bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamics Analysis of a Double-Plunger-Type Turbine Overspeed Protection Mechanism
Authors: Jiang Haicheng , Wang Decheng , Cheng Peng , Shao Chenxi
Published: November 2023

A double-plunger type overspeed protection mechanism has excellent performance in turbine overspeed protection, but the complicated operating principle thereof makes long-term use difficult. In this study, a dynamic model is established to explain the relationship between the motion state and the force situation. Through the analysis of the dynamic model, the specific process of the linked plunger for achieving overspeed protection with tripping action is determined, and the motion law of the inner and outer plungers is clarified. Additionally, the complete solution method and calculation procedure for the tripping speed of the submerged plunger are established, and the specific process for solving the relevant equations with numerical iteration is clarified. Finally, a rigid-flexible coupling virtual prototype model is established, which validates the conclusion that initial eccentric distance and spring stiffness are key factors affecting tripping speed and the motion process of inner and outer plungers.
Tyre–Road Heat Transfer Coefficient Equation Proposal
Authors: Cattani Paolo , Cattani Lucia , Magrini Anna
Published: November 2023

Tyres are one of the most important elements of a vehicle because they are the link to the road and have a huge impact on traffic-related pollution. Knowing their behaviour, thus being able to use them at their best and reducing their wear rate, is one of the means of improving their lifetime, which means decreasing traffic environmental impact. In order to understand how tyres behave and to predict the real-time tyre–road coefficient of friction, which is strongly influenced by the temperature, in the last few years several complex thermo-mechanical models of heat transfer inside the tyre have been developed. However, in the current state of the art of the literature and practice, there is still an important parameter regarding such models that is not deeply studied. This parameter is the heat transfer coefficient between the tyre and the road at the contact patch, which usually is considered as a constant. The current research paper allows understanding that such an approximation is not always valid for all of the speeds and tyre loads of city and race cars; instead, it is developed an equation that, for the first time, calculates the real-time, dynamic tyre–road heat transfer coefficient, taking into account the tyre’s travelling speed and the footprint length. The equation results are in good agreement with the empirical values coming from the literature and permit understanding how much such a parameter can vary, depending on the tyre use range. The formulation is simple enough to be easily implemented in existing thermodynamic tyre models without requiring meaningful computational time.
Sliding Mode Controller for Autonomous Tractor-Trailer Vehicle Reverse Path Tracking
Authors: Bin Salamah Yasser
Published: November 2023

In the past few years, there has been a growing interest among researchers in developing control systems for autonomous vehicles, specifically for tractor-trailer systems. This newfound interest is driven by the potential benefits of enhancing safety, reducing costs, and addressing labor shortages in the industry. Two industries that could reap the rewards of these systems’ advancements are cargo and agriculture transportation. One of the challenging tasks for the truck trailer vehicle is driving in reverse. Backward path tracking of tractor-trailers is a complex control problem with practical applications. The difficulty in controlling the vehicle arises due to its unstable internal dynamics, coupled nonlinear terms, and the under-actuated nature of the system. There is also a limit to the angle at which the steering can be turned before the risk of a jackknife accident increases significantly. In response to these challenges, this paper introduces a robust sliding mode controller designed for path tracking in reverse-driving tractor-trailer systems. The novelty of our work lies in addressing these challenges, which have not been extensively studied in the past. The proposed controller is analyzed, and its performance is tested and verified using different scenarios. The simulation examples show superior control performance, and we anticipate that this novel controller holds the potential to be widely adopted as a fundamental component in the path-tracking algorithms of autonomous truck trailer systems.
Synthesis and Preliminary Screening of the Biological Activity of Sulindac Sulfoximine Derivatives
Authors: Cardellicchio Cosimo , Laquintana Valentino , Iacobazzi Rosa Maria , Denora Nunzio , Scilimati Antonio , Perrone Maria Grazia , Capozzi Maria Annunziata M.
Published: November 2023

Sulindac is a well-known anti-inflammatory agent, sometimes employed as an adjuvant in antitumor therapy. Due to the recent interest in sulfoximine for its potential chemotherapeutics, we decided to transform sulindac and its methyl ester into the corresponding sulfoximines to test their antitumor activity. These compounds were fully characterized. Eventually, sulindac, sulindac methyl ester and the two novel corresponding sulfoximines were tested against malignant cells of U-87 glioblastoma, MCF-7 human breast cancer, HepG2 human liver hepatocellular carcinoma, CaCo-2 human colon cancer, and HeLa human cervical cancer. Interesting preliminary results were observed that encourage new investigations in this research theme.
Experimental and Statistical Study on the Formation Characteristics and Discrimination Criteria of River Blockages Caused by Landslides
Authors: Zhuo Li , Hu Yun-Feng , Xiao Ming-Li , Luo Yu , Liu Huai-Zhong , Xie Hong-Qiang , Pei Jian-Liang
Published: November 2023

The discrimination of river blockages is very important for the risk assessment of landslide disasters and secondary hazards. Experimental studies and statistical analyses were carried out to explore the formation process and discriminant criteria of river blockages caused by landslides. An adjustable slide chute was designed and built to conduct forty-five landslide experiments. According to the experimental results, river blockage was identified as having six types based on the differences between the water depth and the height of the landslide dam, and the degree of river blockage increased from 70% to 100% as the chute angle, particle size, and landslide volume increased. It is also found that landslide volume controls the landslide dam height and degree of blockage, and particle size and slide angle control both the landslide velocity as it enters the river and the cross-section shape of the landslide dam. To investigate more influence factors, a statistical investigation of 60 real landslide cases was carried out, and it revealed that some geometric attributes related to landslide volume have the highest correlation with river blockage, especially landslide thickness. Finally, an improved probability model was proposed to assess the possibility of complete blockage, and it has overall accuracies of 91.1% and 83.3% when applied to predict experimental landslide cases and real landslide cases, respectively.
The Determination of Combustion for Different Pellets Based on Ostwald Diagrams in a Domestic Stove under Experimental Conditions
Authors: González Juan Félix , Álvarez Murillo Andrés , Díaz García Diego , Nogales-Delgado Sergio
Published: November 2023

The global energy scenario is becoming a vital aspect of the sustainable economic development of regions and countries. Current changes in energy production, mainly due to scarcity and geopolitical factors, have proven the need for changes in energy distribution towards a lower energy dependence. Moreover, a considerable amount of biomass waste is generated in many regions, because of agro-industrial activities, whose management could contribute to energy production. The aim of this work was to study the optimization of the combustion process in a biomass stove by using different raw materials as fuels, such as pine, poplar, and plum tree pellets. For that purpose, power, excess air, and biomass content were optimized, among other parameters, and exhaust gases were analyzed with a Testo 335 analyzer, while temperatures were recorded with temperature probes. In conclusion, high yields were found for the optimized parameters of the studied biomass products (ranging from 91.1% for poplar pellets to 92.34% for pine pellets), making these three biomass fuels suitable for combustion in the abovementioned stove. Also, increasing biomass flow by 25% in the stove contributed to a higher efficiency of the process, especially in the case of plum tree pellets.
Morphological Evaluation of the Incisive Canal in the Aspect of the Diagnosis and Planning of Orthodontic Treatment—CBCT Study
Authors: Kuc Anna Ewa , Kotuła Jacek , Nawrocki Jakub , Szeląg Ewa , Kawala Beata , Lis Joanna , Sarul Michał
Published: November 2023

Background: Understanding the anatomy of the incisive canal is crucial for effective diagnosis and treatment planning in clinical orthodontics. This is because, during orthodontic tooth movement, there is a risk of contact between the roots of the upper central incisors and the incisive canal. Objective: The aim of this study was to assess the anatomical variability of the incisive canal using cone beam computed tomography (CBCT), as well as to evaluate its correlation with age, sex, and the position of the maxillary central incisors. There are only a few studies on this topic. Materials and methods: We analysed CBCT data from 67 patients aged from 13 to 49 years. This study was conducted at the Wroclaw Medical University. Measurements were performed twice by two independent researchers, and intra-observer error and correlation were calculated. The mean difference between the first and second observations and between observers was also assessed. We examined the dimensions of the incisive canal and its relationship to the roots of the upper central incisors in relation to age and gender. Results: Our study results revealed a significant correlation between the width and length of the incisive canal. Males exhibited a significantly greater canal length at the lowest point of the incisive canal on the palatal wall. Additionally, males had wider canals compared to females. The analysis of canal width and distance between the most mesial point of the root and the line passing through the most anterior point of the incisive canal showed a negative correlation in all age groups of men. The analysis of incisal inclination and incisal canal inclination showed a very strong relationship, especially in the age group of 13 to 20 years. Several potential risk groups of contact between the roots of central incisors and the incisive canal have been identified based on their structure and the planned incisors’ orthodontic movement. Conclusions and implications: Knowledge of the anatomy of the incisive canal and the use of 3D imaging in high-risk patients can prevent resorption of the incisor root by considering the individual anatomical conditions of the patient when planning orthodontic tooth movement. We recommend performing a CBCT scan before starting orthodontic treatment in the case of moderate and significant retraction of the incisors, or a significant change in their inclination due to the wide anatomical diversity of the incisive canal, especially in adult patients.
Construction and Application of Fuzzy Comprehensive Evaluation Model for Rockburst Based on Microseismic Monitoring
Authors: Li Xuelong , Chen Deyou , Fu Jianhua , Liu Shumin , Geng Xuesheng
Published: November 2023

Based on the relationship between rockburst and microseismic event indicators, this study proposes that the risk of rockburst in mine working faces, roadways, and even the entire mine should be studied through the “double high” risk evaluation of microseismic events. The 43 evaluation indexes of rockburst were optimized into eight indexes by using the expert scoring method. Considering the eight indexes as the basic events and the “double high” risk of microseismic events as the top event, the “double high” accident tree of microseismic events was established. According to the qualitative analysis results of the accident tree, the microseismic activity evaluation index was determined, and the “double high” risk evaluation index system was constructed for microseismic events. The system included three first-level indicators and eight second-level indicators. The fuzzy hierarchical comprehensive evaluation model was used to evaluate the “double high” risk of microseismic events in the Yanbei Coal Mine. In this paper, a microseismic monitoring and evaluation index model is constructed to simplify the existing evaluation system, which is convenient to effectively establish the connection between microseismic monitoring data and rockburst index and provide important theoretical support for underground monitoring and rockburst prevention.
Design and Implementation of Health and Risk Level Assessment for Socially Disadvantaged Patients
Authors: Son Mingeun , Lee Gangpyo , Park Jaeyong , Choi Min
Published: November 2023

The platform developed in this study provides users with mental and physical health status monitoring services. Recently, it has been difficult for people with various respiratory diseases and mental health conditions to receive regular examinations/observation from specialized medical institutions. Therefore, in this study, because the need for the continuous health status measurement/monitoring of subjects was recognized, a device for constantly monitoring trends and changes in blood oxygen saturation was developed to ensure safety. Oxygen saturation below 90 indicates an emergency. In addition, we conducted a hypothesis test for analyzing the relationship between sensor data and depression and anxiety. The sensor data used in the research were location, gyroscope metrics, touch, data usage, and sleep mode from a smartphone. Correlation analysis and multiple regression analysis were conducted to analyze the relationship between the collected sensor data and depression and anxiety. As a result of the research, it was found that among the general characteristics, income, location, sleep duration, and gyroscope were found to have a negative effect on depression and anxiety, whereas touch was shown to have a positive (+) effect. It was confirmed that sensor data measured over runtime can be used as information that can explain users’ depression and anxiety.
Examining Gait Characteristics in People with Osteoporosis Utilizing a Non-Wheeled Smart Walker through Spatiotemporal Analysis
Authors: Ejaz Nazia , Khan Saad Jawaid , Azim Fahad , Faiz Mehwish , Teuțan Emil , Pleșa Alin , Ianosi-Andreeva-Dimitrova Alexandru , Stan Sergiu-Dan
Published: November 2023

Fragility fractures, caused by low-energy trauma, are a significant global health concern, with 158 million people aged 50 and over at risk. Hip fractures, a common issue in elderly patients, are often linked to underlying conditions such as osteoporosis. This study proposed a cost-effective solution using a non-wheeled smart walker with load sensors to measure gait parameters, addressing the high cost of traditional gait analysis equipment, the prototype used PASCO load cells PS2200 for force measurement, eliminating the need for Arduino UNO or microcontroller-based hardware. A lightweight amplifier PS2198 amplified the signal, which was transmitted via USB to a personal computer. PASCO capstone software was used for data recording and visualization. The smart walker was tested on forty volunteers divided into two equal groups: those with osteoporosis and those without, by performing a 10 m walk test three times. ANOVA comparing spatiotemporal parameters (TSPs) of the two participant groups (α = 0.05) showed that significant differences lay in terms of time taken to complete the walk test (p < 0.01), left step length (p = 0.03), walking speed (p = 0.02), and stride length (p < 0.02). The results indicate that this smart walker is a reliable tool for assessing gait patterns in individuals with osteoporosis. The proposed system can be an alternative for time consuming and costly methods such as motion capture, and for socially stigmatizing devices such as exoskeletons. It can also be used further to identify risk factors of osteoporosis.
Experimental and Simulation Studies for Purification and Etherification of Glycerol from the Biodiesel Industry
Authors: Silva Silvia S. O. , Nascimento Matheus R. , Lima Ricardo J. P. , Luna Francisco Murilo Tavares , Cavalcante Júnior Célio Loureiro
Published: November 2023

In this study, a purification route was applied to crude glycerol and its valorization via etherification was evaluated. Crude glycerol samples were obtained through transesterification reactions of soybean oil with methanol using potassium hydroxide as catalyst. A set of separation steps (acidification, neutralization, salt precipitation, evaporation and removal of contaminants using ion-exchange resins) was performed for purification of crude glycerol. The glycerol contents of crude samples were 46% wt., and for purified samples they were above 98% wt. The etherification reactions were carried out with purified samples and different alcohols (ethanol, isopropanol and 3-methyl-1-butanol) placed into a batch reactor, using a small amount of Amberlyst 15 as a catalyst, with autogenous pressure and solvent-free conditions. The glycerol conversion, selectivity and yield to ethers were evaluated. A glycerol conversion of up to 97% wt. was obtained when using ethanol. For isopropanol, the glycerol conversion rate was 85% (97.1% of monoether and 2.8% of diether). However, the selectivity to ethers for 3-methyl-1-butanol was negligible (<3% wt.). A process simulation for the purification and etherification steps integrated with a biodiesel production process was assessed in terms of productivity and energy consumption, considering different scenarios of glycerol/alcohol molar ratios. Finally, main impacts on the overall energy consumption were evaluated for the purification processes (glycerol and ethers).
Dynamics Analysis of a Discrete-Time Commensalism Model with Additive Allee for the Host Species
Authors: Chong Yanbo , Kashyap Ankur Jyoti , Chen Shangming , Chen Fengde
Published: November 2023

We propose and study a class of discrete-time commensalism systems with additive Allee effects on the host species. First, the single species with additive Allee effects is analyzed for existence and stability, then the existence of fixed points of discrete systems is given, and the local stability of fixed points is given by characteristic root analysis. Second, we used the center manifold theorem and bifurcation theory to study the bifurcation of a codimension of one of the system at non-hyperbolic fixed points, including flip, transcritical, pitchfork, and fold bifurcations. Furthermore, this paper used the hybrid chaos method to control the chaos that occurs in the flip bifurcation of the system. Finally, the analysis conclusions were verified by numerical simulations. Compared with the continuous system, the similarities are that both species’ densities decrease with increasing Allee values under the weak Allee effect and that the host species hastens extinction under the strong Allee effect. Further, when the birth rate of the benefited species is low and the time is large enough, the benefited species will be locally asymptotically stabilized. Thus, our new finding is that both strong and weak Allee effects contribute to the stability of the benefited species under certain conditions.
Characteristics of Sensory Neuron Dysfunction in Amyotrophic Lateral Sclerosis (ALS): Potential for ALS Therapy
Authors: Seki Soju , Kitaoka Yoshihiro , Kawata Sou , Nishiura Akira , Uchihashi Toshihiro , Hiraoka Shin-ichiro , Yokota Yusuke , Isomura Emiko Tanaka , Kogo Mikihiko , Tanaka Susumu
Published: November 2023

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder characterised by the progressive degeneration of motor neurons, resulting in muscle weakness, paralysis, and, ultimately, death. Presently, no effective treatment for ALS has been established. Although motor neuron dysfunction is a hallmark of ALS, emerging evidence suggests that sensory neurons are also involved in the disease. In clinical research, 30% of patients with ALS had sensory symptoms and abnormal sensory nerve conduction studies in the lower extremities. Peroneal nerve biopsies show histological abnormalities in 90% of the patients. Preclinical research has reported several genetic abnormalities in the sensory neurons of animal models of ALS, as well as in motor neurons. Furthermore, the aggregation of misfolded proteins like TAR DNA-binding protein 43 has been reported in sensory neurons. This review aims to provide a comprehensive description of ALS-related sensory neuron dysfunction, focusing on its clinical changes and underlying mechanisms. Sensory neuron abnormalities in ALS are not limited to somatosensory issues; proprioceptive sensory neurons, such as MesV and DRG neurons, have been reported to form networks with motor neurons and may be involved in motor control. Despite receiving limited attention, sensory neuron abnormalities in ALS hold potential for new therapies targeting proprioceptive sensory neurons.
Applications of Machine Learning to Diagnosis of Parkinson’s Disease
Authors: Lai Hong , Li Xu-Ying , Xu Fanxi , Zhu Junge , Li Xian , Song Yang , Wang Xianlin , Wang Zhanjun , Wang Chaodong
Published: November 2023

Background: Accurate diagnosis of Parkinson’s disease (PD) is challenging due to its diverse manifestations. Machine learning (ML) algorithms can improve diagnostic precision, but their generalizability across medical centers in China is underexplored. Objective: To assess the accuracy of an ML algorithm for PD diagnosis, trained and tested on data from different medical centers in China. Methods: A total of 1656 participants were included, with 1028 from Beijing (training set) and 628 from Fuzhou (external validation set). Models were trained using the least absolute shrinkage and selection operator–logistic regression (LASSO-LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGboost), support vector machine (SVM), and k-nearest neighbor (KNN) techniques. Hyperparameters were optimized using five-fold cross-validation and grid search techniques. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity (recall), specificity, precision, and F1 score. Variable importance was assessed for all models. Results: SVM demonstrated the best differentiation between healthy controls (HCs) and PD patients (AUC: 0.928, 95% CI: 0.908–0.947; accuracy: 0.844, 95% CI: 0.814–0.871; sensitivity: 0.826, 95% CI: 0.786–0.866; specificity: 0.861, 95% CI: 0.820–0.898; precision: 0.849, 95% CI: 0.807–0.891; F1 score: 0.837, 95% CI: 0.803–0.868) in the validation set. Constipation, olfactory decline, and daytime somnolence significantly influenced predictability. Conclusion: We identified multiple pivotal variables and SVM as a precise and clinician-friendly ML algorithm for prediction of PD in Chinese patients.
Estimating Optimal Cost, Insulation Layer Thickness, and Structural Layer Thickness of Different Composite Insulation External Walls Using Computational Methods
Authors: Alrasheed Mohammed R. A.
Published: November 2023

All the modern gadgets and space conditioning in buildings consume lots of energy. Energy consumption can be optimized using Composite Insulation External Walls (CIEW) built from mortar plaster and structural and insulation layers. This study aimed to improve the overall performance of CIEW by optimizing the structural and insulation layer thickness. The objective was to minimize the Life Cycle Cost (LCC) and maximize the Life Cycle Savings (LCS) of CIEW. The nonlinear Least Squares Estimation (LSE) optimization technique for optimizing LCC and LCS of CIEW was used in the study. The study considered three insulation materials—Extruded Polystyrene (XPS), Rock Wool (RW), and Glass Wool (GW)—across three heat sources, including Circulating Fluidized Bed (CFB), Grate-Fired Boiler (GFB), and Air-Source Heat Pump (ASHP). The Life Cycle Cost Analysis (LCCA) methodology suggested by Huang using a traditional optimization technique was used as a basis for mathematical formulations and result comparison. The payback period of CIEW with optimal structural and insulation layer thickness was computed. The findings revealed that applying the LSE method enabled greater economic efficiency than the LCCA method, with an up to 9.12% increase in LCS value and an up to 7.41% decrease in LCC value. The research also revealed significant correlations between insulation and structural layer thicknesses and economic parameters.
A Novel Predictive Multi-Marker Test for the Pre-Surgical Identification of Ovarian Cancer
Authors: Stephens Andrew N. , Hobbs Simon J. , Kang Sung-Woon , Bilandzic Maree , Rainczuk Adam , Oehler Martin K. , Jobling Tom W. , Plebanski Magdalena , Allman Richard
Published: November 2023

Ovarian cancer remains the most lethal of gynecological malignancies, with the 5-year survival below 50%. Currently there is no simple and effective pre-surgical diagnosis or triage for patients with malignancy, particularly those with early-stage or low-volume tumors. Recently we discovered that CXCL10 can be processed to an inactive form in ovarian cancers and that its measurement has diagnostic significance. In this study we evaluated the addition of processed CXCL10 to a biomarker panel for the discrimination of benign from malignant disease. Multiple biomarkers were measured in retrospectively collected plasma samples (n = 334) from patients diagnosed with benign or malignant disease, and a classifier model was developed using CA125, HE4, Il6 and CXCL10 (active and total). The model provided 95% sensitivity/95% specificity for discrimination of benign from malignant disease. Positive predictive performance exceeded that of “gold standard” scoring systems including CA125, RMI and ROMA% and was independent of menopausal status. In addition, 80% of stage I-II cancers in the cohort were correctly identified using the multi-marker scoring system. Our data suggest the multi-marker panel and associated scoring algorithm provides a useful measurement to assist in pre-surgical diagnosis and triage of patients with suspected ovarian cancer.
Palbociclib Combined with an Aromatase Inhibitor in Patients with Breast Cancer with Lung or Liver Metastases in US Clinical Practice
Authors: Brufsky Adam , Liu Xianchen , Li Benjamin , McRoy Lynn , Chen Connie , Layman Rachel M. , Rugo Hope S.
Published: November 2023

A cyclin-dependent kinase 4/6 inhibitor combined with endocrine therapy is the standard of care for patients with hormone receptor-positive/human epidermal growth factor 2-negative (HR+/HER2−) metastatic breast cancer (mBC), but real-world effectiveness data for patients with lung or liver metastases are limited. This retrospective study included data from the US Flatiron Health database of patients with HR+/HER2− mBC and lung or liver metastases treated with first-line palbociclib (PAL) plus an aromatase inhibitor (AI) or an AI alone in routine clinical practice. Overall survival (OS) and real-world progression-free survival (rwPFS) were assessed. A total of 891 patients were included (622 with lung metastasis, 376 with liver metastasis, and 107 with both lung and liver metastasis). After stabilized inverse probability of treatment weighting to balance patient characteristics, PAL + AI versus AI alone was associated with significantly prolonged OS (HR = 0.62; p < 0.001) and rwPFS (HR = 0.55; p < 0.001) in patients with lung metastases and numerically longer OS (HR = 0.73; p = 0.056) and significantly longer rwPFS (HR = 0.57, p < 0.001) for those with liver metastases. Overall, PAL + AI versus AI alone was associated with prolonged OS and rwPFS in routine clinical practice, supporting the use of first-line PAL + AI for patients with HR+/HER2− mBC with lung and/or liver metastases.
Polycaprolactone Composites/Blends and Their Applications Especially in Water Treatment
Authors: Kayan Gizem Özge , Kayan Asgar
Published: November 2023

Biodegradable poly(ɛ-caprolactone) (PCL) and its composites or blends have received a lot of attention in the last decade because of their potential applications in human life and environmental remediation. Greater efforts have been made to develop biodegradable chemical materials as adsorbents that do not pollute the environment in order to replace traditional materials. Among the numerous types of degradable materials, PCL is currently the most promising, the most popular, and the best material to be developed, and it is referred to as a “green” eco-friendly material. Membranes and adsorbents for water treatment, packaging and compost bags, controlled drug carriers, and biomaterials for tissues such as bone, cartilage, ligament, skeletal muscle, skin, cardiovascular and nerve tissues are just some of the applications of this biodegradable polymer (PCL). The goal of this review is to present a brief overview of PCL, syntheses of PCL, its properties, PCL composites, and PCL blends and to provide a detailed investigation into the utility of PCL/PCL-based adsorbing agents in the removal of dyes/heavy metal ions. Overall, it can be confirmed that PCL blends and composites were found to be significant competitors to other well-known adsorbents in the treatment of wastewaters, necessitating a thorough investigation of their manufacture.
Chemical Sensors for Volatile Organic Compound Detection
Authors: Meng Fanli , Yuan Zhenyu , Meng Dan
Published: November 2023

The detection of volatile organic compounds (VOCs) is in high demand in various fields, such as environmental pollution monitoring, early disease screening, and food freshness assessment [...]
Primary Failure Eruption: Genetic Investigation, Diagnosis and Treatment: A Systematic Review
Authors: Inchingolo Francesco , Ferrara Irene , Viapiano Fabio , Ciocia Anna Maria , Palumbo Irene , Guglielmo Mariafrancesca , Inchingolo Alessio Danilo , Palermo Andrea , Bordea Ioana Roxana , Inchingolo Angelo Michele , Di Venere Daniela , Dipalma Gianna
Published: November 2023

Aim: The aim of this systematic review is to explore the pathology, diagnosis, treatment, and genetic basis of Primary Failure of Eruption (PFE) in the field of pediatric dentistry and orthodontics. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed for this review. The databases PubMed, Science Direct, Scopus, and Web of Science were searched from 1 July 2013 to 1 July 2023, using keywords “primary failure of tooth eruption” OR “primary failure of eruption” OR “tooth eruption failure” OR “PFE” AND “orthodontics”. The study selection process involved screening articles based on the inclusion and exclusion criteria. Results: A total of 1151 results were obtained from the database search, with 14 papers meeting the inclusion criteria. The review covers various aspects of PFE, including its clinical features, diagnosis, treatment options, and genetic associations with mutations in the PTH1R gene. Differentiation between PFE and Mechanical Failure of Eruption (MFE) is crucial for accurate treatment planning. Orthodontic and surgical interventions, along with multidisciplinary approaches, have been employed to manage PFE cases. Genetic testing for PTH1R mutations plays a significant role in confirming the diagnosis and guiding treatment decisions, although some cases may not be linked to this mutation. Conclusions: This systematic review provides valuable insights into the diagnosis, treatment, and genetic basis of PFE. Early diagnosis and personalized treatment planning are crucial for successful management. Genetic testing for PTH1R mutations aids in accurate diagnosis and may influence treatment decisions. However, further research is needed to explore the complex genetic basis of PFE fully and improve treatment outcomes for affected individuals.
Deep Learning Enriched Automation in Damage Detection for Sustainable Operation in Pipelines with Welding Defects under Varying Embedment Conditions
Authors: Shang Li , Zhang Zi , Tang Fujian , Cao Qi , Yodo Nita , Pan Hong , Lin Zhibin
Published: November 2023

Welded joints in metallic pipelines and other structures are used to connect metallic structures. Welding defects, such as cracks and lack of fusion, are vulnerable to initiating early-age cracking and corrosion. The present damage identification techniques use ultrasonic-guided wave procedures, which depend on the change in the physical characteristics of waveforms as they propagate to determine damage states. However, the complexity of geometry and material discontinuity (e.g., the roughness of a weldment with or without defects) could lead to complicated wave reflection and scatters, thus increasing the difficulty in the signal processing. Artificial intelligence and machine learning exhibit their capability for data fusion, including processing signals originally from ultrasonic-guided waves. This study aims to utilize deep learning approaches, including a convolutional neural network (CNN), Long-short term memory network (LSTM), or hybrid CNN-LSTM model, to demonstrate the capability in automation for damage detection for pipes with welded joints embedded in soil. The damage features in terms of welding defect types and severity as well as multiple defects are used to understand the effectiveness of the hybrid CNN-LSTM model, which is further compared to the two commonly used deep learning approaches, CNN and LSTM. The results showed the hybrid CNN-LSTM model has much higher classification accuracy for damage states under all scenarios in comparison with the CNN and LSTM models. Furthermore, the impacts of the pipelines embedded in different types of materials, ranging from loose sand to stiff soil, on signal processing and data classification were further calibrated. The results demonstrated these deep learning approaches can still perform well to detect various pipeline damage under varying embedment conditions. However, the results demonstrate when concrete is used as an embedding material, high attention to absorbing the signal energy of concrete could pose a challenge for the signal processing, particularly under high noise levels.
A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations
Authors: Firmli Soukaina , Chiadmi Dalila
Published: November 2023

The graph model enables a broad range of analyses; thus, graph processing (GP) is an invaluable tool in data analytics. At the heart of every GP system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, GP systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and fast, low-memory graph mutations. Existing graph structures offer a hard tradeoff among read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics, and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists (ALs) to achieve the best of both worlds. We compare CSR++ to CSR, ALs from the Boost Graph Library (BGL), and the following state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular GP algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average) while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed those of several state-of-the-art graph structures while maintaining low memory consumption when the workload includes updates.
Obstructed Hemi-Vagina with Ipsilateral Renal Agenesis Syndrome in Adulthood: A Diagnostic Challenge
Authors: Grammatis Alexandros Loukas , Ajibade Femi , Warakaulle Dinuke , Dada Tunde
Published: November 2023

A patient in her early 20s presented with constant and progressive lower abdominal and back pain, mainly on the right side of the abdomen, purulent vaginal discharge and pyrexia. A radiological assessment revealed a possible tubo-ovarian abscess and the incidental diagnosis of ipsilateral renal agenesis. The patient was treated for pelvic inflammatory disease (PID); however, after antibiotic administration and since the symptoms did not resolve, an abdominal MRI was requested, which revealed uterus didelphys with two cervices, an obstructed haemivagina and evidence of haematocolpos. The diagnosis of Obstructed Hemi-Vagina with Ipsilateral Renal Agenesis (OHVIRA) syndrome was confirmed, and the patient underwent the excision of the vaginal septum, the drainage of the haematopyocolpos and the laparoscopic drainage of the tubo-ovarian abscess. She achieved a good recovery.
Are Tumor Marker Tests Applied Appropriately in Clinical Practice? A Healthcare Claims Data Analysis
Authors: Stollberg Sabrina , Näpflin Markus , Nagler Michael , Huber Carola
Published: November 2023

Tumor markers (TM) are crucial in the monitoring of cancer treatment. However, inappropriate requests for screening reasons have a high risk of false positive and negative findings, which can lead to patient anxiety and unnecessary follow-up examinations. We aimed to assess the appropriateness of TM testing in outpatient practice in Switzerland. We conducted a retrospective cohort study based on healthcare claims data. Patients who had received at least one out of seven TM tests (CEA, CA19-9, CA125, CA15-3, CA72-4, Calcitonin, or NSE) between 2018 and 2021 were analyzed. Appropriate determinations were defined as a request with a corresponding cancer-related diagnosis or intervention. Appropriateness of TM determination by patient characteristics and prescriber specialty was estimated by using multivariate analyses. A total of 51,395 TM determinations in 36,537 patients were included. An amount of 41.6% of all TM were determined appropriately. General practitioners most often determined TM (44.3%) and had the lowest number of appropriate requests (27.8%). A strong predictor for appropriate determinations were requests by medical oncologists. A remarkable proportion of TM testing was performed inappropriately, particularly in the primary care setting. Our results suggest that a considerable proportion of the population is at risk for various harms associated with misinterpretations of TM test results.
Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging
Authors: Kalantar Reza , Curcean Sebastian , Winfield Jessica M. , Lin Gigin , Messiou Christina , Blackledge Matthew D. , Koh Dow-Mu
Published: November 2023

T2-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T2W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595–0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568–0.776), although the difference was not statistically significant (p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T2W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model’s ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications.
“Building Roots”—Developing Agency, Competence, and a Sense of Belonging through Education outside the Classroom
Authors: Lauterbach Gabriele
Published: November 2023

This study investigates how Education Outside the Classroom (EOtC) was used to support the students’ basic psychological needs, i.e., their need for competence, autonomy, and relatedness as described in Self-Determination Theory (SDT). The framework of Ecological Psychology (EP) was applied to explicate how the EOtC teaching settings helped the students to become effective, agentic, and connected. The findings show that this process was driven by their active engagement with environmental affordances which facilitated embodied experiences. Moreover, the interplay with place and people specifically reinforced their satisfaction of relatedness and created a deepened sense of belonging to their community. It is concluded that in order to foster the children’s healthy psychological and physiological development in EOtC, teachers should make use of the affordances and invitations of specific places as a starting point for their teaching to support the students to develop agency, competence and sense of belonging.
Impact of Communication System Characteristics on Electric Vehicle Grid Integration: A Large-Scale Practical Assessment of the UK’s Cellular Network for the Internet of Energy
Authors: Zeinali Mehdi , Erdogan Nuh , Bayram Islam Safak , Thompson John S.
Published: November 2023

The ever-increasing number of plug-in electric vehicles (PEVs) requires appropriate electric vehicle grid integration (EVGI) for charging coordination to maintain grid stability and enhance PEV user convenience. As such, the widespread adoption of electric mobility can be successful. EVGI is facilitated through charging stations and empowers PEV users to manage their charging demand by using smart charging solutions. This makes PEV grids assets that provide flexibility to the power grid. The Internet of Things (IoT) feature can make smooth EVGI possible through a supporting communication infrastructure. In this regard, the selection of an appropriate communication protocol is essential for the successful implementation of EVGI. This study assesses the efficacy of the UK’s 4G network with TCP and 4G UDP protocols for potential EVGI operations. For this, an EVGI emulation test bed is developed, featuring three charging parking lots with the capacity to accommodate up to 64 PEVs. The network’s performance is assessed in terms of data packet loss (e.g., the data-exchange capability between EVGI entities) and latency metrics. The findings reveal that while 4G TCP often outperforms 4G UDP, both achieve latencies of less than 1 s with confidence intervals of 90% or greater for single PEV cases. However, it is observed that the high penetration of PEVs introduces a pronounced latency due to queuing delays in the network including routers and the base station servers, highlighting the challenges associated with maintaining efficient EVGI coordination, which in turn affects the efficient use of grid assets.
Optimization of Weight Matrices for the Linear Quadratic Regulator Problem Using Algebraic Closed-Form Solutions
Authors: Choi Daegyun , Kim Donghoon , Turner James D.
Published: November 2023

This work proposes an analytical gradient-based optimization approach to determine the optimal weight matrices that make the state and control input at the final time close to zero for the linear quadratic regulator problem. Most existing methodologies focused on regulating the diagonal elements using only bio-inspired approaches or analytical approaches. The method proposed, contrarily, optimizes both diagonal and off-diagonal matrix elements based on the gradient. Moreover, by introducing a new variable composed of the steady-state and time-varying terms for the Riccati matrix and using the coordinate transformation for the state, one develops algebraic equationsbased closed-form solutions to generate the required states and numerical partial derivatives for an optimization strategy that does not require the computationally intensive numerical integration process. The authors test the algorithm with one- and two-degrees-of-freedom linear plant models, and it yields the weight matrices that successfully satisfy the pre-defined requirement, which is the norm of the augmented states less than 10−5. The results suggest the broad applicability of the proposed algorithm in science and engineering.
BFE-Net: Object Detection with Bidirectional Feature Enhancement
Authors: Zhang Rong , Zhu Zhongjie , Li Long , Bai Yongqiang , Shi Jiong
Published: November 2023

In realistic scenarios, existing object detection models still face challenges in resisting interference and detecting small objects due to complex environmental factors such as light and noise. For this reason, a novel scheme termed BFE-Net based on bidirectional feature enhancement is proposed. Firstly, a new multi-scale feature extraction module is constructed, which uses a self-attention mechanism to simulate human visual perception. It is used to capture global information and long-range dependencies between pixels, thereby optimizing the extraction of multi-scale features from input images. Secondly, a feature enhancement and denoising module is designed, based on bidirectional information flow. In the top-down, the impact of noise on the feature map is weakened to further enhance the feature extraction. In the bottom-up, multi-scale features are fused to improve the accuracy of small object feature extraction. Lastly, a generalized intersection over union regression loss function is employed to optimize the movement direction of predicted bounding boxes, improving the efficiency and accuracy of object localization. Experimental results using the public dataset PASCAL VOC2007test show that our scheme achieves a mean average precision (mAP) of 85% for object detection, which is 2.3% to 8.6% higher than classical methods such as RetinaNet and YOLOv5. Particularly, the anti-interference capability and the performance in detecting small objects show a significant enhancement.