bluuebunny/crossref_metadata_2025_split huggingface dataset:
Constraint-Following Control for Uncertain Mechanical Systems: An Intelligent Multi-Agent Game-Theoretic Approach
Authors: Yu Rongrong , Wang Guofeng , Bai Jindong , Chen Ye-Hwa , Zhang Qiang 
Published: April 2022

Abstract
Practically all performance requirement of systems can be formulated as constraints. In engineering, systems are tasked, via proper control design, to follow the constraints. There are however two major challenges. The first is that the system possesses uncertainty, which may be (possibly fast) time-varying. The second is that systems are mostly nonlinear, due to effects such as nonlinear elasticity, etc. To resolve the challenges, a novel performance measure β^ is introduced as a dynamic depiction of the constraint-following error. A new control design is proposed based on this measure. The control design procedure includes two phases. The first phase is to design a control scheme, based on a feasible parameter (meaning the parameter only needs to be selected within a certain range), which renders guaranteed performance regardless of the uncertainty. After that, the second phase is to seek an optimal design for the control parameters. This task is resolved by an intelligent multi-agent game-theoretic approach. We consider there are multi-agents in the design process, each addresses a design consideration. However these considerations are opposite: with one's decrease (or increase) will increase (or decrease) the other. Agents are to cooperate with each other intelligently to reach the optimal choice of the design parameters. The design is applied to an active suspension system and the performance is verified.
Predicting the potential global distribution of an invasive alien pest Trioza erytreae (Del Guercio) (Hemiptera: Triozidae)
Authors: Aidoo Owusu Fordjour , Souza Philipe Guilherme Corcino , Silva Ricardo Siqueira , Júnior Paulo Antonio Santana , Picanço Marcelo Coutinho , Kyerematen Rosina , Sètamou Mamoudou , Ekesi Sunday , Borgemeister Christian 
Published: April 2022

Abstract
The impact of invasive alien pests on agriculture, food security, and biodiversity conservation has been worsened by climate change caused by the rising earth’s atmospheric greenhouse gases. The African citrus triozid, Trioza erytreae (Del Guercio; Hemiptera: Triozidae), is an invasive alien pest of all citrus species. It vectors the phloem-limited bacteria “Candidatus Liberibacter africanus” and “Ca. L. asiaticus”, the causal agents of citrus greening disease or huanglongbing (HLB). Understanding the global distribution of T. erytreae is critical for surveillance, monitoring, and eradication programs. Therefore, we combined geospatial and physiological data of T. erytreae to predict its global distribution using the CLIMEX model. The model’s prediction matches T. erytreae present-day distribution and shows that large areas in the Mediterranean region are suitable for the pest. The model predicts habitat suitability in the major citrus-producing countries, such as Brazil, China, India, and the USA. In the SRES A1B and A2 scenarios, the model predicts a reduction in habitat suitability from the current time to 2070. The findings show that global citrus production will continue to be threatened by T. erytreae. However, our study provides relevant information for biosecurity and risk assessment.
Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches
Authors: Ozbek Arif , Ünal Şaban , Bilgili Mehmet 
Published: April 2022

Abstract
Because hurricanes, droughts, floods, and heat waves are all important factors in measuring environmental changes, they can all result from changes in atmospheric air temperature and relative humidity (RH). Besides, climate, weather, industry, human health, and plant growth are all affected by RH. Accurately and consistently forecasting RH is a challenge due to its non-linear nature. The present study tried to predict one day ahead of RH in determined provinces from different climatic regions of Turkey (Ankara, Erzurum, Samsun, Diyarbakır, Antalya, and Bilecik) using long short-term memory (LSTM) and adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) based machine learning models. As evaluation criteria, root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) were employed. The outcomes from the forecasting models were also validated using observed data. During the testing stage, the smallest MAE and RMSE values were discovered to be 5.76% and 7.51%, respectively, in Erzurum province, with an R-value of 0.892 when using the LSTM method. Moreover, the smallest MAE and RMSE values were obtained to be 5.95% and 7.67% respectively, in Erzurum province with an R-value of 0.887 using the ANFIS model according to the hourly RH prediction. The results indicate that both the LSTM and ANFIS approaches performed well in daily RH prediction, with the LSTM and ANFIS approaches producing nearly identical results.
Effect of pituitary adenoma consistency on surgical outcomes in patients undergoing endonasal endoscopic transsphenoidal surgery
Authors: Cancela Alberto Acitores , Rodríguez Berrocal V , Pian Héctor , Díez Juan José , Iglesias Pedro 
Published: April 2022

Abstract
Background: Most pituitary adenomas (PAs) are considered to have a soft tumor consistency. However, there is a non-negligible percentage (5-13%) of tumors presenting or exhibiting a fibrous consistency that would entail a more difficult and complicated surgical excision with higher surgical morbidity and mortality rates.Purpose: To analyze the clinical consequences of PA tumor consistency on the surgical outcomes in patients undergoing endonasal endoscopic transsphenoidal (EET) pituitary surgery.Methods: An ambispective study of patients with PAs operated on through an EET approach in two Spanish tertiary hospitals over the last 12 years. A total of 226 consecutive interventions were carried out in the Neurosurgery Departments of the Hospital Universitario Ramón y Cajal (HURC) and the Hospital Universitario Puerta del Sur by the same neurosurgeon. PAs were grouped into soft (n = 150) and fibrous (n = 76). All patients underwent hormonal and magnetic resonance imaging (MRI) studies before and after surgery. In addition, neurosurgical complications were recorded in each patient.Results: Fibrous adenomas were independently associated with lower resection rates compared to soft adenomas (fibrous gross total resection [GTR] rate 48.7% vs. 76.3%, p < 0.001), even in those adenomas without invasion of the cavernous sinus (Knosp grades 0, I, and II). There were more intraoperative cerebrospinal fluid (CSF) leaks in patients with fibrous PAs. Moreover, fibrous PAs showed higher rates of postoperative hypopituitarism, permanent diabetes insipidus (DI) and postoperative treatments (hormonal treatment and radiotherapy). The excision of a fibrous PA required a longer surgical time (22.5 minutes more than soft PAs, p = 0.014), regardless of other factors.
Theoretical Assessments, Optical and Electrochemical Properties of the Alkoxylated Bischalcone as Emissive Material in Single Layer OLED
Authors: Saidin Syaharil , Khairul Wan M. , Rahamathullah Rafizah , Mohammed Mas , Yusoff Farhanini , Sarih Norazilawati Muhamad 
Published: April 2022

Abstract
A new bischalcone consisting hexyloxy (-C6H2n + 1) chain based on ‘Donor (D)-π-Donor (D)’ scheme was successfully designed and synthesized to demonstrate as emitting material for single layer OLEDs. Density functional theory assessment (DFT) at B3LYP/6–31G(d,p) were computed to obtain frontier molecular orbitals (FMO), chemical reactivity and molecular electrostatic potential (MEP). The utilization of alkoxy towards the chalcone moiety has increased the solubility and contributed to HOMO-LUMO gap energy level 3.473 eV by UV–vis spectroscopy and were found to have good agreement with theoretical calculations. The investigation on their optical, electrochemical and thermal behaviour also were conducted via UV-Vis, cyclic voltammetry (CV), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The entitled alkoxylated bischalcone (CSAB) revealed good thermal stability up to 300˚C and showed high glass transition temperatures. At positive potential, a quasi-reversible oxidation (Eox 1/2) peak at 2.40 V and negative potentials exhibited reduction peak at 0.78 V, respectively. The application of CSAB was tested in the form thin film in respect of their conductivity of electrical current and electroluminescence behaviour. It gave an intense yellow emission has provided the fundamental understanding on its potentiality featuring alkoxylated bischalcone moiety as solution-processed OLEDs material in optoelectronic interest.
Enhancing the multi-encoder-based cutting force estimation along the stationary axis of a machine tool with multiple inertia dynamics
Authors: Yamamoto Keisuke , Kamba Aya , Takeuchi Kazuhiro , Fujita Jun , Fujimagari Yusuke , Kakinuma Yasuhiro 
Published: April 2022

Abstract
Wideband cutting force sensing is a key technology for process monitoring. Sensorless cutting force estimation using the internal servo information of a machine tool with ball-screw-driven stages has been studied owing to its high maintainability and ease of introduction. In the motor current-based method, the cutting force estimation along the stationary axis is challenging, and the estimation bandwidth is significantly limited owing to the low sensitivity of the motor current in the high-frequency range. The dual-inertia model-based load-side disturbance observer (LDOB) can estimate the cutting force along the stationary axis using the relative position obtained from the rotary encoder and linear encoder, which is installed relatively near the cutting point and has a high sensitivity in the high-frequency range. However, this approach is not applicable to machine tools with complicated structural dynamics. To address this challenge, we propose a cutting force estimation method along the stationary axis using the Kalman filter (KF) based on a multiple inertia model derived solely from the relative position signal. The dynamics, depending on the stage position of the feed drive, were modeled using linear interpolation. Through end milling tests, we confirmed that the cutting force estimation accuracy along the stationary axis of a machine tool with multiple inertia dynamics was significantly improved by the proposed method compared to the current and LDOB-based methods. Additionally, the wideband cutting force could be estimated using the proposed method for bandwidths up to 1000 Hz.
Machine Learning Based Regional Epidemic Transmission Risks Precaution in Digital Society
Authors: Shi Zhengyu , Qian Haoqi , Li Yao , Wu Fan , Wu Libo 
Published: April 2022

Abstract
The contact and interaction of human is considered to be one of the important factors affecting the epidemic transmission, and it is critical to model the heterogeneity of individual activities in epidemiological risk assessment. In digital society, massive data makes it possible to implement this idea on large scale. Here, we use the mobile phone signaling to track the users’ trajectories and construct contact network to describe the topology of daily contact between individuals dynamically. We show the spatiotemporal contact features of about 7.5 million mobile phone users during the outbreak of COVID-19 in Shanghai, China. Furthermore, the individual feature matrix extracted from contact network enables us to carry out the extreme event learning and predict the regional transmission risk, which can be further decomposed into the risk due to the inflow of people from epidemic hot zones and the risk due to people close contacts within the observing area. This method is much more flexible and adaptive, and can be taken as one of the epidemic precautions with high efficiency and low cost.
Paleohydrological change during the Early-Middle Eocene: Insights from Long-lived Green River Formation
Authors: Kuma Ryusei , Hasegawa Hitoshi , Whiteside Jessica H. , Elson Amy L. , Yamamoto Koshi , Mimura Koichi 
Published: April 2022

Abstract
The evolution of Earth’s climate and oceans during the Early Eocene Climatic Optimum (EECO) has been a focus of extensive research, but the continental response to global climate change in this warmer than modern world is poorly understood. This study quantitatively reconstructs terrestrial paleoenvironment and paleohydrological changes as recorded in the early–middle Eocene (52.7‒43.8 Ma) lacustrine sedimentary succession of the Green River Formation exposed in Indian Canyon, Uinta Basin, northern Utah, USA. Detailed lithofacies variations and whole rock elemental and mineralogical data reflect lake level changes over the 1000 m-thick succession. By comparing the lithofacies and the major element compositions and clay mineralogies, we show that Ca/Al ratio can be utilized as a proxy of lake level, Mn/Fe ratio as a proxy of lake bottom-water redox conditions, and K/Al ratio and kaolinite abundance as proxies of chemical weathering rates. Based on principal component analysis of the major elements and mineralogy, the first principal component is interpreted to represent paleo-hydrological changes, involving variations in the lake level and degree of chemical weathering. Comparison of this record of paleohydrological change for the mid-latitude region of North America with stable δ18O and δ13C data for benthic foraminifera, which reflect global climatic change, indicates wetter periods correspond to intervals of lighter δ18O and heavier δ13C values, and vice versa. These results show that the extremely organic-rich “Mahogany Zone” in this sequence corresponds to the transitional period between the EECO and cooler middle Eocene (ca. 48.7 Ma). This implies that climatic instability and resultant environmental stress during this transitional climate state led to massive blooms of Botryococcus green algae in Lake Uinta, the remains of which are the source of organics preserved in the Mahogany Zone.
Amphoterin-Induced Gene and Open Reading Frame 2 (AMIGO2) Interacts with CAPN2 and Promotes the Proliferation and Migration of Hypopharyngeal Cancer Cell
Authors: Gu Wei , Niu Yanyan , Huo Hong , Yang Dahai , Jin Xiaofeng , Wang Jian 
Published: April 2022

Abstract
Background: Amphoterin-induced gene and open reading frame 2 (AMIGO2) has been reported to play a detrimental role in multiple cancer types. However, the precise role of AMIGO2 in hypopharyngeal squamous cell carcinoma (HPSCC) remains elusive. Methods: Bioinformatic analysis was employed to identify the differential expression and prognostic value of AMIGO2 in the head and neck cancer. Proliferation, apoptosis, and migration assays were then conducted after short hairpin RNA-mediated knockdown of AMIGO2 in FaDu cell line and in xenograft mouse model. Co-immunoprecipitation and mass spectrometry analysis were performed to determine the protein interaction between AMIGO2 and CAPN2. Rescue experiments with respect to the overexpression of CAPN2 were also performed. RNA sequencing after AMIGO2 knockdown with and without CAPN2 overexpression were conducted, followed by pathway enrichment analysis.Results: AMIGO2 expression was up-regulated in head and neck cancer and correlated with a poor prognosis. Functionally, HPSCC cell proliferation and migration were significantly inhibited in vivo and in vitro upon AMIGO2 knockdown. AMIGO2 binds to CAPN2 in cell context, and overexpression of CAPN2 could effectively reverse the inhibition from AMIGO2 knockdown. Bioinformatics analysis based on RNA sequencing indicated that the interaction between AMIGO2 and CAPN2 might be involved in multiple signaling pathways.Conclusions: Our work suggests that AMIGO2 promotes the proliferation and migration of HPSCC by interacting with CAPN2, implying that AMIGO2 can be therapeutically targeted to treat HPSCC.
Multi-scale chirp sparse representation for autonomous system non-stationary signal with Fast Varying Instantaneous Frequency
Authors: Yan Lu , Juan Du 
Published: April 2022

Abstract
Autonomous system is an emerging AI technology based on latest advances in intelligence, cognition, computing and systems science without human intervention, which is widely applied in the field of Internet of things, mechanical condition monitoring system and so on. With the rapid development of industry 4.0, autonomous system’s natural characteristic and application field determine that its corresponding signal presents multi-component, modulation coupling, non-stationary feature with fast time-varying instantaneous frequency (IF). Considering non-stationary signal contains system state information, how to effectively compute IF and realize signal decoupling separation plays an important role in autonomous system perception and making decision. In this paper, a sparse representation method called multi-scale chirp sparse representation (MSCSR) is introduced to to identify, extract and trend IF for achieving a highly concentrated time-frequency energy comparable to the standard IF estimation methods, simulation demonstrates that the proposed method performs better than traditional Hilbert-Huang transform and variational mode decomposition(VMD) combination with synchrosqueezing wavelet transform. Furthermore, an adaptive time-varying filter is constructed using the extracted instantaneous frequency to decouple non-stationary fast signal. Ultimately, by rapid instantaneous frequency fluctuation experiment, the effectiveness of proposed method for fast strong time-varying signal is validated, it can effectively extract rapid oscillation instantaneous frequency, and the error is less than 5%.
Celsr3 inactivation in the brainstem impairs spinal-descending axon development and mouse behaviors in motor coordination and mechanic-induced response
Authors: Chen Boli , Li Fuxiang , Jia Bin , So Kwok-Fai , Wei Ji-An , Liu Yuchu , Qu Yibo , Zhou Libing 
Published: April 2022

Abstract
Inactivation of Celsr3 in the forebrain results in defects of longitudinal axonal tracts such as the corticospinal tract. In this study, we inactivated Celsr3 in the brainstem using En1-Cre mice (Celsr3 cKO) and analyzed axonal and behavioral phenotypes. Celsr3 cKO animals showed an 83% reduction of rubrospinal axons and 30% decrease of corticospinal axons in spinal segments, associated with increased branching of dopaminergic fibers in the ventral horn. A decrease of spinal motoneurons, neuromuscular junctions and electromyographic signal amplitude of the biceps were also found in mutant animals. Mutant mice had impaired motor coordination and defective response to heavy mechanical stimulation, but no disability in walking and food-pellet handling. Transsynaptic tracing demonstrated that rubrospinal axons synapse on spinal neurons in the deep layer of the dorsal horn, and mechanical stimulation of hindpaws induced strong calcium signal of red nuclei in control mice, which was less prominent in mutant mice. In conclusion, Celsr3 regulates development of spinal-descending axons and the motor-network in cell and non-cell autonomous manners, and the maturation of the rubrospinal system is required for motor coordination and response to mechanical stimulation.
An Optimal Operation Method for Parallel Hydropower Systems Combining Reservoir Level Control and Power Distribution
Authors: Wu Xinyu , Lei Yuan , Cheng Chuntian , Ying Qilin 
Published: April 2022

Abstract
Parallel hydropower systems encounter a high risk of spillage in flood periods. Therefore, controlling spillage should be considered when hydropower systems increase power generation and satisfy power grid demand. To meet multiple operation targets, an effective operation method needs to precisely control reservoir level and quickly adjust power schedule based on real-time load changes. In this paper, an optimal operation method for parallel hydropower systems combining reservoir level control and power distribution is proposed. The method generates reservoir level control rules using a multi-objective simulation method for no gird demands operation, and obtain power distribution schedule considering spill risk and non-storage losses for gird demands. Taking a parallel system in southwest China as an example, the results show that the method can product annual power generation of 55.2 billion kWh, which has a 6.1% improvement compared to regular method. And it has the ability to precisely control daily water level and quickly adjust power distribution. It can be applied to parallel systems and has favorable results in power generation, peak shaving, and especially spillage reducing.
Chemical composition of Artemesia herba alba essential oil and its larvicidal and pupicidal effects against Culex pipiens (Diptera; Culicidae)
Authors: Abdelali Salma kaoutar , Souttou Karim , Elhassani Mohamed Kacimi , Aissaoui Linda 
Published: April 2022

Abstract
Aretemisia herba alba Asso (A.herba alba) (Asteraceae) is widely used in herbal medicine it has a real mine of natural molecules like davanone which is a very interesting product on the international market. The present research proposes a method for controlling the pre-imaginary stages of Culex pipiens (L4 and pupae) based on essential oil of Artemisia herba alba. The aerial part of this plant was extracted by hydrodistillation which gave a yield of 1.5. Then it was analyzed by gas chromatography coupled to the mass spectrometry (CPG / SM) for the determination of its chemical composition. The results of the analysis showed that the oil of A.herba alba is a davanone chemotype which consists mainly of davanone (48.8%). Three concentrations (1µl/ml, 5µl/ml and 10µl/ml) are prepared and directly tested on larvae (L4) and pupae. The results show that the essential oils have an important larvicidal and pupicidal activity. This efficiency is expressed by the calculated toxicological parameters which are successively LC50 and LC90, for larvae 3.278 µl/ml and 7.573 µl/ml, and for pupae 1.213 µl/ml and 2.288 µl/ml.
Failure mode classification for condition-based maintenance in a bearing ring grinding machine
Authors: Ahmer Muhammad , Sandin Fredrik , Marklund Pär , Gustafsson Martin , Berglund Kim 
Published: April 2022

Abstract
Technical failures in machines are major sources of unplanned downtime in any production and result in reduced efficiency and system reliability. Despite the well established potential of Machine Learning techniques in condition-based maintenance (CBM), the lack of access to failure data in production machines has limited the development of a holistic approach to address machine-level CBM. This paper presents a practical approach for failure mode prediction using multiple sensors installed in a bearing ring grinder for process control as well as condition monitoring. Bearing rings are produced in a set of 7 experimental runs, including 5 frequently occurring production failures in the critical subsystems. An advanced data acquisition setup, implemented for CBM in the grinder, is used to capture information about each individual grinding cycle. The dataset* is pre-processed and segmented into grinding cycle stages before time and frequency domain feature extraction. A sensor ranking algorithm is proposed to optimize feature selection for failure classification and the installation cost. Random forest models, bench-marked as best performing classifiers, are trained in a two-step classification framework. The presence of failure mode is predicted in the first step and the failure mode type is identified in the second step using the same feature set. Defining the feature set in the failure detection step improves the predictor generalization with the classifiers' performance accuracy of 99% on the test dataset. The presented approach demonstrates an efficient failure mode classification by selecting crucial sensors resulting in a cost effective CBM implementation in a bearing ring grinder