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Fe–x at% Ga (x = 17, 20, 23 and 25) were prepared by vacuum arc melting and subsequently exposed to different thermal treatments. The microstructure and magnetostrictive properties of the alloys exposed to various thermal histories have been investigated in detail. Detailed microstructural investigation indicates that at lower Ga concentration (17 and 20 at%), the ordering of A2 phase into DO3 occurs through diffusion assisted nucleation and growth transformation. On the contrary, the alloys richer in Ga exhibits ordering of A2 to DO3 through successive continuous ordering processes of A2→B2 and B2→DO3. The continuous ordering transformations being spontaneous process, the ordering could not be suppressed even by water quenching. The ordering is found to have profound influence on magnetostriction of the Fe-Ga alloy as higher magnetostriction was observed in either completely disordered A2 or completely ordered DO3. The magnetostriction is found to decrease in partially ordered material where DO3 phase co-exists with A2. [ABSTRACT FROM AUTHOR]

Delivery times represent a key factor influencing the competitive advantage, as manufacturing companies strive for timely and reliable deliveries. As companies face multiple challenges involved with meeting established delivery dates, research on the accurate estimation of delivery dates has been source of interest for decades. In recent years, the use of machine learning techniques in the field of production planning and control has unlocked new opportunities, in both academia and industry practice. In fact, with the increased availability of data across various levels of manufacturing companies, machine learning techniques offer the opportunity to gain valuable and accurate insights about production processes. However, machine learning-based approaches for the prediction of delivery dates have not received sufficient attention. Thus, this study aims to investigate the ability of machine learning to predict delivery dates early in the ordering process, and what type of information is required to obtain accurate predictions. Based on the data provided by two separate manufacturing companies, this paper presents a machine learning-based approach for predicting delivery times as soon as a request for an offer is received considering the desired customer delivery date as a feature. [ABSTRACT FROM AUTHOR]

The smart mobile Internet of Things (IoT) is a novel paradigm that is rapidly gaining ground in the field of modern wireless telecommunications. Picking operations are the key link in an entire warehousing operation, and congestion problems significantly contribute to managers' decisions regarding the warehousing layout, the storage strategy, and the number of pickers. In this paper, the discrete Markov method is used to study the problem of picking congestion in a narrow-aisle double picking system with a traditional layout, an S-type picking path and a classified storage strategy. First, congestion models are constructed when the picker picks one item with a picking speed that is equal to the walking speed and when the picker picks multiple items on one picking surface under a picking speed that is very different from the walking speed. A sensitivity analysis involving parameter changes was carried out. Second, the paper compares and analyzes situations in which the picking speed is equal to the walking speed and in which there is a large difference between the picking speed and the walking speed and determines the impact of the picking speed, the number of goods picked on a picking surface, the differentiated classified storage strategy, and the number of picking surfaces on the picking congestion rate. The effectiveness of the mathematical model was verified by a MATLAB simulation, and the exploration and discovery for subsequent research were performed. [ABSTRACT FROM AUTHOR]

Robot technology is considered one of the most promising technologies to achieve intelligent production, with picking robots being the most common type. Picking robots are highly integrated mechatronic systems, which autonomously complete tasks including picking, carrying, and sorting. The application of picking robots enhances the efficiency of production across various environments. In this paper, a classification-design-optimization-application integrated framework of picking robots is addressed, contributing to theoretical research and application of picking robots. Classification of picking robot is established and analyzed considering the differences of overall form and end-effector to guide the development of research strategies and approaches of picking robot. Design of picking robot is described from different aspects of the target, structure, monitoring, and control design. Additionally, the commonly used optimization methods for picking robots, including structural parameters, kinematics and dynamics, and energy consumption, are discussed. Finally, the application of picking robots under different environments is expounded, and the challenges and prospects of picking robots are highlighted. This study could present theoretical support and application measures for picking robot study and technology development. [ABSTRACT FROM AUTHOR]

The just-in-time concept, mass customization, omnichannel distribution, and the rising global population have all fueled the logistics sector. Consequently, using automation inside the warehouses to make them more dynamic and sustainable for the future is one of the crucial components to adapt to this quick shift. Giants in the industry and technology are becoming more interested in the "smart warehouse" system, built with innovative warehousing technologies, as an achievable solution for the development of warehouses in the future. To contextualize the past and provide light on prospective future directions, a study of current articles in the literature is important. This study evaluates works published in the previous 32 years related to flexible automation in warehouses to create a framework that future academics might use to guide them in establishing an original conceptual model that might be implemented at warehouses. One hundred eleven selected, examined, and categorized publications were published between 1990 and 2022 to establish a significant foundation for earlier research. Results indicated that combining automated machinery, collecting data technologies, and management systems are essential to creating a flexible automated warehouse. Finally, based on the examined literature, a flexible automated warehouse framework is suggested to direct future research in this field. [ABSTRACT FROM AUTHOR]

Distributional estimates in Bayesian approaches in structure learning have advantages compared to the ones performing point estimates when handling epistemic uncertainty. Differentiable methods for Bayesian structure learning have been developed to enhance the scalability of the inference process and are achieving optimistic outcomes. However, in the differentiable continuous setting, constraining the acyclicity of learned graphs emerges as another challenge. Various works utilize post-hoc penalization scores to impose this constraint which cannot assure acyclicity. The topological ordering of the variables is one type of prior knowledge that contains valuable information about the acyclicity of a directed graph. In this work, we propose a framework to guarantee the acyclicity of inferred graphs by integrating the information from the topological ordering into the inference process. Our integration framework does not interfere with the differentiable inference process while being able to strictly assure the acyclicity of learned graphs and reduce the inference complexity. Our extensive empirical experiments on both synthetic and real data have demonstrated the effectiveness of our approach with preferable results compared to related Bayesian approaches. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the impact of music training on the processing of temporal order in Mandarin sentence reading using event-related potentials (ERPs). Two-clause sentences with temporal connectives ("before" or "after") were presented to both musicians and non-musicians. Additionally, a verbal N-back task was utilized to evaluate the participants' working memory capacities. The findings revealed that musicians, but not nonmusicians, demonstrated a more negative amplitude in the second clauses of "before" sentences compared with "after" sentences. In the N-back task, musicians exhibited faster reaction times than nonmusicians in the two-back condition. Furthermore, a correlation was observed between the ERP amplitude differences (before vs. after) and reaction time differences in the N-back task (0-back vs. 2-back) among musicians. These findings suggested that music training enhances the depth of temporal order processing, potentially mediated by improvements in working memory capacity. [ABSTRACT FROM AUTHOR]

Rapid, high-precision pickup of microseismic P- and S-waves is an important basis for microseismic monitoring and early warning. However, it is difficult to provide fast and highly accurate pickup of micro-seismic P- and S-waves arrival-time. To address this, the study proposes a lightweight and high-precision micro-seismic P- and S-waves arrival times picking model, lightweight adversarial U-shaped network (LAU-Net), based on the framework of the generative adversarial network, and successfully deployed in low-power devices. The pickup network constructs a lightweight feature extraction layer (GHRA) that focuses on extracting pertinent feature information, reducing model complexity and computation, and speeding up pickup. We propose a new adversarial learning strategy called application-aware loss function. By introducing the distribution difference between the predicted results and the artificial labels during the training process, we improve the training stability and further improve the pickup accuracy while ensuring the pickup speed. Finally, 8986 and 473 sets of micro-seismic events are used as training and testing sets to train and test the LAU-Net model, and compared with the STA/LTA algorithm, CNNDET+CGANet algorithm, and UNet++ algorithm, the speed of each pickup is faster than that of the other algorithms by 11.59ms, 15.19ms, and 7.79ms, respectively. The accuracy of the P-wave pickup is improved by 0.221, 0.01, and 0.029, respectively, and the S-wave pickup accuracy is improved by 0.233, 0.135, and 0.102, respectively. It is further applied in the actual project of the Shengli oilfield in Sichuan. The LAU-Net model can meet the needs of practical micro-seismic monitoring and early warning and provides a new way of thinking for accurate and fast on-time picking of micro-seismic P- and S-waves. [ABSTRACT FROM AUTHOR]

Warehouses recently face increasing stress imposed by a volatile customer demand and increasing customer expectations in terms of ever shorter order response times. In that respect, warehouses more and more offer same-day and next-day shipment conditions. However, same-day shipment promises are challenging to fulfil, especially as the order fulfilment process operates against fixed deadlines imposed by the predefined truck departure times. As a natural mitigation strategy, warehouses set a cutoff point and offer same-day shipment only to customers that order until the cutoff point, but next-day shipment to all customers ordering thereafter. Setting an appropriate cutoff point is challenging as it affects multiple facets of the service quality, such as the order response time and the service level. In this paper, we study the design of cutoff-based shipment promises for stochastic deadline-oriented order fulfilment processes in warehouses. We present a discrete-time Markov chain model for exact steady-state performance analysis and propose two novel performance measures – α - and β - cutoff service level – for service level measurement in these systems. We numerically show the benefit of cutoff-based shipment promises. Even with a late cutoff point, there is a significant gain in the system performance. Furthermore, we find that warehouses should set the cutoff point such that it balances customer expectations in terms of service level and order response time. Finally, warehouses can improve their shipment promises when referring to β - instead of α - cutoff service level and by implementing measures reducing the utilisation and the variabilities of the order fulfilment process. [ABSTRACT FROM AUTHOR]

The human mitochondrial genome is transcribed into two RNAs, containing mRNAs, rRNAs and tRNAs, all dedicated to produce essential proteins of the respiratory chain. The precise excision of tRNAs by the mitochondrial endoribonucleases (mt-RNase), P and Z, releases all RNA species from the two RNA transcripts. The tRNAs then undergo 3′-CCA addition. In metazoan mitochondria, RNase P is a multi-enzyme assembly that comprises the endoribonuclease PRORP and a tRNA methyltransferase subcomplex. The requirement for this tRNA methyltransferase subcomplex for mt-RNase P cleavage activity, as well as the mechanisms of pre-tRNA 3′-cleavage and 3′-CCA addition, are still poorly understood. Here, we report cryo-EM structures that visualise four steps of mitochondrial tRNA maturation: 5′ and 3′ tRNA-end processing, methylation and 3′-CCA addition, and explain the defined sequential order of the tRNA processing steps. The methyltransferase subcomplex recognises the pre-tRNA in a distinct mode that can support tRNA-end processing and 3′-CCA addition, likely resulting from an evolutionary adaptation of mitochondrial tRNA maturation complexes to the structurally-fragile mitochondrial tRNAs. This subcomplex can also ensure a tRNA-folding quality-control checkpoint before the sequential docking of the maturation enzymes. Altogether, our study provides detailed molecular insight into RNA-transcript processing and tRNA maturation in human mitochondria. Mitochondrial tRNAs are less structurally stable than nuclear tRNAs, and their maturation pathway is unique. Here, the authors reveal how human mitochondrial precursor tRNAs are recognised, processed, methylated and prepared for full functionality in mitochondrial translation. [ABSTRACT FROM AUTHOR]

Astrocyte-derived pyruvate is considered to have neuroprotective functions. In order to investigate the processes that are involved in astrocytic pyruvate release, we used primary rat astrocyte cultures as model system. Depending on the incubation conditions and medium composition, astrocyte cultures established extracellular steady state pyruvate concentrations in the range between 150 µM and 300 µM. During incubations for up to 2 weeks in DMEM culture medium, the extracellular pyruvate concentration remained almost constant for days, while the extracellular lactate concentration increased continuously during the incubation into the millimolar concentration range as long as glucose was present. In an amino acid-free incubation buffer, glucose-fed astrocytes released pyruvate with an initial rate of around 60 nmol/(h × mg) and after around 5 h an almost constant extracellular pyruvate concentration was established that was maintained for several hours. Extracellular pyruvate accumulation was also observed, if glucose had been replaced by mannose, fructose, lactate or alanine. Glucose-fed astrocyte cultures established similar extracellular steady state concentrations of pyruvate by releasing pyruvate into pyruvate-free media or by consuming excess of extracellular pyruvate. Inhibition of the monocarboxylate transporter MCT1 by AR-C155858 lowered extracellular pyruvate accumulation, while inhibition of mitochondrial pyruvate uptake by UK5099 increased the extracellular pyruvate concentration. Finally, the presence of the uncoupler BAM15 or of the respiratory chain inhibitor antimycin A almost completely abolished extracellular pyruvate accumulation. The data presented demonstrate that cultured astrocytes establish a transient extracellular steady state concentration of pyruvate which is strongly affected by modulation of the mitochondrial pyruvate metabolism. [ABSTRACT FROM AUTHOR]

In e-commerce logistics, it is critical to enhance the efficiency of the order-picking system. Motivated by applications of automatic logistics, we consider the mobile robot based order batching problem. In this problem, mobile robots carry shelves to the picking station for order picking and then return them. The objective is to reduce shelf movements while minimizing the number of delayed orders. We introduce a hyper-heuristic method based on deep reinforcement learning to optimize the order batching strategy in the system. The proposed method adaptively selects the order batching strategy, significantly improving the sequential decision-making process in order picking. Through extensive tests, we demonstrate the superiority of the proposed method over several existing heuristic methods in a range of test scenarios. The results show that the proposed method outperforms other existing heuristic methods in a range of test scenarios, offering more stable and effective solutions. This study is a pioneer in the application of deep reinforcement learning to the mobile robot based order batching problem, offering a novel perspective and methodology to overcome the challenges of sequential decision-making optimization in order picking systems. [ABSTRACT FROM AUTHOR]

The Self-Exciting Threshold Autoregressive model (SETAR) is non-linear and considers threshold values to model time series affected by regimes. It is extended through the Multivariate SETAR (MSETAR) model, where the threshold variable can also be a multivariate process. The stationary marginal density (smd) of an MSETAR process of order one corresponds to a Unified Skew-Normal density. In this paper, the smd of an MSETAR of order one process was considered to compute explicit expressions of differential entropy and Kullback–Leibler and Jeffrey's divergences between two MSETAR(1) processes. In addition, two asymptotic tests based on divergences were built for statistical significance testing of the disparity between MSETAR(1) processes and the threshold coefficient matrix. Information measures considered involved high-dimensional integrals that likewise depended on multivariate cumulative density normal function. To solve these integrals, Genz's algorithm was considered based on Cholesky decomposition and Monte Carlo approximation. Some numerical experiments and applications to fish condition factor and Chilean economic perception time series illustrate performance. [ABSTRACT FROM AUTHOR]

To probe the processing of gaze-dependent positional information and gaze-independent order information when matching strings of characters, we compared effects of visual similarity (hypothesized to affect gaze-centered position coding) with the effects of character transpositions (hypothesized to affect the processing of gaze-independent order information). In Experiment 1, we obtained empirical measures of visual similarity for pairs of characters, separately for uppercase consonants and keyboard symbols. These similarity values were then used in Experiment 2 to create pairs of four-character stimuli (four letters or four symbols) that could differ by substituting one character with a different character from the same category that was visually similar (e.g., FJDK–FJBK) or dissimilar (e.g., FJVK–FJBK). We also compared the effects of transposing two characters (e.g., FBJK–FJBK) with substituting two characters (e.g., FHSK–FJBK). "Different" responses were harder to make in the single substitution condition when the substituted character was visually similar, and this effect was not conditioned by character type. On the other hand, transposition costs (i.e., greater difficulty in detecting a difference with transpositions compared with double substitutions) were greater for letters compared with symbols. We conclude that visual similarity mainly affects the generic gaze-dependent processing of complex visual features, and that the encoding of letter order involves a mechanism that is specific to reading. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has become a widely used technique in 3D printing, but it has proven to be a very costly process, even when optimizing parameters in existing models. Due to the characteristics of AM, and in order to optimize its process, a new approach is introduced to the problem: the discretization of each layer to be printed. This involves establishing an order relation based on the sequence in which the layers should be printed. The valid orders for the execution of the process, referred to as compatible with the order relation, will be characterized. Additionally, algorithms will be provided to obtain new compatible orders from others that were already compatible, and strategies will be presented to optimally and efficiently reorder non-compatible orders, converting them into compatible ones. [ABSTRACT FROM AUTHOR]

Conical pick cutters are by far the most common type of rock cutting tools used on variety of excavation equipment in mining and civil applications. Prediction of the forces acting on pick cutters when cutting rock can be an important input towards cutterhead design and performance estimation for such equipment. The cutting forces are a function of the rock strength and pick tip geometry, which is impacted by the bit wear. To develop a prediction model, a database of mean cutting force (MCF) measured in full-scale testing has been compiled and subsequently analyzed using regression methods to find the empirical equations linking MCF to the bit, cutting geometry and rock properties. Full-scale cutting tests are known to offer precise measurement of the cutting forces for a given bit geometry, cutting geometry (spacing between the cuts and depth of penetration), and combined rock mechanic characteristics of the sample; consequently, the information can be used to develop models of prediction of cutting forces. Linear and log-linear regression and tree-based machine learning models (random forest, decision tree, and extreme gradient boost) were used for the analysis of the experimental data. The results demonstrated that while using input parameters including uniaxial compressive strength (UCS), spacing, Brazilian tensile strength (BTS), penetration, and pick cutter's tip radius and tip angle, models can offer a reasonable prediction of the cutting forces. Among the models that have been examined, regression tree models, especially extreme gradient boost shows the highest coefficient of determination (R2), and the lowest mean absolute error (MAE). Highlights: The key input parameters that affect MCF are recognized, and their influence on MCF is thoroughly examined. The most important parameters for predicting cutting forces are UCS, tip angle, penetration, and BTS. Tree-based machine learning models demonstrate superior performance in predicting MCF. [ABSTRACT FROM AUTHOR]

Monte Carlo event generators for hard hadronic collisions depend on the evolution of parton showers backwards from a high-scale subprocess to the hadronization scale. The evolution is treated as a branching process with a sequence of resolvable parton emissions. The criterion of resolvability involves cutoffs that determine the no-emission probability (NEP) for a given range of the evolution scale. Existing event generators neglect cutoff-dependent terms in the NEP that, although formally power-suppressed, can have significant phenomenological effects. We compute such terms and study their consequences. One important result is that it is not possible for the backward shower to faithfully reproduce the cutoff-independent parton distribution functions (PDFs) used to generate it. We show that the computed NEP corrections mitigate but do not eliminate this problem. An alternative approach is to use cutoff-dependent PDFs that are consistent with the uncorrected NEP. Then one must apply cutoff-dependent corrections to hard subprocess matrix elements. We compute those corrections to the first nontrivial order for the Drell-Yan process and for Higgs production by gluon fusion. [ABSTRACT FROM AUTHOR]

The flexibility and programmability of SDN enable dynamic and automated network configuration and traffic routing. However, this also provides more avenues for malicious code propagation, leading to serious risks such as service disruptions and privacy breaches. To address this problem, we first designed three modules to suppress malicious code propagation: the abnormal traffic detection module, the malicious code analysis module, and the abnormal traffic tracing module. Then, the sharing mechanism is introduced. In order to analyze the process of malicious code propagation more clearly, based on the above strategy, this paper introduces the warning node into the classical SIR model, which can be exploited for studying how to control malicious code propagation to prevent large-scale outbreaks. The propagation threshold and equilibrium point of the proposed model are obtained through calculations. By constructing a Lyapunov function, the equilibrium point is proven stable. Finally, numerical simulation results indicate that when the detection rate reaches 90%, approximately 86.3% fewer nodes are infected at the peak point. Through comparative analysis, our system demonstrates optimal performance, validating the effectiveness of the analytical results. [ABSTRACT FROM AUTHOR]

Chinese lunar landing mission Chang'E-4 reached the far side of the Moon in January 2019 and has been providing unprecedented Lunar Penetrating Radar data able to explore the lunar subsurface down to more than 40 m (with its more resolutive high frequency band). Data are periodically released to the scientific community in raw PDS4 format. Here we provide different versions of the radar dataset after editing (i.e. pre-processing), partial, and full processing in order to provide a complete ready-to-use dataset to end-users (data collected since 4th January 2019 until 27th March 2023) which can be directly exploited for analysis, interpretation, inversion, as well as integration with imagery or other information. In particular, we implemented an efficient and objective way to remove duplicated traces representing more than 90% of original data, as well as a processing flow able to retain all the original data information, while avoiding redundancies. The provided datasets can be implemented with future data releases and straightforwardly exploited for any future analysis. [ABSTRACT FROM AUTHOR]

Owing to the development of optical precision and computer technology, the number of experiments based on visual methods for processing data from track detectors is growing. The use of neural networks to identify clusters in images obtained using new-generation scanning stations will both speed up image processing in nuclear emulsions and make the track reconstruction process an order of magnitude more efficient. [ABSTRACT FROM AUTHOR]

Salient object detection (SOD) must capture the multi-level features from both global and local view. Furthermore, interiors and boundaries of salient objects must be processed simultaneously in order to generate a clear salient map with sharp boundaries. In addition, the object-part relationship should be taken into consideration to segment the salient object as a whole. To address above issues, we propose a novel multi-source feature extraction network (MFEN), which is capable of integrating salient features, boundary features and global feature, simultaneously. First of all, the multi-source global and local module (MGLM) is introduced to integrate multi-source features, composing of a series of hybrid dilation convolution modules with different dilated rate. Furthermore, the boundary detection module is introduced to predict the boundary map and boundary features, helping for locating the salient object and sharpen edge. In addition, the adjacent features from MGLM are fused progressively to generate the final salient map by feature fusion modules. Experimental results on five datasets demonstrate that our proposed MFEN outperforms recent 18 SOD methods. More importantly, the ablation study shows that the MGLM is an effective feature fusion module for multi-level and multi-source feature. [ABSTRACT FROM AUTHOR]

An MIAPP-NALT system is an order picking system in which cases are picked at multiple in-the-aisle pick positions (MIAPP) and storage and retrieval operations are performed by a narrow aisle lift truck (NALT). In this paper, the operation of such a system involving three classes of stock keeping units with random demands for storage and retrieval operations is modeled as an M/G/1 queue, where "customers" are storage and retrieval requests, the "server" is the NALT, and retrieval requests have non-preemptive priority over storage requests. Our goal is to explore a methodology and solution method to obtain the optimal layout design of a class-based MIAPP-NALT system with stochastic demands and priority service. To this end, an operation time model of the system is developed and the first two moments for the operation time are derived. To overcome the challenge in finding the desired optimal layout, a near-optimal layout obtained via a heuristic approach is obtained at first and is improved afterwards. Based on the optimal layout, some valuable queueing results demonstrate the benefit of using a priority-based discipline. Moreover, some useful insights regarding the selection of dedicated versus random storage policies are obtained. [ABSTRACT FROM AUTHOR]

People continuously adapt their movements to ever-changing circumstances, and particularly in skills training and rehabilitation, it is crucial that we understand how to optimize implicit adaptation in order for these processes to require as little conscious effort as possible. Although it is generally assumed that the way to do this is by introducing perturbations gradually, the literature is ambivalent on the effectiveness of this approach. Here, we tested whether there are differences in motor performance when adapting to an abrupt compared to a ramped visuomotor rotation. Using a within-subjects design, we tested this question under 3 different rotation sizes: 30-degrees, 45-degrees, and 60-degrees, as well as in 3 different populations: younger adults, older adults, and patients with mild cerebellar ataxia. We find no significant differences in either the behavioural outcomes, or model fits, between abrupt and gradual learning across any of the different conditions. Neither age, nor cerebellar ataxia had any significant effect on error-sensitivity either. These findings together indicate that error-sensitivity is not modulated by introducing a perturbation abruptly compared to gradually, and is also unaffected by age or mild cerebellar ataxia. [ABSTRACT FROM AUTHOR]

In order to improve process safety, product purity, and production efficiency in the synthesis of N-n-butyl-N-(2-nitroxy-ethyl)nitramine (BuNENA), a two-stage continuous flow microreactor system was constructed by sequentially connecting the self-designed heart-shaped channel microreactor and the caterpillar microreactor. n-Butylethanolamine was used as the raw material, nitric acid and acetic anhydride were used as the nitrating agents. The results showed that when the flow rate of n-butylethanolamine was 1.00 mL.min− 1, the temperature of the heart-shaped channel microreactor was 10 ℃, the temperature of the caterpillar microreactor was 35 ℃, the molar ratio of ZnCl2 to n-butylethanolamine was 2%, the molar ratio of nitric acid to n-butylethanolamine was 2.4, and the molar ratio of ZnCl2 to n-butylethanolamine was 2.4, the result was best. Under the conditions, the reaction time was shortened to 300 s, the purity of BuNENA was up to 98.1%, and the yield was 87.1%. [ABSTRACT FROM AUTHOR]

Cation ordering/disordering features in perovskite-like oxides LnBaMn2O6−δ where Ln = Pr, Nd, Sm were studied by means of a combination of thermogravimetry and X-ray powder diffraction techniques in a wide range of temperatures. Variation in lanthanide size was shown to be an effective route for the shift in temperature of ordering and disordering. As a result, it was found coexistence of three different phases which can be obtained separately: disordered cubic Ln0.5Ba0.5MnO3–δ, and ordered kinds of LnBaMn2O6−δ having δ ≈ 0 or 1.0. The respective ambient conditions around stability boundaries for the whole studied phases were successfully determined. Additionally the main reasons for inducing ordering as well as disordering processes were clarified by the use of in-situ XRD and DSC methods. A combination of the experimentally obtained data with the results of limited studies reported in literature allows one to organize the available information about double manganites giving some explanation of ordering processes as well as appropriate conditions used to obtain oxides with desired ordering degree. [ABSTRACT FROM AUTHOR]

The paper introduces a high level speed control method for the coordination of multiple autonomous vehicles (AVs) in roundabout scenarios. The aim of the control method is to guarantee collision-free motion of the AVs, and similarly, to minimize their traveling time. In the method a priority-based ordering process of the AVs is used, which enforces the time-efficient motion of the AVs. The collision-free motion is guaranteed through an optimization-based method including control input constraints. The ordering process and the optimization form a low complexity solution, which requires low computation effort. The proposed control strategy is involved in the high level of a hierarchical control structure. The effectiveness of the proposed control strategy is illustrated by simulation examples and Hardware-in-the-Loop demonstration. [ABSTRACT FROM AUTHOR]

Recently, few methods for understanding machine learning model's outputs have been developed. SHAP and LIME are two well-known examples of these methods. They provide individual explanations based on feature importance for each instance. While remarkable scores have been achieved for individual explanations, understanding the model's decisions globally remains a complex task. Methods like LIME were extended to face this complexity by using individual explanations. In this approach, the problem was expressed as a submodular optimization problem. This algorithm is a bottom-up method aiming at providing a global explanation. It consists of picking a group of individual explanations which illustrate the global behavior of the model and avoid redundancy. In this paper, we propose CoSP (Co-Selection Pick) framework that allows a global explainability of any black-box model by selecting individual explanations based on a similarity preserving approach. Unlike submodular optimization, in our method the problem is considered as a co-selection task. This approach achieves a co-selection of instances and features over the explanations provided by any explainer. The proposed framework is more generic given that it is possible to make the co-selection either in supervised or unsupervised scenarios and also over explanations provided by any local explainer. Preliminary experimental results are made to validate our proposal. [ABSTRACT FROM AUTHOR]

The resourcefulness of crowdsourcing can be used to handle a wide range of complex macro-tasks, such as travel planning, translation, and software development. Multi-phase tasks are a type of macro-task that consists of several subtasks distributed across multiple sequential phases. Due to the recent work's disregard for the task's sequential correlation, it is difficult for them to handle multi-stage tasks effectively. This work bridges this gap. We call this novel approach Hint, which incorporates task design, pre hoc worker coordination, and post hoc crowd work coordination. Starting with the task interface design, Hint makes workers aware of the relationship between phases in order to improve their processing abilities. Second, pre hoc coordination of workers is to organize the workers to do the tasks to lower the monetary costs required to meet a specific quality standard. Third, post hoc coordination of crowd work is through a decision tree-based coordination strategy. Extensive tests are carried out on real-world datasets to validate the desirable qualities of the suggested mechanism. [ABSTRACT FROM AUTHOR]

Estimates of the onset times of P phases from active source experiments can effectively be used in developing wavespeed models, and the large number of recordings typical of such experiments incentivizes the development of automated approaches to generate these estimates. The simplicity and repeatability of an airgun source such as that used in the 2016 Pisagua/Iquique Crustal Tomography to Understand the Region of the Earthquake Source (PICTURES) project in northern Chile suggested that a straightforward application of waveform cross-correlation would suffice for arrivals recorded by a network of inland seismic stations, but did not work well due to significant variations in waveform morphology. For similar reasons, the machine learning application PhaseNet also did not work well. Application of an alternative algorithm typically used in passive source investigations that is indifferent to waveform morphology, the Regressive ESTimator (REST) autopicking package, also proved unsatisfactory, largely because the limited spectral bandwidth of the airgun source and the frequent occurrence of local seismicity led to numerous false picks. This motivated the development of a new approach, named CORREL, that is a hybrid of REST and cross-correlation, with the addition of a constraint in the form of a polynomial function based on the REST picks that provides a reasonable prediction of an onset time. Compared to the results obtained originally from REST and simple waveform correlation, the application of CORREL to the PICTURES data both significantly increased the number of arrivals estimated and greatly reduced the number of outliers. The combination of techniques retains the precision afforded by cross correlation while taking advantage of the accuracy afforded by absolute time picking. The predictive polynomial also provides CORREL a better means to discriminate true shots from the abundant natural seismicity. [ABSTRACT FROM AUTHOR]

Fe3Al powders with different order degrees were fabricated by a high-energy ball milling process and ordering transformation process. The order degree of the Fe3Al powders with plate structures can be tuned by the calcination temperature. It is found that the order degree of the obtained Fe3Al powders affects the magnetic properties. When the sample is calcined at 500 °C, the obtained Fe3Al contains the most DO3 superlattice structure with an order degree of 40.2%, thus exhibiting the best magnetic properties. Moreover, the best impedance matching ratio and attenuation constant are also achieved, leading to the best electromagnetic wave absorption performance. When the thickness is 1.35 mm, the effective absorption bandwidth with RL ≤ − 10 dB is as wide as 7.2 GHz. [ABSTRACT FROM AUTHOR]

In this work, we provide a comprehensive set of differential cross-section distributions for photon + di-jet production in proton-proton collisions with next-to-next-to-leading order precision in massless QCD. The event selection corresponds to recent measurements by the ATLAS collaboration. We observe an improved description of data in comparison to lower-order calculations in the case of observables that are expected to be well described by perturbation theory. The results also show better agreement with data than parton-shower-matched and multi-jet-merged predictions generated for the ATLAS analysis using the Sherpa Monte Carlo. A particular highlight of our study is the use of exact five-point two-loop virtual amplitudes. This is the first calculation of a complete two-to-three hadron-collider process at next-to-next-to-leading order in QCD that does not rely on the leading-colour approximation at two loops. We demonstrate, nevertheless, that the sub-leading-colour effects present in the infraredand ultraviolet-finite double-virtual contributions are negligible in view of the remaining scale uncertainties. [ABSTRACT FROM AUTHOR]

Theoretical studies predict that the light magnetic doping of Dirac semimetals leads to the occurrence of extraordinary properties and quantum states, including the Weyl semimetal, axionic insulator, topological superconductor, and others. However, thespecific materials that can exhibit these phenomena, as well as the characteristic concentrations of magnetic atoms are still unknown. In this work, the ab initio study of the electronic and magnetic properties of the Cd3As2 Dirac semimetal doped isoelectronically with Mn atoms at concentrations of 4, 6, and 8% has been carried out. In the analysis of the results, the main focus has been on the break of the spatial and time symmetry in the alloys, the behavior of the electron spectrum near the top of the Dirac cone, and spin ordering processes in Mn atoms. The results obtained have been compared with the previous theoretical and experimental data and, based on these results, a detailed picture of the effect of isoelectronic magnetic doping on the properties of the Cd3As2 Dirac semimetal has been presented. [ABSTRACT FROM AUTHOR]

Using a particle-based model for skyrmions, we numerically investigate the ordering processes that take place when subjecting interacting skyrmions in a narrow channel to a drive. Due to the interplay of repulsive wall–skyrmion interaction, repulsive skyrmion–skyrmion interaction, and velocity-dependent Magnus force, skyrmions are arranged in lines with different densities. In this ordering process, the Magnus force plays a facilitating role that accelerates the formation of the ordered lines when starting from a disordered initial state. Besides constant drive, we also consider the situation where the drive is periodically switched on and off, which leads to skyrmions transitioning from one arrangement to another. During this rearrangement, the Magnus force is found to impede the relaxation process. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty in locating the picking points of table grapes in the natural environment, this paper proposed a Yolov5-CBAM-Fourth Detection Layer-Decoupled (Yolov5-CFD) network based on Yolov5 to realize grape and stem recognition. Meanwhile, the fast localization of the picking point was realized using a geometric method. First, to enhance the feature extraction ability of the Backbone module, it was improved by integrating the Convolutional Block Attention Module (CBAM) attention mechanism. Second, aiming at the problem that a small target was difficult to detect, the fourth layer of detection was added to the Neck module. In addition, the paper improved the Head module of Yolov5 by borrowing from Decoupled structure in Yolox, which optimized the classification and regression performance of the network. Further, a geometric method was used to quickly and accurately locate the picking points of table grapes. In order to verify the effectiveness of the proposed network model, about 10,000 grape images were used for training. The results showed that the detection precision, recall, mAP_0.5 and mAP_0.5:0.95 of the Yolov5s-CFD model were 0.857, 0.804, 0.855, 0.642, respectivaly. And the detection precision, recall, mAP_0.5 and mAP_0.5:0.95 of the Yolov5m-CFD model were 0.986, 0.987, 0.993 and 0.910, respectivaly. In addition, the success rate of picking point localization was compared with the corresponding network structure. The results showed that the success rate of picking point positioning of the Yolov5s-CFD model was increased by 11.53% compared with the initial Yolov5s, and the success rate of the Yolov5m-CFD model was increased by 5.84% compared with the initial Yolov5. Although the recognition time of the improved Yolov5 model is increased compared to the initial Yolov5 model, it is still acceptable. It can fully meet the requirements of mechanized picking of table grapes and provide a theoretical basis for mechanized picking of table grapes. [ABSTRACT FROM AUTHOR]

The effect of the location of an additional Au layer (top, intermediate, and bottom) on the residual stresses, structure, the transformation of the disordered A1 phase into the ordered L10 FePt phase, and magnetic properties of layered FePt(15 nm)-Au films on SiO2(100 nm)/Si(001) substrate is investigated. The ordering processes in the films separated from the substrates during in situ annealing are studied as well. It is shown that the level of mechanical stresses depends on the Au layer location and the number of interfaces in FePt–Au films. Both films (on the substrate and separated from it) with an intermediate Au layer had the lowest temperature of the beginning of L10 phase formation. During annealing, diffusion of Au and its redistribution along the grain boundaries of the L10 FePt phase occur. An increase in the Au concentration along the grain boundaries reduces the exchange interaction between them and increases the coercivity of the FePt/Au and Au/FePt films. [ABSTRACT FROM AUTHOR]

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking. [ABSTRACT FROM AUTHOR]

This paper aims to construct a general framework of coupling tumor–bone remodeling processes in order to produce plausible outcomes of the effects of tumors on the number of osteoclasts, osteoblasts, and the frequency of the bone turnover cycle. In this document, Komarova's model has been extended to include the effect of tumors on the bone remodeling processes. Thus, we explored three alternatives for coupling tumor presence into Komarova's model: first, using a "damage" parameter that depends on the tumor cell concentration. A second model follows the original structure of Komarova, including the tumor presence in those equations powered up to a new parameter, called the paracrine effect of the tumor on osteoclasts and osteoblasts; the last model is replicated from Ayati and collaborators in which the impact of the tumor is included into the paracrine parameters. Through the models, we studied their stability and considered some examples that can reproduce the tumor effects seen in clinic and experimentally. Therefore, this paper has three parts: the exposition of the three models, the results and discussion (where we explore some aspects and examples of the solution of the models), and the conclusion. [ABSTRACT FROM AUTHOR]

The robotic mobile fulfillment system, where mobile robots carry racks to stationary stations for human pickers, is widely used in e-commerce warehouses. The operational efficiency of the system is largely affected by the organization of the order picking process. In this paper, we investigate an order picking tactic in a robotic mobile fulfillment system that allows racks moving between multiple picking stations in order to feed more orders during one visit and therefore save the overall throughput time. To evaluate this operational tactic's effectiveness, we propose a mathematical model to jointly optimize the order assignment, order sequencing, rack selection, and rack sequencing. A two-stage hybrid heuristic algorithm framework is then presented, including order assignment in the first stage and order and rack sequencing in the second stage. We conduct numerical experiments to validate the proposed algorithms' performance under different strategies and find out that the inter-station operation can significantly save order throughput time on the testing cases. We also investigate the effects of several factors, including the number of picking stations, their capacities, the stock keeping unit diversity, and queue length. Furthermore, a solid simulation is carried out to show the rationale of using rack moves as the objective rather than the completion time. [ABSTRACT FROM AUTHOR]

This study aims to minimize the travel time in multi-block high-level storage systems considering height level constraints for picking devices to leave aisles. Considering these operating environments, the formulation of minimum travel times between each pair of storage positions is proposed and the picker routing problem (PRP) is solved by means of Genetic Algorithms (GA) and Ant Colony Optimization (ACO). A parameter tuning is performed for both metaheuristics, and the performance of the GA and ACO is compared with the optimal solution for small-sized problems demonstrating the reliability of the algorithms solving the PRP. Then, the performance of the GA and ACO is tested under several warehouse configurations and pick-list sizes obtaining that both metaheuristics provide high-quality solutions within short computing times. It is concluded that the GA outperforms the ACO in both efficiency and computing time, so it is recommended to implement the GA to solve the PRP in joint order picking problems. [ABSTRACT FROM AUTHOR]

Processing a sequence of events is different from encoding the relative order of the elements composing the sequence. Whether order processing arises automatically from the sequential processing of events is yet unknown, however the literature suggests that order processing can occur at an automatic level when the order of stimuli is not detected consciously. In the present study, we aimed to investigate the question of automatic order processing in a difficult visual task where participants identified one among two possible target luminances. The luminance of the targets was contingent on the order of presentation of two visual cues separated by a subthreshold asynchrony. Participants' performance was compared to that in a control condition where the cues were presented synchronously. In a first experiment, participants' performance benefited from the use of subthreshold order information compared to the control condition, however this facilitation effect was transient and disappeared over the course of the experiment. In a second experiment, we investigated and confirmed the role of motivation, via a monetary incentive, on the previously observed effect. Taken together, our results suggest that the processing of temporal order of sub-threshold asynchronies is possible, although fragile and likely dependent on task requirements. [ABSTRACT FROM AUTHOR]

Comprehensive studies on emerging contaminants like volatile methyl siloxanes in settled dust from different micro-environments are still limited. In this study, concentrations and distribution of cyclic volatile methyl siloxanes (CVMSs) including D3, D4, D5, and D6 were examined in indoor dust samples collected from various micro-environments in northern and central Vietnam. Concentrations of total CVMSs in the dust samples ranged from 86.0 to 5890 (median 755) ng/g and decreased in the order: waste processing workshops (median 1560; range 329–5890) > common houses (650; 115–1680) > university classrooms (480; 86.0–1540) > vehicle repair shops (295; 126–1950) ng/g. This observation suggests that informal waste processing activities are sources of CVMSs. Among the studied CVMSs, D5 was the most predominant compound (41 ± 14%), followed by D6 (26 ± 13%), D4 (23 ± 12%), and D3 (11 ± 11%). Moderate positive correlations between D3/D4, D4/D5, and D5/D6 were found. Median daily intake doses of D3, D4, D5, and D6 through dust ingestion were 0.016, 0.051, 0.11, and 0.054 ng/kg/d, respectively, which were comparable to water consumption and markedly lower than the air inhalation pathway. [ABSTRACT FROM AUTHOR]

We present all-order predictions for Higgs boson production plus at least one jet which are accurate to leading logarithm in s ̂ / p ⊥ 2 . Our calculation includes full top and bottom quark mass dependence at all orders in the logarithmic part, and to highest available order in the tree-level matching. The calculation is implemented in the framework of High Energy Jets (HEJ). This is the first cross section calculated with log( s ̂ ) resummation and matched to fixed order for a process requiring just one jet, and our results also extend the region of resummation for processes with two jets or more. This is possible because the resummation is performed explicitly in phase space. We compare the results of our new calculation to LHC data and to next-to-leading order predictions and find a numerically significant impact of the logarithmic corrections in the shape of key distributions, which remains after normalisation of the cross section. [ABSTRACT FROM AUTHOR]

In the French West Indies, and particularly in Guadeloupe, agricultural policies mainly support the banana and sugarcane export sectors. However, driven by consumer demand, policy-makers are increasingly interested in developing local and agroecological food systems. Plantain (Musa spp., AAB), cultivated by a wide range of farmers, plays a key role in the diversification of local production and food systems, contributing to food security. However, important gaps in knowledge about plantain cropping systems are hindering the understanding of their contribution to the agroecological transition of farms. Farmers are also requesting more guidance from technical advisers and research. The aim of the work, presented in this article, was to co-design agroecological plantain cropping systems with farmers in order to fill this knowledge gap and to support local food systems. The co-design process was based on the characterization of the diversity of plantain farming systems, the evaluation of changes in practices implemented between 2017 and 2019, and the co-design of a system experiment. We identified six types of plantain farming systems defined by the role of plantain in the production strategy of the farm and a gradient of agroecology in plantain management practices. Our results also show progress toward agroecology between 2017 and 2019. Four innovative plantain cropping systems were designed based on a combination of existing knowledge held by farmers from the six types of farming systems. These results confirm that plantain cropping systems are contributing to the agroecological transition of farms in Guadeloupe and highlight that there are multiple possible agroecological transition pathways for plantain farmers. These results also provide a concrete example of integration of academic and non-academic knowledge for the co-design of agroecological systems. [ABSTRACT FROM AUTHOR]

The paper proposes a predictive approach to assessing special classes of dangerous states in the development of technological processes in order to make proactive decisions. The developed approach is based on a hybrid model based on the combination of an evidence-based classifier, fuzzy logic, and a Dempster–Shafer probabilistic scheme for evidence combination. The article presents a formal description of the predictor of critical states of the technological process. The resulting approach is universal and applicable in the automation of any complex technical systems. As an example, this article considers the application of the developed approach to solve the problem for assessing the safety of the technological process of shunting trains on a hump yard. The presented example shows the high efficiency and practical usefulness of the developed approach. [ABSTRACT FROM AUTHOR]

Visualizations may help students understand the network of connections among multiple documents. This study explored how ninth-grade students used a novel digital document mapping scaffold to construct models of multiple documents. We examined students' maps and mapping processes in order to understand how they used the scaffold to visually represent multiple scientific documents on a controversial issue. We also investigated the relations between document mapping and argumentative writing in order to clarify how mapping might support written argumentation. Lastly, we explored how students perceived the purposes of document mapping. We found that students used the scaffold to construct several types of models that varied in their degree of source vs. content separation and integration. About half of the students constructed full documents models. Interleaving reading and mapping led to construction of more elaborate and well-integrated maps. Students who created more elaborate and two-sided maps also wrote essays that better integrated multiple documents in order to synthesize and weigh competing arguments. Students who revisited the maps and the documents while writing wrote essays that included and cited more documents. Students described document mapping as a tool for selecting and connecting sources and claims in order to support understanding and writing. These findings reveal how document mapping can support comprehension and integration of multiple documents. The findings also shed new light on how students construct documents models and on the relations between representations of multiple documents and argumentative writing. These results have implications for both theory and instruction. [ABSTRACT FROM AUTHOR]

The commonly-used superstrate configuration (depositing front subcell first and then depositing back subcell) in all-perovskite tandem solar cells is disadvantageous for long-term stability due to oxidizable narrow-bandgap perovskite assembled last and easily exposable to air. Here we reverse the processing order and demonstrate all-perovskite tandems in a substrate configuration (depositing back subcell first and then depositing front subcell) to bury oxidizable narrow-bandgap perovskite deep in the device stack. By using guanidinium tetrafluoroborate additive in wide-bandgap perovskite subcell, we achieve an efficiency of 25.3% for the substrate-configured all-perovskite tandem cells. The unencapsulated devices exhibit no performance degradation after storage in dry air for 1000 hours. The substrate configuration also widens the choice of flexible substrates: we achieve 24.1% and 20.3% efficient flexible all-perovskite tandem solar cells on copper-coated polyethylene naphthalene and copper metal foil, respectively. Substrate configuration offers a promising route to unleash the commercial potential of all-perovskite tandem solar cells. The superstate configuration in all-perovskite tandem solar cells is disadvantageous for long-term stability. Here, the authors reverse the processing order and demonstrate substrate configuration to bury oxidizable narrow-bandgap perovskites, and achieve efficiency of 25.3% with long stability. [ABSTRACT FROM AUTHOR]

Tillering and yield are linked in rice, with significant efforts being invested to understand the genetic basis of this phenomenon. However, in addition to genetic factors, tillering is also influenced by the environment. Exploiting experiments in which seedlings were first grown in elevated CO2 (eCO2) before transfer and further growth under ambient CO2 (aCO2) levels, we found that even moderate exposure times to eCO2 were sufficient to induce tillering in seedlings, which was maintained in plants grown to maturity plants in controlled environment chambers. We then explored whether brief exposure to eCO2 (eCO2 priming) could be implemented to regulate tiller number and yield in the field. We designed a cost-effective growth system, using yeast to increase the CO2 level for the first 24 days of growth, and grew these seedlings to maturity in semi-field conditions in Malaysia. The increased growth caused by eCO2 priming translated into larger mature plants with increased tillering, panicle number, and improved grain filling and 1000 grain weight. In order to make the process more appealing to conventional rice farmers, we then developed a system in which fungal mycelium was used to generate the eCO2 via respiration of sugars derived by growing the fungus on lignocellulosic waste. Not only does this provide a sustainable source of CO2, it also has the added financial benefit to farmers of generating economically valuable oyster mushrooms as an end-product of mycelium growth. Our experiments show that the system is capable of generating sufficient CO2 to induce increased tillering in rice seedlings, leading eventually to 18% more tillers and panicles in mature paddy-grown crop. We discuss the potential of eCO2 priming as a rapidly implementable, broadly applicable and sustainable system to increase tillering, and thus yield potential in rice. [ABSTRACT FROM AUTHOR]

During excavation of roadheader, specific energy (SE) is a key component of rock cuttability evaluation and cutting head design. Previous studies have shown that the specific energy is simultaneously affected by physical and mechanical parameters of rock, pick geometry, and pick operation parameters. In the paper, six machine learning (ML) algorithms (back-propagation neural network, Elman neural network, extreme learning machine, kernel extreme learning machine, random forest, support vector regression) optimized by sparrow search algorithm (SSA) for SE prediction are developed by simultaneously considering two rock mechanical parameters (tensile strength of the rock σt and uniaxial compressive strength of the rock σc), one pick geometry (cone angle θ) and five pick operation parameters (cutting depth d, tool spacing s, rake angle α, attack angle γ, back-clearance angle β). 213 rock samples containing 26 rock types were selected to build the SSA-ML model. Mean absolute error (MAE), mean absolute percentage error (MAPE) and determination coefficient (R2) between the measured and predicted values are assigned as evaluation indicators to compare prediction performance of SSA-ML models. The importance of input variables is calculated internally using random forest (RF) algorithm. The results indicated that SSA-RF model with MAE of (0.7938 and 1.0438), MAPE of (12.76% and 16.98%), R2 of (0.9632 and 0.8943) on the training set and testing set has the most potential for SE prediction. The sensitive analysis shows the d, σc and σt are the most significant input variables for SE prediction. [ABSTRACT FROM AUTHOR]

A comparison study examines six different metal oxides (CuO, ZnO, Fe3O4, Co3O4, NiO, and α-MnO2) for the degradation of malachite green dye using four distinct processes. These processes are as follows: sonocatalysis (US/metal oxide), sonocatalysis under ultra-violet irradiation (US/metal oxide/UV), sonocatalysis in the presence of hydrogen peroxide (US/metal oxide/H2O2), and a combination of all these processes (US/metal oxide/UV/H2O2). The effective operating parameters, such as the dosage of metal oxide nanoparticles (MONPs), the type of the process, and the metal oxides' efficiency order, were studied. At the same reaction conditions, the sonophotocatalytic is the best process for all six MOsNPs, CuO was the better metal oxide than other MOsNPs, and at the sonocatalysis process, ZnO was the best metal oxide in other processes. It was found that the metal oxide order for sonocatalytic process is CuO > α-MnO2 ≥ ZnO > NiO ≥ Fe3O4 ≥ Co3O4 within 15–45 min. The order of (US/metal oxide/UV) process is ZnO ≥ NiO ≥ α-MnO2 > Fe3O4 ≥ CuO ≥ Co3O4 within 5–40 min. The order of (US/ MOsNPs/ H2O2) process is ZnO ≥ CuO ≥ α-MnO2 ≥ NiO > Co3O4 > Fe3O4 within 5–20 min. The maximum removal efficiency order of the sonophotocatalytic process is ZnO ≥ CuO > α-MnO2 > NiO > Fe3O4 ≥ Co3O4 within 2–8 min. The four processes degradation efficiency was in the order US/MOsNPs ˂ US/MOsNPs/UV ˂ US/MOsNPs/H2O2 ˂ (UV/Ultrasonic/MOsNPs/H2O2). Complete degradation of MG was obtained at 0.05 g/L MONPs and 1 mM of H2O2 using 296 W/L ultrasonic power and 15 W ultra-violet lamp (UV-C) within a reaction time of 8 min according to the MOsNPs type at the same sonophotocatalytic/H2O2 reaction conditions. The US/metal oxide/UV/H2O2 process is inexpensive, highly reusable, and efficient for degrading dyes in colored wastewater. [ABSTRACT FROM AUTHOR]

We have proposed the maintenance effort assessment model based on two Wiener processes for the operation of open source software (OSS) used in the edge computing in the past. In particular, we consider that this proposed model can assess the reliability by using three dimensional graph. Then, we have proposed two-dimensional modeling based on the effort management in the past. In this paper, we propose new expanded maintenance model considering OSS edge computing by expanding the existing two Wiener processes model in order to consider the network environment under the edge OSS operation. Especially, it is important to control the amount of maintenance effort expense in the long-term phase. Then, we propose the optimization method based on the past two-dimensional Wiener processes model. Thereby, it will be helpful to assess the operation effort expenditures with network environment of edge OSS service. Moreover, actual effort data sets are analyzed to show numerical examples of the proposed optimization method considering the network environment under the edge OSS operation. [ABSTRACT FROM AUTHOR]