bluuebunny/crossref_metadata_2025 huggingface dataset:
Feasibility Study to Byproduce Medical Radioisotopes in a Fusion Reactor
Authors: Li Jia , Zheng Shanliang
Published: February 2023

Currently, international nuclear fission reactors producing medical isotopes face the problem of shutdown and maintenance, decommissioning, or dismantling, while the production capacity of domestic research reactors for medical radioisotopes is inadequate, and the supply capacity for medical radioisotopes faces major challenges in the future. Fusion reactors are characterized by high neutron energy, high flux density, and the absence of highly radioactive fission fragments. Additionally, compared to fission reactors, the reactivity of the fusion reactor core is not significantly affected by the target material. By building a preliminary model of the China Fusion Engineering Test Reactor (CFETR), a Monte Carlo simulation was performed for particle transport between different target materials at a fusion power of 2 GW. The yields (specific activity) of six medical radioisotopes (14C, 89Sr, 32P, 64Cu, 67Cu, and 99Mo) with various irradiation positions, different target materials, and different irradiation times were studied, and compared with those of other high-flux engineering test reactors (HFETR) and the China Experimental Fast Reactor (CEFR). The results show that this approach not only provides competitive medical isotope yield, but also contributes to the performance of the fusion reactor itself, e.g., tritium self-sustainability and shielding performance.
Potential Anticancer Activity of Juniperus procera and Molecular Docking Models of Active Proteins in Cancer Cells
Authors: Alhayyani Sultan , Akhdhar Abdullah , Asseri Amer H. , Mohammed Abdelhafeez M. A. , Hussien Mostafa A. , Roselin L. Selva , Hosawi Salman , AlAbbasi Fahad , Alharbi Khadijah H. , Baty Roua S. , Kalantan Abdulaziz A. , Ali Ehab M. M.
Published: February 2023

Medicinal plants provide a wide range of active compounds that can be exploited to create novel medicines with minimal side effects. The current study aimed to identify the anticancer properties of Juniperus procera (J. procera) leaves. Here, we demonstrate that J. procera leaves’ methanolic extract suppresses cancer cells in colon (HCT116), liver (HepG2), breast (MCF-7), and erythroid (JK-1) cell lines. By applying GC/MS, we were able to determine the components of the J. procera extract that might contribute to cytotoxicity. Molecular docking modules were created that used active components against cyclin-dependent kinase 5 (Cdk5) in colon cancer, aromatase cytochrome P450 in the breast cancer receptor protein, the -N terminal domain in the erythroid cancer receptor of the erythroid spectrin, and topoisomerase in liver cancer. The results demonstrate that, out of the 12 bioactive compounds generated by GC/MS analysis, the active ingredient 2-imino-6-nitro-2H-1-benzopyran-3-carbothiamide proved to be the best-docked chemical with the chosen proteins impacted by DNA conformational changes, cell membrane integrity, and proliferation in molecular docking studies. Notably, we uncovered the capacity of J. procera to induce apoptosis and inhibit cell growth in the HCT116 cell line. Collectively, our data propose that J. procera leaves’ methanolic extract has an anticancer role with the potential to guide future mechanistic studies.
Signals for Muscular Protein Turnover and Insulin Resistance in Critically Ill Patients: A Narrative Review
Authors: Chapela Sebastián P. , Simancas-Racines Daniel , Montalvan Martha , Frias-Toral Evelyn , Simancas-Racines Alison , Muscogiuri Giovanna , Barrea Luigi , Sarno Gerardo , Martínez Pablo I. , Reberendo María J. , Llobera Natalia D. , Stella Carlos A.
Published: February 2023

Sarcopenia in critically ill patients is a highly prevalent comorbidity. It is associated with a higher mortality rate, length of mechanical ventilation, and probability of being sent to a nursing home after the Intensive Care Unit (ICU). Despite the number of calories and proteins delivered, there is a complex network of signals of hormones and cytokines that affect muscle metabolism and its protein synthesis and breakdown in critically ill and chronic patients. To date, it is known that a higher number of proteins decreases mortality, but the exact amount needs to be clarified. This complex network of signals affects protein synthesis and breakdown. Some hormones regulate metabolism, such as insulin, insulin growth factor glucocorticoids, and growth hormone, whose secretion is affected by feeding states and inflammation. In addition, cytokines are involved, such as TNF-alpha and HIF-1. These hormones and cytokines have common pathways that activate muscle breakdown effectors, such as the ubiquitin–proteasome system, calpain, and caspase-3. These effectors are responsible for protein breakdown in muscles. Many trials have been conducted with hormones with different results but not with nutritional outcomes. This review examines the effect of hormones and cytokines on muscles. Knowing all the signals and pathways that affect protein synthesis and breakdown can be considered for future therapeutics.
Effectiveness of Dietary Management for Moderate Wasting among Children > 6 Months of Age—A Systematic Review and Meta-Analysis Exploring Different Types, Quantities, and Durations
Authors: Cichon Bernardette , Das Jai K. , Salam Rehana A. , Padhani Zahra A. , Stobaugh Heather C. , Mughal Muzna , Pajak Patrizia , Rutishauser-Perera Alexandra , Bhutta Zulfiqar A. , Black Robert E.
Published: February 2023

Currently, no World Health Organization guidelines exist for the management of approximately 31.8 million moderately wasted children globally. The objective of this review was to synthesise evidence on the optimal type, quantity, and duration of dietary treatment for moderate wasting. Ten electronic databases were searched until the 23rd of August 2021. Experimental studies comparing interventions for the dietary management of moderate wasting were included. Meta-analyses were conducted and results were presented as risk ratios or mean differences with 95% confidence intervals. Seventeen studies comparing specially formulated foods were included involving 23,005 participants. Findings suggest little or no difference in recovery between Fortified Blended Foods (FBFs) with improved micronutrient and/or milk content (enhanced FBFs) and lipid-based nutrient supplements (LNS), whereas children treated with non-enhanced FBFs (locally produced FBFs or standard corn–soy blend) may have lower recovery rates than those treated with LNS. There was no difference in recovery when ready-to-use therapeutic and ready-to-use supplementary food were compared. Other outcomes mostly aligned with results for recovery. In conclusion, LNSs improve recovery compared to non-enhanced FBFs, but are comparable to enhanced FBFs. Programmatic choice of supplement should consider factors such as cost, cost-effectiveness, and acceptability. Further research is required to determine optimal dosing and duration of supplementation.
An Assessment of Mushroom Consumption on Cardiometabolic Disease Risk Factors and Morbidities in Humans: A Systematic Review
Authors: Uffelman Cassi N. , Chan Nok In , Davis Eric M. , Wang Yu , McGowan Bethany S. , Campbell Wayne W.
Published: February 2023

Mushrooms, unique edible fungi, contain several essential nutrients and bioactive compounds which may positively influence cardiometabolic health. Despite a long history of consumption, the health benefits of mushrooms are not well documented. We conducted a systematic review to assess the effects of and associations between mushroom consumption and cardiometabolic disease (CMD)-related risk factors and morbidities/mortality. We identified 22 articles (11 experimental and 11 observational) from five databases meeting our inclusion criteria. Limited evidence from experimental research suggests mushroom consumption improves serum/plasma triglycerides and hs-CRP, but not other lipids, lipoproteins, measures of glucose control (fasting glucose and HbA1c), or blood pressure. Limited evidence from observational research (seven of 11 articles with a posteriori assessments) suggests no association between mushroom consumption and fasting blood total or LDL cholesterol, glucose, or morbidity/mortality from cardiovascular disease, coronary heart disease, or type 2 diabetes mellitus. Other CMD health outcomes were deemed either inconsistent (blood pressure, HDL cholesterol, and triglycerides) or insufficient (HbA1c/hyperglycemia, hs-CRP, cerebrovascular disease, and stroke). The majority of the articles vetted were rated “poor” using the NHLBI study quality assessment tool due to study methodology and/or poor reporting issues. While new, high-quality experimental and observational research is warranted, limited experimental findings suggest greater mushroom consumption lowers blood triglycerides and hs-CRP, indices of cardiometabolic health.
Effects of a 16-Week Digital Intervention on Sports Nutrition Knowledge and Behavior in Female Endurance Athletes with Risk of Relative Energy Deficiency in Sport (REDs)
Authors: Fahrenholtz Ida L. , Melin Anna K. , Garthe Ina , Hollekim-Strand Siri Marte , Ivarsson Andreas , Koehler Karsten , Logue Danielle , Lundström Petra , Madigan Sharon , Wasserfurth Paulina , Torstveit Monica K.
Published: February 2023

Female endurance athletes are considered a high-risk group for developing Relative Energy Deficiency in Sport (REDs). Due to the lack of educational and behavioral intervention studies, targeting and evaluating the effects of the practical daily management of REDs, we developed the Food and nUtrition for Endurance athletes—a Learning (FUEL) program, consisting of 16 weekly online lectures and individual athlete-centered nutrition counseling every other week. We recruited female endurance athletes from Norway (n = 60), Sweden (n = 84), Ireland (n = 17), and Germany (n = 47). Fifty athletes with symptoms of REDs and with low risk of eating disorders, with no use of hormonal contraceptives and no chronic diseases, were allocated to either the FUEL intervention (n = 32) (FUEL) or a 16-week control period (n = 18) (CON). All but one completed FUEL, while 15 completed CON. We found strong evidence for improvements in sports nutrition knowledge, assessed via interviews, and moderate to strong evidence in the ratings concerning self-perceived sports nutrition knowledge in FUEL versus CON. Analyses of the seven-day prospective weighed food record and questions related to sports nutrition habits, suggested weak evidence for improvements in FUEL versus CON. The FUEL intervention improved sports nutrition knowledge and suggested weak evidence for improved sports nutrition behavior in female endurance athletes with symptoms of REDs.
Low Phase Angle Values Are Associated with Malnutrition according to the Global Leadership Initiative on Malnutrition Criteria in Kidney Transplant Candidates: Preliminary Assessment of Diagnostic Accuracy in the FRAILMar Study
Authors: Muñoz-Redondo Elena , Morgado-Pérez Andrea , Pérez-Sáez María-José , Faura Anna , Sánchez-Rodríguez Dolores , Tejero-Sánchez Marta , Meza-Valderrama Delky , Muns María Dolors , Pascual Julio , Marco Ester
Published: February 2023

Malnutrition has a negative impact on patients with chronic diseases and its early identification is a priority. The primary objective of this diagnostic accuracy study was to assess the performance of the phase angle (PhA), a bioimpedance analysis (BIA)-derived parameter, for malnutrition screening using the Global Leadership Initiative for Malnutrition (GLIM) criteria as the reference standard in patients with advanced chronic kidney disease (CKD) waiting for kidney transplantation (KT); criteria associated with low PhA in this population were also analyzed. Sensitivity, specificity, accuracy, positive and negative likelihood ratios, predictive values, and area under the receiver operating characteristic curve were calculated for PhA (index test) and compared with GLIM criteria (reference standard). Of 63 patients (62.9 years old; 76.2% men), 22 (34.9%) had malnutrition. The PhA threshold with the highest accuracy was ≤4.85° (sensitivity 72.7%, specificity 65.9%, and positive and negative likelihood ratios 2.13 and 0.41, respectively). A PhA ≤ 4.85° was associated with a 3.5-fold higher malnutrition risk (OR = 3.53 (CI95% 1.0–12.1)). Considering the GLIM criteria as the reference standard, a PhA ≤ 4.85° showed only fair validity for detecting malnutrition, and thus cannot be recommended as a stand-alone screening tool in this population.
Comment on Sung et al. Body Fat Reduction Effect of Bifidobacterium breve B-3: A Randomized, Double-Blind, Placebo Comparative Clinical Trial. Nutrients 2023, 15, 28
Authors: Lee Sang Yeoup
Published: February 2023

I read with interest the paper by Sung et al. entitled “Body Fat Reduction Effect of Bifidobacterium breve B-3: A Randomized, Double-Blind, Placebo Comparative Clinical Trial” where a reduction in body fat mass after Bifidobacterium breve B-3 (BB-3) ingestion for 12 weeks was reported [...]
Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery
Authors: Kwofie Samuel K. , Adams Joseph , Broni Emmanuel , Enninful Kweku S. , Agoni Clement , Soliman Mahmoud E. S. , Wilson Michael D.
Published: February 2023

The effect of Ebola virus disease (EVD) is fatal and devastating, necessitating several efforts to identify potent biotherapeutic molecules. This review seeks to provide perspectives on complementing existing work on Ebola virus (EBOV) by discussing the role of machine learning (ML) techniques in the prediction of small molecule inhibitors of EBOV. Different ML algorithms have been used to predict anti-EBOV compounds, including Bayesian, support vector machine, and random forest algorithms, which present strong models with credible outcomes. The use of deep learning models for predicting anti-EBOV molecules is underutilized; therefore, we discuss how such models could be leveraged to develop fast, efficient, robust, and novel algorithms to aid in the discovery of anti-EBOV drugs. We further discuss the deep neural network as a plausible ML algorithm for predicting anti-EBOV compounds. We also summarize the plethora of data sources necessary for ML predictions in the form of systematic and comprehensive high-dimensional data. With ongoing efforts to eradicate EVD, the application of artificial intelligence-based ML to EBOV drug discovery research can promote data-driven decision making and may help to reduce the high attrition rates of compounds in the drug development pipeline.
Vortical Differential Scattering of Twisted Light by Dielectric Chiral Particles
Authors: Wang Ju , Cui Zhiwei , Shi Yiyu , Guo Shenyan , Wu Fuping
Published: February 2023

Twisted light carrying orbital angular momentum inherently possesses a handedness, which would produce chiroptical responses by chiral matter. In this work, a scheme of vortical differential scattering (VDS) was utilized to investigate the chiroptical responses of dielectric chiral particles to the twisted light. The simulation results showed that the dielectric chiral particles have obvious VDS signals under the illumination of twisted light with opposite topological charges. The larger the relative chiral parameter of the particles, the more obvious the VDS signals. The extreme value of the VDS signals can be enhanced by reducing the waist radius of the twisted light or by adopting the circularly polarized twisted light. In addition, non-spherical dielectric chiral particles exhibit more obvious VDS signals compared with spherical ones. These findings are expected to find potential applications in the detection and identification of chiral substances.
Towards Microbial Food Safety of Sprouts: Photodynamic Decontamination of Seeds
Authors: Fellner Andreas , Hamminger Christoph , Fefer Michael , Liu Jun , Plaetzer Kristjan
Published: February 2023

The climate crisis is one of the biggest challenges for humanity in the 21st century. Production and consumption of meat contributes to global warming by causing emissions of climate-relevant gases. Freshly grown sprouts are part of an alternative, as they are less polluting but still a nutritious food. However, warm humid sprouting conditions may cause pathogenic microorganisms to thrive. Decontamination methods for raw sprouts are therefore relevant. Photodynamic Inactivation (PDI) is a novel approach that uses photoactivatable molecules (photosensitisers, PS) and visible or near-infrared light to produce reactive oxygen species (ROS). These ROS kill microorganisms by oxidative processes. Here, we test the application of PDI based on sodium-magnesium-chlorophyllin (Chl, approved as food additive E140) for photo-decontamination of mung bean, radish, and buckwheat seeds. Seeds were contaminated with Listeria innocua, serving as a model system for Listeria monocytogenes, subjected to PDI using an LED array with 395 nm and tested for remaining bacterial contamination by CFU counting. PDI based on 100 µM Chl reduces the bacterial load of mung bean and radish seeds by 99.9% (radiant exposure 56.4 J/cm2 and 28.2 J/cm2, respectively), and of buckwheat seeds by <90% reduction after illumination with 28.2 J/cm2. Neither weight nor the germination rates of seeds are affected by PDI. Interestingly, the effect of PDI on seeds is partially maintained on stored sprouts after germination: The bacterial load on mung bean sprouts is reduced by more than 99.9% after phototreatment of seeds with 100 µM Chl and illumination at 56.4 J/cm2. In conclusion, we suggest PDI based on Chl as an effective and biocompatible method for the decontamination of seeds and sprouts for human consumption from Listeria.
Process-Specific Topology Optimization Method Based on Laser-Based Additive Manufacturing of AlSi10Mg Components: Material Characterization and Evaluation
Authors: Czink Steffen , Holoch Jan , Renz Robert , Schulze Volker , Albers Albert , Dietrich Stefan
Published: February 2023

In the laser powder bed fusion process (PBF-LB), components are built up incrementally by locally melting metal powder with a laser beam. This process leads to inhomogeneous material properties of the manufactured components. By integrating these specific material properties into a topology optimization algorithm, product developers can be supported in the early phases of the product development process, such as design finding. For this purpose, a topology optimization method was developed, which takes the inhomogeneous material properties of components fabricated in the PBF-LB process into account. The complex pore architecture in PBF-LB components was studied with micro-computed tomography (µCT). Thereby, three characteristic regions of different porosity were identified and analyzed. The effective stiffness in each of these regions was determined by means of resonant ultrasonic spectroscopy (RUS) as well as finite element analysis. Afterward, the effective stiffness is iteratively considered in the developed topology optimization method. The resulting design proposals of two optimization cases were analyzed and compared to design proposals derived from a standard topology optimization. To evaluate the developed topology optimization method, the derived design proposals were additionally manufactured in the PBF-LB process, and the characteristic pore architecture was analyzed by means of µCT.
Exergoeconomic Analysis of an Integrated Solar Combined Cycle in the Al-Qayara Power Plant in Iraq
Authors: Talal Wadah , Akroot Abdulrazzak
Published: February 2023

Enhancing the sustainability and diversification of Iraq’s electricity system is a strategic objective. Achieving this goal depends critically on increasing the use of renewable energy sources (RESs). The significance of developing solar-powered technologies becomes essential at this point. Iraq, similar to other places with high average direct normal irradiation, is a good location for concentrated solar thermal power (CSP) technology. This study aims to recover the waste heat from the gas turbine cycle (GTC) in the Al-Qayara power plant in Iraq and integrate it with a solar power tower. A thermoeconomic analysis has been done to support the installation of an integrated solar combined cycle (ISCC), which uses concentrated solar tower technology. The results indicate that the examined power plant has a total capacity of 561.5 MW, of which 130.4 MW is due to the waste heat recovery of G.T.s, and 68 MW. is from CSP. Due to the waste heat recovery of GTC, the thermal and exergy efficiencies increase by 10.99 and 10.61%, respectively, and the overall unit cost of production is 11.43 USD/MWh. For ISCC, the thermal and exergy efficiencies increase by 17.96 and 17.34%, respectively, and the overall unit cost of production is 12.39 USD/MWh. The integrated solar combined cycle’s lowest monthly capacity was about 539 MW in September, while its highest monthly capacity was approximately 574.6 MW in April.
Improving the Modeling of Pressure Pulsation and Cavitation Prediction in a Double-Volute Double-Suction Pump Using Mosaic Meshing Technology
Authors: Arocena Virgel M. , Danao Louis Angelo M.
Published: February 2023

Over the years, Computational Fluid Dynamics (CFD) has been an integral part of most pump design processes. Unfortunately, as calculation schemes and flow investigations become more complicated, the cost of conducting numerical simulations also becomes more expensive in terms of computational time. To remedy this, cutting-edge technology, together with novel calculation techniques, are continuously introduced with the end target of producing more accurate results and faster computing time. In this paper, CFD simulations are run on a numerical model of a double-volute double-suction pump prepared using ANSYS Fluent Mosaic meshing technology. Poly-Hexcore, the first application of Mosaic technology, fills the bulk region with octree hexes, keeps a high-quality layered poly-prism mesh in the boundary layer, and conformally connects these two meshes with general polyhedral elements. This technology promises to provide a lower number of cells along with a significant increase in computing speed. In this paper, steady state results of the model with Mosaic Poly-Hexcore mesh with ~37% fewer cells produced comparable results with a similarly sized model prepared with multi-block structured hexagonal mesh. The predicted pump head, efficiency and shaft power under the design conditions were within 1% for both models, while calculation time was reduced by ~25%. Additional simulations using the Poly-Hexcore mesh showed that the model was able to closely predict the pump’s NPSH3 for 0.8QD, 1.0QD, and 1.2QD compared with the manufacturer’s data. Under cavitating flow conditions, the formation of vapor bubbles was observed on the suction side, starting at the leading edge of the blade and slowly forming as thin sheets towards the trailing edge as the suction pressure is reduced. Lastly, pressure fluctuations were observed from pressure coefficient data collected at several monitoring points in the volute and the impeller. It was seen that due to the interaction between the stationary casing and the rotating impeller, pulsations were equivalent to the blade passing frequency and its harmonics.
Prediction of the Soil Permeability Coefficient of Reservoirs Using a Deep Neural Network Based on a Dendrite Concept
Authors: Kim Myeong Hwan , Song Chul Min
Published: February 2023

Changes in the pore water pressure of soil are essential factors that affect the movement of structures during and after construction in terms of stability and safety. Soil permeability represents the quantity of water transferred using pore water pressure. However, these changes cannot be easily identified and require considerable time and money. This study predicted and evaluated the soil permeability coefficient using a multiple regression (MR) model, adaptive network-based fuzzy inference system (ANFIS), general deep neural network (DNN) model, and DNN using the dendrite concept (DNN−T, which was proposed in this study). The void ratio, unit weight, and particle size were obtained from 164 undisturbed samples collected from the embankments of reservoirs in South Korea as input variables for the aforementioned models. The data used in this study included seven input variables, and the ratios of the training data to the validation data were randomly extracted, such as 6:4, 7:3, and 8:2, and were used. The analysis results for each model showed a median correlation of r = 0.6 or less and a low model efficiency of Nash–Sutcliffe efficiency (NSE) = 0.35 or less as a result of predicting MR and ANFIS. The DNN and DNN−T both have good performance, with a strong correlation of r = 0.75 or higher. Evidently, the DNN−T performance in terms of r, NSE, and root mean square error (RMSE) improved more than that of the DNN. However, the difference between the mean absolute percent error (MAPE) of DNN−T and the DNN was that the error of the DNN was small (11%). Regarding the ratio of the training data to the verification data, 7:3 and 8:2 showed better results compared to 6:4 for indicators, such as r, NSE, RMSE, and MAPE. We assumed that this phenomenon was caused by the DNN−T thinking layer. This study shows that DNN−T, which changes the structure of the DNN, is an alternative for estimating the soil permeability coefficient in the safety inspection of construction sites and is an excellent methodology that can save time and budget.
Current and Expected Development of Corporate Strategies for Managing Environmental Risks in Hungary
Authors: Fekete-Berzsenyi Hajnalka , Molnárné Barna Katalin , Koczor-Keul Melinda
Published: February 2023

Environmental challenges often present businesses with unexpected situations, and in order to address them, innovation in the direction of sustainability must become an unavoidable activity. This entails the transformation and development of the existing business models, assuming a great business risk. The occurrence of the risk and its extent can only be estimated, which is why it is important to have management models that are able to handle the challenges posed by new, constantly arising risk factors. We analyzed the largest companies based on the number of employees with headquarters or sites in Hungary with regard to the management methods used by them to manage environmental risks. The methods used were the analysis of variance and cluster analysis. Based on the results of the research it is clear that the companies surveyed are already very concerned with environmental opportunities and risks, and they expect that the role of innovations applied to manage them to play a more prominent role in their future target system. However, the level of this is significantly different and does not depend on the financial performance, and at the same time companies can be divided into distinct groups according to the level of environmental risk management.
Method on Early Identification of Low-Frequency Debris Flow Gullies along the Highways in the Chuanxi Plateau
Authors: Hu Guisheng , Huang Hong , Tian Shufeng , Rahman Mahfuzur , Shen Haowen , Yang Zhiquan
Published: February 2023

Low-frequency debris flows are characterized by strong concealment, high potential danger, and difficulty achieving an early warning; hence early identification of low-frequency debris flow gullies is crucial to mitigation. Here, an identification system for low-frequency debris flow gullies along the traffic arteries in the Chuanxi Plateau is proposed based on the identification and stability calculation of colluvium deposits in a hollow region (CDH) and the quantitative roundness analysis for the stones in a deposit fan. At first, for the watershed without a deposit fan, the CDH is identified and analyzed using the geomorphologic change point detection method combined with high-precision remote sensing images and field investigation. The watershed can be identified as a low-frequency debris flow gully with the safety factors (Fs) of all CDHs greater than 1. Then, the roundness of stones in the deposit fan is quantitatively analyzed. The watershed can also be identified as a low-frequency debris flow gully with the average roundness of the stones ranging from 0.30 to 0.41. Lastly, the identification system was tested and verified based on another ten watersheds along three traffic arteries. It shows that the method proposed in this paper has good applicability and high accuracy. Here we try to achieve the accurate early identification of low-frequency debris flow gullies by combining remote sensing interpretation and field investigation, which can provide theoretical support for predicting and mitigating debris flows in mountainous areas.
Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model
Authors: Ustad Astrid , Logacjov Aleksej , Trollebø Stine Øverengen , Thingstad Pernille , Vereijken Beatrix , Bach Kerstin , Maroni Nina Skjæret
Published: February 2023

Activity monitoring combined with machine learning (ML) methods can contribute to detailed knowledge about daily physical behavior in older adults. The current study (1) evaluated the performance of an existing activity type recognition ML model (HARTH), based on data from healthy young adults, for classifying daily physical behavior in fit-to-frail older adults, (2) compared the performance with a ML model (HAR70+) that included training data from older adults, and (3) evaluated the ML models on older adults with and without walking aids. Eighteen older adults aged 70–95 years who ranged widely in physical function, including usage of walking aids, were equipped with a chest-mounted camera and two accelerometers during a semi-structured free-living protocol. Labeled accelerometer data from video analysis was used as ground truth for the classification of walking, standing, sitting, and lying identified by the ML models. Overall accuracy was high for both the HARTH model (91%) and the HAR70+ model (94%). The performance was lower for those using walking aids in both models, however, the overall accuracy improved from 87% to 93% in the HAR70+ model. The validated HAR70+ model contributes to more accurate classification of daily physical behavior in older adults that is essential for future research.
Investigating the Improvement of Autonomous Vehicle Performance through the Integration of Multi-Sensor Dynamic Mapping Techniques
Authors: Seo Hyoduck , Lee Kyesan , Lee Kyujin
Published: February 2023

The emergence of autonomous vehicles marks a shift in mobility. Conventional vehicles have been designed to prioritize the safety of drivers and passengers and increase fuel efficiency, while autonomous vehicles are developing as convergence technologies with a focus on more than just transportation. With the potential for autonomous vehicles to serve as an office or leisure space, the accuracy and stability of their driving technology is of utmost importance. However, commercializing autonomous vehicles has been challenging due to the limitations of current technology. This paper proposes a method to build a precision map for multi-sensor-based autonomous driving to improve the accuracy and stability of autonomous vehicle technology. The proposed method leverages dynamic high-definition maps to enhance the recognition rates and autonomous driving path recognition of objects in the vicinity of the vehicle, utilizing multiple sensors such as cameras, LIDAR, and RADAR. The goal is to improve the accuracy and stability of autonomous driving technology.
CST: Complex Sparse Transformer for Low-SNR Speech Enhancement
Authors: Tan Kaijun , Mao Wenyu , Guo Xiaozhou , Lu Huaxiang , Zhang Chi , Cao Zhanzhong , Wang Xingang
Published: February 2023

Speech enhancement tasks for audio with a low SNR are challenging. Existing speech enhancement methods are mainly designed for high SNR audio, and they usually use RNNs to model audio sequence features, which causes the model to be unable to learn long-distance dependencies, thus limiting its performance in low-SNR speech enhancement tasks. We design a complex transformer module with sparse attention to overcome this problem. Different from the traditional transformer model, this model is extended to effectively model complex domain sequences, using the sparse attention mask balance model’s attention to long-distance and nearby relations, introducing the pre-layer positional embedding module to enhance the model’s perception of position information, adding the channel attention module to enable the model to dynamically adjust the weight distribution between channels according to the input audio. The experimental results show that, in the low-SNR speech enhancement tests, our models have noticeable performance improvements in speech quality and intelligibility, respectively.
Monitoring Irrigation in Small Orchards with Cosmic-Ray Neutron Sensors
Authors: Brogi Cosimo , Pisinaras Vassilios , Köhli Markus , Dombrowski Olga , Hendricks Franssen Harrie-Jan , Babakos Konstantinos , Chatzi Anna , Panagopoulos Andreas , Bogena Heye Reemt
Published: February 2023

Due to their unique characteristics, cosmic-ray neutron sensors (CRNSs) have potential in monitoring and informing irrigation management, and thus optimising the use of water resources in agriculture. However, practical methods to monitor small, irrigated fields with CRNSs are currently not available and the challenges of targeting areas smaller than the CRNS sensing volume are mostly unaddressed. In this study, CRNSs are used to continuously monitor soil moisture (SM) dynamics in two irrigated apple orchards (Agia, Greece) of ~1.2 ha. The CRNS-derived SM was compared to a reference SM obtained by weighting a dense sensor network. In the 2021 irrigation period, CRNSs could only capture the timing of irrigation events, and an ad hoc calibration resulted in improvements only in the hours before irrigation (RMSE between 0.020 and 0.035). In 2022, a correction based on neutron transport simulations, and on SM measurements from a non-irrigated location, was tested. In the nearby irrigated field, the proposed correction improved the CRNS-derived SM (from 0.052 to 0.031 RMSE) and, most importantly, allowed for monitoring the magnitude of SM dynamics that are due to irrigation. The results are a step forward in using CRNSs as a decision support system in irrigation management.
Effective Techniques for Multimodal Data Fusion: A Comparative Analysis
Authors: Pawłowski Maciej , Wróblewska Anna , Sysko-Romańczuk Sylwia
Published: February 2023

Data processing in robotics is currently challenged by the effective building of multimodal and common representations. Tremendous volumes of raw data are available and their smart management is the core concept of multimodal learning in a new paradigm for data fusion. Although several techniques for building multimodal representations have been proven successful, they have not yet been analyzed and compared in a given production setting. This paper explored three of the most common techniques, (1) the late fusion, (2) the early fusion, and (3) the sketch, and compared them in classification tasks. Our paper explored different types of data (modalities) that could be gathered by sensors serving a wide range of sensor applications. Our experiments were conducted on Amazon Reviews, MovieLens25M, and Movie-Lens1M datasets. Their outcomes allowed us to confirm that the choice of fusion technique for building multimodal representation is crucial to obtain the highest possible model performance resulting from the proper modality combination. Consequently, we designed criteria for choosing this optimal data fusion technique.
Quality Management System for an IoT Meteorological Sensor Network—Application to Smart Seoul Data of Things (S-DoT)
Authors: Park Moon-Soo , Baek Kitae
Published: February 2023

Meteorological data with a high horizontal resolution are essential for user-specific weather application services, such as flash floods, heat waves, strong winds, and road ice, in urban areas. National meteorological observation networks, such as the Automated Synoptic Observing System (ASOS) and Automated Weather System (AWS), provide accurate but low horizontal resolution data to address urban-scale weather phenomena. Many megacities are constructing their own Internet of Things (IoT) sensor networks to overcome this limitation. This study investigated the status of the smart Seoul data of things (S-DoT) network and the spatial distribution of temperature on heatwave and coldwave event days. The temperature at above 90% of S-DoT stations was higher than that at the ASOS station, mainly because of different surface covers and surrounding local climate zones. A quality management system for an S-DoT meteorological sensor network (QMS-SDM) comprising pre-processing, basic quality control, extended quality control, and data reconstruction using spatial gap-filling was developed. The upper threshold temperatures for the climate range test were set higher than those adopted by the ASOS. A 10-digit flag for each data point was defined to discriminate between normal, doubtful, and erroneous data. Missing data at a single station were imputed using the Stineman method, and the data with spatial outliers were filled with values at three stations within 2 km. Using QMS-SDM, irregular and diverse data formats were changed to regular and unit-format data. QMS-SDM application increased the amount of available data by 20–30%, and significantly improved data availability for urban meteorological information services.
Adaptive Predefined-Time Sliding Mode Control for QUADROTOR Formation with Obstacle and Inter-Quadrotor Avoidance
Authors: Liu Hao , Tu Haiyan , Huang Shan , Zheng Xiujuan
Published: February 2023

In this paper, aiming at the problem of control and obstacle avoidance in quadrotor formation when mathematical modeling is not accurate, the artificial potential field method with virtual force is used to plan the obstacle avoidance path of quadrotor formation to solve the problem that the artificial potential field method may fall into local optimal. The adaptive predefined-time sliding mode control algorithm based on RBF neural networks enables the quadrotor formation to track the planned trajectory in a predetermined time and also adaptively estimates the unknown interference in the mathematical model of the quadrotor to improve the control performance. Through theoretical derivation and simulation experiments, this study verified that the proposed algorithm can make the planned trajectory of the quadrotor formation avoid obstacles and make the error between the true trajectory and the planned trajectory converge within a predetermined time under the premise of adaptive estimation of unknown interference in the quadrotor model.
Modeling of Packet Error Rate Distribution Based on Received Signal Strength Indications in OMNeT++ for Wake-Up Receivers
Authors: Baazaoui Mohamed Khalil , Ketata Ilef , Fakhfakh Ahmed , Derbel Faouzi
Published: February 2023

Wireless sensor network (WSN) with energy-saving capabilities have drawn considerable attention in recent years, as they are the key for long-term monitoring and embedded applications. To improve the power efficiency of wireless sensor nodes, a wake-up technology was introduced in the research community. Such a device reduces the system’s energy consumption without affecting the latency. Thereby, the introduction of wake-up receiver (WuRx)-based technology has grown in several sectors. The use of WuRx in a real environment without consideration of physical environmental conditions, such as the reflection, refraction, and diffraction caused by different materials, that affect the reliability of the whole network. Indeed, the simulation of different protocols and scenarios under such circumstances is a success key for a reliable WSN. Simulating different scenarios is required to evaluate the proposed architecture before its deployment in a real-world environment. The contribution of this study emerges in the modeling of different link quality metrics, both hardware and software metrics that will be integrated into an objective modular network testbed in C++ (OMNeT++) discrete event simulator afterward are discussed, with the received signal strength indicator (RSSI) for the hardware metric case and the packet error rate (PER) for the software metric study case using WuRx based on a wake-up matcher and SPIRIT1 transceiver. The different behaviors of the two chips are modeled using machine learning (ML) regression to define parameters such as sensitivity and transition interval for the PER for both radio modules. The generated module was able to detect the variation in the PER distribution as a response in the real experiment output by implementing different analytical functions in the simulator.
Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance
Authors: Martins Alexandre , Fonseca Inácio , Farinha José Torres , Reis João , Cardoso António J. Marques
Published: February 2023

Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected by the sensors are reliable, it is necessary to have metrological traceability made by successive calibrations from higher standards to the sensors used in the factories. To ensure the reliability of the data, a calibration strategy must be put in place. Usually, sensors are only calibrated on a periodic basis; so, they often go for calibration without it being necessary or collect data inaccurately. In addition, the sensors are checked often, increasing the need for manpower, and sensor errors are frequently overlooked when the redundant sensor has a drift in the same direction. It is necessary to acquire a calibration strategy based on the sensor condition. Through online monitoring of sensor calibration status (OLM), it is possible to perform calibrations only when it is really necessary. To reach this end, this paper aims to provide a strategy to classify the health status of the production equipment and of the reading equipment that uses the same dataset. A measurement signal from four sensors was simulated, for which Artificial Intelligence and Machine Learning with unsupervised algorithms were used. This paper demonstrates how, through the same dataset, it is possible to obtain distinct information. Because of this, we have a very important feature creation process, followed by Principal Component Analysis (PCA), K-means clustering, and classification based on Hidden Markov Models (HMM). Through three hidden states of the HMM, which represent the health states of the production equipment, we will first detect, through correlations, the features of its status. After that, an HMM filter is used to eliminate those errors from the original signal. Next, an equal methodology is conducted for each sensor individually and using statistical features in the time domain where we can obtain, through HMM, the failures of each sensor.
LoRa Technology in Flying Ad Hoc Networks: A Survey of Challenges and Open Issues
Authors: Paredes William David , Kaushal Hemani , Vakilinia Iman , Prodanoff Zornitza
Published: February 2023

The Internet of Things (IoT) and Flying Ad Hoc Networks (FANETs) have become hot topics among researchers because of the increased availability of Unmanned Aerial Vehicles (UAVs) and the electronic components required to control and connect them (e.g., microcontrollers, single board computers, and radios). LoRa is a wireless technology, intended for the IoT, that requires low power and provides long-range communications, which can be useful for ground and aerial applications. This paper explores the role that LoRa plays in FANET design by presenting a technical overview of both, and by performing a systematic literature review based on a breakdown of the communications, mobility and energy topics involved in a FANET implementation. Furthermore, open issues in protocol design are discussed, as well as other challenges associated with the use of LoRa in the deployment of FANETs.
Smart Cementitious Sensors with Nano-, Micro-, and Hybrid-Modified Reinforcement: Mechanical and Electrical Properties
Authors: Thomoglou Athanasia K. , Falara Maria G. , Gkountakou Fani I. , Elenas Anaxagoras , Chalioris Constantin E.
Published: February 2023

The current paper presents the results of an experimental study of carbon nano-, micro-, and hybrid-modified cementitious mortar to evaluate mechanical performance, energy absorption, electrical conductivity, and piezoresistive sensibility. Three amounts of single-walled carbon nanotubes (SWCNTs), namely 0.05 wt.%, 0.1 wt.%, 0.2 wt.%, and 0.3 wt.% of the cement mass, were used to prepare nano-modified cement-based specimens. In the microscale modification, 0.05 wt.%, 0.5 wt.%, 1.0 wt.% carbon fibers (CFs) were incorporated in the matrix. The hybrid-modified cementitious specimens were enhanced by adding optimized amounts of CFs and SWCNTs. The smartness of modified mortars, indicated by their piezoresistive behavior, was investigated by measuring the changes in electrical resistivity. The effective parameters that enhance the composites’ mechanical and electrical performance are the different concentrations of reinforcement and the synergistic effect between the types of reinforcement used in the hybrid structure. Results reveal that all the strengthening types improved flexural strength, toughness, and electrical conductivity by about an order of magnitude compared to the reference specimens. Specifically, the hybrid-modified mortars presented a marginal reduction of 1.5% in compressive strength and an increase in flexural strength of 21%. The hybrid-modified mortar absorbed the most energy, 1509%, 921%, and 544% more than the reference mortar, nano-modified mortar, and micro-modified mortar, respectively. The change rate of impedance, capacitance, and resistivity in piezoresistive 28-day hybrid mortars improved the tree ratios by 289%, 324%, and 576%, respectively, for nano-modified mortars and by 64%, 93%, and 234%, respectively, for micro-modified mortars.
Wafer Type Ion Energy Monitoring Sensor for Plasma Diagnosis
Authors: Han Chansu , Koo Yoonsung , Kim Jaehwan , Choi Kwangwook , Hong Sangjeen
Published: February 2023

We propose a wafer-type ion energy monitoring sensor (IEMS) that can measure the spatially resolved distribution of ion energy over the 150 mm plasma chamber for the in situ monitoring of the semiconductor fabrication process. The IEMS can directly be applied to the semiconductor chip production equipment without further modification of the automated wafer handling system. Thus, it can be adopted as an in situ data acquisition platform for plasma characterization inside the process chamber. To achieve ion energy measurement on the wafer-type sensor, the injected ion flux energy from the plasma sheath was converted into the induced currents on each electrode over the wafer-type sensor, and the generated currents from the ion injection were compared along the position of electrodes. The IEMS operates without problems in the plasma environment and has the same trends as the result predicted through the equation.