Start-stop Time Research Articles

Entropy-based fluid–solid–thermal coupled wear prediction of journal bearing during repeated starting and stopping

The novelty of the present study is to construct an Entropy-based wear and fluid-solid-thermal (FST) coupled model for journal bearings, which reveals the transient interaction behavior between wear evolution and FST evolution during repeated starting and stopping. The wear rate is measured experimentally and the contact temperature, friction coefficient and normal contact force are obtained from the validated FST model. Wear tests for the lubricated journal bearing at three constant speeds are used to validate the predicted wear rate. Numerical calculations give the time-varying wear and FST evolution of journal bearings during repeated starting and stopping at the same or different start-stop time, including wear rate, wear depth, temperature, fluid pressure and contact pressure. The effects of the bearing thickness, maximum speed, radial clearance and load on the wear and FST performances are evaluated. The simulation results reveal that the rate of change of wear rate in the start-stop cycle first increases rapidly and then decreases slowly as the number of start-stop cycles increases. The distributions of the contact pressure and the fluid pressure become wider along the circumferential direction with the increase in the number of start-stop cycles while the maximum values become smaller. The maximum wear depth increases with increasing start-stop time for an identical number of start-stop cycles, and the shorter the start-stop time is, the higher the maximum temperature tends to be. Furthermore, the effect on the maximum wear depth and maximum temperature of journal bearings will become significant when the maximum speed and bearing thickness are relatively small or the radial clearance and load are relatively large.

Experimental and numerical investigations on operation characteristics of seasonal borehole underground thermal energy storage

To investigate operation characteristics of seasonal borehole underground thermal energy storage (SBUTES) with different operational strategies, a model test platform with reduced size was established based on similarity principle. The test results show that the larger the start-stop time ratio, the smaller the average heat exchange rate per unit depth (HERPUD) of borehole, and the lower the energy storage efficiency, but the thermal energy storage and extraction capacity increase. For the time ratio of thermal energy storage to extraction (TROSE), the larger the TROSE, the larger the thermal imbalance rate of energy storage body, which will lead to the gradual increase or decrease of temperature of energy storage body and is not conducive to the operation of energy storage. Compared with the asynchronous mode of storage and extraction, the synchronous mode of storage and extraction can effectively reduce the average temperature of energy storage body and increase the heat exchange efficiency of boreholes during the thermal energy storage. The focused energy storage mode can improve the energy storage efficiency and soil temperature recovery rate, balance the thermal energy storage and extraction capacity, and thus the performance of SBUTES can be optimized. A 3-D CFD model of borehole energy storage was established to further find the influences of borehole layout forms, layout spacing and depths on characteristics of the SBUTES. It can be found that for the energy storage efficiency, the hexagonal layout is the highest, the rectangular layout is the lowest, and the circular layout is slightly higher than the square one. Under the same volume of boreholes group, increasing the spacing of inner boreholes can effectively alleviate thermal interference of inner boreholes during the thermal energy storage, and the heat exchange capacity undertaken by inner boreholes can be improved. But it is not conducive to thermal energy extraction. Although increasing borehole depth can increase thermal energy storage and extraction capacity and system operation efficiency, the increase degree is decreasing with the increase of borehole depth, and the greater the borehole depth, the smaller the influence of borehole depth on characteristics of SBUTES.

Experimental study on a novel battery cooling system containing dual flow medium

The refrigerant cooling scheme can effectively deal with the problem of battery cooling but with a poor economy. A novel battery thermal management (BTMS) called dual flow medium system (DFMS) is proposed in this paper, which is more economical than the refrigerant cooling scheme. The DFMS contains the refrigerant cooling system (RCS) and the coolant cooling system (CCS). The alternate cooling mode and the simultaneous cooling mode of the DFMS were studied under different coolant flow rates, the control range of the batteries' average temperature, and ambient temperature. The results show that the alternate cooling mode makes power consumption saved by 49.15% maximumly when the ambient temperature is 18.3°C and the compressor start-stop interval time is prolonged. The simultaneous cooling mode can prolong the compressor operating time by 16.3% and save a little energy under an appropriate coolant flow rate. The DFMS provides a novel option for the design of BTMS, and the research on the operating modes of the DFMS lays a good foundation for its further development.

Study on occupant behaviour using air conditioning of high-rise residential buildings in hot summer and cold winter zone in China

Over the past two decades, the electricity consumption of residential air conditioning (AC) has increased by 13.4 times. Moreover, carbon emissions from building operations remain high. Occupant behaviour related to AC operation is the most significant factor affecting the energy consumption of residential ACs. Hence, in this study, occupant behaviour towards using air conditioning in high-rise residential buildings was analysed to provide suggestions for increasing the AC usage efficiency. Annual data on the AC power consumption, indoor temperature, and humidity of typical high-rise residential buildings in hot summer and cold winter zones in China were collected. Five target parameters—AC opening rate, AC standby rate, start–stop time, start–stop temperature, and stable temperature—were statistically analysed. The results reveal that the AC start–stop time follows a normal distribution. The start and stop times of the AC during the cooling period are 22:32 and 7:05 the next day, respectively, whereas during the heating periods these values were 20:42 and 23:02 or 7:56 the next day, respectively. The standard deviation of the time distribution of AC starting or stopping at night is larger than that during the day. The AC start-up temperature followed an exponential distribution. The indoor temperature during the cooling and heating periods is more than 29.42 °C and less than 11.08 °C, respectively. The opening rate of the room AC is 80 %.

Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning

Aiming at the scarcity of Low Earth Orbit (LEO) satellite spectrum resources, this paper proposes an algorithm of interference signal feature extraction and pattern classification based on deep learning to further improve the stability of satellite–ground communication links. The algorithm can successfully predict the interference signal pattern, start–stop time, frequency change range and other parameters, and has the advantages of excellent interference detection performance, high detection accuracy and small parameter prediction error, etc. It can be applied in the field of channel monitoring of communication satellite-to-ground communication links, and realize the repeated and efficient utilization of spectrum resources. Experiments show that the precision and recall of the algorithm for detecting five kinds of interference signals are all close to 100%, the prediction error of starting and ending time is less than 4 ms, and the prediction error of starting and ending frequency is less than 6 KHz.

A comparative assessment of the battery liquid‐cooling system employing two coolants: Phase change material emulsion and water

This work proposes a novel liquid-cooling system that employs the phase change material (PCM) emulsion as the coolant for the battery pack. To compare the proposed scheme with the traditional water cooling system, a thermal model is developed for the battery pack with cooling systems, where the system start-stop control and time hysteresis phenomenon are considered in simulations. The results show that either increasing the inlet velocity or temperature improves the battery temperature uniformity for the two schemes, but it enlarges the pumping energy consumption during the whole discharging process. Compared with the water cooling system, the proposed PCM emulsion cooling scheme can not only significantly decrease the battery temperature difference but also effectively shorten the system operating time. According to the calculation, the pumping energy consumption with using the PCM emulsions as the coolant decreases 18.9% in minimum and 66.6% in maximum than that with using the water as the coolant. Consequently, the proposed PCM emulsion cooling system is superior to the water cooling system in terms of the heat dissipation capacity, the temperature uniformity, the pump power requirement, and the pumping energy consumption, which is well-suited for the cooling system of the battery pack in electric vehicles. In addition, the proposed thermal model can be used well to simulate the thermal characteristics of the vehicle-mounted battery liquid-cooling system under actual operating conditions.

Time-Varying Insomnia Symptoms and Incidence of Cognitive Impairment and Dementia among Older US Adults

There is conflicting evidence regarding the association between insomnia and the onset of mild cognitive impairment (MCI) or dementia. This study aimed to evaluate if time-varying insomnia is associated with the development of MCI and dementia. Data from the Health and Retirement Study (n = 13,833) from 2002 to 2014 were used (59.4% female). The Brief Insomnia Questionnaire was used to identify insomnia symptoms compiled in an insomnia severity index, ranging from 0 to 4. In the analysis, participants’ symptoms could vary from wave-to-wave. Dementia was defined using results from the Health and Retirement Study (HRS) global cognitive assessment tool. Respondents were classified as either having dementia, MCI or being cognitively healthy. Cox proportional hazards models with time-dependent exposure using the counting process (start-stop time) were used for analysis. For each one-unit increase in the insomnia symptom index, there was a 5-percent greater hazard of MCI (HR = 1.05; 95% CI: 1.04–1.06) and dementia (HR = 1.05; 95% CI: 1.03–1.05), after fully adjusting. Using a nationally representative sample of adults aged 51 and older, this study found that time-varying insomnia symptoms are associated with the risk of MCI and dementia. This highlights the importance of identifying sleep disturbances and their change over time as potentially important risk factors for MCI and dementia.

Simulation Analysis of Circuit and Designing of PCB Layout of a CMOS based NOR Logic Gate using Open-Source Software eSim

Abstract: There are various basic gates like NAND, NOR gates which are extensively used in the designing of the more complex circuits with use higher number of transistors such as MUXs, ADCs and any other circuits. In this paper, we have carried out the modelling of NOR gate at 130 nm technology, yet maintaining comparable performance than conventional CMOS NOR gate logic structure. The modelling includes schematics design and PCB layout design run of the above gates. Also, the simulation results of the gates are obtained at the same node with start time, step time, stop time, rise time, fall time and delay and power dissipation. In this all process have been carried out of a CMOS based NOR Logic Gate using Open-Source Software eSim. Keywords: Simulation, PCB, NOR, CMOS, eSim

Time-Varying Insomnia Symptoms and Incidence of Cognitive Impairment and Dementia among Older US Adults.

There is conflicting evidence regarding the association between insomnia and the onset of mild cognitive impairment (MCI) or dementia. This study aimed to evaluate if time-varying insomnia is associated with the development of MCI and dementia. Data from the Health and Retirement Study (n = 13,833) from 2002 to 2014 were used (59.4% female). The Brief Insomnia Questionnaire was used to identify insomnia symptoms which were compiled in an insomnia severity index, ranging from 0 to 4. In analysis, participants’ symptoms could vary from wave-to-wave. Dementia was defined using results from the Health and Retirement Study (HRS) global cognitive assessment tool. Respondents were classified as either having dementia, MCI, or being cognitively healthy. Cox proportional hazards models with time-dependent exposure using the counting process (start-stop time) were used for analysis. For each one-unit increase in the insomnia symptom index, there was a 5-percent greater hazard of MCI (HR = 1.05; 95% CI: 1.04–1.06) and dementia (HR = 1.05; 95% CI: 1.03–1.05), after fully adjusting. Using a nationally representative sample of adults age 51 and older, this study found that time-varying insomnia symptoms are associated with risk of MCI and dementia. This highlights the importance of identifying sleep disturbances and their change over time as potentially important risk factors for MCI and dementia.

An Efficient and Accurate Method for Detection and Classification of Power Quality Disturbances

Aiming to the problem of large computation amount and high redundancy of traditional S‐transform methods available in the detection and classification of transient power quality disturbances, an efficient and accurate method for the detection and classification is proposed. First, the maximum power spectrum dynamics method is used to extract the main frequency points. Then, a called modified incomplete S‐transform proposed in the paper is performed on the main frequency points so as to obtain a modulus time‐frequency matrix involving the characteristic information of disturbances. The modified incomplete S‐transform is implemented by introducing a bi‐Gaussian window function with two parameters to replace the Gaussian window function of the traditional S‐transform. And then, the characteristic quantities for detecting power quality disturbance and identifying the classification of the disturbances are extracted based on the matrix. Finally, the disturbance parameters such as the amplitude, start time, and stop time of the disturbances are calculated based on the relevant theories and rules, and also the classification of the disturbances is identified by extracting some important characteristic quantities. A great number of simulation experiments are conducted. The results obtained show that by the method, the start time, stop time, and amplitude changes of power quality disturbance signals can be fast and accurately detected, and the classification of the disturbances can be fast and accurately identified, and that the method has the characteristics of less operation time and strong anti‐interference ability. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

An Event-Driven Convolutional Neural Architecture for Non-Intrusive Load Monitoring of Residential Appliance

Nowadays, the advancement of non-intrusive load monitoring (NILM) is hastened by the everincreasing requirements for smart power utilization and demand side management. Thus, an intelligent event-driven non-intrusive load monitoring method based on convolutional neural network (CNN) is proposed in this article to profile residential consumer behavior, which focused on limited applicability and low recognition accuracy of existing NILM techniques. The deep convolutional neural architecture improves the performance of load monitoring procedure, and extends its application range to monitoring process in terms of various and combined characteristics. Meanwhile, the event-driven procedure is conducted to find the start-stop time and state change of the appliance accurately, including zero-cross detection, current similarity detection, threshold evaluation, and event current acquisition. Subsequently, the current-to-image conversion is carried out to represent the characteristics of residential appliance as the input of CNN. And the convolutional neural architecture is proposed to identify the power consumption load and its working condition. Selected experimental results are performed that the average recognition accuracy is over 93%, which demonstrates the effectiveness and superiority of the proposed methodology for home energy management.

Non-intrusive monitoring and disaggregation of industrial load based on Petri net theory

In this paper, the non-intrusive monitoring and disaggregation method of industrial load based on Petri net theory is studied. First, the basic concepts of Petri nets are introduced. On the basis of prototype Petri nets, time factor is added. Then, the Petri net with time factor is combined with industrial load production process. Considering the production sequence, start-stop time and production time constraints of industrial load, a non-intrusive monitoring and disaggregation model of industrial load is constructed. Finally, according to the actual production process of industrial load, the electricity consumption situation of each production link is calculated, and the detailed analysis results of industrial user’s electricity consumption behavior are obtained. The research could provide an important reference for realization of friendly interaction between industrial users and power grid.

A Deep Learning Framework for Signal Detection and Modulation Classification.

Deep learning (DL) is a powerful technique which has achieved great success in many applications. However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems. In this work, a DL framework for multi-signals detection and modulation recognition is proposed. Compared to some existing methods, the signal modulation format, center frequency, and start-stop time can be obtained from the proposed scheme. Furthermore, two types of networks are built: (1) Single shot multibox detector (SSD) networks for signal detection and (2) multi-inputs convolutional neural networks (CNNs) for modulation recognition. Additionally, the importance of signal representation to different tasks is investigated. Experimental results demonstrate that the DL framework is capable of detecting and recognizing signals. And compared to the traditional methods and other deep network techniques, the current built DL framework can achieve better performance.

A smart community energy management scheme considering user dominated demand side response and P2P trading

This paper proposed a Peer-to-Peer (P2P) local community energy pool and a User Dominated Demand Side Response (UDDSR) that can help energy sharing and reduce energy bills of smart community. The proposed UDDSR allows energy users within the community to submit flexible Demand Response (DR) bids to Community Energy Management Scheme (EMS) with flexible start time, stop time and response durations with regard to users’ comfort zones for electric heating systems, electric vehicles and other home appliances, which gives maximum freedom to the DR participants. The scheduling of the DR bids, originally a multi-objective optimization problem (maximize the total flexible demand and the flexible demand in every interval during the whole DR duration), is transferred to a single objective optimization problem (maximize the total demand with penalty for demand imbalance during the whole DR duration) that can significantly decrease the computational complexity. Furthermore, to facilitate efficient energy usage among neighbourhoods, a local energy pool is also proposed to enable the energy trading among users aiming to facilitate the usage of surplus energy within the community. The electricity price of energy pool is determined by the real-time demand/supply ratio, and upper/lower limit for the price is configured to ensure the profitability for all the participants within the pool. To evaluate the performance of proposed UDDSR and local energy pool, comprehensive numerical analysis is conducted. It is found that the energy pool participants without PV can get at least 6.16% savings on electricity bill (when PV penetration level equals to 20%). The energy pool participants with PV can get much better return (at least 13.4% profit increase) on the PV generation compared to the conventional Feed-in-Tariff. If energy users join the UDDSR scheme, the participants can get further return, and the proposed UDDSR can provide a constant load reduction/increase during the every time interval of the whole DR event. If Battery Energy Storage System (BESS) is included in the DR operation, the usage efficiency of customers’ flexible loads can achieve more than 85%.

Complexity–entropy causality plane based on power spectral entropy for complex time series

The complexity–entropy causality plane based on permutation entropy is a powerful tool to discriminate signals from different systems. In this paper, we combine traditional statistical complexity measure and power spectral entropy and construct complexity–entropy causality plane in frequency domain. The power spectral entropy is derived from Fourier transformation, so some features that are obscure in time domain can be extracted in frequency domain. Comparing to permutation entropy, this method is free of parameters. Several time series generated from different classes of systems are analyzed to demonstrate the measure. Results show that these signals can be clearly distinguished in our plane. Then by adding sinusoidal abnormal signal into original one, the abnormal information can be efficiently detected. Finally, we apply it to bearing vibration signals. Empirical consequences illustrate that the start–stop time and classification of fault signal can be clearly determined.

Investigation of nursing interruptions during endoscopic surgeries

Objective To observe interruption events during endoscopic surgery. Methods Total 63 laparoscopic surgeries(185.1 h totally) had been observed from March to September 2015, through observation recorded source, type, the affairs occupying nurses when the interruptions occurred, results, years of experience and start-stop time. Results Total 702 interruptions had been observed, reasons accounting for interruptions: surgeons (23.1%,162/702) and circumstances (16.7%,117/702); the major type: intrusion (39.7%,279/702); the affairs occupying nurses when the interruptions occurred: sterile operation(19.2%,135/702) and equipment inventory(14.7%,103/702); the major result: negative (84.6%,594/702). Conclusions There is a high level frequency of interruption events, intraoperative workflows need to be well designed to reduce unnecessary interruptions. Key words: Laparoscopes; Nursing interruptions; Operation safe; Observation

Attribute driven process architecture for additive manufacturing

In additive manufacturing (AM) process, the manufacturing attributes are highly dependent upon the execution of hierarchical plan. Among them, material deposition plan can frequently interrupt the AM process due to tool-path changes, tool start-stop and non-deposition time, which can be challenging during free-form part fabrication. In this paper, the layer geometries for both model and support structure are analyzed to identify the features that create change in deposition modality. First, the overhanging points on the part surface are identified using the normal vector direction of the model surface. A k-th nearest point algorithm is implemented to generate the 3d boundary support contour which is used to construct the support structure. Both model and support structures are sliced and contours are evaluated. The layer contour, plurality, concavity, number of contours, geometric shape, size and interior islands are considered to generate an AM deposition model. The proposed model is solved for minimizing the change in deposition modality by maximizing the continuity and connectivity in the material deposition plan. Both continuity and connectivity algorithms are implemented for model and support structure for free-form object. The proposed algorithm provides the optimum deposition direction that results in minimum number of tool-path segments and their connectivity while minimizing contour plurality effect. This information is stored as a generic digital file format named Part Attributable Motion (PAM). A common application program interface (API) platform is also proposed in this paper, which can access the PAM and generate machine readable file for different existing 3D printers. The proposed research is implemented on three free-form objects with complex geometry and parts are fabricated. Also, the build time is evaluated and the results are compared with the available 3d printing software. The layer geometries for AM are analyzed to minimize the change in deposition modality.Overhanging points are identified and k-th nearest point algorithm is implemented to construct the support structure.Continuity and connectivity algorithms are implemented for free-form shaped model and support structure.A generic digital file format named Part Attributable Motion (PAM) is proposed to store the outcome from the methodology.The build time is evaluated and the results are compared with the available 3d printing software.

MP22-12 THE RELATIONSHIP BETWEEN OPERATIVE TIMES AND COMPLICATION RATES IN PERCUTANEOUS NEPHROSTOLITHOTOMY PROCEDURES

You have accessJournal of UrologyStone Disease: Surgical Therapy I1 Apr 2016MP22-12 THE RELATIONSHIP BETWEEN OPERATIVE TIMES AND COMPLICATION RATES IN PERCUTANEOUS NEPHROSTOLITHOTOMY PROCEDURES Julie Stein, Kaitlan Cobb, John Michael DiBianco, and Patrick Mufarrij Julie SteinJulie Stein More articles by this author , Kaitlan CobbKaitlan Cobb More articles by this author , John Michael DiBiancoJohn Michael DiBianco More articles by this author , and Patrick MufarrijPatrick Mufarrij More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.700AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Prolonged operative time has long been associated with postoperative complications. Percutaneous nephrostolithotomy (PCNL) is a common approach for the treatment of complex stone disease, often requiring lengthy operative times and/or multiple surgical stages. Complications of PCNL, including hemorrhage, injury to adjacent organs/structures, and infection, have been reported to occur at higher rates in procedures that have had prolonged operative times. Additionally, some of these complications may limit the duration of PCNL procedures, thereby decreasing stone-free rates or requiring more surgical stages. We aimed to investigate the relationship between operative times and postoperative complications as classified by the Clavien-Dindo System of Surgical Complications. METHODS A retrospective review was performed on 66 patients who underwent PCNL by a single endourologist at George Washington University Hospital and Sibley Memorial Hospital in Washington, D.C. from October 2012 to December 2014. Data on basic patient demographics and characteristics, operative times, and complications were collected. Operative time was defined by surgery start time and stop time as recorded by the anesthesiologist. Complications were classified by the Clavien-Dindo system on a scale of 0 to 5. RESULTS Mean age of patients was 57.3 +/- 12.2 years. Overall complication rate was 14.9% with 0% Clavien I, 9.0% Clavien II, 0% Clavien III, 6.0% Clavien IV, and 0% Clavien V. Mean operative time was 232.0 +/- 72.7 minutes. A simple linear regression model revealed that operative time accounted for 6.1% of the variance in Clavien score (R2 = .061, p < 0.05). One-way Analysis of Variance (ANOVA) demonstrated that the presence of infection stones differed significantly between Clavien classes (F(2, 60) = 3.32, p = 0.043). Post-hoc analyses showed that infection stones were associated with a Clavien II complication (p = 0.05), but not with a Clavien IV complication (p > 0.05). CONCLUSIONS In our investigation, increased operative time of PCNL procedures was not associated with an increase in complications. The increase in mid-level complications observed in patients identified to have infection-based stones corroborates previous data on this particular group of stone patients. While, in general, it is advisable to minimize operative duration, this data does not suggest an increased complication risk with lengthier PCNL procedures. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e257-e258 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Julie Stein More articles by this author Kaitlan Cobb More articles by this author John Michael DiBianco More articles by this author Patrick Mufarrij More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Passive Control Shuttle-Type Check Valve – a New Nuclear-Class Fluid Control Component

A novel check valve - passive control shuttle-type check valve is introduced in this paper based on the comparison of the existing swing check valves including the liquid, structural principle, flow resistance coefficient, airproof and start-stop performances. The experimental and simulation analysis validated that shuttle-type check valve has the advantages of symmetric structure along the pipeline axis, balance circle flow, low coefficient streamline, adjustable start-stop time, multi-airproof, zero leakage and less noise.

Modelling concentrations of volcanic ash encountered by aircraft in past eruptions

Abstract Prolonged disruption to aviation during the April–May 2010 eruption of Eyjafjallajokull, Iceland resulted in pressure to predict volcanic ash plume concentrations for the purpose of allowing aircraft to fly in regions with low ash contamination. Over the past few decades there have been a number of incidents where aircraft have encountered volcanic ash resulting in damage to the aircraft and loss of power to engines. Understanding the volcanic ash concentrations that these aircraft have encountered provides important input to determining a safe concentration limit. Aircraft encounters with six volcanic eruption plumes have been studied and ash concentrations predicted using the atmospheric dispersion model NAME. The eruptions considered are Galunggung 1982, Soputan 1985, Redoubt 1989, Pinatubo 1991, Hekla 2000 and Manam 2006. Uncertainties in the eruption source details (start time, stop time and eruption height) and in the aircraft encounter location and flight path are found to be major limitations in some cases. Errors in the driving meteorological data (which is often coarse in resolution for historic studies) and the lack of eruption plume dynamics (e.g. umbrella cloud representation) results in further uncertainties in the predicted ash concentrations. In most of the case studies, the dispersion modelling shows the presence of ash at the aircraft encounter location. Maximum ash concentrations in the vicinity of the aircraft are predicted to be at least 4000 μg m −3 although confidence in the estimated concentrations is low and uncertainties of orders of magnitude are shown to be possible.