bluuebunny/crossref_metadata_2025 huggingface dataset:
Pyrolysis and Volatile Evolution Behaviors of Cold-Rolling Oily Sludge
Authors: Que Zhigang , Fu Yinxuan , Shi Jinming , Ai Xianbin , Xu Chunbao
Published: March 2022

Cold-rolling oily sludge contains high amounts of oil and iron resources that can be recycled by pyrolysis. We investigated the pyrolysis behavior and volatile products of oily sludge by thermogravimetric analysis (TG) coupled with Fourier transform infrared spectroscopy (FTIR) and a pyrolyzer (PY) coupled with gas chromatography/mass spectrometry (GC/MS). The pyrolysis process was divided into three stages: H2O drying and CO2 desorption at low temperatures (below 393 K); the volatilization of low-molecular-weight organics and the covalent bond cleavage of C=C, C-O, and C-H in the medium-molecular-weight organics at medium temperatures (393–844 K); and chain scission of the high-molecular-weight organics and reduction of iron oxides by CO at high temperatures (above 844 K). The weight losses of oily sludge in the three stages were 0.4 wt %, 47.9 wt %, and 14.7 wt %, respectively. According to the kinetic models, stage 2 and stage 3 could be described with the second-order and third-order reaction models, and their activation energies were 40.22 kJ/mol and 214.99 kJ/mol, respectively. The compounds in the volatile products were identified by FTIR and GC/MS. The organics in the volatile products from stage 2 pyrolysis mainly consisted of aliphatic hydrocarbons, fatty acids, esters, ketones, and nitrogen compounds, while the volatile products from stage 3 predominantly contained aliphatic hydrocarbons, mononuclear aromatic hydrocarbons, and small amounts of nitrogen compounds and CO, suggesting the occurrence of chain scission of heavy organics.
Comparison of Knudsen Diffusion and the Dusty Gas Approach for the Modeling of the Freeze-Drying Process of Bulk Food Products
Authors: Levin Patrick , Buchholz Moritz , Meunier Vincent , Kessler Ulrich , Palzer Stefan , Heinrich Stefan
Published: March 2022

Freeze-drying is generally used to achieve high quality products and preserve thermal sensitive components; however, it is also considered as a high energy and costly process. Modeling of the process can help to optimize the process to reduce these drawbacks. In this work, a mathematical model is presented to predict the heat and mass transfer behavior for freeze-drying of porous frozen food particles during freeze-drying to optimize the process. For the mass transfer, a comparison between Knudsen diffusion and the more complex dusty-gas approach is performed. Simulation results of a single particle are validated by experiments of single-layer drying to extend the usage of this model from a single particle to a particle bed. For the moisture transfer, adaption parameters are introduced and evaluated. A comparison shows a good agreement of the model with experimental results. The results furthermore suggest a strong correlation of the drying kinetics with pore size and particle porosity. An increase in the pore diameter strongly improves the overall mass transfer rates and hence is a suitable parameter for an effective increase of the drying rates in freeze-drying.
Systemic Efficacy of Sirolimus via the ERBB Signaling Pathway in Breast Cancer
Authors: Kim Shinuk
Published: March 2022

Rapamycin, also known as sirolimus, inhibits the mTOR pathway in complex diseases such as cancer, and its downstream targets are ribosomal S6 kinases (RPS6K). Sirolimus is involved in regulating cell growth and cell survival through roles such as the mediation of epidermal growth factor signaling. However, the systemic efficacy of sirolimus in pathway regulation is unclear. The purpose of this study is to determine systemic drug efficacy using computational methods and drug-induced datasets. We suggest a computational method using gene expression datasets induced by sirolimus and an inverse algorithm that simultaneously identifies parameters referring to gene–gene interactions. We downloaded two sirolimus-induced microarray gene expression datasets and used a computational method to obtain the most enriched pathway, then adopted an inverse algorithm to discover the gene–gene interactions of that pathway. In the results, RPS6KB1 was a target gene of sirolimus and was associated with genes in the pathway. The common gene interactions from two datasets were a hub gene, RPS6KB1, and 10 related genes (AKT3, CBLC, MAP2K7, NRG1/2, PAK3, PIK3CD/G, PRKCG, and SHC3) in the epidermal growth factor (ERBB) signaling pathway.
Structural Changes of Alkali Lignin under Ozone Treatment and Effect of Ozone-Oxidized Alkali Lignin on Cellulose Digestibility
Authors: Wang Hongyuan , Zhao Lihong , Ren Junli , He Beihai
Published: March 2022

In this study, the structural changes of alkali lignin induced by ozonation were investigated, and the effect of ozone-treated alkali lignin and its mechanism on Avicel enzymatic hydrolysis was examined. The physicochemical properties of alkali lignin were analyzed by FTIR, 1H-13C HSQC NMR, and GPC. It was revealed that ozone pretreatment increased the content of carboxyl and/or aldehyde groups and the negative zeta potential of alkali lignin, which enhanced the electrostatic repulsion between alkali lignin and cellulase; The S/G ratio was reduced, indicating the hydrophobic interaction was diminished. The Langmuir adsorption isotherm showed that the cellulase binding strength of ozone pretreated alkali lignin (OL-pH3, OL-pH7, and OL-pH12 were 16.67, 13.87, and 44.05 mL/g, respectively) was significantly lower than that of alkali lignin (161.29 mL/g). The 72 h hydrolysis yields of Avicel added with OL-pH3, OL-pH7, and OL-pH12 were 55.4%, 58.6%, and 54.9% respectively, which were 2.6–6.3% higher than that of Avicel added with AL (52.3%). This research aimed to reduce the non-productive adsorption between cellulase and lignin by investigating the structural changes of lignin caused by ozone treatment. For the first time, we discovered that ozone-treated alkali lignin has a further promotion effect on the enzymatic digestion of cellulose, providing a green and feasible pretreatment process for the enzymatic hydrolysis of lignocellulose and aiding in the more efficient utilization of biomass.
Evaluation Residual Compressive Strength of Tunnel Lining Concrete Structure after Fire Damage Based on Ultrasonic Pulse Velocity and Shear-Wave Tomography
Authors: Wang Qiang , Chen Daqing , Zhu Kai , Zhai Zitai , Xu Juntao , Wu Linlin , Hu Dong , Xu Weirong , Huang Huandong
Published: March 2022

In this study, ultrasonic pulse velocity (UPV) and ultrasonic shear-wave tomography are combined to measure the residual compressive strength (RCS) of small-scale lining concrete blocks and to detect inner defects in the lining structure. The characteristics of and variations in the RCS of test blocks after being exposed to elevated temperatures (200–800 °C) and constant heating times (2 h, 3 h, and 4 h) were studied. At 800 °C, the RCS values reduced by 64.4%, 69.2%, and 74.6% at heating times of 2 h, 3 h, and 4 h. Scanning electron microscopy (SEM) was used for the micro-phase analysis of the samples that had been exposed to high temperatures. The heating time and RCS as well as the SEM micro-structure relationship were compared. Finally, a tunnel lining slab sample was designed to simulate the post-fire damage inside the blocks. Additionally, shear-wave tomography with 32 probes was able to detect the ϕ10 mm void defects at a depth of 200 mm.
Extreme Body Size Variation in Pleistocene Dwarf Elephants from the Siculo-Maltese Palaeoarchipelago: Disentangling the Causes in Time and Space
Authors: Scarborough Matthew Edward
Published: March 2022

The phenomenon of insular dwarfism in proboscideans is particularly well represented on the Siculo-Maltese Palaeoarchipelago, an island group on which a species complex of palaeoloxodont elephants evolved during the Middle-Late Pleistocene. This likely included three species from Malta, four from Sicily (and possibly its palaeo-islands), and one from Favignana Island, ranging in size from the 1 m-tall Palaeoloxodon falconeri to continental-sized 4m-tall P. antiquus. However, our understanding of the causes for extreme differences in body size among insular samples in such a small geographic region is still limited. Here, I document the full range in size of elephants from the palaeoarchipelago, and discuss the reasons for size differences on the three islands in time and space in relation to predation, competition, resource limitation, accelerated life history, and duration of isolation. Differences in size between larger and smaller Sicilian elephants from Luparello Fissure, as well as possibly also in P. ex gr. P. mnaidriensis from Puntali Cave, San Teodoro Cave, and Favignana Island, may relate to the duration of isolation, or alternatively environmental stressors associated with the Last Glacial Maximum in the case of the Favignana elephant. Additionally, small but significant differences in size observable in Middle Pleistocene P. ex gr. P. falconeri from different localities on Sicily, as well as in Maltese P. ‘melitensis’ may also relate to duration of isolation, highlighting the need for better geochronological data in order to better distinguish macro-ecological causes driving body size change from more subtle effects relating to duration of isolation.
Intradural Lumbar Disc Herniation: Updated Report of a Case with Literature Review
Authors: Nguyen Hoang-Long , Do Manh-Hung , Vo Hoang-Long , Nguyen Bao-Tien L.
Published: March 2022

Intradural lumbar disc herniation is a rare complication of disc disease. The mechanism by which a herniated disc tears the dura matter remains unknown. The preoperative diagnosis of an intradural lumbar disc herniation is still difficult. We report our experience dealing with a case of intradural lumbar disc herniation at level L3/4 in a 34-year-old man. Based on current experience, we found that attachment of the ventral dura to the posterior longitudinal ligament can be a favorable factor indicating intradural lumbar disc herniation. One should pay attention to those with cauda equina syndrome, as it can thereby promptly suggest a preoperative diagnosis of intradural lumbar disc herniation. Surgeons need to avoid omitting intraoperative lesions by palpating the dura mater during surgery for suspected tumor cases.
A Complex Vibration Analysis of a Drive System Equipped with an Innovative Prototype of a Flexible Torsion Clutch as an Element of Pre-Implementation Testing
Authors: Wieczorek Andrzej N. , Konieczny Łukasz , Burdzik Rafał , Wojnar Grzegorz , Filipowicz Krzysztof , Kuczaj Mariusz
Published: March 2022

The paper presents how an important aspect of introducing new machines, especially in the mining industry, is testing a prototype under laboratory conditions. For this purpose, advanced methods of analyzing the vibrations of a drive system equipped with an innovative prototype of a flexible torsion clutch are presented. The main goal is to present a comprehensive method for analyzing vibration signals in various dimensions of the signal analysis. As a result of this approach, it can be seen how much important information about the tested clutch can be obtained by using various analysis methods in terms of time–frequency distributions or order analysis. To emphasize the differences in the functioning of the tested clutch and the possibility of monitoring these differences on the basis of the observation of residual processes, such as vibrations, the results for the flexible and locked clutch are compared.
Assessing Respiratory Activity by Using IMUs: Modeling and Validation
Authors: Monaco Vito , Giustinoni Carolina , Ciapetti Tommaso , Maselli Alessandro , Stefanini Cesare
Published: March 2022

This study aimed to explore novel inertial measurement unit (IMU)-based strategies to estimate respiratory parameters in healthy adults lying on a bed while breathing normally. During the experimental sessions, the kinematics of the chest wall were contemporaneously collected through both a network of 9 IMUs and a set of 45 uniformly distributed reflective markers. All inertial kinematics were analyzed to identify a minimum set of signals and IMUs whose linear combination best matched the tidal volume measured by optoelectronic plethysmography. The resulting models were finally tuned and validated through a leave-one-out cross-validation approach to assess the extent to which they could accurately estimate a set of respiratory parameters related to three trunk compartments. The adopted methodological approach allowed us to identify two different models. The first, referred to as Model 1, relies on the 3D acceleration measured by three IMUs located on the abdominal compartment and on the lower costal margin. The second, referred to as Model 2, relies on only one component of the acceleration measured by two IMUs located on the abdominal compartment. Both models can accurately estimate the respiratory rate (relative error < 1.5%). Conversely, the duration of the respiratory phases and the tidal volume can be more accurately assessed by Model 2 (relative error < 5%) and Model 1 (relative error < 5%), respectively. We further discuss possible approaches to overcome limitations and improve the overall accuracy of the proposed approach.
A Memetic Algorithm for Solving the Robust Influence Maximization Problem on Complex Networks against Structural Failures
Authors: Huang Delin , Tan Xiaojun , Chen Nanjie , Fan Zhengping
Published: March 2022

Many transport systems in the real world can be modeled as networked systems. Due to limited resources, only a few nodes can be selected as seeds in the system, whose role is to spread required information or control signals as widely as possible. This problem can be modeled as the influence maximization problem. Most of the existing selection strategies are based on the invariable network structure and have not touched upon the condition that the network is under structural failures. Related studies indicate that such strategies may not completely tackle complicated diffusion tasks in reality, and the robustness of the information diffusion process against perturbances is significant. To give a numerical performance criterion of seeds under structural failure, a measure has been developed to define the robust influence maximization (RIM) problem. Further, a memetic optimization algorithm (MA) which includes several problem-orientated operators to improve the search ability, termed RIMMA, has been presented to deal with the RIM problem. Experimental results on synthetic networks and real-world networks validate the effectiveness of RIMMA, its superiority over existing approaches is also shown.
Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification
Authors: Mochammad Solichin , Noh Yoojeong , Kang Young-Jin , Park Sunhwa , Lee Jangwoo , Chin Simon
Published: March 2022

In the fault classification process, filter methods that sequentially remove unnecessary features have long been studied. However, the existing filter methods do not have guidelines on which, and how many, features are needed. This study developed a multi-filter clustering fusion (MFCF) technique, to effectively and efficiently select features. In the MFCF process, a multi-filter method combining existing filter methods is first applied for feature clustering; then, key features are automatically selected. The union of key features is utilized to find all potentially important features, and an exhaustive search is used to obtain the best combination of selected features to maximize the accuracy of the classification model. In the rotating machinery examples, fault classification models using MFCF were generated to classify normal and abnormal conditions of rotational machinery. The obtained results demonstrated that classification models using MFCF provide good accuracy, efficiency, and robustness in the fault classification of rotational machinery.
Wireless Capsule Endoscope Localization with Phase Detection Algorithm and Adaptive Body Model
Authors: Oleksy Paweł , Januszkiewicz Łukasz
Published: March 2022

Wireless capsule endoscopes take and send photos of the human digestive tract, which are used for medical diagnosis. The capsule’s location enables exact identification of the regions with lesions. This can be carried out by analyzing the parameters of the electromagnetic wave received from the capsule. Because the human body is a complex heterogeneous environment that impacts the propagation of wireless signals, determining the distance between the transmitter and the receiver based on the received power level is challenging. An enhanced approach of identifying the location of endoscope capsules using a wireless signal phase detection algorithm is presented in this paper. For each capsule position, this technique uses adaptive estimation of human body model permittivity. This approach was tested using computer simulations in Remcom XFdtd software using a numerical, heterogeneous human body model, as well as measurements with physical phantom. The type of transmitting antenna employed in the capsule also has a significant impact on the suggested localization method’s accuracy. As a result, the helical antenna, which is smaller than the dipole, was chosen as the signal’s source. For both the numerical and physical phantom studies, the proposed technique with adaptive body model enhances localization accuracy by roughly 30%.
Non-Destructive Testing for Cavity Damages in Automated Machines Based on Acoustic Emission Tomography
Authors: Su Yueyuan , Dong Longjun , Pei Zhongwei
Published: March 2022

Damage detection is important for the maintenance of automated machines. General non-destructive testing techniques require static equipment and complex analysis processes, which restricts the maintenance of automated machines. Therefore, this paper proposes an acoustic emission (AE) tomography method for detecting cavity damage in automated machines, combining the fast sweeping method (FSM) and the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method. This approach overcomes the limitations of real-time AE detection for cavity damage in continuous and homogeneous materials. The proposed method has been applied in numerical and laboratory experiments to validate its feasibility. The results show that the inversed low-velocity regions correspond to the actual cavity regions, and the sources of cavity damage can be effectively detected. This paper provides a new perspective for AE testing technologies, and also lays the foundation for other non-destructive testing techniques, in terms of cavity damage detection.
Validity and Reliability of the Insole3 Instrumented Shoe Insole for Ground Reaction Force Measurement during Walking and Running
Authors: Cramer Leora A. , Wimmer Markus A. , Malloy Philip , O’Keefe Joan A. , Knowlton Christopher B. , Ferrigno Christopher
Published: March 2022

Pressure-detecting insoles such as the Insole3 have potential as a portable alternative for assessing vertical ground reaction force (vGRF) outside of specialized laboratories. This study evaluated whether the Insole3 is a valid and reliable alternative to force plates for measuring vGRF. Eleven healthy participants walked overground at slow and moderately paced speeds and ran at a moderate pace while collecting vGRF simultaneously from a force plate (3000 Hz) and Insole3 (100 Hz). Intraclass correlation coefficients (ICC) demonstrated excellent vGRF agreement between systems during both walking speeds for Peak 1, Peak 2, the valley between peaks, and the vGRF impulse (ICC > 0.941). There was excellent agreement during running for the single vGRF peak (ICC = 0.942) and impulse (ICC = 0.940). The insoles slightly underestimated vGRF peaks (−3.7% to 0.9% bias) and valleys (−2.2% to −1.8% bias), and slightly overestimated impulses (4.2% to 5.6% bias). Reliability between visits for all three activities was excellent (ICC > 0.970). The Insole3 is a valid and reliable alternative to traditional force plates for assessing vGRF during walking and running in healthy adults. The excellent ICC values during slow walking suggests that the Insole3 may be particularly suitable for older adults in clinical and home settings.
Joint Semi-Blind Self-Interference Cancellation and Equalisation Processes in 5G QC-LDPC-Encoded Short-Packet Full-Duplex Transmissions
Authors: Vuong Bao Quoc , Gautier Roland , Ta Hien Quang , Nguyen Lap Luat , Fiche Anthony , Marazin Mélanie
Published: March 2022

The paper proposes a joint semi-blind algorithm for simultaneously cancelling the self-interference component and estimating the propagation channel in 5G Quasi-Cyclic Low-Density Parity-Check (QC-LDPC)-encoded short-packet Full-Duplex (FD) transmissions. To avoid the effect of channel estimation processes when using short-packet transmissions, this semi-blind algorithm was developed by taking into account only a small number (four at least) pilot symbols, which was integrated with the intended information sequence and used for the feedback loop of the estimation of the channels. The results showed that this semi-blind algorithm not only achieved nearly optimal performance, but also significantly reduced the processing time and computational complexity. This semi-blind algorithm can also improve the performances of the Mean-Squared Error (MSE) and Bit Error Rate (BER). The results of this study highlight the potential efficiency of this joint semi-blind iterative algorithm for 5G and Beyond and/or practical IoT transmission scenarios.
No-Reference Video Quality Assessment Using Multi-Pooled, Saliency Weighted Deep Features and Decision Fusion
Authors: Varga Domonkos
Published: March 2022

With the constantly growing popularity of video-based services and applications, no-reference video quality assessment (NR-VQA) has become a very hot research topic. Over the years, many different approaches have been introduced in the literature to evaluate the perceptual quality of digital videos. Due to the advent of large benchmark video quality assessment databases, deep learning has attracted a significant amount of attention in this field in recent years. This paper presents a novel, innovative deep learning-based approach for NR-VQA that relies on a set of in parallel pre-trained convolutional neural networks (CNN) to characterize versatitely the potential image and video distortions. Specifically, temporally pooled and saliency weighted video-level deep features are extracted with the help of a set of pre-trained CNNs and mapped onto perceptual quality scores independently from each other. Finally, the quality scores coming from the different regressors are fused together to obtain the perceptual quality of a given video sequence. Extensive experiments demonstrate that the proposed method sets a new state-of-the-art on two large benchmark video quality assessment databases with authentic distortions. Moreover, the presented results underline that the decision fusion of multiple deep architectures can significantly benefit NR-VQA.
Assessment of the Measurement Performance of the Multimodal Fibre Optic Shape Sensing Configuration for a Morphing Wing Section
Authors: Nazeer Nakash , Groves Roger M. , Benedictus Rinze
Published: March 2022

In this paper, with the final aim of shape sensing for a morphing aircraft wing section, a developed multimodal shape sensing system is analysed. We utilise the method of interrogating a morphing wing section based on the principles of both hybrid interferometry and Fibre Bragg Grating (FBG) spectral sensing described in our previous work. The focus of this work is to assess the measurement performance and analyse the errors in the shape sensing system. This includes an estimation of the bending and torsional deformations of an aluminium mock-up section due to static loading that imitates the behaviour of a morphing wing trailing edge. The analysis involves using a detailed calibration procedure and a multimodal sensing algorithm to measure the deflection and shape. The method described In this paper, uses a standard single core optical fibre and two grating pairs on both the top and bottom surfaces of the morphing section. A study on the fibre placement and recommendations for efficient monitoring is also included. The analysis yielded a maximum deflection sensing error of 0.7 mm for a 347 × 350 mm wing section.
An Accelerating Reduction Approach for Incomplete Decision Table Using Positive Approximation Set
Authors: Yan Tao , Han Chongzhao , Zhang Kaitong , Wang Chengnan
Published: March 2022

Due to the explosive growth of data collected by various sensors, it has become a difficult problem determining how to conduct feature selection more efficiently. To address this problem, we offer a fresh insight into rough set theory from the perspective of a positive approximation set. It is found that a granularity domain can be used to characterize the target knowledge, because of its form of a covering with respect to a tolerance relation. On the basis of this fact, a novel heuristic approach ARIPA is proposed to accelerate representative reduction algorithms for incomplete decision table. As a result, ARIPA in classical rough set model and ARIPA-IVPR in variable precision rough set model are realized respectively. Moreover, ARIPA is adopted to improve the computational efficiency of two existing state-of-the-art reduction algorithms. To demonstrate the effectiveness of the improved algorithms, a variety of experiments utilizing four UCI incomplete data sets are conducted. The performances of improved algorithms are compared with those of original ones as well. Numerical experiments justify that our accelerating approach enhances the existing algorithms to accomplish the reduction task more quickly. In some cases, they fulfill attribute reduction even more stably than the original algorithms do.
Knowledge-Aware Multispace Embedding Learning for Personalized Recommendation
Authors: Jian Meng , Zhang Chenlin , Fu Xin , Wu Lifang , Wang Zhangquan
Published: March 2022

Recommender systems help users filter items they may be interested in from massive multimedia content to alleviate information overload. Collaborative filtering-based models perform recommendation relying on users’ historical interactions, which meets great difficulty in modeling users’ interests with extremely sparse interactions. Fortunately, the rich semantics hidden in items may be promising in helping to describing users’ interests. In this work, we explore the semantic correlations between items on modeling users’ interests and propose knowledge-aware multispace embedding learning (KMEL) for personalized recommendation. KMEL attempts to model users’ interests across semantic structures to leverage valuable knowledge. High-order semantic collaborative signals are extracted in multiple independent semantic spaces and aggregated to describe users’ interests in each specific semantic. The semantic embeddings are adaptively integrated with a target-aware attention mechanism to learn cross-space multisemantic embeddings for users and items, which are fed to the subsequent pairwise interaction layer for personalized recommendation. Experiments on real-world datasets demonstrate the effectiveness of the proposed KMEL model.
Efficient Violence Detection in Surveillance
Authors: Vijeikis Romas , Raudonis Vidas , Dervinis Gintaras
Published: March 2022

Intelligent video surveillance systems are rapidly being introduced to public places. The adoption of computer vision and machine learning techniques enables various applications for collected video features; one of the major is safety monitoring. The efficacy of violent event detection is measured by the efficiency and accuracy of violent event detection. In this paper, we present a novel architecture for violence detection from video surveillance cameras. Our proposed model is a spatial feature extracting a U-Net-like network that uses MobileNet V2 as an encoder followed by LSTM for temporal feature extraction and classification. The proposed model is computationally light and still achieves good results—experiments showed that an average accuracy is 0.82 ± 2% and average precision is 0.81 ± 3% using a complex real-world security camera footage dataset based on RWF-2000.
Simultaneous Electrochemical Sensing of Indole-3-Acetic Acid and Salicylic Acid on Poly(L-Proline) Nanoparticles–Carbon Dots–Multiwalled Carbon Nanotubes Composite-Modified Electrode
Authors: Li Mengxue , Kuang Yiwen , Fan Ziyan , Qin Xiaoli , Hu Shiyu , Liang Zhanning , Liu Qilin , Zhang Weizhong , Wang Birui , Su Zhaohong
Published: March 2022

Sensitive simultaneous electrochemical sensing of phytohormones indole-3-acetic acid and salicylic acid based on a novel poly(L-Proline) nanoparticles–carbon dots composite consisting of multiwalled carbon nanotubes was reported in this study. The poly(L-Proline) nanoparticles–carbon dots composite was facilely prepared by the hydrothermal method, and L-Proline was used as a monomer and carbon source for the preparation of poly(L-Proline) nanoparticles and carbon dots, respectively. Then, the poly(L-Proline) nanoparticles–carbon dots–multiwalled carbon nanotubes composite was prepared by ultrasonic mixing of poly(L-Proline) nanoparticles–carbon dots composite dispersion and multiwalled carbon nanotubes. Scanning electron microscope, transmission electron microscope, Fourier transform infrared spectroscopy, ultraviolet visible spectroscopy, energy dispersive spectroscopy, cyclic voltammetry, electrochemical impedance spectroscopy, and linear sweep voltammetry were used to characterize the properties of the composite. poly(L-Proline) nanoparticles were found to significantly enhance the conductivity and sensing performance of the composite. Under optimal conditions, the composite-modified electrode exhibited a wide linear range from 0.05 to 25 μM for indole-3-acetic acid and from 0.2 to 60 μM for salicylic acid with detection limits of 0.007 μM and 0.1 μM (S/N = 3), respectively. In addition, the proposed sensor was also applied to simultaneously test indole-3-acetic acid and salicylic acid in real leaf samples with satisfactory recovery.
Incremental Ant-Miner Classifier for Online Big Data Analytics
Authors: Al-Dawsari Amal , Al-Turaiki Isra , Kurdi Heba
Published: March 2022

Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT environments where datasets are highly dynamic, evolving over time, it is highly advised to adopt an online (also called incremental) machine learning model that can analyze incoming data instantaneously, rather than an offline model (also called static), that should be retrained on the entire dataset as new records arrive. The main contribution of this paper is to introduce the Incremental Ant-Miner (IAM), a machine learning algorithm for online prediction based on one of the most well-established machine learning algorithms, Ant-Miner. IAM classifier tackles the challenge of reducing the time and space overheads associated with the classic offline classifiers, when used for online prediction. IAM can be exploited in managing dynamic environments to ensure timely and space-efficient prediction, achieving high accuracy, precision, recall, and F-measure scores. To show its effectiveness, the proposed IAM was run on six different datasets from different domains, namely horse colic, credit cards, flags, ionosphere, and two breast cancer datasets. The performance of the proposed model was compared to ten state-of-the-art classifiers: naive Bayes, logistic regression, multilayer perceptron, support vector machine, K*, adaptive boosting (AdaBoost), bagging, Projective Adaptive Resonance Theory (PART), decision tree (C4.5), and random forest. The experimental results illustrate the superiority of IAM as it outperformed all the benchmarks in nearly all performance measures. Additionally, IAM only needs to be rerun on the new data increment rather than the entire big dataset on the arrival of new data records, which makes IAM better in time- and resource-saving. These results demonstrate the strong potential and efficiency of the IAM classifier for big data analytics in various areas.
The Bright and Dark Sides of Reactive Oxygen Species Generated by Copper–Peptide Complexes
Authors: Komarnicka Urszula K. , Lesiów Monika K. , Witwicki Maciej , Bieńko Alina
Published: March 2022

Copper ions bind to biomolecules (e.g., peptides and proteins) playing an essential role in many biological and physiological pathways in the human body. The resulting complexes may contribute to the initiation of neurodegenerative diseases, cancer, and bacterial and viral diseases, or act as therapeutics. Some compounds can chemically damage biological macromolecules and initiate the development of pathogenic states. Conversely, a number of these compounds may have antibacterial, antiviral, and even anticancer properties. One of the most significant current discussions in Cu biochemistry relates to the mechanisms of the positive and negative actions of Cu ions based on the generation of reactive oxygen species, including radicals that can interact with DNA molecules. This review aims to analyze various peptide–copper complexes and the mechanism of their action.
Study on the Desliming Performance of a Novel Hydrocyclone Sand Washer
Authors: Yang Xinghua , Yang Guanghui , Liu Peikun , Li Xiaoyu , Jiang Lanyue , Zhang Jiashun
Published: March 2022

A novel hydrocyclone sand washer featured by connecting a cylindrical hydrocyclone and a conical-cylindric hydrocyclone in series was developed to improve the poor grading performance in current machine-made sand processing technology. The former hydroycyclone with a flat bottom was designed to enhance the centrifugal intensity, thereby achieving the pre-grading of fine and coarse particles and ensuring the discharge of most fine mud particles from the overflow pipe. The latter hydrocyclone was designed to achieve the secondary fine separation and therefore reduce the content of fine particles in the underflow product. Firstly, the flow field inside the consecutive hydrocyclones was simulated using an RSM and VOF model. The DPM model was introduced to trace the particle motion trajectory and validate the feasibility of hydrocyclone separation. Then, the experimental study was performed using the control variable method, and the effects of the first-section overflow pipe diameter, the feeding rate, and the mud–sand mixing ratio on the desliming performance were examined. Results show that the content of particles with a diameter of below 75 μm in the second-section underflow drops significantly after the separation in the hydrocyclone sand washer. When the first-section overflow pipe diameter, the feeding rate, and the mud–sand mixing ratio are set to 34 mm, 60 kg/h and 1:1, respectively, the desliming rate of the novel hydrocyclone sand washer can reach 94.31% and the loss rate of quartz sand is only 1.28%.
Where You From? Examining the Relationship between Gang Migrants and Gang-Related Homicide
Authors: Scott Daniel
Published: March 2022

Research has frequently focused on the increased likelihood of violence and homicide among gang-involved individuals, as well as on the factors that contribute to this violence. Such work has examined the relationship between immigration and the frequency of crime, as well. However, there is a dearth of research examining the likelihood of gang-related homicide and the presence of both gang migrants from within the U.S. and those from abroad in a given community. The current paper utilizes National Youth Gang Survey data to examine the relationship between law enforcement perceptions of gang migrants in their jurisdiction and the frequency of gang-related homicide. The results reveal that gang-related homicides have a significant and negative association with the presence of gang migrants. These findings have important policy implications for understanding and addressing serious gang violence and homicide at the community level.
What Is the Flag We Rally Around? Trust in Information Sources at the Outset of the COVID-19 Pandemic in Latvia
Authors: Rožukalne Anda , Kleinberga Vineta , Tīfentāle Alise , Strode Ieva
Published: March 2022

Trust in information sources about COVID-19 may influence the public attitude toward the disease and the imposed restrictions, thus determining the course of the COVID-19 pandemic in a given country. Acknowledging an increase in trust in the government or the so-called rally ‘round the flag’ effect around the world at the beginning of the COVID-19 pandemic, this study explores possible determinants of this effect in Latvia, looking at such variables as the perceived disease risk, gender, age, education, income, and language spoken in the family. Presuming that risk perception may be amplified by trust in various information sources, we investigate a spill-over of the rally ‘round the flag’ effect on healthcare professionals, media, and interpersonal networks. Studying data from a nationally representative sociological survey conducted in September 2020, we confirm a positive relationship between trust in all information sources, except friends, relatives, and colleagues, and perceived disease risk. Correlations are also strong regarding trust in almost all information sources and the measured socio-demographic variables, except gender. Interpersonal trust seems to be relatively stable, and in most cases the correlations are statistically insignificant. With this study we suggest that increase in trust in government institutions as well as other information sources, even in crisis situations, does not depend on any single element, but instead presents a more complex phenomenon.
The Impact of COVID-19-Related Mitigation Measures on the Health and Fitness Status of Primary School Children in Austria: A Longitudinal Study with Data from 708 Children Measured before and during the Ongoing COVID-19 Pandemic
Authors: Jarnig Gerald , Kerbl Reinhold , van Poppel Mireille
Published: March 2022

The COVID-19-related closing of schools and sport facilities resulted in major changes to daily routines worldwide. It was the aim of this study to investigate the impact of COVID-19-related mitigation measures on the health and fitness status of primary school children in Austria. Seven hundred and eight primary school children (7–10 years old) participated in the longitudinal study. Data on height, weight, waist circumference, and fitness were collected before (September 2019) and during the course of the COVID-19 pandemic (June 20, September 20, March 21, June 21). A significant increase in EQUI BMIAUT (ηp2 = 0.087) and significant changes (ηp2 = 0.355) in waist circumference were found. Cardiorespiratory endurance (ηp2 = 0.440) and action speed (ηp2 = 0.221) decreased dramatically following lockdowns/school closures. In contrast, muscle strength showed no significant changes. The COVID-19-related mitigation measures intended to contain a communicable disease resulted in an acceleration of the pre-existing pandemic of overweight and obesity. The adverse combination of increasing BMI and the loss of physical fitness is likely to result in long-term negative effects on the health status of growing and developing individuals. Health professionals should therefore not only support further longitudinal observations of this “non-communicable disease” but also support intervention programs to reverse this worrying side-effect of COVID-19-associated containment policies.
The Pullout Mechanical Properties of Shrub Root Systems in a Typical Karst Area, Southwest China
Authors: Ruan Shihang , Tang Lixia , Huang Tongli
Published: March 2022

Roots play a major role in reinforcing and stabilizing soil. The pullout mechanical characteristics of soil reinforcement and slope protection of the root systems of dominant shrub species (Pyracantha and Geranium) were estimated by in-situ pullout tests in a karst area, in which roots were pulled out from soil to reliably test the pulling force. The goals of this study were to discover the pullout mechanical properties of roots in karst areas and to try to analyse the impact of the root system on landslide control. The F–s curves were multipeak curves with a noticeable main peak and main double peaks. The curves showed a linear increasing trend at the initial stage of drawing and decreased rapidly after reaching the peak. The F–s curves of root systems inserted into rock cracks showed secondary fluctuations in the later stage of drawing, and rock cracks stimulated the tensile efficiency of the root system more effectively. Field in situ pullout results indicate that tree roots fail progressively rather than simultaneously. The maximum pulling force had a linear relationship with the increase in soil thickness and a disproportionate increasing trend with the increasing number of broken roots. The displacement of the maximum peak was different between the two tree species and was concentrated at 5–15 cm and 5–25 cm for Pyracantha and Geranium, respectively. The maximum pulling force of Geranium was 1.29 times that of Pyracantha, and the root system of Geranium had strong pullout resistance. We concluded that the peak distribution of the F–s curves was affected by broken roots and rock cracks, while soil thickness and the number of broken roots had positive effects on the maximum pulling force, all of which is helpful in understanding the effect of root pullout mechanical properties on landslides in karst areas.
COVID-19 Lockdown Has No Significant Impact on Trauma Epidemiology and Outcomes in a Tertiary Trauma Center—Retrospective Cohort Study
Authors: Bala Miklosh , Almogy Gidon , Pikarsky Alon , Abu Salem Samer , Kedar Asaf
Published: March 2022

Background: The aim of this study was to describe the impact of a COVID-19 lockdown on the variation in the volumes and types of injuries at a level one trauma center while focusing on preserving trauma care resources. Methods: A retrospective, descriptive study of prospectively collected data from the Trauma Registry. Data collection included patient demographics, injury mechanism, injury type and treatment required. The time periods studied corresponded with the lockdown period in Israel and a parallel period in 2019. Results: Overall, there was no reduction in all injury-related admissions. There was a significant reduction in pedestrian injuries (p < 0.02) and a non-significant increase in children admissions aged 0–2 years. Compared to the previous years, the severity of injuries during the March–April 2020 lockdown was unchanged. Hospital resources (number and percentage of trauma patients who required an operation, ICU stay and LOS) were not different between the lockdown period compared to the previous 5 years. Less trauma patients arrived with a Trauma Team Activation code during the lockdown period (58, 33% decrease compared to the control), but a significantly higher proportion of those patients required hospitalization (77.6%, p < 0.0001). Conclusions: During a lockdown period, road accidents were still the main cause for major trauma admissions, resulting in prolonged and complex surgeries, and hospitals should continue to provide full services until resource limitations are unavoidable. Maintenance of an effective full-staffed trauma service is vital throughout a COVID-19 lockdown.
Phase-Matching Continuous-Variable Measurement-Device-Independent Quantum Key Distribution
Authors: Huang Peng , Wang Tao , Huang Duan , Zeng Guihua
Published: March 2022

Continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) allows remote parties to share information-theoretical secure keys while defending all the side-channel attacks on measurement devices. However, the secure transmission distance and the secret key rate are quite limited due to the high untrusted equivalent excess noise in the Gaussian modulation. More particularly, extremely high-efficiency homodyne detections are required for even non-zero secure transmission distances, which directly restrict its practical realization. Here, we propose a CV-MDI-QKD protocol by encoding the key information into matched discrete phases of two groups of coherent states, which decreases the required detection efficiency for ideally asymmetric cases, and makes it possible to practically achieve secure key distribution with current low-efficiency homodyne detections. Besides, a proof-of-principle experiment with a locally generated oscillator is implemented, which, for the first time, demonstrates the realizability of CV-MDI-QKD using all fiber-based devices. The discrete-modulated phase-matching method provides an alternative direction of an applicable quantum key distribution with practical security.
Benzimidazoles and Plants: Uptake, Transformation and Effect
Authors: Podlipná Radka
Published: March 2022

In recent years, there has been increasing concern over the environmental risks of the so called “Emerging pollutants (EPs)” that are defined as synthetic or naturally occurring chemicals that are not commonly monitored in the environment but which have the potential to enter the environment and cause adverse ecological and (or) human health effects [...]
Digital Storytelling and Community Engagement to Find Missing TB Cases in Rural Nuh, India
Authors: Quraishi Subhi , Quraishi Hilmi , Yadav Hemlata , Singh Ayushi , Fasih Ilmana , Vasquez Nathaly Aguilera , Huria Lavanya , Pande Tripti , Mumba Olive , Kamineni Vishnu Vardhan , Khan Amera
Published: March 2022

Nuh, Haryana, is one of India’s least developed districts. To improve TB case notifications, ZMQ carried out an active case-finding (ACF) intervention conducted by community health workers (MIRAs) using a digital TB storytelling platform to create TB awareness in the community. The combined storytelling and ACF intervention were conducted house-to-house or in community group settings. Steps included (A) the development of digital TB awareness-raising stories using a participatory approach called Story Labs; (B) the implementation of the intervention; and (C) process, outcome, and impact evaluation of these activities. Six digital stories were created and used during ACF in which 19,345 people were screened and 255 people were diagnosed with TB. Of 731 participants surveyed, the stories were well received and resulted in an increase in TB knowledge. ACF activities resulted in a 56% increase in bacteriologically confirmed TB and an 8% decrease in all forms of TB compared to baseline. All form notifications may have been impacted by COVID-19 lockdowns. Digital TB storytelling can improve TB awareness and knowledge, particularly for low-literacy populations. The use of these tools may benefit ACF campaigns and improve TB case finding.
The History of Intravenous and Oral Rehydration and Maintenance Therapy of Cholera and Non-Cholera Dehydrating Diarrheas: A Deconstruction of Translational Medicine: From Bench to Bedside?
Authors: Nalin David R.
Published: March 2022

The “bench to bedside” (BTB) paradigm of translational medicine (TM) assumes that medical progress emanates from basic science discoveries transforming clinical therapeutic models. However, a recent report found that most published medical research is false due, among other factors, to small samples, inherent bias and inappropriate statistical applications. Translation-blocking factors include the validity (or lack thereof) of the underlying pathophysiological constructs and related therapeutic paradigms and adherence to faulty traditional beliefs. Empirical discoveries have also led to major therapeutic advances, but scientific dogma has retrospectively retranslated these into the BTB paradigm. A review of the history of intravenous (I.V.) and oral therapy for cholera and NDDs illustrates some fallacies of the BTB model and highlights pitfalls blocking translational and transformative progress, and retro-translational factors, including programmatic modifications of therapeutic advances contradicting therapeutic paradigms and medical economic factors promoting more expensive and profitable medical applications inaccessible to resource-limited environments.