Large Machines Research Articles

Biomarkers for cognitive impairment in alpha-synucleinopathies: an overview of systematic reviews and meta-analyses

Cognitive impairment (CI) is common in α-synucleinopathies, i.e., Parkinson’s disease, Lewy bodies dementia, and multiple system atrophy. We summarize data from systematic reviews/meta-analyses on neuroimaging, neurophysiology, biofluid and genetic diagnostic/prognostic biomarkers of CI in α-synucleinopathies. Diagnostic biomarkers include atrophy/functional neuroimaging brain changes, abnormal cortical amyloid and tau deposition, and cerebrospinal fluid (CSF) Alzheimer’s disease (AD) biomarkers, cortical rhythm slowing, reduced cortical cholinergic and glutamatergic and increased cortical GABAergic activity, delayed P300 latency, increased plasma homocysteine and cystatin C and decreased vitamin B12 and folate, increased CSF/serum albumin quotient, and serum neurofilament light chain. Prognostic biomarkers include brain regional atrophy, cortical rhythm slowing, CSF amyloid biomarkers, Val66Met polymorphism, and apolipoprotein-E ε2 and ε4 alleles. Some AD/amyloid/tau biomarkers may diagnose/predict CI in α-synucleinopathies, but single, validated diagnostic/prognostic biomarkers lack. Future studies should include large consortia, biobanks, multi-omics approach, artificial intelligence, and machine learning to better reflect the complexity of CI in α-synucleinopathies.

Modular shadowgraph imaging for zooplankton ecological studies in diverse field and mesocosm settings

AbstractVarious iterations of shadowgraph imaging have been used to quantify zooplankton in situ with high spatial resolution. Because these systems can image relatively large volumes of water, they are especially useful for resolving less common meso‐ or macrozooplankton taxa (< 50 ind. m−3), such as larval fishes and gelatinous animals. However, larger volume imagers are typically integrated with heavy towed vehicles and deployed from research vessels, which introduces high costs and limits sampling approaches. Here we demonstrate that versatile configurations of shadowgraph imaging, including modular benchtop, handheld, and towed, compact vehicle systems (along with customizable software), allow for tailoring sampling to a variety of marine and freshwater settings (including mesocosms). These systems encompass a suite of possible architectures, designed for adapting the imaging depth of field, acquisition rates, sensor configuration, and deployment method to fit a wide range of sampling protocols, with high vertical resolution (~ 5 cm) and adequate taxonomic capabilities for > 0.5 mm organisms. The benchtop system facilitates an interactive approach to observe and quantify zooplankton behaviors and optical properties. Video footage from the benchtop system generates thousands of regions of interest min−1 for target organisms with variable orientations and swimming postures. When used in conjunction with in situ imaging, the benchtop system can build large machine learning training libraries targeted toward rare or morphologically diverse zooplankton, which often includes the larval stages of economically valuable taxa. These modular hardware and software components increase affordability and versatility while broadening the scope of scientific questions addressed by plankton imaging systems.

Synchronization characteristics and stable states of four co-rotating vibrators with balanced slanted elliptical trajectory

In the petroleum drilling and mining screening industries, our goal is to enhance excitation force, integrate circular screening with rectilinear transport to obtain the balanced slanted elliptical trajectory (BSET), and reduce screen mesh clogging. This work investigates the synchronization characteristics and stable states of four co-rotating vibrators. The novelty of this work lies in using a novel vibration model to achieve the BSET in the synchronization state and in further considering the influence of a new skewing angle between two eccentric rotors (ERs), which are few in previous vibration screening field. Initially, the vibration model is extracted from the designed prototype. Then, in accordance with Routh–Hurwitz criterion, the synchronization and stability are theoretically analyzed using small parameters of vibrator velocity and phase differences (PDs). The synchronization characteristics related to mechanical structures are discussed via numerical calculation and experimental verification. Results reveal that the synchronization implementation is adversely affected by the skewing angle, even leading to unordered behaviors. Achieving zero-phase or in-phase synchronization with four vibrators is only feasible with a larger symmetric distance and installation angle. This work can contribute a foundation for designing some new types of large vibration screening and material conveyance machines.

Evaluating the Translation Accuracy of ChatGPT and DeepL Through the Lens of Implied Subjects

This research aims to offer deeper insights into the translation quality of large language models and neural machine translation systems. It compares the performance of two state-of-the-art models, ChatGPT and DeepL, to explore which system achieves higher translation accuracy. This research employs a highly focused and specialized method to evaluate translation performance, enabling the identification of nuanced differences between advanced AI/machine models. It focuses on implied subjects in contemporary Chinese writer Qiuyu Yu’s Iraq travelogue and examines how the two models handle these implied subjects when translating the text into English. The analysis shows that both ChatGPT and DeepL exhibit high-level intelligence when dealing with the implied subjects. They can flexibly adjust sentence structures to maintain the implicitness or to make the implied subjects explicit. The analysis further reveals that ChatGPT performs better in accurately inferring the implied subjects, which thereby reflects ChatGPT’s superior contextual understanding capability. The comparison of the source text and target texts also shows that ChatGPT’s translation is more succinct, nuanced, and easier to read. DeepL’s translation, by comparison, prioritizes reducing potential misunderstanding. This research tentatively concludes that, though not specialized for translation, ChatGPT translates with higher accuracy and demonstrates more sophisticated intelligence compared to DeepL when translating literary texts.

First SOLPS-ITER modelling of an X-point radiator in ITER

Abstract For the first time, an X-point radiating (XPR) regime using neon (Ne) seed impurity has been obtained in ITER modelling with the SOLPS-ITER code, including fluid drifts. The highly radiating pattern is qualitatively similar to those observed experimentally in ASDEX Upgrade and JET. Comparison of the simulation results with those obtained from modelling of the same regime, with the same approach, in ASDEX Upgrade is used to analyse the difference between the XPR in large and medium-sized machines. Analysis demonstrates that the cold X-point formation is controlled principally by the Ne radiation. The simulation results for the poloidal scale of the intense radiating zone are in good agreement with analytic estimates.

Recycling lithium-ion batteries: A review of current status and future directions

Lithium-ion batteries (LIBs) have become a widely adopted energy source for various electrical devices, ranging from small devices to large machines, such as cell phones, and electric vehicles (EVs). The increasing number of EVs, and other electrical devices has led to the enormous amount of discarded spent LIBs into the landfill. The amount of LIB waste generated in 2019 alone from EVs was 500,000 tons. This amount is expected to reach 8,000,000 tons by 2040. Globally, only 5 % of discarded spent LIBs is presently being recycled. The need to recycle LIBs stems from the desire to conserve raw materials, and save cost. Also, LIBs comprise heavy metals (Ni, Li, Co, Cu, Mn, Fe, and Al), and hazardous chemicals, which cause serious environmental hazards and threaten human lives; thus, pointing out the need to recycle LIBs. This work reviewed different recycling techniques and, the latest technological advancements in pretreatment methods, hydrometallurgy, pyrometallurgy, and direct recycling methods. Also, future perspectives and prospects are provided herein.

A [formula omitted]-means triangular synthesis large margin classifier with unified pinball loss for imbalanced data

Large Margin Distribution Machine (LDM) improves the Support Vector Machine (SVM) by integrating the marginal distribution of samples into the objective function, which exhibits excellent classification and generalization performance. However, most of the existing LDM-based models exhibit inherent flaws: (1) The assumption of a category uniform distribution, resulting in an inability to effectively address the issue of class imbalance. (2) The inheritance of the hinge loss in SVM, leading to inferior anti-noise performance. Given the shortcomings above, this paper constructs a novel K-means Triangular Synthesis (KTS) method for imbalanced data classification and introduces Unified Pinball (UP) loss to ensure robust performance, demonstrated as a novel KTS large margin classifier with UP Loss (KTS-UPLMC) model. Specifically, the KTS method utilizes the triangular synthesis and generates samples based on the cluster density, effectively mitigating the problems of introducing noise samples and strip-shaped sample distribution in the traditional synthetic minority oversampling technique, further alleviating the LDM’s classification line offset. In addition, the UP Loss considers quantile distance, improving the anti-noise performance of the model. Comparative experiments on artificial datasets and benchmark datasets validate the effectiveness and noise resistance of the proposed KTS-UPLMC in imbalanced data classification problems. Furthermore, the stability of the KTS-UPLMC is substantiated by the parameter sensitivity analysis experiment. In conclusion, the KTS method effectively addresses category imbalance, while the integration of the UP Loss enhances noise resistance.

Data-driven multi-objective optimization of aerodynamics and aeroacoustics in dual Savonius wind turbines using large eddy simulation and machine learning

Savonius rotor is a popular form of vertical-axis wind turbine (VAWT) for small-scale and urban applications because of its straightforward design and self-starting ability. Dual VAWTs present challenges in terms of wake interactions and noise, particularly in urban areas. Optimizing these parameters is essential for future wind energy adoption. This research is the first to analyze how the interaction of wakes from adjacent rotors, combined with a deflector, affects both the aerodynamic performance and noise levels of dual Savonius rotors. Large Eddy Simulation is applied, as it effectively captures detailed turbulent wind flows and their interactions with wind turbines. A multi-objective optimization method combining Machine Learning and Computational Fluid Dynamics (CFD) is developed to optimize rotors for maximum power efficiency and minimum noise, considering their wake interactions with a unique deflector system. First, the influence of geometric parameters on aerodynamics and aeroacoustics characteristics of rotors is analyzed, and the database is generated using Design of Experiment approach. Next, the CFD model is replaced by Artificial Neural Network (ANN) model established for predicting rotor performances. A Multi-Objective Genetic Algorithm method is used to optimize aerodynamics and aeroacoustics characteristics of rotors. Finally, optimal design parameters are identified from the Pareto front using the technique for order of preference by similarity to ideal solution decision-making method. The ANN model demonstrated high accuracy with an RANN2 of 0.995 and 0.971 for the average power coefficient (CP) and overall sound pressure level (OSPL) predictions, respectively. Multi-objective optimization revealed the best configuration of the deflector with bleed jets, improving the average CP up to 57.5% and reducing OSPL to an almost 5.2% compared to the dual rotor case at TSR = 0.8.

Distributed learning with compressed gradient differences*

Training large machine learning models requires a distributed computing approach, with communication of the model updates being the bottleneck. For this reason, several methods based on the compression of updates were recently proposed using sparsification or quantization. However, none of the prior methods are able to learn the gradients, which renders them incapable of converging to the true optimum in the batch mode. In this work we propose a new method – DIANA – which resolves this issue via compression of gradient differences. We provide theory in the strongly convex and nonconvex settings that shows improved convergence rates, and use it to obtain the first convergence rate for the previously proposed method TernGrad. Finally, we provide theory to support non-smooth regularizers.

GPCRSPACE: A New GPCR Real Expanded Library Based on Large Language Models Architecture and Positive Sample Machine Learning Strategies.

The quest for novel therapeutics targeting G protein-coupled receptors (GPCRs), essential in numerous physiological processes, is crucial in drug discovery. Despite the abundance of GPCR-targeting drugs, many receptors lack selective modulators, indicating a significant untapped therapeutic potential. To bridge this gap, we introduce GPCRSPACE, a novel GPCR-focused purchasable real chemical library developed using the G protein-coupled receptors large language models (GPCR LLM) architecture. Different from traditional machine learning models, GPCR LLM uses a positive sample machine learning strategy for training and does not need to construct any negative samples. This not only reduces false negatives but also reduces the time to label negative samples. GPCR LLM accelerates the identification and screening of potential GPCR-interactive compounds by learning the chemical space of GPCR-targeting molecules. GPCRSPACE, built on GPCR LLM, outperforms existing chemical data sets in synthesizability, structural diversity, and GPCR-likeness, making it a valuable tool for GPCR drug discovery.

Identifying Psychosocial and Ecological Determinants of Enthusiasm In Youth: Integrative Cross-Sectional Analysis Using Machine Learning.

Understanding the factors contributing to mental well-being in youth is a public health priority. Self-reported enthusiasm for the future may be a useful indicator of well-being and has been shown to forecast social and educational success. Typically, cross-domain measures of ecological and health-related factors with relevance to public policy and programming are analyzed either in isolation or in targeted models assessing bivariate interactions. Here, we capitalize on a large provincial data set and machine learning to identify the sociodemographic, experiential, behavioral, and other health-related factors most strongly associated with levels of subjective enthusiasm for the future in a large sample of elementary and secondary school students. The aim of this study was to identify the sociodemographic, experiential, behavioral, and other health-related factors associated with enthusiasm for the future in elementary and secondary school students using machine learning. We analyzed data from 13,661 participants in the 2019 Ontario Student Drug Use and Health Survey (OSDUHS) (grades 7-12) with complete data for our primary outcome: self-reported levels of enthusiasm for the future. We used 50 variables as model predictors, including demographics, perception of school experience (i.e., school connectedness and academic performance), physical activity and quantity of sleep, substance use, and physical and mental health indicators. Models were built using a nonlinear decision tree-based machine learning algorithm called extreme gradient boosting to classify students as indicating either high or low levels of enthusiasm. Shapley additive explanations (SHAP) values were used to interpret the generated models, providing a ranking of feature importance and revealing any nonlinear or interactive effects of the input variables. The top 3 contributors to higher self-rated enthusiasm for the future were higher self-rated physical health (SHAP value=0.62), feeling that one is able to discuss problems or feelings with their parents (SHAP value=0.49), and school belonging (SHAP value=0.32). Additionally, subjective social status at school was a top feature and showed nonlinear effects, with benefits to predicted enthusiasm present in the mid-to-high range of values. Using machine learning, we identified key factors related to self-reported enthusiasm for the future in a large sample of young students: perceived physical health, subjective school social status and connectedness, and quality of relationship with parents. A focus on perceptions of physical health and school connectedness should be considered central to improving the well-being of youth at the population level.

Research on Satellite Navigation Control of Six‐Crawler Machinery Based on Fuzzy PID Algorithm

ABSTRACTThe six‐crawler driving mechanism plays a crucial role in the operation of large machines such as bucket‐wheel excavators, dumping machines, and mobile crushing stations, as it serves functions like bearing, movement and steering. The driving characteristics of this mechanism directly influence the safety and efficiency of these machinery systems. To enhance the design methodology for multi‐crawler machinery, improve path controllability, and achieve adaptive driving, a satellite navigation control system for six‐crawler machinery was developed based on the principles of real‐time kinematic (RTK) satellite positioning. This system utilizes the distance deviation and heading angle deviation between the actual path and the predetermined path of the six‐crawler machinery as inputs to a fuzzy proportion integration differentiation (fuzzy PID) controller. This controller regulates the deviation angle of the steering crawler and the driving speeds of each track, thereby ensuring precise path tracking control. To evaluate the path tracking control performance under both straight and curved driving conditions, a virtual prototype model of the six‐crawler mechanical system was established, and co‐simulation analysis was conducted. In addition, an experimental platform for path tracking control of six‐crawler machinery was established to validate the efficacy of the satellite navigation system. The actual tracking data obtained from various driving conditions and initial deviations demonstrated that the RTK satellite navigation path tracking control system exhibited excellent control performance.

ANALISIS STUDI KOMPARATIF KEBERHASILAN BERWIRAUSAHA PADA PENGUSAHA VCO (VIRGIN COCONUT OIL) DI KABUPATEN BULELENG

This study aims to determine how the analysis of Comparative Studies of Entrepreneurial Success in VCO (Virgin Coconut Oil) Entrepreneurs in Buleleng Regency. This research uses a qualitative approach, with data collection techniques using structured interview techniques to extract information and documentation techniques to collect a number of documents as information data material, namely sound recordings and photographs to make conclusions and data analysis. The source triangulation technique is used as a data validity technique by asking several questions to the sources so that conclusions can be drawn from the answers that have been given. Data analysis techniques used in this study using SWOT analysis, namely strengths, opportunities, weaknesses, and threats. The results of the study analyzed the entrepreneurial success of VCO products in Buleleng Regency from internal and external factors, which is said to be successful is Bali Pure VCO. From the S-O strategy where this strategy uses the strengths of the company, to take advantage of its great opportunities. This can be seen from the situation and condition of the company that is being run, which has followed the development of technology by using large industrial machines and unique packaging techniques and maintained sterilization. In addition, Bali Pure products have also innovated a lot by utilizing coconut fruit not only as pure coconut oil but also as a bath soap product with a variety of natural aromas

The Use of AI in Detecting Rare Diseases

Rare diseases, affecting fewer than 1 in 2,000 individuals, present significant challenges in diagnosis and treatment due to their diversity, limited clinical data, and lack of awareness among healthcare professionals. Artificial intelligence (AI) is emerging as a valuable tool in rare disease detection, leveraging large datasets and machine learning algorithms to analyze symptoms, genetic data, and medical literature. This paper investigates how AI enhances diagnostic accuracy, shortens the diagnostic process, and addresses the complexities associated with rare diseases. Additionally, we examine case studies, discuss the ethical considerations of AI-based diagnosis, and highlight the potential for AI to revolutionize rare disease care. Keywords: Artificial Intelligence (AI), Rare Diseases, Machine Learning, Diagnosis, Genetic Testing.

PMSM Design for Elevators: Determination of the Basic Topology Affecting Performance

In recent years, with the increase in high-rise building construction, there has been a rise in demand for elevators. This demand has spurred mutual growth between the vertical transportation sector and high-rise building construction. Both sectors are improving technology, security, and comfort standards. Traditional elevator traction machines typically employ asynchronous motors coupled with geared reducers, despite their low energy efficiency and requirement for large machine rooms. However, with the proliferation of rare-earth magnet materials and advancements in driver technologies, the use of Permanent Magnet Synchronous Motors (PMSMs) in elevator traction machines has increased. PMSMs operate with direct drive, eliminating the need for reducers. High efficiency and passenger comfort are crucial parameters for elevator traction machines. This study focuses on identifying the fundamental topology that could influence the performance of PMSMs for gearless elevator machines. Factors such as stator materials, rotor structures, permanent magnets, winding configurations, slot/pole combinations, cogging torque and torque ripple have been examined to select suitable design topologies for elevator motors. Important topics such as the magnetic properties of grain-oriented silicon steel sheets used in stator construction, B-H curves, lamination forming and stacking factors have been addressed. Two different rotor topologies and magnet arrangements, namely internal and external, have been investigated in PMSM designs. Additionally, the use of four different magnet materials, namely AlNiCo, Ferrite, NdFeB, and SmCo have been compared for PMSM traction motors in elevators. Single-layer and double-layer winding configurations, distributed and concentrated winding configurations have been compared. Fractional slot/pole combinations, which directly affect the performance and comfort of PMSMs, have also been examined based on information obtained from the literature. Particularly, structures with high winding factors, such as 12/10, 12/14, 18/16, 18/20, 24/22, and 24/26 have been identified. Finally, studies on skewing to reduce cogging torque and torque ripple have been addressed.

Co-design of an unmanned cable shovel for structural and control integrated optimization: A highly heterogeneous constrained multi-objective optimization algorithm

Unmanned cable shovel is an intelligence-based large machine used for excavating in open-pit coal mines, with significant implications for the security and development of energy resources. The co-design of unmanned cable shovels, considering elements from both structural and control systems, has demonstrated superior performance in obtaining optimal solutions compared to the traditional approaches that focus on individual systems. However, the inherent complexities of diverse systems often lead to heterogeneous function evaluations, posing challenges for the existing optimization algorithms. To address highly heterogeneous multi-objective optimization problems characterized by surrogate-approximated expensive functions and explicit inexpensive functions, this study proposes a highly heterogeneous constrained multi-objective optimization (HHCMO) algorithm. Leveraging NSGA-III as an evolutionary optimizer and the Kriging model for approximating expensive functions, HHCMO strategically addresses the challenges posed by highly heterogeneous objective evaluations. A Monte Carlo sampling-based expected hypervolume improvement (EHVI) criterion, employing a new nearest point approximation method in a unified sampling space, effectively handles the impact of heterogeneous objective evaluations on the sequential infill of the Kriging model. Thorough evaluations on both unconstrained and constrained multi-objective benchmark problems validate the correctness of HHCMO and its superiority relative to state-of-the-art algorithms. Finally, the effectiveness of the proposed method is successfully demonstrated in an engineering scenario of an unmanned cable shovel, where it adeptly handles expensive functions from the structural system and inexpensive functions from the control system.

The simulation and analysis of the spraying status of large sprinkler machines loaded with atomizing micro-sprinklers

Due to the typically high flow rate of nozzles on large sprinkler machines, direct use for pesticide application can lead to pesticide waste and non-point source pollution. To fulfill the multifunctional needs of large sprinkler machines for irrigation, fertilization, and pesticide application, this study installed atomizing micro-sprinklers on the machines to enable their pesticide application capabilities. Based on the spraying patterns of the atomizing micro-sprinklers, three types were selected for this study: hollow cone, solid cone, and fan-shaped micro-sprinklers. Three working pressure levels were set at 0.2 MPa, 0.3 MPa, and 0.5 MPa, with the height of the micro-sprinklers above the ground set at three levels: 0.8 m, 1.2 m, and 1.5 m. A completely randomized design was used for the experiment, which included hydraulic performance tests and software simulation. The results indicated that the spray volume of the hollow cone, solid cone, and fan-shaped atomizing micro-sprinklers approximately presented a hollow conical, solid conical, and normal distribution, respectively. The spray intensity (average and maximum values) of the atomizing micro-sprinklers increased with the increase in pressure, while the median showed an initial increase followed by a decrease as pressure increased. Under the same pressure conditions, the spray intensity of the different atomizing micro-sprinklers decreased with the increase in height above the ground, while the spray width increased with height. By comparing the spraying characteristics of micro-sprinklers with various patterns, large sprinkler machines can select atomizing micro-sprinklers in different spraying statuses according to actual conditions and determine the optimal height of the atomizing micro-sprinklers above the ground to achieve efficient and uniform pesticide application effects. This reduces the evaporation and drift loss of liquid medicine. The research findings provide a scientific basis for achieving the pesticide application function of large sprinkler machines and expanding their usage capabilities.

Devising a calculation method for modern structures of current-conducting elements in large electric machines in a three-dimensional statement

The jumpers of rotor pole-to-pole connections are highly stressed elements in a hydraulic generator structure. These assemblies often fail due to deformation that exceeds the size of an air gap. Existing methods do not take into account the thermal component and attempts to improve the design are not based on mathematical models that make it possible to perform calculations with an accuracy of more than 50 %. The method devised in this work makes it possible to obtain boundary conditions of the third kind on the basis of three-dimensional mathematical modeling of the ventilation system of the hydraulic unit without simplifications. The method accuracy is explained by taking into account the spatial thermal component. The heat transfer coefficient determined by this method in the pole-to-pole connections area was ~250 W/(m2·K). Using FEM, mathematical modeling of the thermal stress state of pole-to-pole connections was carried out, taking into account mechanical and thermal factors. This made it possible to design the improved connection structure with additional fastening elements, which make it possible to reduce the displacement to 0.03 mm, and the stress to 53 MPa at the rotor rotation frequency of 880 rpm. This design makes it possible to enable reliable operation of the hydraulic unit under the condition of increasing the rotor rotation frequency to overspeed with disconnected combinatorial dependence, provided that the actual stresses are 0.95 % of the material yield strength. The convergence of the values obtained by the proposed method and by the HSS method exceeded 99 %. The practical result is the proposals for the hydraulic generator design modernization

Forecasting Inflation Using Economic Narratives

We use economic narratives to forecast inflation with a large news corpus and machine learning algorithms. The economic narratives from the full text content of over 880,000 Wall Street Journal articles are decomposed into multiple time series representing interpretable news topics, which are then used to predict inflation. The results indicate that narrative-based forecasts are more accurate than the benchmarks, especially during recession periods. Narrative-based forecasts perform better in long-run forecasting and provide incremental predictive information even after controlling macroeconomic big data. In particular, information about inflation expectations and prices of specific goods embedded in narratives contributes to their predictive power. Overall, we provide a novel representation of economic narratives and document the important role of economic narratives in inflation forecasting.

Research on the Pricing Model of Financial Derivatives

Based on the importance of the pricing model of financial derivatives, this paper points out the defects and risks of the current model and proposes some possible solutions. The research method is mainly to summarize the framework of the existing model by searching the academic literature and commercial examples related to the pricing model. The final conclusion is that an obvious flaw of the pricing model is that it is based on an idealized market state and cannot predict hidden market trends, but through random volatility, consideration of market friction, and the development of mathematical technology, the market can refine the pricing model. Research and forecast risk premium, market liquidity and asset price changes in advance when considering pricing. In addition, the market is also supported to consider the application of large machines in the portfolio as well as the issue of options panels, so as to deepen the understanding of the movement of financial products.