Miracle-dz/UniFAR-FTUs huggingface dataset:
Game Theory Approach on Modeling of Residential Electricity Market by Considering the Uncertainty due to the Battery Electric Vehicles (BEVs)

The rapid progression of sophisticated advance metering infrastructure (AMI), allows us to have a better understanding and data from demand-response (DR) solutions. There are vast amounts of research on the internet of things and its application on the smart grids has been examined to find the most optimized bill for the user; however, we propose a novel approach of house loads, combined with owning a battery electric vehicle (BEV) equipped with the BEV communication controllers and vehicle-to-grid (V2G) technology. In this paper we use the Stackelberg game approach to achieve an efficient and effective optimized algorithm for the users (followers) based on time dependent pricing. We also assumed an electricity retailer company (leader) and a two-way bilateral communication procedure. The usage-based side of the game has been studied together with demand side management (DSM). Real-time pricing (RTP) from time-of-use (TOU) companies has been used for better results, and Monte Carlo simulation (MCS) handles the uncertain behavior of BEV drivers. Numerical results compared to those from the simulation show that with this method we can reshape the customer's demand for the best efficiency.
DADI: Block-Level Image Service for Agile and Elastic Application Deployment

Businesses increasingly need agile and elastic computing infrastructure to respond quickly to real world situations. By offering efﬁcient process-based virtualization and a layered image system, containers are designed to enable agile and elastic application deployment. However, creating or updating large container clusters is still slow due to the image downloading and unpacking process. In this paper, we present DADI Image Service, a block-level image service for increased agility and elasticity in deploying applications. DADI replaces the waterfall model of starting containers (downloading image, unpacking image, starting container) with ﬁne-grained on-demand transfer of remote images, realizing instant start of containers. DADI optionally relies on a peer-to-peer architecture in large clusters to balance network trafﬁc among all the participating hosts. DADI efﬁ-ciently supports various kinds of runtimes including cgroups, QEMU, etc., further realizing “build once, run anywhere”. DADI has been deployed at scale in the production environment of Alibaba, serving one of the world’s largest ecommerce platforms. Performance results show that DADI can cold start 10,000 containers on 1,000 hosts within 4 seconds.
Impacts of Residual Stress on Micro Vibratory Platform Used for Inertial Sensor Calibration

A micro vibratory platform driven by converse piezoelectric effects is a promising in-situ recalibration platform to eliminate the influence of bias and scale factor drift caused by long-term storage of micro-electro–mechanical system (MEMS) inertial sensors. The calibration accuracy is critically determined by the stable and repeatable vibration of platform, and it is unavoidably impacted by the residual stress of micro structures and lead zirconate titanate (PZT) hysteresis. The abnormal phenomenon of the observed displacement response in experiments was investigated analytically using the stiffness model of beams and hysteresis model of piezoelectric material. Rather than the hysteresis, the initial deflection formed by the residual stress of the beam was identified as the main cause of the response error around the zero position. This conclusion provides guidelines to improve the performance and control of micro vibratory platforms.
A Mutual Selection Mechanism of Ride-Hailing Based on Hidden Points

As a new way to travel, ride-hailing has brought great convenience to the public. However, the demand expression ability of passengers in the existing ride-hailing platforms is weak, and the accuracy of the matching results is low, resulting in a large number of transaction losses and the loss of platform revenue. In this paper, we propose a kind of mutual selection mechanism of ride-hailing based on hidden points, which is composed of platform benefit calculation algorithm and driver assignment algorithm. The platform benefit calculation algorithm mainly calculates the benefits obtained by the platform after the order is completed, while the driver allocation algorithm meets the mutual selection needs of passengers and drivers. Through experiments and theoretical analysis, the mutual selection mechanism proposed in this paper has improved user satisfaction by 5%-21% compared to the traditional methods of order-snatching mechanism and order-sending mechanism, which has significantly improved passenger satisfaction and solved the problem that the existing platform cannot meet the passengers’ individual travel needs and the problem that the passengers’ choice is not fair. The issue of platform revenue was discussed in the context of improved passenger satisfaction.
Positive effect of combining high pressure and biopreservation treatments on lowering the bacterial growth during storage of diced cooked ham with reduced nitrite salt

We investigated the combined effects of biopreservation and high pressure treatment on bacterial communities of diced cooked ham prepared with diminished nitrite salt. First, bacterial communities of four commercial brands of dice cooked ham from local supermarkets, sampled near the use-by-date, were characterised. The four ham microbiota showed a relative low diversity but harboured quite dissimilar communities. Two ham samples were dominated by different Proteobacteria (Pseudomonas, Serratia for one, Psychrobacter and Vibrio for the other one) while the two others were dominated by Firmicutes (Latilactobacillus and Leuconostoc). Second, sterile diced cooked ham, prepared with reduced level of nitrite was inoculated with the two Proteobacteria-rich microbiota collected from the aforementioned commercial samples together with a Lactococcus lactis protective strain. Dices were then treated at 500 MPa for 5 minutes and bacterial dynamics was monitored during storage at 8°C. Applied alone, none of the treatments stabilized durably the growth of hams microbiota. Nevertheless, the combination of biopreservation and high pressure treatment was efficient to reduce the growth of Proteobacteria spoilage species. However, this effect was dependent on the nature of the initial microbiota, showing that use of biopreservation and high pressure treatment as an alternative to nitrite reduction for ensuring cooked ham microbial safety merits attention but still requires improvement.
Nopalito and forage productivity of Opuntia spp. and Nopalea sp. (Cactaceae) growing under greenhouse hydroponics system

The aim of this research was to assess the year-long productivity (nopalito and fodder) of Nopalea cochenillifera, Opuntia ficus-indica, O. robusta ssp. larreyi and O. undulata x O. tomentosa, growing under greenhouse hydroponics. Cladodes were harvested when they reached the tender nopalito size (growth stage 1, GS1), developed cladode size (GS4), or one of two intermediate stages (GS2 and GS3), regardless oftheir chronological age. Productivity was determined by dividing total production by the number of days that passed from the first sprout to the last harvest. Productivity increased (p < 0.001) exponentially with growth stage in O. undulata x O. tomentosa, O. ficus-indica and N. cochenillifera, and linearly in O. robusta ssp. larreyi. As nopalito, the productivity of Opuntia (21 and 31 kg DM ha -1 day -1 for GS1 and GS2, respectively) doubled that of Nopalea, although this difference did not achieve statistical significance. In stages GS3 and GS4, O. robusta ssp. larreyi and O. undulata x O. tomentosa showed the highest productivity (p<0.05), 63 and 57, and 82 and 93 kg DM ha -1 day -1 , respectively, although the latter rose significantly from GS3 to GS4. Biomas-accumulation pattern for treatments fit sigmoid model, which is characteristic of perennial species. With the harvest criterion adopted, the interval between harvests depended on the species and increased, on average, from 10 days in GS1 to 28 days in GS4. Cactus pear productivity depended on the species, and was likely associated with the daily and seasonal temperature variations in its natural habitat. Differences in productivity were more evident at the genus level. Productivity rose as larger cladodes were harvested, but with different rates between species. Nopalito productivity was similar in the four species, but they differed in forage productivity.
Kinetochore individualization in meiosis I is required for centromeric cohesin removal in meiosis II

Partitioning of the genome in meiosis occurs through two highly specialized cell divisions, named meiosis I and II. Step-wise cohesin removal is required for chromosome segregation in meiosis I, and sister chromatid segregation in meiosis II. In meiosis I, mono-oriented sister kinetochores appear as fused together when examined by high resolution confocal microscopy, whereas they are clearly separated in meiosis II, when attachments are bipolar. It has been proposed that bipolar tension applied by the spindle is responsible for the physical separation of sister kinetochores, removal of cohesin protection and chromatid separation in meiosis II. We show here that this is not the case, and initial separation of sister kinetochores occurs already in anaphase I, when attachments are still monopolar, and independently of pericentromeric Sgo2 removal. This kinetochore individualization occurs also independently of spindle forces applied on sister kinetochores, but importantly, depends on cleavage activity of Separase. Crucially, without kinetochore individualization by Separase in meiosis I, oocytes separate bivalents into chromosomes and not sister chromatids in meiosis II, showing that whether centromeric cohesin is removed or not is determined by the kinetochore structure prior to meiosis II.
Reliability-Oriented Electricity Distribution System Switch and Tie Line Optimization

In the past decade, enhancing the reliability of distribution networks by means of optimal switch placement has attracted much attention. In the case of failures in a distribution feeder, such disconnect switches will isolate the faulted section, and the customers downstream of the faulted point can be supplied by neighboring feeders through tie lines. Nevertheless, such reserve branches not only might experience failures themselves but also may not even exist prior to the switch placement. Accordingly, this paper presents a mathematical-programming-based model for the concurrent placement of disconnect switches and tie lines in the distribution networks to enhance the service reliability, considering both practical benefits and drawbacks of such reserve branches. In the proposed model, installation of remote-controlled and manual switches at various locations of distribution feeders together with potential tie lines are considered. Also, practical operational constraints regarding the utilization of tie lines, and the impact of failures in such reserve branches on the reliability indices are meticulously modeled in the proposed formulation. Unreliability cost is estimated based on a reward-penalty scheme and the revenue lost due to the not supplied demand during the network contingencies. As an instance of mixed-integer linear programming, the proposed optimization model can be efficiently solved to the global optimality using commercially available software. Aiming at investigating the applicability of the proposed model, it is implemented on a test network, and the results are thoroughly analyzed through various case studies.
Generalizability of SuperAlarm via Cross-Institutional Performance Evaluation

Bedside patient monitors are ubiquitous tools in modern critical care units to provide timely patient status. However, current systems suffer from high volume of false alarms leading to alarm fatigue, one of top technical hazards in clinical settings. Many studies are racing to develop improved algorithms towards precision patient monitoring, while little has been done to investigate the aspect of algorithm generalizability across different health institutions. Our group has been developing an evolving framework termed SuperAlarm that extracts multivariate patterns in data streams (monitor alarms, electronic health records and physiologic waveforms) of modern health enterprise to predict patient deterioration and has demonstrated great potential in mitigating alarm fatigue. In this study, we further investigate the generalizability of SuperAlarm by designing a comprehensive approach to achieve performance comparison in predicting in-hospital code blue (CB) events across two health institutions. SuperAlarm model trained with alarm data in one institution is tested on both internal and external test sets. Results show comparable performance with sensitivity up to 80% within one-hour window of events and over 90% in reduction of false alarms in both institutions. Cross-institutional performance agreement can be further improved by predicting a more stringent CB subtype (cardiopulmonary arrest), with internal sensitivity lying within 95% confident interval of external one up to 8-hour before event onset. The cross-institutional performance comparison offers first-hand knowledge on both advantages and challenges in generalizing a prediction algorithm across different institutions, which hold key information to guide the design of model training and deployment strategy.
An Accurate Object Detector With Effective Feature Extraction by Intrinsic Prior Knowledge

Deep learning-based object detection algorithms play an increasingly important role in many computer vision tasks. Many detection algorithms are trying to increase the depth of networks to improve their feature expression capacity. However, how to better extract the essential features from a fixed number of training samples is often ignored. Thus, we propose CoReSh network, using the prior knowledge of intrinsic features, to address this problem. First, the relevant optical factors which may affect the detection performance are explored, and the intrinsic features are proven to be able to represent the essential information of objects. Second, a new data augmentation is proposed to increase the feature diversities of training samples through intrinsic decomposition. Third, a deep detection neural network incorporating color, reflectance, and shading image’s information is designed. To optimize the performance of our network, different learning rate setting tactics are compared. Besides, a contrast experiment on the Pascal VOC dataset was set up to verify it. The result shows that our network is 10% better than ordinary deep learning-based detection networks in precision, and roughly the same as the network loaded with pre-training weights from ILSVRC. It shows that our network has greatly improved the precision of object detection and it is suitable when the training process lacks sufficient training samples.
Temporal Importance of Greater Light Interception to Increased Yield in Narrow-Row Soybean

Increased light interception is considered the main factor explaining greater yield in narrow- compared to wide-row spacing in soybean [Glycine max (L.) Merr. ]. Controversy exists, as to when during the growth cycle the greater ligh interception of the narrow rows has an enhancing effect on yield. The objectives of this study were to analyze crop grown rate and yield components to determine the importance of greater light interception during vegetative (E-R1), early reproductive (R1-R5), and late reproductive (R5-R7) periods to increased yield in narrow-row culture. Field studies were conducted during 1989 and 1990 at a late (July) planting date with Centennial' soybean (determinate, Maturity Group VI) at row spacings of 100, 75, 50 and 25 cm (.)
Importance for Phylogeny of Single and Multiple Stem-group Fossil Species with Examples from Freshwater Fishes

The phylogenetic significance of fossils is limited in many groups, such as freshwater fishes, by the sparse fossil record that is available. Nevertheless, even these fossils have a role in phylogenetic analysis. Fossils can be a source of new species, characters, and character states. Fossils can also suggest different character polarities. They often clarify the order in which synapomorphies were acquired and demonstrate convergence in the apparent synapomorphies of living species. In extreme cases, they can change estimated phylogenies based only on living species. Species that diverged early in the history of their group usually have more primitive features than younger species, but these early forms tend to be removed from the fauna by extinction
A Deep Learning Based Fault Diagnosis Method With Hyperparameter Optimization by Using Parallel Computing

Bearing fault diagnosis is of great significance to ensure the safe operation of mechanical equipment. This paper proposes an intelligent fault diagnosis method of rolling bearings based on deep belief network (DBN) with hyperparameter optimization by using parallel computing. Different with traditional diagnosis methods that extract the features manually depending on much prior knowledge about signal processing techniques and diagnostic expertise, DBN extracts fault features automatically by machine learning mechanism. Considering the time consuming problem, parallel computing is adopted to the DBN training process by using a Master/Slave mode to improve the training speed so that the global optimization with Genetic Algorithm and higher diagnosis accuracy can be achieved. Finally, the proposed method is verified with the public datasets provided by Case Western Reserve University (CWRU) with various fault depths in different locations and loads of rolling bearings. The results indicate that the proposed method can identify bearing faults under different conditions correctly which significantly enhances the intelligence of fault classification and reduces the time for parameter selection of deep learning models.
Synchronverter: A Comprehensive Review of Modifications, Stability Assessment, Applications and Future Perspectives

The increase in the penetration of renewable energy sources (RES) has reduced the effective inertia of the power system dominated by synchronous machines. The reduction in inertia has paved the way to the development of inertia emulation techniques, which complements the increased penetration of RES. The synchronverter technology is one among the virtual inertia emulation techniques which resembles close to the synchronous machine. The advancements in control and adaptations of synchronverter for various applications demand a comprehensive yet critical review. Thus, the main objective of this paper is to present a critical review of synchronverter technology which could increase the inertial response of RES. A critical analysis of modifications made to original synchronverter and the stability assessment techniques is presented. The effect of parameter variation on stability and dynamic performance is brought out as a decision matrix by analysing the movement of eigenvalues presented in various works. Towards the end, a synoptic overview is presented to depict the research trend and emphasize the domain which requires further attention. Furthermore, a brief discussion of the research gaps perceived during the phase of review is presented to leverage future research in the field.
SAnE: Smart Annotation and Evaluation Tools for Point Cloud Data

Addressing the need for high-quality, time efficient, and easy to use annotation tools, we propose SAnE, a semiautomatic annotation tool for labeling point cloud data. The contributions of this paper are threefold: (1) we propose a denoising pointwise segmentation strategy enabling a fast implementation of one-click annotation, (2) we expand the motion model technique with our guided-tracking algorithm, and (3) we provide an interactive, yet robust, open-source point cloud annotation tool, targeting both skilled and crowdsourcing annotators. Using the KITTI dataset, we show that the SAnE speeds up the annotation process by a factor of 4 while achieving Intersection over Union (IoU) agreements of 84%. Furthermore, in experiments using crowdsourcing services, SAnE achieves more than 20% higher IoU accuracy compared to the existing annotation tool and its baseline, while reducing the annotation time by a factor of 3. This result shows the potential of SAnE, for providing fast and accurate annotation labels for large-scale datasets with a significantly reduced price. SAnE is open-sourced at https://github.com/hasanari/sane.
A Review of Acoustic Impedance Matching Techniques for Piezoelectric Sensors and Transducers

The coupling of waves between the piezoelectric generators, detectors, and propagating media is challenging due to mismatch in the acoustic properties. The mismatch leads to the reverberation of waves within the transducer, heating, low signal-to-noise ratio, and signal distortion. Acoustic impedance matching increases the coupling largely. This article presents standard methods to match the acoustic impedance of the piezoelectric sensors, actuators, and transducers with the surrounding wave propagation media. Acoustic matching methods utilizing active and passive materials have been discussed. Special materials such as nanocomposites, metamaterials, and metasurfaces as emerging materials have been presented. Emphasis is placed throughout the article to differentiate the difference between electric and acoustic impedance matching and the relation between the two. Comparison of various techniques is made with the discussion on capabilities, advantages, and disadvantages. Acoustic impedance matching for specific and uncommon applications has also been covered.
Insecticidal activities of essential oils from fruits of Litsea salicifolia Roxb. ex Wall. against Sitophilus zeamais Motschulsky and Tribolium castaneum (Herbst).

The insecticidal activities of essential oil extracted from Litsea salicifolia to Sitophilus zeamais and Tribolium castaneum were investigated under laboratory conditions. Essential oil was extracted by hydro-distillation method and then analyzed by GC/MS. The major components of L. salicifolia were (E)-citral and (Z)-citral. Litsea salicifolia had repellency effect on both insect species even at the lowest application rate (0.16 µg/cm 2 ). In addition, it exhibited fumigant toxicity to S. zeamais, contact toxicity to both species and high antifeedant toxicity to T. castaneum compared to S. zeamais. Hence, L. salicifolia might be considered as a grain protectant to major stored product insects.
A Preliminary Framework for Recognizing iStar Hand Drafts

Despite the rapid development of iStar modeling tools, drafting iStar models on paper is convenient for illustration purposes and can be inspiring during brainstorming sessions. Digitizing such hand drafts and generating corresponding model files can enable modelers to save and reuse model snippets. This paper presents a preliminary technical framework for achieving this goal. Adopting the divide and conquer strategy, the proposed framework consists of three separate recognition modules dealing with node recognition, text recognition, and element composition. Upon implementing the framework, the recognized model drafts will be transformed into iStarML files to be easily imported to iStar modeling tools. This paper focuses on discussing the feasibility of the proposed approach and presents some preliminary.