bluuebunny/crossref_metadata_2025 huggingface dataset:
A Traumatic Neuroma Formation Following Fasciotomy for the Treatment of Tibialis Anterior Muscle Herniation: A Case Report
Authors: Yokoe Takuji , Tajima Takuya , Yamaguchi Nami , Morita Yudai , Chosa Etsuo
Published: February 2023

Muscle herniation of the lower extremity, such as tibialis anterior muscle herniation (TAMH), is not a rare cause of leg pain in athletes. However, a few studies have reported surgical treatment for TAMH, and the optimal surgical procedure remains controversial. Fasciotomy was reported to be effective for patients with TAMH. However, this procedure would be associated with a risk of intraoperative injury to the superficial peroneal nerve (SPN), although no previous literature has reported this complication. This case report aimed to report a case of bilateral TAMHs in which a traumatic neuroma of the SPN developed after fasciotomy. A 16-year-old baseball player presented with painful swelling lesions of the bilateral lower extremities (1 lesion on the right, 3 lesions on the left) after sports activities. An ultrasonographic evaluation showed swelling lesions of the anterolateral parts of the bilateral lower extremities in the standing position after dashing, while these lesions were not detected in the supine position. A fasciotomy of the crural fascia was performed after conservative treatment failed. Several days after surgery, the patient presented with weakened touch sensation over the dorsal area of the left foot. At the three-month follow-up examination, a swelling lesion with hard elasticity was identified. The palpation of this lesion caused a radiating sensation in the area supplied by the SPN. He was able to return to playing baseball six months after surgery. The patient was asymptomatic without palpation of the traumatic neuroma of the SPN at the latest follow-up examination. In conclusion, the present case report suggests that orthopedic surgeons need to consider the risk of iatrogenic injury to the SPN during fasciotomy for the treatment of TAMHs. However, there may be a risk of injuring the SPN because of the many variants of the course of the SPN within the compartment of the lower extremities.
Combination of St. John’s Wort Oil and Neem Oil in Pharmaceuticals: An Effective Treatment Option for Pressure Ulcers in Intensive Care Units
Authors: Özdemir Samet , Bostanabad Saber Yari , Parmaksız Ayhan , Canatan Halil Can
Published: February 2023

Background and Objectives: Phytotherapeutically, various herbal remedies, such as St. John’s wort oil, have been introduced as wound care options. Recently, Neem oil has been considered a herbal option for the management of superficial wounds. Wound care is a complex process that involves several factors including the patient, caregiver, and medications. Herbal combinations could be an alternative to the chemical counterparts in the wound care area. This report includes an investigation of the possible supportive impacts of the St. John’s wort and Neem oil containing ointment (W Cura G Plus ®) in the management of pressure ulcers (PUs) in three intensive care unit (ICU) patients. Materials and Methods: The ointment was administered to individuals once daily for 42 consecutive days. The status of individuals was macroscopically monitored by measuring the PU area and histopathological assessment of the tissue sections taken on the first and last days of wound treatment. Results: The outcomes of the macroscopic and histopathological techniques exhibited that St. John’s wort and Neem oil containing ointment provided a remarkable supportive impact on the patients that suffered from PUs in the ICUs. Conclusions: The combination of St. John’s wort and Neem oil could be suggested as an efficient active phytoconstituent for the management of PUs. The herbal ointments may be suggested as an alternative for the patients that have PUs in the ICUs.
Relationship between Transition Shock, Professional Stressors, and Intent to Leave the Nursing Profession during the COVID-19 Pandemic
Authors: Nakić Dario , Gusar Ivana , Franov Ivana , Sarić Marijana Matek , Ljubičić Marija
Published: February 2023

Background and Objectives: Moving nurses to a COVID-19 department may cause the phenomenon of transition shock, which occurs when already employed nurses change jobs. A set of confusing and uncertain feelings arise due to the unfamiliar work environment, which may lead to their intention to leave the nursing profession. The aim of this study was to examine transition shock and the contribution of both the respondents’ characteristics and the presence of stressors to the occurrence of transition shock in nurses assigned to work in COVID-19 departments. Materials and Methods: A cross-sectional study with 120 nurses employed in COVID-19 departments was conducted. Several linear regression models were used to assess the association between transition shock, personal and professional COVID-19 stressors, and the intention to leave the nursing profession. Results: Nurses who intended to leave the profession showed higher transition shock and higher personal and professional stressors (p < 0.001). Female nurses had lower transition shock (β = −0.16; p = 0.036) and higher personal COVID-19 stressors (β = 0.27; p < 0.001). University education contributes to the lowering of nurses’ transition shock (β = −0.16; p = 0.038). Nurses who did not intend to leave the nursing profession had lower personal COVID-19 stressors (β = −0.15; p = 0.044). Transition shock was associated with personal COVID-19 stressors (β = 0.39; p < 0.001) and professional COVID-19 stressors (β = 0.29; p < 0.001), and vice versa. Conclusions: The phenomenon of transition shock was present after nurses transitioned to working in COVID-19 departments. Transition shock may cause more nurses to leave the profession, which may have a strong impact on the health system in many countries that are already facing a shortage of nurses. Additional education on and preparation for adapting to new working conditions with psychological support could have an influence by lowering the level of nurses’ transition shock.
House Flies Are Underappreciated Yet Important Reservoirs and Vectors of Microbial Threats to Animal and Human Health
Authors: Nayduch Dana , Neupane Saraswoti , Pickens Victoria , Purvis Tanya , Olds Cassandra
Published: February 2023

House flies are well recognized as filth-associated organisms and public nuisances. House flies create sanitation issues when they bridge the gap between microbe-rich breeding environments and animal/human habitations. Numerous scientific surveys have demonstrated that house flies harbor bacterial pathogens that pose a threat to humans and animals. More extensive and informative surveys incorporating next-generation sequencing technologies have shown that house fly carriage of pathogens and harmful genetic elements, such as antimicrobial resistance genes, is more widespread and dangerous than previously thought. Further, there is a strong body of research confirming that flies not only harbor but also transmit viable, and presumably infectious, bacterial pathogens. Some pathogens replicate and persist in the fly, permitting prolonged shedding and dissemination. Finally, although the drivers still have yet to be firmly determined, the potential range of dissemination of flies and their associated pathogens can be extensive. Despite this evidence, the house flies’ role as reservoirs, disseminators, and true, yet facultative, vectors for pathogens have been greatly underestimated and underappreciated. In this review, we present key studies that bolster the house fly’s role both an important player in microbial ecology and population biology and as transmitters of microbial threats to animal and human health.
Role of Neuroglia in the Habenular Connection Hub of the Dorsal Diencephalic Conduction System
Authors: Loonen Anton J. M.
Published: February 2023

Astrocytes and microglia play important roles in organizing the structure and function of neuronal networks in the central nervous system (CNS). The dorsal diencephalic connection system (DDCS) is a phylogenetically ancient regulatory system by which the forebrain influences the activity of cholinergic and ascending monoaminergic pathways in the midbrain. The DDCS is probably important in inducing aspects of mental disorders, such as depression and addiction. The habenula is the small but highly complex connecting center of the DDCS in the epithalamus that consists of a medial (MHb) and lateral (LHb) division. MHb and LHb are built differently and connect different brain structures. Studies in animal models and human biomarker research provide good evidence that astroglia and microglia also affect the symptoms of mental disorders (such as depression). The significance of these neuroglia in habenular neurotransmission has not been extensively studied. This review article provides arguments for doing so more thoroughly.
The Perspectives of Plant Natural Products for Mitigation of Obesity
Authors: Li Daotong , Chen Fang
Published: February 2023

Obesity is a metabolic disease caused by an imbalance between energy intake and consumption, which leads to excessive fat accumulation in adipose tissues [...]
Enabling Access to Pharmacy Law Teaching during COVID-19: Student Perceptions of MyDispense and Assessment Outcomes
Authors: Slater Natasha , Mason Thuy , Micallef Ricarda , Ramkhelawon Madhvee , May Leanne
Published: February 2023

During the COVID-19 pandemic, it was not always possible to teach pharmacy practice and practical dispensing skills in person. Second-year pharmacy students (n = 147) were given access to a virtual simulation tool, MyDispense, to supplement their learning. This software enabled students to work remotely and access exercises in a ‘safe’ community pharmacy setting. This study evaluated second-year pharmacy students’ perceptions of MyDispense, and the impact on assessment performance in the pharmacy law and ethics module. Students were able to access 22 MyDispense activities throughout the academic year (2020/2021). Exercise completion rates and assessment marks were analysed, along with findings from a cross-sectional survey about user experience. MyDispense data were available for all students (n = 147), and 76.1% (n = 115) completed the survey. The mean number of MyDispense exercises completed was nine. Higher levels of MyDispense exercise completion were associated with higher pass rates and mean scores (no exercises completed versus 1–10 activities completed: mean score 77.1 versus 83.1, respectively) and a statistically significant association between exercise usage and in-class assessed prescription scores. Overall, 46.1% (n = 53/115), 33.0% (n = 38/115) and 33.9% (n = 39/115) of students felt that MyDispense had helped them to prepare for their assessed prescriptions, mid-module test, and final exam, respectively. MyDispense has provided an accessible alternative to in-person teaching for students during the COVID-19 pandemic, and results showed a positive association with assessment performance in pharmacy law and ethics.
Tailoring Large Asymmetric Laguerre–Gaussian Beam Array Using Computer-Generated Holography
Authors: Singh Sumit Kumar , Adachi Yoshikazu , Kinashi Kenji , Tsutsumi Naoto , Sakai Wataru , Jackin Boaz Jessie
Published: February 2023

Laguerre–Gaussian beams are structured light beams with a donut-shaped symmetric intensity profile and a helical phase profile. The beam profile is defined by a quantized parameter known as the mode number which extends to infinity. The availability of unbounded modes makes these beams a promising candidate for next-generation optical computing, and optical communication technologies. The symmetric intensity profile of a Laguerre–Gaussian beam can be made asymmetric through certain techniques and these beams are known by the term ‘asymmetric Laguerre–Gaussian beams’. Here, the asymmetricity adds another degree of freedom to the beam (apart from its mode number) which helps in encoding more information compared to a symmetric beam. However, in order to harness the benefits of all the available degrees of freedom, it is required to generate a large number of such beams in a multiplexed fashion. Here, we report the generation of such a large array of asymmetric Laguerre–Gaussian beams for the first time. Computer-generated holography and spatial multiplexing techniques were employed to generate a large array comprising of 12 × 16 = 192 asymmetric Laguerre–Gaussian beams with an arbitrary mode index and asymmetricity.
CO2 Decomposition in Microwave Discharge Created in Liquid Hydrocarbon
Authors: Batukaev Timur S. , Bilera Igor V. , Krashevskaya Galina V. , Lebedev Yuri A. , Nazarov Nurlan A.
Published: February 2023

The task of CO2 decomposition is one of the components of the problem associated with global warming. One of the promising directions of its solution is the use of low-temperature plasma. For these purposes, different types of discharges are used. Microwave discharge in liquid hydrocarbons has not been studied before for this problem. This paper presents the results of a study of microwave discharge products in liquid Nefras C2 80/120 (petroleum solvent, a mixture of light hydrocarbons with a boiling point from 33 to 205 °C) when CO2 is introduced into the discharge zone, as well as the results of a study of the discharge by optical emission spectroscopy and shadow photography methods. The main gas products are H2, C2H2, C2H4, CH4, CO2, and CO. No oxygen was found in the products. The mechanisms of CO2 decomposition in the discharge are considered. The formation of H2 occurs simultaneously with the decomposition of CO2 in the discharge, with a volumetric rate of up to 475 mL/min and energy consumption of up to 81.4 NL/kWh.
Study of Varietal Differences in the Composition of Heteropolysaccharides of Oil Flax and Fiber Flax
Authors: Ozhimkova Elena , Uschapovsky Igor , Manaenkov Oleg
Published: February 2023

Flaxseed mucilage and its derivatives have been extensively investigated over the last decade, mainly due to their inherent techno-functional (thickening, gelling, interface-stabilizing, and film-forming) properties that are relevant in the food industry. Hydrocolloids are used to modify food properties, such as for stabilization and emulsion, and are also used to control the microstructure of the food. Increasing research attention has been paid to the application of hydrocolloid materials in gel particles for encapsulation or texture control in food, pharmaceutical, cosmetic, and probiotic products. Thus, it is important to investigate the properties of hydrocolloids manufactured from various sources and explore their possible applications in the food industry. The applied nature of the study of plant mucus substances is associated with the ever-increasing demand for their use in the food, cosmetic, and pharmacological industries, determining the related research priorities, including the development of the most effective methods for the extraction of glycans and the search for highly productive raw materials for the production of polysaccharides. The aim of this work was to study varietal differences in the compositions of heteropolysaccharides in the mucus samples of oilseed and fiber flax varieties using a modern methodological approach for obtaining glycans based on the ultrasonic extraction of polysaccharides. The seeds of 10 flax varieties were studied, differing in their morphotype, place, and time of creation. The obtained results indicated significant differences in the quantitative and qualitative compositions of the heteropolysaccharides of flax seeds of various varieties. The contents of reducing sugars in the studied varieties ranged from 5.61 ± 0.01 to 18.81 ± 0.01 mg/g, indicating significant differences in the structural organization of glycans in different flax varieties. Additionally, the results obtained here allowed us to conclude that the range of reducing sugars for flax heteropolysaccharides is significantly less than this range for oilseed flax varieties. The obtained results of the study of the composition of flax seed heteropolysaccharides allowed us to consider them as selection trait and genetic markers.
A Casing Deformation Prediction Model Considering the Properties of Cement
Authors: Zeng Bo , Zhou Xiaojin , Cao Jing , Zhou Feng , Wang Yao , Wang Yezhong , Song Yi , Hu Junjie , Du Yurou
Published: February 2023

A large amount of casing deformation has occurred in shale gas wells during the complex fracturing process, which affects the fracturing construction and single well production. Based on the statistical analysis of casing deformation wells and caliper logging interpretation, the main mechanism of casing deformation of shale gas wells is revealed as formation slip. By comprehensively considering the rotating speed under casing running condition, the cement solidification heat release under cementing condition, and the fracturing fluid temperature drop under fracturing condition, the safe service margin of the casing is large. Moreover, simply increasing the casing steel grade and wall thickness has no obvious effect on casing deformation prevention, so full wellbore casing deformation prevention measures should be considered. By using the method of unconventional oil and gas well casing string simulation test and numerical simulation, the mechanical response of wellbore and the mechanism of bridge plug resistance are analyzed. By analyzing the influence of elastic modulus and wall thickness of cement on the casing minimum drift diameter after shear deformation, an analytical model of the minimum drift diameter of shale gas casing under the effect of fracture slip is established and verified, which provides technical support for parameter selection of cement and measures to prevent casing deformation.
A Fault-Tolerant and Reconfigurable Control Framework: Modeling, Design, and Synthesis
Authors: Tahiri Imane , Philippot Alexandre , Carré-Ménétrier Véronique , Riera Bernard
Published: February 2023

Manufacturing systems (MS) have become increasingly complex due to constraints induced by a changing environment, such as flexibility, availability, competition, and key performance indicators. This change has led to a need for flexible systems capable of adapting to production changes while meeting productivity and quality criteria and reducing the risk of failures. This paper provides a methodology for designing reconfigurable and fault-tolerant control for implementation in a Programmable Logic Controller (PLC). The main contribution of this methodology is based on a safe control synthesis founded on timed properties. If a sensor fault is detected, the controller switches from normal behavior to a degraded one, where timed information replaces the information lost from the faulty sensor. Switching between normal and degraded behaviors is ensured through reconfiguration rules. The primary objective of this method is to implement the obtained control into a PLC. In order to achieve this goal, a method is proposed to translate the controllers of the two behaving modes and the reconfiguration rules into different Grafcets. This approach relies on the modular architecture of manufacturing systems to avoid the combinatorial explosion that occurs in several approaches.
Review of Emission Characteristics and Purification Methods of Volatile Organic Compounds (VOCs) in Cooking Oil Fume
Authors: Tao Chong , He Limo , Zhou Xuechen , Li Hanjian , Ren Qiangqiang , Han Hengda , Hu Song , Su Sheng , Wang Yi , Xiang Jun
Published: February 2023

Volatile organic compounds (VOCs) in cooking oil fumes need to be efficiently removed due to the significant damage they cause to the environment and human health. This review discusses the emission characteristics, which are influenced by different cooking temperatures, cooking oils, and cuisines. Then, various cooking oil fume purification methods are mainly classified into physical capture, chemical decomposition, and combination methods. VOCs removal rate, system operability, secondary pollution, application area, and cost are compared. The catalytic combustion method was found to have the advantages of high VOC removal efficiency, environmental protection, and low cost. Therefore, the last part of this review focuses on the research progress of the catalytic combustion method and summarizes its mechanisms and catalysts. The Marse-van Krevelen (MVK), Langmuir-Hinshelwood (L-H), and Eley-Rideal (E-R) mechanisms are analyzed. Noble metal and non-noble metal catalysts are commonly used. The former showed excellent activity at low temperatures due to its strong adsorption and electron transfer abilities, but the high price limits its application. The transition metals primarily comprise the latter, including single metal and composite metal catalysts. Compared to single metal catalysts, the interaction between metals in composite metal catalysts can further enhance the catalytic performance.
Analytical Solution to DGLAP Integro-Differential Equation in a Simple Toy-Model with a Fixed Gauge Coupling
Authors: Álvarez Gustavo , Cvetič Gorazd , Kniehl Bernd A. , Kondrashuk Igor , Parra-Ferrada Ivan
Published: February 2023

We consider a simple model for QCD dynamics in which DGLAP integro-differential equation may be solved analytically. This is a gauge model which possesses dominant evolution of gauge boson (gluon) distribution and in which the gauge coupling does not run. This may be N=4 supersymmetric gauge theory with softly broken supersymmetry, other finite supersymmetric gauge theory with a lower level of supersymmetry, or topological Chern–Simons field theories. We maintain only one term in the splitting function of unintegrated gluon distribution and solve DGLAP analytically for this simplified splitting function. The solution is found using the Cauchy integral formula. The solution restricts the form of the unintegrated gluon distribution as a function of momentum transfer and of Bjorken x. Then, we consider an almost realistic splitting function of unintegrated gluon distribution as an input to DGLAP equation and solve it by the same method which we have developed to solve DGLAP equation for the toy-model. We study a result obtained for the realistic gluon distribution and find a singular Bessel-like behavior in the vicinity of the point x=0 and a smooth behavior in the vicinity of the point x=1.
The Internet of Things for the Intelligent Management of the Heating of a Swimming Pool by Means of Smart Sensors
Authors: de la Puente-Gil Álvaro , de Simón-Martín Miguel , González-Martínez Alberto , Diez-Suárez Ana-María , Blanes-Peiró Jorge-Juan
Published: February 2023

By using various smart sensors integrated in a global domotic system, a proper solar thermal management is executed. The goal is to properly manage solar energy for heating swimming pool using various devices installed at home. Swimming pools are a necessity in many communities. In summer, they are a source of refreshment. However, maintaining a swimming pool at an optimal temperature can be a challenge even in the summer months. The use of the Internet of Things in homes has enabled proper management of solar thermal energy, thus significantly improving the quality of life by making homes more comfortable and safer without using additional resources. The houses built today have several smart devices that manage to optimize the energy consumption of the house. The solutions proposed in this study to improve energy efficiency in swimming pool facilities include the installation of solar collectors to heat swimming pool water more efficiently. The installation of smart actuation devices (to efficiently control energy consumption of a pool facility via different processes) together with sensors that provide valuable information on energy consumption in the different processes of a pool facility, can optimize energy consumption thus reducing overall consumption (by 90%) and economic cost (by more than 40%). Together, these solutions can help to significantly reduce energy consumption and economic costs and extrapolate it to different processes of similar characteristics in the rest of the society.
3.6 mW Active-Electrode ECG/ETI Sensor System Using Wideband Low-Noise Instrumentation Amplifier and High Impedance Balanced Current Driver
Authors: Nguyen Xuan Tien , Ali Muhammad , Lee Jong-Wook
Published: February 2023

An active electrode (AE) and back-end (BE) integrated system for enhanced electrocardiogram (ECG)/electrode-tissue impedance (ETI) measurement is proposed. The AE consists of a balanced current driver and a preamplifier. To increase the output impedance, the current driver uses a matched current source and sink, which operates under negative feedback. To increase the linear input range, a new source degeneration method is proposed. The preamplifier is realized using a capacitively-coupled instrumentation amplifier (CCIA) with a ripple-reduction loop (RRL). Compared to the traditional Miller compensation, active frequency feedback compensation (AFFC) achieves bandwidth extension using the reduced size of the compensation capacitor. The BE performs three types of signal sensing: ECG, band power (BP), and impedance (IMP) data. The BP channel is used to detect the Q-, R-, and S-wave (QRS) complex in the ECG signal. The IMP channel measures the resistance and reactance of the electrode-tissue. The integrated circuits for the ECG/ETI system are realized in the 180 nm CMOS process and occupy a 1.26 mm2 area. The measured results show that the current driver supplies a relatively high current (>600 μApp) and achieves a high output impedance (1 MΩ at 500 kHz). The ETI system can detect resistance and capacitance in the ranges of 10 mΩ–3 kΩ and 100 nF–100 μF, respectively. The ECG/ETI system consumes 3.6 mW using a single 1.8 V supply.
HAAN: Learning a Hierarchical Adaptive Alignment Network for Image-Text Retrieval
Authors: Wang Shuhuai , Liu Zheng , Pei Xinlei , Xu Junhao
Published: February 2023

Image-text retrieval aims to search related results of one modality by querying another modality. As a fundamental and key problem in cross-modal retrieval, image-text retrieval is still a challenging problem owing to the complementary and imbalanced relationship between different modalities (i.e., Image and Text) and different granularities (i.e., Global-level and Local-level). However, existing works have not fully considered how to effectively mine and fuse the complementarities between images and texts at different granularities. Therefore, in this paper, we propose a hierarchical adaptive alignment network, whose contributions are as follows: (1) We propose a multi-level alignment network, which simultaneously mines global-level and local-level data, thereby enhancing the semantic association between images and texts. (2) We propose an adaptive weighted loss to flexibly optimize the image-text similarity with two stages in a unified framework. (3) We conduct extensive experiments on three public benchmark datasets (Corel 5K, Pascal Sentence, and Wiki) and compare them with eleven state-of-the-art methods. The experimental results thoroughly verify the effectiveness of our proposed method.
Neural Network Models for Driving Control of Indoor Autonomous Vehicles in Mobile Edge Computing
Authors: Kwon Yonghun , Kim Woojae , Jung Inbum
Published: February 2023

Mobile edge computing has been proposed as a solution for solving the latency problem of traditional cloud computing. In particular, mobile edge computing is needed in areas such as autonomous driving, which requires large amounts of data to be processed without latency for safety. Indoor autonomous driving is attracting attention as one of the mobile edge computing services. Furthermore, it relies on its sensors for location recognition because indoor autonomous driving cannot use a GPS device, as is the case with outdoor driving. However, while the autonomous vehicle is being driven, the real-time processing of external events and the correction of errors are required for safety. Furthermore, an efficient autonomous driving system is required because it is a mobile environment with resource constraints. This study proposes neural network models as a machine-learning method for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the range data measured with the LiDAR sensor. We designed six neural network models to be evaluated according to the number of input data points. In addition, we made an autonomous vehicle based on the Raspberry Pi for driving and learning and an indoor circular driving track for collecting data and performance evaluation. Finally, we evaluated six neural network models in terms of confusion matrix, response time, battery consumption, and driving command accuracy. In addition, when neural network learning was applied, the effect of the number of inputs was confirmed in the usage of resources. The result will influence the choice of an appropriate neural network model for an indoor autonomous vehicle.
Medical Devices with Embedded Sensor Systems: Design and Development Methodology for Start-Ups
Authors: Arandia Nerea , Garate Jose Ignacio , Mabe Jon
Published: February 2023

Embedded systems have become a key technology for the evolution of medical devices. However, the regulatory requirements that must be met make designing and developing these devices challenging. As a result, many start-ups attempting to develop medical devices fail. Therefore, this article presents a methodology to design and develop embedded medical devices while minimising the economic investment during the technical risk stages and encouraging customer feedback. The proposed methodology is based on the execution of three stages: Development Feasibility, Incremental and Iterative Prototyping, and Medical Product Consolidation. All this is completed in compliance with the applicable regulations. The methodology mentioned above is validated through practical use cases in which the development of a wearable device for monitoring vital signs is the most relevant. The presented use cases sustain the proposed methodology, for the devices were successfully CE marked. Moreover, ISO 13485 certification is obtained by following the proposed procedures.
Induction Motor Fault Diagnosis Using Support Vector Machine, Neural Networks, and Boosting Methods
Authors: Kim Min-Chan , Lee Jong-Hyun , Wang Dong-Hun , Lee In-Soo
Published: February 2023

Induction motors are robust and cost effective; thus, they are commonly used as power sources in various industrial applications. However, due to the characteristics of induction motors, industrial processes can stop when motor failures occur. Thus, research is required to realize the quick and accurate diagnosis of faults in induction motors. In this study, we constructed an induction motor simulator with normal, rotor failure, and bearing failure states. Using this simulator, 1240 vibration datasets comprising 1024 data samples were obtained for each state. Then, failure diagnosis was performed on the acquired data using support vector machine, multilayer neural network, convolutional neural network, gradient boosting machine, and XGBoost machine learning models. The diagnostic accuracies and calculation speeds of these models were verified via stratified K-fold cross validation. In addition, a graphical user interface was designed and implemented for the proposed fault diagnosis technique. The experimental results demonstrate that the proposed fault diagnosis technique is suitable for diagnosing faults in induction motors.
Can Satellites Predict Yield? Ensemble Machine Learning and Statistical Analysis of Sentinel-2 Imagery for Processing Tomato Yield Prediction
Authors: Darra Nicoleta , Espejo-Garcia Borja , Kasimati Aikaterini , Kriezi Olga , Psomiadis Emmanouil , Fountas Spyros
Published: February 2023

In this paper, we propose an innovative approach for robust prediction of processing tomato yield using open-source AutoML techniques and statistical analysis. Sentinel-2 satellite imagery was deployed to obtain values of five (5) selected vegetation indices (VIs) during the growing season of 2021 (April to September) at 5-day intervals. Actual recorded yields were collected across 108 fields, corresponding to a total area of 410.10 ha of processing tomato in central Greece, to assess the performance of Vis at different temporal scales. In addition, VIs were connected with the crop phenology to establish the annual dynamics of the crop. The highest Pearson coefficient (r) values occurred during a period of 80 to 90 days, indicating the strong relationship between the VIs and the yield. Specifically, RVI presented the highest correlation values of the growing season at 80 (r = 0.72) and 90 days (r = 0.75), while NDVI performed better at 85 days (r = 0.72). This output was confirmed by the AutoML technique, which also indicated the highest performance of the VIs during the same period, with the values of the adjusted R2 ranging from 0.60 to 0.72. The most precise results were obtained with the combination of ARD regression and SVR, which was the most successful combination for building an ensemble (adj. R2 = 0.67 ± 0.02).
A Low-Cost AI-Empowered Stethoscope and a Lightweight Model for Detecting Cardiac and Respiratory Diseases from Lung and Heart Auscultation Sounds
Authors: Zhang Miao , Li Min , Guo Liang , Liu Jianya
Published: February 2023

Cardiac and respiratory diseases are the primary causes of health problems. If we can automate anomalous heart and lung sound diagnosis, we can improve the early detection of disease and enable the screening of a wider population than possible with manual screening. We propose a lightweight yet powerful model for simultaneous lung and heart sound diagnosis, which is deployable in an embedded low-cost device and is valuable in remote areas or developing countries where Internet access may not be available. We trained and tested the proposed model with the ICBHI and the Yaseen datasets. The experimental results showed that our 11-class prediction model could achieve 99.94% accuracy, 99.84% precision, 99.89% specificity, 99.66% sensitivity, and 99.72% F1 score. We designed a digital stethoscope (around USD 5) and connected it to a low-cost, single-board-computer Raspberry Pi Zero 2W (around USD 20), on which our pretrained model can be smoothly run. This AI-empowered digital stethoscope is beneficial for anyone in the medical field, as it can automatically provide diagnostic results and produce digital audio records for further analysis.
A Multi-Label Based Physical Activity Recognition via Cascade Classifier
Authors: Mo Lingfei , Zhu Yaojie , Zeng Lujie
Published: February 2023

Physical activity recognition is a field that infers human activities used in machine learning techniques through wearable devices and embedded inertial sensors of smartphones. It has gained much research significance and promising prospects in the fields of medical rehabilitation and fitness management. Generally, datasets with different wearable sensors and activity labels are used to train machine learning models, and most research has achieved satisfactory performance for these datasets. However, most of the methods are incapable of recognizing the complex physical activity of free living. To address the issue, we propose a cascade classifier structure for sensor-based physical activity recognition from a multi-dimensional perspective, with two types of labels that work together to represent an exact type of activity. This approach employed the cascade classifier structure based on a multi-label system (Cascade Classifier on Multi-label, CCM). The labels reflecting the activity intensity would be classified first. Then, the data flow is divided into the corresponding activity type classifier according to the output of the pre-layer prediction. The dataset of 110 participants has been collected for the experiment on PA recognition. Compared with the typical machine learning algorithms of Random Forest (RF), Sequential Minimal Optimization (SMO) and K Nearest Neighbors (KNN), the proposed method greatly improves the overall recognition accuracy of ten physical activities. The results show that the RF-CCM classifier has achieved 93.94% higher accuracy than the 87.93% obtained from the non-CCM system, which could obtain better generalization performance. The comparison results reveal that the novel CCM system proposed is more effective and stable in physical activity recognition than the conventional classification methods.
Editorial: Special Issue “Optical Signal Processing Technologies for Communication, Computing, and Sensing Applications”
Authors: Du Jiangbing , Yue Yang , Zhao Jian , Liu Yange
Published: February 2023

Optical technology is one of the key technologies that have been widely used for communication, computing and sensing [...]
Cost-Sensitive YOLOv5 for Detecting Surface Defects of Industrial Products
Authors: Liu Ben , Gao Feng , Li Yan
Published: February 2023

Owing to the remarkable development of deep learning algorithms, defect detection techniques based on deep neural networks have been extensively applied in industrial production. Most existing surface defect detection models assign equal costs to the classification errors among different defect categories but do not strictly distinguish them. However, various errors can generate a great discrepancy in decision risk or classification costs and then produce a cost-sensitive issue that is crucial to the manufacturing process. To address this engineering challenge, we propose a novel supervised classification cost-sensitive learning method (SCCS) and apply it to improve YOLOv5 as CS-YOLOv5, where the classification loss function of object detection was reconstructed according to a new cost-sensitive learning criterion explained by a label–cost vector selection method. In this way, the classification risk information from a cost matrix is directly introduced into the detection model and fully exploited in training. As a result, the developed approach can make low-risk classification decisions for defect detection. It is applicable for direct cost-sensitive learning based on a cost matrix to implement detection tasks. Using two datasets of a painting surface and a hot-rolled steel strip surface, our CS-YOLOv5 model outperforms the original version with respect to cost under different positive classes, coefficients, and weight ratios, but also maintains effective detection performance measured by mAP and F1 scores.
Traditional Importance, Phytochemistry, Pharmacology, and Toxicological Attributes of the Promising Medicinal Herb Carissa spinarum L.
Authors: Sharma Nitin , Kumar Vikas , Gupta Nidhi , Shekhar Pratyush , Kaur Palki Sahib
Published: February 2023

Carissa spinarum L. (Apocynaceae), commonly known as Garna or Jungli Karonda, has a rich history of use in indigenous traditional medicinal systems owing to its tremendous medicinal and nutritional benefits. The present review aims to discuss the traditional uses, ethnopharmacology, bioactive composition, toxicity analysis, and biotechnological applications of Carissa spinarum L. (CS) to identify the gap between current applications and research conducted on this plant. We collected the literature published before December 2022 on the phytochemical composition, pharmacological properties, and biotechnological applications of CS. Literature in English from scientific databases such as Google Scholar, PubMed, ScienceDirect, Springer, and Wiley, along with books on CS, was analyzed and summarized to prepare this review. The plant taxonomy was verified using the “World Flora Online” database. The in vitro and in vivo pharmacological studies on CS revealed its anthelmintic, anticonvulsant, anti-arthritic, anti-inflammatory, antimicrobial, antioxidant, antidiabetic, hepatoprotective, vasorelaxant, antihypertensive, antitumor, wound-healing, anti-venom, and antipyretic effects. Toxicological studies on CS also indicated the absence of any adverse effects even at high doses after oral administration. Although CS showed remarkable therapeutic activities against several diseases—such as diabetes, cancer, inflammation, and hepatitis B virus—there are several drawbacks in previous reports, including the lack of information on the drug dose, standards, controls, and mechanism of action of the extract or the phytocompounds responsible for its activity. Extensive research with proper in vivo or in vitro model systems is required to validate its reported activities.
Study of Phenol Red Photocatalytic Decomposition on KBrO3-Supported TiO2 Nanoparticles for Wastewater Treatment
Authors: Almulhem Najla Khaled , Awada Chawki , Shaalan Nagih M.
Published: February 2023

In this study, the enhanced photodegradation of a high-concentration phenol red (PR) using very fine TiO2 nanocrystals by adding a KBrO3 electron acceptor was reported for the first time. The structural study on TiO2 nanocrystals using HRTEM, XRD, Raman, and EDX was performed and it confirmed the anatase phase of TiO2 nanocrystals. UV–Vis absorbance of 20 mg.L−1 PR was measured and the photodegradation was extracted. The KBrO3 concentration effects exhibited an important enhancement in the degradation of PR dye. The efficiency of PR was increased during 110 min from 75% of pure TiO2 to 92% and 98% of TiO2 with 1 mg and 5 mg KBrO3, respectively. For different samples, a first-order kinetic of dye degradation is confirmed. The instantaneous amount of degraded dye increased from 150 to 180 and 197 mg/g TiO2 with 1 mg and 5 mg KBrO3, respectively. The mechanism of the photodegradation reaction confirms the effect of OH- radicals on increasing the photocatalytic activities. The addition of electron acceptors KBrO3 improved the photocatalysis rate, where it prevented e-h recombination through conduction band electron capture, which increases the concentration of hydroxyl radicals. The proposed mechanism and results were supported by photocurrent measurements and a Raman spectra analysis of the final photodegraded products. The photocurrent of TiO2 was observed at 1.2 µA, which was significantly improved up to 13.2, and 21.3 µA with the addition of 1 mg and 5 mg of KBrO3. The Raman spectra of the final products confirmed that SO42− and carbons are byproducts of PR degradation.
Comparison of Conservative Interventions for Proximal Hamstring Tendinopathy: A Systematic Review and Recommendations for Rehabilitation
Authors: Dizon Pilar , Jeanfavre Michael , Leff Gretchen , Norton Rachel
Published: February 2023

Knowledge of muscular forces and adaptations with hamstring-specific exercises can optimize exercise prescription and tendon remodeling; however, studies investigating the effectiveness of the current conservative management of proximal hamstring tendinopathy (PHT) and outcomes are lacking. The purpose of this review is to provide insights into the efficacy of conservative therapeutic interventions in the management of PHT. In January 2022, databases including PubMed, Web of Science, CINAHL, and Embase were searched for studies assessing the effectiveness of conservative intervention compared with that of a placebo or combination of treatments on functional outcomes and pain. Studies that performed conservative management (exercise therapy and/or physical therapy modalities) in adults 18–65 years were included. Studies that performed surgical interventions or whose subjects had complete hamstring rupture/avulsion greater than a 2 cm displacement were excluded. A total of 13 studies were included: five studies compared exercise interventions, while eight studies investigated a multimodal approach of either shockwave therapy and exercise or a hybrid model incorporating exercise, shockwave therapy, and other modalities, such as ultrasound, trigger point needling, or instrument-assisted soft tissue mobilization. This review supports the notion that the conservative management of PHT may best be optimized through a multimodal approach incorporating a combination of tendon-specific loading at an increased length, lumbopelvic stabilization exercises, and extracorporeal shockwave therapy. With regard to hamstring-specific exercise selection, PHT may be optimally managed by including a progressive loading program at combined angles of the hip flexion at 110 degrees and the knee flexion between 45 and 90 degrees.
Performance Evaluation of a Novel Bio-Trickling Filter for Styrene Waste Gas Treatment
Authors: Wang Liyong , Gao Panfeng , Li Shubiao , Fu Haiyan , Yang Aili , Wu Yicheng , Dai Yuan , Liu Shuaihao
Published: February 2023

In recent years, styrene waste gas has become a hot issue in the waste gas treatment industry due to its hydrophobicity and easy polymerization. This study is aimed at the problems of long empty bed residence time and low removal capacity of waste gas from styrene degradation by bio-trickling filter (BTF). A novel bio-trickling filter (NBTF) that we designed was used to explore the effects of styrene inlet concentration, empty bed residence time (EBRT), and starvation period on the performance of NBTF in the degradation of styrene waste gas. The experimental results show that the NBTF can be started in 17 days; when the inlet concentration was lower than 1750 mg/m3 and the EBRT was 59.66 s, the removal efficiency (RE) of styrene can reach 100%. When the inlet concentration was 1000 mg/m3 and the EBRT was greater than 39.77 s, styrene waste gas can also be completely degraded. The above proves that NBTF can complete the degradation of styrene waste gas with high concentration under the condition of short EBRT; in the whole operation process, the maximum elimination capability (EC) of styrene was 112.96 g/m3/h, and NBTF shows excellent degradation performance of styrene. When the starvation period was 2 days, 7 days and 15 days, respectively, NBTF can recover high degradation performance within 2 days after restart. The NBTF has good operation performance in 124 days of operation, which proves that the NBTF can effectively degrade styrene waste gas. This provides a reference basis for industrial treatment of styrene waste gas in the future.
The Impact of Outward Foreign Direct Investment on Product Quality and Export: Evidence from China
Authors: Yan Zhoufu , Sui Shuntian , Wu Fangwei , Cao Li
Published: February 2023

While China’s outward foreign direct investment (OFDI) is growing rapidly, few studies have considered the impact of this growth on product quality. This study uses microdata at the enterprise level to investigate the effects of OFDI on product quality and export trade, from the perspective of enterprise heterogeneity. The results denote that (1) OFDI improves product quality, which is caused by the direct promotional effect of OFDI and the productivity effect brought by OFDI. (2) As OFDI of low productivity enterprises creates a productivity effect and leads to a promotional effect on product quality enhancement, the magnitude is significantly higher than that of high productivity enterprises. (3) Product quality enhancement significantly increases the promotional effect on exports induced by OFDI of low productivity enterprises, while simultaneously significantly suppressing the promotional effect on exports induced by OFDI of high productivity enterprises. (4) The promotional effect of OFDI on exports decreases as productivity increases, and OFDI significantly promotes exports of low productivity enterprises and has no significant effect on high productivity enterprises. These results suggest that China should continue to expand the space for international cooperation and encourage enterprises to invest overseas, especially those facing export pressures.