Elovich model was properly fitted by RPS, PST1, and PST2 biomaterials data. The Freundlich isotherm best described the shrinkage of IC on different sorbents. The good correlation of the experimental data of the IC with respect to the Freundlich isotherm indicated a multilayer adsorption with heterogeneous adsorption sites and different energies. The interest of this work consisted in developing analytical methods for the treatment of water polluted by dyes by using biosorbents, local biological materials widely available and inexpensive. The results collected in this work highlighted the interesting structural, morphological, and physico-chemical properties of the agro-waste used in the study, which properties allowed an important fixation of the target dye in solution. The research showed that the agro-waste used in the study are possible precursors to locally manufacture adsorbents at low cost, thus allowing the efficient removal of waste and dyes in liquid effluents.
Modeling and Simulation of the Impact of Feed Gas Perturbation on CO2 Removal in a Polymeric Hollow Fiber Membrane
Authors: Ghasem Nayef
Published: September 2022

A membrane contactor is a device that attains the transfer of gas/liquid or liquid/liquid mass without dispersion of one phase within another. Membrane contactor modules generally provide 30 times more surface area than can be achieved in traditional gas absorption towers and 500 times what can be obtained in liquid/liquid extraction columns. By contrast, membrane contactor design has limitations, as the presence of the membrane adds additional resistance to mass transfer compared with conventional solvent absorption systems. Increasing mass transfer in the gas and solvent phase boundary layers is necessary to reduce additional resistance. This study aims to increase the mass transfer in the gas phase layer without interfering with membrane structure by oscillating the velocity of the feed gas. Therefore, an unsteady state mathematical model was improved to consider feed gas oscillation. The model equation was solved using Comsol Multiphysics version 6.0. The simulation results reveal that the maximum CO2 removal rate was about 30% without oscillation, and at an oscillation frequency of 0.05 Hz, the CO2 percent removal was almost doubled.
Electrospun-Based Membranes as a Key Tool to Prevent Respiratory Infections
Authors: Guerreiro Sara F. C. , Ferreira Carolina A. M. , Valente Joana F. A. , Patrício Tatiana M. F. , Alves Nuno M. F. , Dias Juliana R.
Published: September 2022

The use of electrospun meshes has been proposed as highly efficient protective equipment to prevent respiratory infections. Those infections can result from the activity of micro-organisms and other small dust particles, such as those resulting from air pollution, that impair the respiratory tract, induce cellular damage and compromise breathing capacity. Therefore, electrospun meshes can contribute to promoting air-breathing quality and controlling the spread of such epidemic-disrupting agents due to their intrinsic characteristics, namely, low pore size, and high porosity and surface area. In this review, the mechanisms behind the pathogenesis of several stressors of the respiratory system are covered as well as the strategies adopted to inhibit their action. The main goal is to discuss the performance of antimicrobial electrospun nanofibers by comparing the results already reported in the literature. Further, the main aspects of the certification of filtering systems are highlighted, and the expected technology developments in the industry are also discussed.
Cell-Laden Composite Hydrogel Bioinks with Human Bone Allograft Particles to Enhance Stem Cell Osteogenesis
Authors: Gharacheh Hadis , Guvendiren Murat
Published: September 2022

There is a growing demand for bone graft substitutes that mimic the extracellular matrix properties of the native bone tissue to enhance stem cell osteogenesis. Composite hydrogels containing human bone allograft particles are particularly interesting due to inherent bioactivity of the allograft tissue. Here, we report a novel photocurable composite hydrogel bioink for bone tissue engineering. Our composite bioink is formulated by incorporating human allograft bone particles in a methacrylated alginate formulation to enhance adult human mesenchymal stem cell (hMSC) osteogenesis. Detailed rheology and printability studies confirm suitability of our composite bioinks for extrusion-based 3D bioprinting technology. In vitro studies reveal high cell viability (~90%) for hMSCs up to 28 days of culture within 3D bioprinted composite scaffolds. When cultured within bioprinted composite scaffolds, hMSCs show significantly enhanced osteogenic differentiation as compared to neat scaffolds based on alkaline phosphatase activity, calcium deposition, and osteocalcin expression.
Two-Way Shear Resistance of FRP Reinforced-Concrete Slabs: Data and a Comparative Study
Authors: Aslam Fahid , AbdelMongy Mohamed , Alzara Majed , Ibrahim Taha , Deifalla Ahmed Farouk , Yosri Ahmed M.
Published: September 2022

This study aims to investigate the two-way shear strength of concrete slabs with FRP reinforcements. Twenty-one strength models were briefly outlined and compared. In addition, information on a total of 248 concrete slabs with FRP reinforcements were collected from 50 different research studies. Moreover, behavior trends and correlations between their strength and various parameters were identified and discussed. Strength models were compared to each other with respect to the experimentally measured strength, which were conducted by comparing overall performance versus selected basic variables. Areas of future research were identified. Concluding remarks were outlined and discussed, which could help further the development of future design codes. The ACI is the least consistent model because it does not include the effects of size, dowel action, and depth-to-control perimeter ratio. While the EE-b is the most consistent model with respect to the size effect, concrete compressive strength, depth to control perimeter ratio, and the shear span-to-depth ratio. This is because of it using experimentally observed behavior as well as being based on mechanical bases.
Preparation and Properties of Fluorosilicone Fouling-Release Coatings
Authors: Wu Tong , Qi Yuhong , Chen Qi’an , Gu Chuanjun , Zhang Zhanping
Published: September 2022

To improve the antifouling performance of silicone fouling-release coatings, some fluorosilicone and silicone fouling-release coatings were prepared and cured at room temperature with hydroxyl-terminated fluoropolysiloxane (FPS) or hydroxy-terminated polydimethylsiloxane (PDMS) as a film-forming resin, tetraethyl orthosilicate (TEOS) as a crosslinking agent, and dibutyltin dilaurate (DBTDL) as a catalyst. The chemical structure, surface morphology and roughness, tensile properties, and antifouling properties of the coating were studied by infrared spectroscopy, a laser confocal scanning microscope, contact angle measurement, tensile tests, and marine bacteria and benthic diatom attachment tests. The results showed that the FPS coatings were not only hydrophobic but also oleophobic, and the contact angles of the FPS coatings were larger than those of the PDMS coatings. The surface free energies of the FPS coatings were much lower than those of the PDMS coatings. Generally, the fluorine groups can improve the antifouling performance of the coating. Introducing nonreactive silicone oil into PDMS or FPS coatings can improve the antifouling performance of the coating to a certain extent. The prepared fluorosilicone fouling-release coatings showed good application prospects.
Comprehensive Investigation of Stoichiometry–Structure–Performance Relationships in Flexible Polyurethane Foams
Authors: Olszewski Adam , Kosmela Paulina , Piasecki Adam , Żukowska Wiktoria , Szczepański Mariusz , Wojtasz Paweł , Barczewski Mateusz , Barczewski Roman , Hejna Aleksander
Published: September 2022

Polyurethane (PU) foams are versatile materials with a broad application range. Their performance is driven by the stoichiometry of polymerization reaction, which has been investigated in several works. However, the analysis was often limited only to selected properties and compared samples differing in apparent density, significantly influencing their performance. In the bigger picture, there is still a lack of comprehensive studies dealing with the stoichiometry impact on PU foams’ performance. Herein, flexible PU foams with a similar apparent density but differing in the isocyanate index (IIso) (from 0.80 to 1.20) were prepared. The stoichiometry–structure–performance relationships were investigated considering cellular and chemical structure, as well as the static and dynamic mechanical properties, thermal stability, thermal insulation, and acoustic performance. For IIso of 1.00, the biggest cell diameters of 274 µm were noted, which was 21–25% higher compared to 0.80 and 1.20 values. Increasing IIso reduced open cell content from 83.1 to 22.4%, which, combined with stiffening of structure (rise of modulus from 63 to 2787 kPa) resulting from crosslinking, limited the sound suppression ability around five times. On the other hand, it significantly strengthened the material, increasing tensile and compressive strength 4 and 13 times, respectively. Changes in the foams’ performance were also induced by the glass transition temperature shift from 6.1 to 31.7 °C, resulting from a greater extent of urethane groups’ generation and additional isocyanate reactions. Generally, the presented work provides important insights into preparing flexible PU foams and could be very useful for the future development of these materials.
Fatigue Analysis of CFRP-Reinforced Concrete Ribbed Girder Bridge Deck Slabs
Authors: Tian Shuai , Zhang Xiaotao , Hu Wenjing
Published: September 2022

This study aims to improve the operational safety of reinforced concrete-ribbed beam bridge decks and prolong their service life by performing fatigue analysis of deck slabs reinforced with carbon-fiber-reinforced polymers (CFRP) and other materials. Based on a 16-m-span ribbed girder bridge, five test beams were designed: three reinforced (with CFRP cloth, CFRP mesh, and strip steel plates) and the remaining unreinforced. To simulate the real force of the bridge deck slabs, a PLS-500 electro-hydraulic servo dynamic and static test system was used and static load failure (monotonic graded loading) and fixed-point constant-amplitude fatigue loading tests (fatigue load of 0.515, loading frequency of 5 Hz) were performed. The main fatigue crack appeared when the number of load cycles exceeded 90% of the fatigue life. In the middle of fatigue, the reinforcement material can reduce the deterioration value of the bridge deck by approximately 50%. When it is reinforced at the cumulative damage degree of 0.4, its fatigue life extends by approximately 53.3–78.9%. The fatigue life of the bridge deck slabs reinforced with CFRP cloth or mesh was 22.1–25.6% more than that of those reinforced with strip steel plates. CFRP cloth is best suited for the reinforcement of bridge deck slabs.
Unified Approach for Prediction of the Volumetric Mass Transfer Coefficients in a Homogeneous and Heterogeneous Bubble Column Based on the Non-Corrected Penetration Theory: Case Studies
Authors: Nedeltchev Stoyan
Published: September 2022

A critical review on the improvement of penetration theory is presented in this work. The volumetric liquid-phase mass transfer coefficients kLa in seven different liquids (1-butanol, 2-propanol, anilin, decalin, nitrobenzene, tetralin, and ethylene glycol) aerated with air in a small bubble column (BC) (inner diameter: 0.095 m) were measured at ambient conditions and further analyzed. It was found that the kLa values can be predicted satisfactorily on the basis of the classical Higbie’s penetration model. The gas–liquid contact time was defined as the ratio of the Sauter-mean bubble diameter to bubble rise velocity. Moreover, the experimental kLa values were well predicted, not only in the homogeneous regime, but also in the transition and heterogeneous regimes. This is a new finding, since to date, it was considered that the penetration theory needs a correction factor for a successful application to any liquid, even in the homogeneous regime. The predictions of the mass transfer coefficients kLa in the above-mentioned seven liquids imply that the mean bubble diameters are always ellipsoidal or spherical, which is the key condition for the applicability (without a correction) of penetration theory. In the presented (in this work) model-based kLa predictions, the Sauter-mean bubble diameters were estimated by means of the reliable correlation of Wilkinson et al., which always predicts a gradually decreasing bubble size at higher gas velocities.
Degradation of the UV Filter Benzophenone-4 by Ferrate (VI) in Aquatic Environments
Authors: Wang Rouyi , Sun Ping , Zhai Zhicai , Liu Hui , Han Ruirui , Liu Hongxia , Fang Yingsen
Published: September 2022

This work demonstrates the potential utility of ferrate(VI)-based advanced oxidation processes for the degradation of a representative UV filter, BP-4. The operational parameters of oxidant dose and temperature were determined with kinetic experiments. In addition, the effects of water constituents including anions (Cl−, HCO3−, NO3−, SO42−), cations (Na+, K+, Ca2+, Mg2+, Cu2+, Fe3+), and humic acid (HA) were investigated. Results suggested that the removal rate of BP-4 (5 mg/L) could reach 95% in 60 min, when [Fe(VI)]:[BP-4] = 100:1, T = 25 °C and pH = 7.0, The presence of K+, Cu2+ and Fe3+ could promote the removal of BP-4, but Cl−, SO42−, NO3−, HA and Na+ could significantly inhibit the removal of BP-4. Furthermore, this Fe(VI) oxidation processes has good feasibility in real water samples. These results may provide useful information for the environmental elimination of benzophenone-type UV filters by Fe(VI).
The Role of Conventional Methods and Artificial Intelligence in the Wastewater Treatment: A Comprehensive Review
Authors: Altowayti Wahid Ali Hamood , Shahir Shafinaz , Othman Norzila , Eisa Taiseer Abdalla Elfadil , Yafooz Wael M. S. , Al-Dhaqm Arafat , Soon Chan Yong , Yahya Izzati Binti , Che Rahim Nur Anis Natasha binti , Abaker Mohammed , Ali Abdulalem
Published: September 2022

Water pollution is a severe health concern. Several studies have recently demonstrated the efficacy of various approaches for treating wastewater from anthropogenic activities. Wastewater treatment is an artificial procedure that removes contaminants and impurities from wastewater or sewage before discharging the effluent back into the environment. It can also be recycled by being further treated or polished to provide safe quality water for use, such as potable water. Municipal and industrial wastewater treatment systems are designed to create effluent discharged to the surrounding environments and must comply with various authorities’ environmental discharge quality rules. An effective, low-cost, environmentally friendly, and long-term wastewater treatment system is critical to protecting our unique and finite water supplies. Moreover, this paper discusses water pollution classification and the three traditional treatment methods of precipitation/encapsulation, adsorption, and membrane technologies, such as electrodialysis, nanofiltration, reverse osmosis, and other artificial intelligence technology. The treatment performances in terms of application and variables have been fully addressed. The ultimate purpose of wastewater treatment is to protect the environment that is compatible with public health and socioeconomic considerations. Realization of the nature of wastewater is the guiding concept for designing a practical and advanced treatment technology to assure the treated wastewater’s productivity, safety, and quality.
Study of Prevention and Control Technology for Roadway Excavation under the Soft and Extra-Thick Coal Roof in Luling Coal Mine
Authors: Huang Shunjie , Zhao Guangming , Meng Xiangrui , Cheng Xiang , Xu Wensong , Liu Gang , Zhu Shikui
Published: September 2022

In view of the problems associated with the poor stability of coal walls, coal slide and leakage of top-coal at the tunnel excavation working face under a soft and extra-thick coal roof, the surrounding rock at the tunnel excavation working face must be strengthened. The theoretical analysis of rock pressure, numerical simulation and other methods were comprehensively used to study the coal-wall-slicing mechanism. Given the characteristics of a soft and extra-thick coal roof, the combined supporting technology of “coal wall water injection + metal roof frame” is proposed. The findings show that in the process of roadway excavation, the coal–rock junctions of the wall and the middle part of the roof are weak areas that are prone to spalling and therefore need to be strengthened. Laboratory tests determined the moisture content of the coal body during tunneling to provide data for the parameter design of coal wall water injection. Safe and efficient excavation of the roadway was ensured by injecting water into the coal wall in combination with a metal roof protection skeleton. The application of this technology not only effectively prevents rib spalling but improves control of the deformation of the surrounding rock. During 40 days of field observation, the maximum deformation of the roof was 24.8 mm, and the distance between the two roadway walls was 21.5 mm. The deformation of the roadway was controlled within a safety zone. The application of this technology reduced the repair rate of the roadway and improved the efficiency of the roadway excavation. It brought significant economic benefits and provides an important reference for similar mines.
A Survey of DEA Window Analysis Applications
Authors: AlKhars Mohammed A. , Alnasser Ahmad H. , AlFaraj Taqi
Published: September 2022

This article aims to review, analyze, and classify the published research applications of the Data Envelopment Analysis (DEA) window analysis technique. The number of filtered articles included in the study is 109, retrieved from 79 journals in the web of science (WoS) database during the period 1996–2019. The papers are classified into 15 application areas: energy and environment, transportation, banking, tourism, manufacturing, healthcare, power, agriculture, education, finance, petroleum, sport, communication, water, and miscellaneous. Moreover, we present descriptive statistics related to the growth of publications over time, the journals publishing the articles, keyword terms used, length of articles, and authorship analysis (including institutional and country affiliations). To the best of the authors knowledge, this is the first survey reviewing the literature of the DEA window analysis applications in the 15 areas mentioned in the paper.
The Content Specificity and Generality of the Relationship between Mathematical Problem Solving and Affective Factors
Authors: Shimizu Yuno
Published: September 2022

Research has revealed that both cognitive factors, such as knowledge, problem solving strategies and affective factors, such as motivation and emotions, strongly influence mathematical problem solving. However, few studies have examined the content specificity and generality of the relationship between mathematical problem solving and affective factors. This study examines the content specificity and generality of the relationship between mathematical problem solving, task value, math anxiety and engagement among high school students. Japanese second-year high school students (n = 240) completed questionnaires. The multilevel structural equation modelling revealed that utility value for entrance examinations and emotional engagement positively affected mathematical problem solving via cognitive engagement between various contents level. Emotional engagement positively affected mathematical problem solving via cognitive engagement within a certain content level. The results suggest that promoting the perception that learning mathematics has high utility value for university entrance examinations across various contents can increase students’ cognitive engagement and, therefore, improve mathematical problem solving. Furthermore, both increasing students’ emotional engagement only when they learn certain content and consistently increasing it may improve cognitive engagement and, therefore, allow learners to better solve mathematical problems. The study’s findings have significant implications for educational practice and mathematical problem-solving research.
Detection of Water Spread Area Changes in Eutrophic Lake Using Landsat Data
Authors: Deoli Vaibhav , Kumar Deepak , Kuriqi Alban
Published: September 2022

Adequate water resource management is essential for fulfilling ecosystem and human needs. Nainital Lake is a popular lake in Uttarakhand State in India, attracting lakhs of tourists annually. Locals also use the lake water for domestic purposes and irrigation. The increasing impact of climate change and over-exploration of water from lakes make their regular monitoring key to implementing effective conservation measures and preventing substantial degradation. In this study, dynamic change in the water spread area of Nainital Lake from 2001 to 2018 has been investigated using the multiband rationing indices, namely normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and water ratio index (WRI). The model has been developed in QGIS 3.4 software. A physical GPS survey of the lake was conducted to check the accuracy of these indices. Furthermore, to determine the trend in water surface area for a studied period, a non-parametric Mann–Kendall test was used. San’s slope estimator test determined the magnitude of the trend and total percentage change. The result of the physical survey shows that NDWI was the best method, with an accuracy of 96.94%. Hence, the lake water spread area trend is determined based on calculated NDWI values. The lake water spread area significantly decreased from March to June and July to October at a 5% significance level. The maximum decrease in water spread area has been determined from March to June (7.7%), which was followed by the period July to October (4.67%) and then November to February (2.79%). The study results show that the lake’s water spread area decreased sharply for the analyzed period. The study might be helpful for the government, policymakers, and water experts to make plans for reclaiming and restoring Nainital Lake. This study is very helpful in states such as Uttarakhand, where physical mapping is not possible every time due to its tough topography and climate conditions.
Efficient Clustering for Continuous Occupancy Mapping Using a Mixture of Gaussian Processes
Authors: Kim Soohwan , Kim Jonghyuk
Published: September 2022

This paper proposes a novel method for occupancy map building using a mixture of Gaussian processes. Gaussian processes have proven to be highly flexible and accurate for a robotic occupancy mapping problem, yet the high computational complexity has been a critical barrier for large-scale applications. We consider clustering the data into small, manageable subsets and applying a mixture of Gaussian processes. One of the problems in clustering is that the number of groups is not known a priori, thus requiring inputs from experts. We propose two efficient clustering methods utilizing (1) a Dirichlet process and (2) geometrical information in the context of occupancy mapping. We will show that the Dirichlet process-based clustering can significantly speed up the training step of the Gaussian process and if geometrical features, such as line features, are available, they can further improve the clustering accuracy. We will provide simulation results, analyze the performance and demonstrate the benefits of the proposed methods.
Evaluating Brazilian Agriculturalists’ IoT Smart Agriculture Adoption Barriers: Understanding Stakeholder Salience Prior to Launching an Innovation
Authors: Strong Robert , Wynn John Thomas , Lindner James R. , Palmer Karissa
Published: September 2022

The study sought to: (1) evaluate agriculturalists’ characteristics as adopters of IoT smart agriculture technologies, (2) evaluate traits fostering innovation adoption, (3) evaluate the cycle of IoT smart agriculture adoption, and, lastly, (4) discern attributes and barriers of information communication. Researchers utilized a survey design to develop an instrument composed of eight adoption constructs and one personal characteristic construct and distributed it to agriculturalists at an agricultural exposition in Rio Grande do Sul. Three-hundred-forty-four (n = 344) agriculturalists responded to the data collection instrument. Adopter characteristics of agriculturalists were educated, higher consciousness of social status, larger understanding of technology use, and more likely identified as opinion leaders in communities. Innovation traits advantageous to IoT adoption regarding smart agriculture innovations were: (a) simplistic, (b) easily communicated to a targeted audience, (c) socially accepted, and (d) larger degrees of functionality. Smart agriculture innovation’s elevated levels of observability and compatibility coupled with the innovation’s low complexity were the diffusion elements predicting agriculturalists’ adoption. Agriculturalists’ beliefs in barriers to adopting IoT innovations were excessive complexity and minimal compatibility. Practitioners or change agents should promote IoT smart agriculture technologies to opinion leaders, reduce the innovation’s complexity, and amplify educational opportunities for technologies. The existing sum of IoT smart agriculture adoption literature with stakeholders and actors is descriptive and limited, which constitutes this inquiry as unique.
A Deep Sequence Learning Framework for Action Recognition in Small-Scale Depth Video Dataset
Authors: Bulbul Mohammad Farhad , Ullah Amin , Ali Hazrat , Kim Daijin
Published: September 2022

Depth video sequence-based deep models for recognizing human actions are scarce compared to RGB and skeleton video sequences-based models. This scarcity limits the research advancements based on depth data, as training deep models with small-scale data is challenging. In this work, we propose a sequence classification deep model using depth video data for scenarios when the video data are limited. Unlike summarizing the frame contents of each frame into a single class, our method can directly classify a depth video, i.e., a sequence of depth frames. Firstly, the proposed system transforms an input depth video into three sequences of multi-view temporal motion frames. Together with the three temporal motion sequences, the input depth frame sequence offers a four-stream representation of the input depth action video. Next, the DenseNet121 architecture is employed along with ImageNet pre-trained weights to extract the discriminating frame-level action features of depth and temporal motion frames. The extracted four sets of feature vectors about frames of four streams are fed into four bi-directional (BLSTM) networks. The temporal features are further analyzed through multi-head self-attention (MHSA) to capture multi-view sequence correlations. Finally, the concatenated genre of their outputs is processed through dense layers to classify the input depth video. The experimental results on two small-scale benchmark depth datasets, MSRAction3D and DHA, demonstrate that the proposed framework is efficacious even for insufficient training samples and superior to the existing depth data-based action recognition methods.
Using Python Modules in Real-Time Plasma Systems for Fusion
Authors: Ferron Nicolo , Manduchi Gabriele
Published: September 2022

One of the most important applications of sensors is feedback control, in which an algorithm is applied to data that are collected from sensors in order to drive system actuators and achieve the desired outputs of the target plant. One of the most challenging applications of this control is represented by magnetic confinement fusion, in which real-time systems are responsible for the confinement of plasma at a temperature of several million degrees within a toroidal container by means of strong electromagnetic fields. Due to the fast dynamics of the underlying physical phenomena, data that are collected from electromagnetic sensors must be processed in real time. In most applications, real-time systems are implemented in C++; however, Python applications are now becoming more and more widespread, which has raised potential interest in their applicability in real-time systems. In this study, a framework was set up to assess the applicability of Python in real-time systems. For this purpose, a reference operating system configuration was chosen, which was optimized for real time, together with a reference framework for real-time data management. Within this framework, the performance of modules that computed PID control and FFT transforms was compared for C++ and Python implementations, respectively. Despite the initial concerns about Python applicability in real-time systems, it was found that the worst-case execution time (WCET) could also be safely defined for modules that were implemented in Python, thereby confirming that they could be considered for real-time applications.
DECAL: A Reconfigurable Monolithic Active Pixel Sensor for Tracking and Calorimetry in a 180 nm Image Sensor Process
Authors: Allport Philip Patrick , Benhammadi Seddik , Bosley Robert Ross , Dopke Jens , Fasselt Lucian , Flynn Samuel , Gonella Laura , Guerrini Nicola , Issever Cigdem , Nikolopoulos Kostas , Kopsalis Ioannis , Philips Peter , Price Tony , Sedgwick Iain , Villani Giulio , Warren Matt , Watson Nigel , Weber Hannsjorg , Winter Alasdair , Wilson Fergus , Worm Steven , Zhang Zhige
Published: September 2022

In this paper, we describe DECAL, a prototype Monolithic Active Pixel Sensor (MAPS) device designed to demonstrate the feasibility of both digital calorimetry and reconfigurability in ASICs for particle physics. The goal of this architecture is to help reduce the development and manufacturing costs of detectors for future colliders by developing a chip that can operate both as a digital silicon calorimeter and a tracking chip. The prototype sensor consists of a matrix of 64 × 64 55 μm pixels, and provides a readout at 40 MHz of the number of particles which have struck the matrix in the preceding 25 ns. It can be configured to report this as a total sum across the sensor (equivalent to the pad of an analogue calorimeter) or the sum per column (equivalent to a traditional strip detector). The design and operation of the sensor are described, and the results of chip characterisation are reported and compared to simulations.
Single-Shot Object Detection via Feature Enhancement and Channel Attention
Authors: Li Yi , Wang Lingna , Wang Zeji
Published: September 2022

Features play a critical role in computer vision tasks. Deep learning methods have resulted in significant breakthroughs in the field of object detection, but it is still an extremely challenging obstacle when an object is very small. In this work, we propose a feature-enhancement- and channel-attention-guided single-shot detector called the FCSSD with four modules to improve object detection performance. Specifically, inspired by the structure of atrous convolution, we built an efficient feature-extraction module (EFM) in order to explore contextual information along the spatial dimension, and then pyramidal aggregation module (PAM) is presented to explore the semantic features of deep layers, thus reducing the semantic gap between multi-scale features. Furthermore, we construct an effective feature pyramid refinement fusion (FPRF) to refine the multi-scale features and create benefits for richer object knowledge. Finally, an attention-guided module (AGM) is developed to balance the channel weights and optimize the final integrated features on each level; this alleviates the aliasing effects of the FPN with negligible computational costs. The FCSSD exploits richer information of shallow layers and higher layers by using our designed modules, thus accomplishing excellent detection performance for multi-scale object detection and reaching a better tradeoff between accuracy and inference time. Experiments on PASCAL VOC and MS COCO datasets were conducted to evaluate the performance, showing that our FCSSD achieves competitive detection performance compared with existing mainstream object detection methods.
Joint User Scheduling and Hybrid Beamforming Design for Massive MIMO LEO Satellite Multigroup Multicast Communication Systems
Authors: Liu Yang , Li Changqing , Li Jiong , Feng Lu
Published: September 2022

In the satellite multigroup multicast communication systems based on the DVB-S2X standard, due to the limitation of the DVB-S2X frame structure, user scheduling and beamforming design have become the focus of academic research. In this work, we take the massive multi-input multi-output (MIMO) low earth orbit (LEO) satellite communication system adopting the DVB-S2X standard as the research scenario, and the LEO satellite adopts a uniform planar array (UPA) based on the fully connected hybrid structure. We focus on the coupling design of user scheduling and beamforming; meanwhile, the scheme design takes the influence of residual Doppler shift and phase disturbance on channel errors into account. Under the constraints of total transmission power and quality of service (QoS), we study the robust joint user scheduling and hybrid beamforming design aimed at maximizing the energy efficiency (EE). For this problem, we first adopt the hierarchical clustering algorithm to group users. Then, the semidefinite programming (SDP) algorithm and the concave convex process (CCCP) framework are applied to tackle the optimization of user scheduling and hybrid beamforming design. To handle the rank-one matrix constraint, the penalty iteration algorithm is proposed. To balance the performance and complexity of the algorithm, the user preselected step is added before joint design. Finally, to obtain the digital beamforming matrix and the analog beamforming matrix in a hybrid beamformer, the alternative optimization algorithm based on the majorization-minimization framework (MM-AltOpt) is proposed. Numerical simulation results show that the EE of the proposed joint user scheduling and beamforming design algorithm is higher than that of the traditional decoupling design algorithms.
Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method
Authors: Nie Guang-Yu , Bodda Saran Srikanth , Sandhu Harleen Kaur , Han Kevin , Gupta Abhinav
Published: September 2022

Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains a variety of keypoints and combines the multi-level strategy with an optical flow algorithm to search the keypoints with a large motion vector for tracking. Then, an outlier removal method based on Hamming distance and interquartile range (IQR) score is introduced to minimize the error. The proposed target tracking method is verified through a lab experiment—a three-story shear building structure subjected to various harmonic excitations. It is compared with existing sparse-optical-flow-based target tracking methods and target tracking methods based on three other types of techniques, i.e., feature matching, dense optical flow, and template matching. The results show that the performance of target tracking is greatly improved through the use of a multi-level strategy and the proposed outlier removal method. The proposed sparse-optical-flow-based target tracking method achieves the best accuracy compared to other existing target tracking methods.
Multiscale Kernel-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM
Authors: Song Qiang , Wang Mingsheng , Lai Wuxuan , Zhao Sifang
Published: September 2022

The diagnosis of an inter-turn short circuit (ITSC) fault at its early stage is very important in permanent magnet synchronous motors as these faults can lead to disastrous results. In this paper, a multiscale kernel-based residual convolutional neural network (CNN) algorithm is proposed for the diagnosis of ITSC faults. The contributions are majorly located on two sides. Firstly, a residual learning connection is embedded into a dilated CNN to overcome the defects of the conventional convolution and the degradation problem of a deep network. Secondly, a multiscale kernel algorithm is added to a residual dilated CNN architecture to extract high-dimension features from the collected current signals under complex operating conditions and electromagnetic interference. A motor fault experiment with both constant operating conditions and dynamics was conducted by setting the fault severity of the ITSC fault to 17 levels. Comparison with five other algorithms demonstrated the effectiveness of the proposed algorithm.
Ultra-Wideband Differential Line-to-Balanced Line Transitions for Super-High-Speed Digital Transmission
Authors: Min Byung Cheol , Lee Gwan Hui , Lee Jung Seok , Nashuha Syifa Haunan , Choi Hyun Chul , Kim Kang Wook
Published: September 2022

A conventional differential line (DL), commonly used on typical digital circuit boards for transmitting high-speed digital data, has fundamental limitations on the maximum signal bandwidth (~10 GHz), mainly due to signal skew, multiple line coupling, and EM interference. Therefore, to support super-high-speed digital data transmission, especially for beyond 5G communications, a practical high-performance transmission structure for digital signals is required. Balanced lines (BLs) can transmit the differential signals with multiple advantages of ultra-wide bandwidth, common-mode rejection, reduced crosstalk, phase recovery, and skew reduction, which enable super-high-speed transmission. In order to utilize the BLs in the DL-based digital circuit, connecting structures between a DL and BLs are required, but the DL-to-BL transition structures dominate the operating bandwidth and signal properties. Therefore, in this paper, properties, and design methods for two ultra-wideband DL-to-BL transitions, i.e., DL-to-CPS (coplanar stripline) and DL-to-PSL (parallel stripline) transitions, are presented. Both implemented DL-to-CPS and DL-to-PSL transitions provide high-quality performance up to 40 GHz or higher, significantly enhancing the frequency bandwidth for the transmission of digital signals while providing compatibility with the DL-based PCBs. The fabricated DL-to-CPS transition performs well from DC to 40 GHz with an insertion loss of less than 0.86 dB and a return loss of more than 10 dB, and the fabricated DL-to-PSL transition also provides good performance from DC to 40 GHz, with an insertion loss of less than 1.34 dB and a return loss of more than 10 dB. Therefore, the proposed DL-to-BL transitions can be applied to achieve super-high-speed digital data transmission with over 40 GHz bandwidth, which is more than four times the bandwidth of the DL, supporting over 200 Gbps of digital data transmission on PCBs for the next generation of advanced communications.
The Development of an Innovative Embedded Sensor for the Optical Measurement of Ex-Vivo Engineered Muscle Tissue Contractility
Authors: Apa Ludovica , Cosentino Marianna , Forconi Flavia , Musarò Antonio , Rizzuto Emanuele , Del Prete Zaccaria
Published: September 2022

Tissue engineering is a multidisciplinary approach focused on the development of innovative bioartificial substitutes for damaged organs and tissues. For skeletal muscle, the measurement of contractile capability represents a crucial aspect for tissue replacement, drug screening and personalized medicine. To date, the measurement of engineered muscle tissues is rather invasive and not continuous. In this context, we proposed an innovative sensor for the continuous monitoring of engineered-muscle-tissue contractility through an embedded technique. The sensor is based on the calibrated deflection of one of the engineered tissue’s supporting pins, whose movements are measured using a noninvasive optical method. The sensor was calibrated to return force values through the use of a step linear motor and a micro-force transducer. Experimental results showed that the embedded sensor did not alter the correct maturation of the engineered muscle tissue. Finally, as proof of concept, we demonstrated the ability of the sensor to capture alterations in the force contractility of the engineered muscle tissues subjected to serum deprivation.
Industrial Soft Sensor Optimized by Improved PSO: A Deep Representation-Learning Approach
Authors: Severino Alcemy Gabriel Vitor , de Lima Jean Mário Moreira , de Araújo Fábio Meneghetti Ugulino
Published: September 2022

Soft sensors based on deep learning approaches are growing in popularity due to their ability to extract high-level features from training, improving soft sensors’ performance. In the training process of such a deep model, the set of hyperparameters is critical to archive generalization and reliability. However, choosing the training hyperparameters is a complex task. Usually, a random approach defines the set of hyperparameters, which may not be adequate regarding the high number of sets and the soft sensing purposes. This work proposes the RB-PSOSAE, a Representation-Based Particle Swarm Optimization with a modified evaluation function to optimize the hyperparameter set of a Stacked AutoEncoder-based soft sensor. The evaluation function considers the mean square error (MSE) of validation and the representation of the features extracted through mutual information (MI) analysis in the pre-training step. By doing this, the RB-PSOSAE computes hyperparameters capable of supporting the training process to generate models with improved generalization and relevant hidden features. As a result, the proposed method can generate more than 16.4% improvement in RMSE compared to another standard PSO-based method and, in some cases, more than 50% improvement compared to traditional methods applied to the same real-world nonlinear industrial process. Thus, the results demonstrate better prediction performance than traditional and state-of-the-art methods.
Kidney-on-a-Chip: Mechanical Stimulation and Sensor Integration
Authors: Wang Dan , Gust Matthew , Ferrell Nicholas
Published: September 2022

Bioengineered in vitro models of the kidney offer unprecedented opportunities to better mimic the in vivo microenvironment. Kidney-on-a-chip technology reproduces 2D or 3D features which can replicate features of the tissue architecture, composition, and dynamic mechanical forces experienced by cells in vivo. Kidney cells are exposed to mechanical stimuli such as substrate stiffness, shear stress, compression, and stretch, which regulate multiple cellular functions. Incorporating mechanical stimuli in kidney-on-a-chip is critically important for recapitulating the physiological or pathological microenvironment. This review will explore approaches to applying mechanical stimuli to different cell types using kidney-on-a-chip models and how these systems are used to study kidney physiology, model disease, and screen for drug toxicity. We further discuss sensor integration into kidney-on-a-chip for monitoring cellular responses to mechanical or other pathological stimuli. We discuss the advantages, limitations, and challenges associated with incorporating mechanical stimuli in kidney-on-a-chip models for a variety of applications. Overall, this review aims to highlight the importance of mechanical stimuli and sensor integration in the design and implementation of kidney-on-a-chip devices.