Search

Herein, a simple, efficient, and environmentally friendly strategy to prepare black TiO 2 nanoparticles has been reported; the procedure combines the ultraviolet light irradiation and low-temperature annealing (300 °C) with continuous N 2 flow under atmospheric pressure. The samples were characterized by the various techniques, such as X-ray diffraction, high resolution transmission electron microcopy, Raman spectroscopy, X-ray photoelectron spectroscopy. The photocatalytic degradation of the organic contaminant Rhodamine B was investigated when black TiO 2 nanoparticles was irradiaed with ultraviolet light and visible light. The black TiO 2 material possesses an anatase phase and is able to absorb a high amount of photon energy in the visible light region, effectively driving photochemical degradation reactions. The enhancement of the photocatalytic activity can be attributed to the following: first, the hydrogen ions decrease the localized Ti 3+ states (which are also the e − -h + recombination centers) leading to a more efficient separation of the electron-hole pairs; secondly, the conduction band electrons in black TiO 2 nanoparticles are responsible for strong absorption of visible light. Consequently, the photocatalytic degradation of Rhodamine B can be significantly enhanced using black TiO 2 nanoparticles. This work proves that hydrogenated black TiO 2 promotes the absorption of the entire electromagnetic spectrum and exhibits high photocatalytic activity under solar light irradiation. [ABSTRACT FROM AUTHOR]

Zn2GeO4:Mn,Eu and Zn2GeO4:Mn powders were synthesized by a high-temperature solid-state reaction. X-ray powder diffraction (XRD) and scanning electron microscopy (SEM) were used to characterize the structures and morphologies of the synthesized powders, respectively. The photocatalytic properties and long persistent luminescence performance were improved by Eu doping. Thermoluminescent (TL) curves showed that the trap concentration in the material was increased with Eu doping, which formed trap centers in Zn2GeO4:Mn. The trap centers can capture the electrons or holes and subsequently increase the separation of photogenerated electrons and holes by suppressing the recombination of captured electrons and holes; thus, resulting in an improved photocatalytic activity and a prolonged persistent luminescence. The present strategy may be used as a general method to improve the photocatalytic activity and persistent luminescence. [ABSTRACT FROM AUTHOR]

Cr-doped zinc gallate (ZnGa2O4) near-infrared (NIR) phosphors were synthesized via a high temperature solid state method. The luminescence properties of the phosphors were studied systematically. A significant spectra overlap between the emission of ZnGa2O4 and the absorption of Cr was observed and 300 nm excitation exhibited the most excellent long-lasting luminescence properties among the three main excitation bands. Luminescence intensity was changed with the ratio of Ga/Cr and the blue host emission of ZnGa2O4 was suppressed when doping Cr into ZnGa2O4. The fluorescence decay curves of blue emission of ZnGa2O4 with different Cr doping concentrations indicated that the lifetime of ZnGa2O4 at 505 nm become shorter with the increase of the Cr concentration. Herein, a possible mechanism of long-lasting luminescence in ZnGa2O4: Cr was proposed that the NIR long-lasting luminescence in ZnGa2O4: Cr comes from the persistent energy transfer from ZnGa2O4 to Cr. [ABSTRACT FROM AUTHOR]

Background Degeneration of intervertebral disks in the lower lumbar spine is associated with significant structural alterations. Finite element model has been widely used in the study of spinal mechanical behaviors. Our study used this technique to characterize the motional influence to the double-level (L4-5 and L5-S) degeneration. Methods and Results Three grades of disk degeneration were modeled with the changes in geometry and material properties. In the extension and flexion of range of motion (ROM), single segment degeneration in L4-5 or L5-S resulted in a decreased angle in itself and increased angle in the other segment. Double-level degeneration resulted in a decreased rotation in both segments. Bending resulted in a decreased ROM in all 3 grades of degeneration in the double-level degeneration. In torsion loading, mild and moderate single degeneration in L4-5 and L5-S increased the rotation angle. In double-level degeneration, mild and moderate L4-5 degeneration increased the L4-5 rotation angle by 14%−19%. In contrast, severe L4-5 decreased L4-5 rotation angle. Concurrently, mild and moderate L5-S degeneration increased the rotation angle, respectively, by 15% and 6%, and severe degeneration decreased the rotation angle by 29%. Conclusions Different loading motions in double-level degeneration had differing effects on the ROM. These changes are important to understand the biomechanics of the progression of disk degeneration in the lower lumbar spine. Our results provide insights for the clinical intervention of double-level intervertebral disks. [ABSTRACT FROM AUTHOR]

The crystal structure, photoluminescence, persistent luminescence and photocatalytic properties of Li 0.15 Zn 0.85 Ga 2 O 4 phosphors were investigated. X-ray diffraction (XRD) patterns indicated that Li-doped ZnGa 2 O 4 had a spinel phase. The result of Raman spectra showed that the first order Raman active modes in spinel ZnGa 2 O 4 were only attributed to Zn 2+ ions in the tetrahedral sites. The Li + ion doping into ZnGa 2 O 4 led to enhancement of the emission intensity, and improved the persistent luminescence properties. When Li + ion was incorporated into the ZnGa 2 O 4 lattice, the new cation vacancy formed for the charge compensation and the cation vacancy would capture some of the excited electrons and acted as electron trap. Moreover, photocatalytic activity test shown that Li-doped ZnGa 2 O 4 phosphor exhibited higher catalytic activities than the pure ZnGa 2 O 4 for the degradation of Rhodamine B by the ultraviolet irradiation. It indicated that Li-doped ZnGa 2 O 4 owned more traps to storage the photoinduced carriers, which decreased the combination of electrons and holes, resulting a higher catalytic activity. [ABSTRACT FROM AUTHOR]

Y 2 O 3 :Eu 3+ and Y 2 O 3 :Eu 3+ , Ho 3+ phosphors were prepared by the solid-state reaction method. X-ray diffraction (XRD) patterns indicate that the Eu 3+ and/or Ho 3+ doping do not show obvious effect to the cubic Y 2 O 3 crystal. Under UV 254 nm excitation, both samples present same photoluminescence (PL) properties, while Y 2 O 3 :Eu 3+ , Ho 3+ phosphor shows a red-emitting long afterglow phenomenon, and the Eu 3+ ion are the luminescent center during the decay process. The decay characteristic of Y 2 O 3 :Eu 3+ , Ho 3+ phosphor is according with the double exponential equation. Thermoluminescence (TL) analysis indicates that the traps in the Y 2 O 3 :Eu 3+ and Y 2 O 3 :Eu 3+ , Ho 3+ host are both origin from Eu 3+ ion. The co-doped Ho 3+ ion shoaled the trap depth, which is benefit for the carriers to escape from the trap, and then the afterglow phenomenon happened. [ABSTRACT FROM AUTHOR]

To improve customer satisfaction and reduce operation costs, transit authorities have been striving to monitor transit service quality and identify the key factors to enhance it. The recent advent of passive data collection technologies, e.g., automated fare collection (AFC) and automated vehicle location (AVL), has shifted a data-poor environment to a data-rich environment and offered opportunities for transit agencies to conduct comprehensive transit system performance measures. However, most AFC and AVL systems are not designed for transit performance measures, implying that additional data processing and visualization procedures are needed to improve both data usability and accessibility. This study attempts to develop a data-driven platform for online transit performance monitoring. The primary data sources come from the AFC and AVL systems in Beijing, where a passenger's boarding stop (origin) and alighting stop (destination) on a flat-rate bus are not recorded. The individual transit rider's origin and destination can be estimated by utilizing a series of data-mining techniques, which are then incorporated into a regional-map platform for transit performance measures. A multilevel framework is proposed to calculate the network-level speed, route-level travel time reliability, stop-level ridership, and headway variance. These statistics are interactively displayed on a map through a simplified transit GIS data model. This platform not only serves as a data-rich visualization platform to monitor transit network performance for planning and operations, it also intends to take advantage of e-science initiative for data-driven transportation research and applications. [ABSTRACT FROM AUTHOR]

Ga 2 O 3 :Cr 3+ 0.01 , Zn 2+ 0.005 and Ga 2 O 3 :Cr 3+ 0.01 phosphors were synthesized by the high temperature solid state reaction. The emission and excitation spectra, persistent luminescence decay curve, thermoluminescence curve and photocatalytic properties were measured. The emission and excitation spectra indicate that the Zn 2+ doping has no effect on photoluminescent properties of Ga 2 O 3 :Cr 3+ 0.01 . The red persistent luminescence of Ga 2 O 3 :Cr 3+ 0.01 , Zn 2+ 0.005 could be observed for 20 min by naked eye. The thermoluminescence (TL) curve shows that a suitable trap was introduced into the Ga 2 O 3 :Cr 3+ host by Zn 2+ doping. The photocatalytic activity toward rhodamineB (RhB) was obtained to evaluate photocatalytic properties by Zn 2+ doping. The results indicate that the Zn 2+ doping could improve the photocatalytic properties of Ga 2 O 3 :Cr 3+ 0.01 phosphors. It was considered that the new traps play an important role for trapping the electrons or holes to decrease the combination of the electrons and holes producing by the light irradiation and increase the photocatalytic activity. [ABSTRACT FROM AUTHOR]

The phosphor ZnGa 2 O 4 was synthesized via H 3 BO 3 assisted solid-state reaction method. The effect of H 3 BO 3 on the crystal structure, photoluminescence, persistent luminescence, and photocatalytic properties of the phosphor was studied in detail. X-ray diffraction patterns indicated that a pure phase ZnGa 2 O 4 could be achieved at the sintered temperature 1200 °C adding H 3 BO 3 of 10 mol%. The samples exhibited a wide emission band peak at 423 nm and a narrow emission peak at 502 nm under ultraviolet excitation (254 nm). Moreover, H 3 BO 3 improved the persistent luminescence properties of ZnGa 2 O 4 phosphor effectively because H 3 BO 3 increased the electron traps and improved the depth of the traps. In addition, photocatalytic activity test shown that ZnGa 2 O 4 phosphor with H 3 BO 3 exhibited lower catalytic activities than the pure ZnGa 2 O 4 for the degradation of Rhodamine B by the ultraviolet irradiation. The reason was that the particle size of the ZnGa 2 O 4 phosphor became larger when the ZnGa 2 O 4 was synthesized using H 3 BO 3 as flux. [ABSTRACT FROM AUTHOR]

Abstract: The analysis of dynamics in traffic flow is an important step to achieve advanced traffic management and control in Intelligent Transportation System (ITS). Complexity and periodicity are definitely two fundamental properties in traffic dynamics. In this study, we first measure the complexity of traffic flow data by Lempel–Ziv algorithm at different temporal scales, and the data are collected from loop detectors on freeway. Second, to obtain more insight into the complexity and periodicity in traffic time series, we then construct complex networks from traffic time series by considering each day as a cycle and each cycle as a single node. The optimal threshold value of complex networks is estimated by the distribution of density and its derivative. In addition, the complex networks are subsequently analyzed in terms of some statistical properties, such as average path length, clustering coefficient, density, average degree and betweenness. Finally, take 2 min aggregation data as example, we use the correlation coefficient matrix, adjacent matrix and closeness to exploit the periodicity of weekdays and weekends in traffic flow data. The findings in this paper indicate that complex network is a practical tool for exploring dynamics in traffic time series. [Copyright &y& Elsevier]

Highlights: [•] The Zn2GeO4:Mn2+, Pr3+ phosphor possess a persistent luminescence property. [•] The green long afterglow of Zn2GeO4:Mn2+, Pr3+ can last for three hours. [•] The Zn2GeO4 may be an excellent host material for Mn2+-based long afterglow. [•] Pr3+ ions served as trap center, which can greatly postpone the afterglow. [ABSTRACT FROM AUTHOR]

Abstract: Zn2GeO4:Eu2+ 0.01 and Zn2GeO4:Mn2+ 0.01 long afterglow phosphors were synthesized via a high temperature solid state reaction. X-ray diffraction (XRD), afterglow spectra, decay curves and thermoluminescence curves were utilized to characterize the samples. The X-ray diffraction phases indicate that the doping of small amount of transition metal ions or rare earth ions has no significant influence on the crystal structure of Zn2GeO4. According to the afterglow spectra, we found that the Zn2GeO4:Eu2+ 0.01 exhibits a broad band emission with a peak at 474nm, which could be ascribed to Eu2+ transition between 4f65d1 and 4f7 electron configurations. The Zn2GeO4:Mn2+ 0.01 shows a narrow band emission peaking at 532nm corresponding to the characteristic transition of Mn2+(4T1→6A1). The thermoluminescence (TL) curves above room temperature are employed for the discussion of the origin of the traps and the mechanism of the persistent luminescence. The results indicate that Zn2GeO4 may be an excellent host material for the rare earth ions or transition metal ions long afterglows. [Copyright &y& Elsevier]

Abstract: A complex network is a powerful tool to research complex systems, traffic flow being one of the most complex systems. In this paper, we use complex network theory to study traffic time series, which provide a new insight into traffic flow analysis. Firstly, the phase space, which describes the evolution of the behavior of a nonlinear system, is reconstructed using the delay embedding theorem. Secondly, in order to convert the new time series into a complex network, the critical threshold is estimated by the characteristics of a complex network, which include degree distribution, cumulative degree distribution, and density and clustering coefficients. We find that the degree distribution of associated complex network can be fitted with a Gaussian function, and the cumulative degree distribution can be fitted with an exponential function. Density and clustering coefficients are then researched to reflect the change of connections between nodes in complex network, and the results are in accordance with the observation of the plot of an adjacent matrix. Consequently, based on complex network analysis, the proper range of the critical threshold is determined. Finally, to mine the nodes with the closest relations in a complex network, the modularity is calculated with the increase of critical threshold and the community structure is detected according to the optimal modularity. The work in our paper provides a new way to understand the dynamics of traffic time series. [Copyright &y& Elsevier]

With the goal of improving pedestrians' convenience and safety, signalised mid-block crossings are commonly installed in highly-populated areas lacking convenient intersection crossings. However, few studies have sought to provide standards for the application of single- and double-phase crossings in highly-populated areas. Moreover, previous studies have not addressed the design capacities of single- or double-phase crossings with their associated vehicle lanes. This paper proposes an approach to determining the applicability of single- and double-phase crossings based on the relationships between control cycles, green time and clearance time at mid-block crossings. Single- and double-phase crossing efficiency along with several key crossing design parameters, such as crossing width, crossing length, traffic demand, are discussed in this paper. The research findings show that double-phase crossings provide superior flexibility for highly populated areas. Findings also show that the design crossing capacity is affected more by the crossing width than by length, while the design lane capacity is affected more by the crossing length than width. Finally, the paper provides a guideline for determining the applicability of single- and double-phase crossings in highly-populated areas. [ABSTRACT FROM AUTHOR]

This article describes a coordinated ramp metering algorithm for systematically mitigating freeway congestion. A preemptive hierarchical control scheme with a three-priority-layer structure is employed in this algorithm. Ramp metering is formulated as a multiobjective optimization problem to enhance system performance. These optimization objectives include promptly tackling freeway congestion, sufficiently utilizing on-ramp storage capacities, and preventing on-ramp vehicles from overflowing to local streets, balancing on-ramp vehicle equity, and maximizing traffic throughputs for the entire system. Instead of relying heavily on accurate estimates of freeway traffic flow evolvement, this new approach models ramp meter control as a linear program and uses real-time traffic sensor measurements for minimizing the indeterminate impacts from the mainstream flow capacities. VISSIM-based simulation experiments are performed to examine its practicality and effectiveness using geometric and traffic demand data from one real-world freeway segment. The simulation test results show that the proposed ramp metering approach performed well in optimizing overall freeway system operations under various traffic conditions. The system-wide optimal control performance can be achieved to quickly mitigate freeway congestion, prevent traffic from overflowing to local streets, and maximize overall traffic throughputs. The proposed ramp metering approach can dynamically assemble relevant ramp meters to work together and effectively coordinate the individual meter rates to leverage their response strengths for minimizing time to clear the congestion. This study demonstrates that utilization of existing freeway infrastructure can be optimized through the proposed algorithm. [ABSTRACT FROM AUTHOR]

Traditionally, education and training in pathology has been delivered using textbooks, glass slides and conventional microscopy. Over the last two decades, the number of web-based pathology resources has expanded dramatically with centralized pathological resources being delivered to many students simultaneously. Recently, whole slide imaging technology allows glass slides to be scanned and viewed on a computer screen via dedicated software. This technology is referred to as virtual microscopy and has created enormous opportunities in pathological training and education. Students are able to learn key histopathological skills, e.g. to identify areas of diagnostic relevance from an entire slide, via a web-based computer environment. Students no longer need to be in the same room as the slides. New human-computer interfaces are also being developed using more natural touch technology to enhance the manipulation of digitized slides. Several major initiatives are also underway introducing online competency and diagnostic decision analysis using virtual microscopy and have important future roles in accreditation and recertification. Finally, researchers are investigating how pathological decision-making is achieved using virtual microscopy and modern eye-tracking devices. Virtual microscopy and digital pathology will continue to improve how pathology training and education is delivered. [ABSTRACT FROM AUTHOR]

Background: Tissue MicroArrays (TMAs) are a valuable platform for tissue based translational research and the discovery of tissue biomarkers. The digitised TMA slides or TMA Virtual Slides, are ultra-large digital images, and can contain several hundred samples. The processing of such slides is time-consuming, bottlenecking a potentially high throughput platform. Methods: A High Performance Computing (HPC) platform for the rapid analysis of TMA virtual slides is presented in this study. Using an HP high performance cluster and a centralised dynamic load balancing approach, the simultaneous analysis of multiple tissue-cores were established. This was evaluated on Non-Small Cell Lung Cancer TMAs for complex analysis of tissue pattern and immunohistochemical positivity. Results: The automated processing of a single TMA virtual slide containing 230 patient samples can be significantly speeded up by a factor of circa 22, bringing the analysis time to one minute. Over 90 TMAs could also be analysed simultaneously, speeding up multiplex biomarker experiments enormously. Conclusions: The methodologies developed in this paper provide for the first time a genuine high throughput analysis platform for TMA biomarker discovery that will significantly enhance the reliability and speed for biomarker research. This will have widespread implications in translational tissue based research. [ABSTRACT FROM AUTHOR]

Abstract: Long persistent phosphors Sr x Ca1−x Al2O4: Eu2+, Dy3+ (x =0, 0.2, 0.4, 0.6, 0.8, 1.0) were prepared through a solution-combustion method at 600°C. The phase structures of the phosphors were characterized by X-ray diffraction. The major phase was CaAl2O4 when x <0.8 with SrAl2O4 phase as a minor phase, whereas the major phase was SrAl2O4 when x ≥0.8. The results indicated that the phase structure of the samples changed from CaAl2O4 to SrAl2O4 when x changed from 0 to 1.0. The emission colors changed accordingly from blue to green-blue and then to green in visible range, as evidenced from the emission spectra. The decay characteristics showed that the life times also varied with the value of x. These results revealed that the emission colors and life times can be adjusted by the value of x. Furthermore, the reason that the emission colors and decay times varied with x has been ascribed to the distortion of crystal lattice. [Copyright &y& Elsevier]

Abstract: The modification effect of the doping of Yb3+ ions, as an auxiliary activator, onto the luminescent properties of SrAl2O4:Eu2+, Dy3+ phosphor was studied for the first time. The phosphorescent nanoparticles were prepared by the combustion method. The experimental results indicate that the appropriate doping of Yb3+ ions largely improves phosphorescence of the phosphors with more intense luminescence, higher brightness, and no change in emission spectrum peaked at 513nm. Meanwhile the decay speed of the phosphor nanoparticles rises increasingly with the doping ratio of Yb3+ ions, whereas an excessive Yb3+ ions doping leads to the disappearance of the pure monoclinic phase of SrAl2O4 and the appearance of the weak diffraction lines of the YbAlO3 phase. The phosphorescent mechanism of the phosphors could be well understood based on the hole, thermally released from the trap levels of Dy3+ and Yb3+. [Copyright &y& Elsevier]

Abstracts: The nanowires of hexagonal metal Zn were prepared in porous alumina membranes by an electrochemical deposition. Scanning electron microscope measurement shows the diameter of the nanowires is about 40nm, in agreement with the pore diameter of the alumina membranes. X-ray diffraction (XRD) indicates the nanowires have a hexagonal structure and texture along c direction. The high-resolution (XRD), with high-temperature attachments, was employed to examine thermal expansion of the nanowires. The results show the Zn nanowires possess smaller thermal expansion compared with the conventional bulk Zn in c-axis direction, but almost is the same in a-axis direction. [Copyright &y& Elsevier]

A microscopic model of freeway rear-end crash risk is developed based on a modified negative binomial regression and estimated using Washington State data. Compared with most existing models, this model has two major advantages: (1) It directly considers a driver’s response time distribution; and (2) it applies a new dual-impact structure accounting for the probability of both a vehicle becoming an obstacle (Po) and the following vehicle’s reaction failure (Pf). The results show for example that truck percentage-mile-per-lane has a dual impact, it increases Po and decreases Pf, yielding a net decrease in rear-end crash probabilities. Urban area, curvature, off-ramp and merge, shoulder width, and merge section are factors found to increase rear-end crash probabilities. Daily vehicle miles traveled (VMT) per lane has a dual impact; it decreases Po and increases Pf, yielding a net increase, indicating for example that focusing VMT related safety improvement efforts on reducing drivers’ failure to avoid crashes, such as crash-avoidance systems, is of key importance. Understanding such dual impacts is important for selecting and evaluating safety improvement plans for freeways. [ABSTRACT FROM AUTHOR]

The Cu nanowires were prepared in alumina membranes with ordered pore arrays by an electrochemical deposition. X-ray diffraction and scan electron microscopy were employed to characterize the nanowires. The results show that the nanowires are located in the channels of the alumina membrane with a diameter of about 40 nm along the [110] direction. The thermal expansion coefficients of the nanowires were measured by in situ high-resolution x-ray diffraction in the temperature range from 298 to 693 K. The results show that the Cu nanowires possess larger lattice parameters and smaller thermal expansion coefficient compared with the conventional bulk Cu. This is because the nanowires consist of a crystalline component and grain boundaries. The grain boundaries usually contain vacancies and vacancy clusters, so they have a larger thermal expansion coefficient than the crystalline component. With elevating temperature, the vacancies and vacancy clusters disappeared and a small thermal expansion coefficient of the Cu nanowires was observed. The high thermal stability of the Cu nanowires is very important for designing nanoelectronic devices and reforming properties of the cermet. [ABSTRACT FROM AUTHOR]

Eu2+ and Dy3+ co-activated CaAl2Si2O8-based long afterglow phosphor was firstly synthesized by solid state reaction. The phosphor emits a visible blue light under UV excitation and the blue afterglow lasts about 1 h in the dark. The phase-forming process of the phosphor was studied by means of differential thermal analysis (DTA) and X-ray diffraction (XRD) methods. The phosphor forms the final anorthite single-phase at about 1200 °C. The fluorescence spectra show that the excitation and emission spectra are both broadband and the emission peak is at 440 nm ascribed to the typical 4f7→4f 65d transition of Eu2+. The decay curves of afterglow indicate that the phosphor has a long afterglow feature and Dy3+ ions play an important role in prolonging the afterglow. [Copyright &y& Elsevier]

Collisions between bicycles and motor vehicles have caused severe life and property losses in many countries. The majority of bicycle–motor vehicle (BMV) accidents occur at intersections. In order to reduce the number of BMV accidents at intersections, a substantial understanding of the causal factors for the collisions is required. In this study, intersection BMV accidents were classified into three types based on the movements of the involved motor vehicles and bicycles. The three BMV accident classifications were through motor vehicle related collisions, left-turn motor vehicle related collisions, and right-turn motor vehicle related collisions. A methodology for estimating these BMV accident risks was developed based on probability theory. A significant difference between this proposed methodology and most current approaches is that the proposed approach explicitly relates the risk of each specific BMV accident type to its related flows. The methodology was demonstrated using a 4-year (1992–1995) data set collected from 115 signalized intersections in the Tokyo Metropolitan area. This data set contains BMV accident data, bicycle flow data, motor vehicle flow data, traffic control data, and geometric data for each intersection approach. For each BMV risk model, an independent explanatory variable set was chosen according to the characteristics of the accident type. Three negative binomial regression models (one corresponding to each BMV accident type) were estimated using the maximum likelihood method. The coefficient value and its significance level were estimated for each selected variable. The negative binomial dispersion parameters for all the three models were significant at 0.01 levels. This supported the choice of the negative binomial regression over the Poisson regression for the quantitative analyses in this study. [Copyright &y& Elsevier]

At signalized intersections, rear-end accidents are frequently the predominant accident type. These accidents result from the combination lead-vehicle deceleration and the ineffective response of the following vehicle’s driver to this deceleration. This paper mathematically represents this process, by expressing accident probability as the product of the probability of the lead vehicle decelerating and the probability of the driver in the following failing to respond in time to avoid a collision. Using this premise, a model of rear-end accident probabilities is estimated using information on traffic flow, traffic regulations, roadway geometrics, and human factors from four-legged signalized intersections in Tokyo, Japan. Estimation findings provide some important preliminary evidence for the development of countermeasures to reduce the frequency of rear-end accidents at signalized intersections. [ABSTRACT FROM AUTHOR]

Real-time speed and vehicle-classification data are important inputs for modern freeway traffic control and management systems. However, these data are not directly measurable by single-loop detectors. Although dual-loop detectors provide speeds and classified vehicle volumes, there are too few of them on our current freeway systems to meet the practical ATMS (Advanced Traffic Management System) needs, and the cost of upgrading from a single-loop detector to a dual-loop detector is high. This makes it extremely desirable to develop appropriate algorithms to make single-loop detectors capable of performing the tasks of double loops. This paper presents just such an algorithm, i.e., one that uses single-loop measurements to provide accurate speed and vehicle-classification estimates. There are three steps in the algorithm: (1) to separate intervals with long vehicles (LVs) from those without; (2) to use measurements of intervals without LVs for speed estimation; and (3) to identify LV volumes for the intervals with LVs using the estimated speed. Preliminary tests for both spatial transferability and temporal transferability of the algorithm were performed, and the results were encouraging. [ABSTRACT FROM AUTHOR]

AgI nanocrystals were synthesised by autoclave at 140 °C. XRD measurement shows that AgI has a single β-phase and the particle size is about 25 nm. This is a simple and efficient method to prepare β-AgI nanocrystals at a large scale. The absorption spectra of β-AgI were compared with the absorption spectra of colloid AgI (usually a mixture of β-AgI and γ-AgI at room temperature). [Copyright &y& Elsevier]

The customized bus (CB) is an emerging type of public transportation system, which not only provides a flexible and reliable demand-responsive service, but also reduces the usage of private car to alleviate traffic congestion in metropolitan cities. The customized bus route design problem (CBRDP) is a crucial procedure in the CB service system designing. In this work, we develop a new type of problem scenario: Multi-Trip Multi-Pickup and Delivery Problem with Time Windows, to describe CBRDP by simultaneously optimizing the operating cost and passenger profit, where excess travel time is introduced to estimate passenger extra cost compared with taxi service, and each vehicle is allowed to perform multiple trips for operational cost savings. To solve this problem, a constructive two-stage heuristic algorithm is presented to obtain the Pareto solution. Taking a benchmark problem and Beijing commuting corridor as case studies, we calculate and compare the monetary and travel costs of CB with other travel modes, and quantitatively confirm that the CB can be a cost-effective choice for passengers. • The customized bus route design issue is modeled as a Multi-Trip MPDPTW problem. • A bi-objective optimization model to minimize passenger and operating costs • A heuristic algorithm with adaptive variable neighborhood search is presented. • Customized bus is proved to be economically efficient by using empirical data. [ABSTRACT FROM AUTHOR]

• Reinforcement learning for safe, efficient, comfortable vehicle velocity control. • A reward function is developed by combining driving features. • Collision avoidance strategy is incorporated for safety and faster convergence. • The model outperforms human drivers and has faster running speed than MPC. A model used for velocity control during car following is proposed based on reinforcement learning (RL). To optimize driving performance, a reward function is developed by referencing human driving data and combining driving features related to safety, efficiency, and comfort. With the developed reward function, the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards, through trials and errors in the simulation environment. To avoid potential unsafe actions, the proposed RL model is incorporated with a collision avoidance strategy for safety checks. The safety check strategy is used during both model training and testing phases, which results in faster convergence and zero collisions. A total of 1,341 car-following events extracted from the Next Generation Simulation (NGSIM) dataset are used to train and test the proposed model. The performance of the proposed model is evaluated by the comparison with empirical NGSIM data and with adaptive cruise control (ACC) algorithm implemented through model predictive control (MPC). The experimental results show that the proposed model demonstrates the capability of safe, efficient, and comfortable velocity control and outperforms human drivers in that it 1) has larger TTC values than those of human drivers, 2) can maintain efficient and safe headways around 1.2s, and 3) can follow the lead vehicle comfortably with smooth acceleration (jerk value is only a third of that of human drivers). Compared with the MPC-based ACC algorithm, the proposed model has better performance in terms of safety, comfort, and especially running speed during testing (more than 200 times faster). The results indicate that the proposed approach could contribute to the development of better autonomous driving systems. Source code of this paper can be found at https://github.com/MeixinZhu/Velocity_control. [ABSTRACT FROM AUTHOR]

Abstract: The Eu2+ and Dy3+ co-activated Ca–Sr feldspar solid solution compounds, (Ca0.98− x Sr x )Al2Si2O8:Eu0.01,Dy0.01 (), have been prepared by solid-state reaction in weak reductive atmosphere. The fluorescence spectra showed that the emission peaks of these phosphors shifts to shorter wavelength with an increase of Sr2+ solid solution quantity and correspondingly the afterglow color changes from blue to purple. All the changes can be attributed to the various crystal fields around Eu2+ ions. The afterglow with special short wavelength in near ultraviolet region can be obtained by adjusting the host chemical composition. [Copyright &y& Elsevier]

The AgI nanowire arrays were prepared in the ordered porous alumina membrane by an electrochemical method. Transmission electron microscopy observation shows that the AgI nanowires are located in the channels of the alumina membrane. In situ x-ray diffractions show that the nanowire arrays possess hexagonal close-packed structure (β-AgI) at 293 K, orienting along the (002) plane, whereas at 473 K, the nanowire arrays possess a body-centered cubic structure (α-AgI), orienting along the (110) plane. The AgI nanowire arrays exhibit a negative thermal expansion property from 293 to 433 K, and a higher transition temperature from the β to α phase. We ascribe the negative thermal expansion behavior to the phase transition from the β to α phase, and the elevated transition temperature to the radial restriction by the channels of alumina membrane. [ABSTRACT FROM AUTHOR]

Rhodamine B degradation rate (A) and rate constant (B) with different photocatalysts. [Display omitted] • Results showed that constructed photocatalysis of this work greatly enhanced the utilization rate of solar energy. • Photocatalytic for the degradation of liquid organic pollutants with H x WO 3 is believed to have a bright feature. Solar energy is considered sustainable green energy to prevent environmental pollution. Tungsten trioxide (WO 3) is one of the attractive solar energy-driven photocatalysts and photocatalytic supports. Besides, Hydrogen (H) has a small size and can move into many inorganic compounds, and occupy the sites in microstructure as well, with little space structure expansion. H doping in semiconductors attracts more and more attention due to its effect of providing free carriers, resulting in localized surface plasmon resonances which can improve the utilization efficiency of solar energy. In our research, we developed a new method to prepare the H-doped WO 3 whose color turned green, and to prepare Ag/H x WO 3 , which could extend the solar light response range to near-infrared. Furthermore, the photocatalytic activity to degrade RhB solution under UV light and visible light was both improved. The density of free electrons is based on the Drude model to discuss the mechanism of photocatalysis upon the visible. The contact of noble metal Ag and H x WO 3 can play a vital effect in the separation of photogenerated electron-hole pairs. The localized surface plasmon resonances and green coloration bring the tungsten trioxide more remarkable performance together. Results showed that constructed photocatalysis of this work greatly enhanced the utilization rate of solar energy. Photocatalytic for the Degradation of liquid organic pollutants with H x WO 3 is believed to have a bright feature. [ABSTRACT FROM AUTHOR]

Use of smartphone apps to detect driving risks and/or provide warning information to drivers is a hot topic in traffic safety research. However, scientific evidence on the effectiveness of such apps to reduce dangerous driving is still limited. To provide additional insights, this study examines whether and how driving risks diagnoses and real-time warning information provided through a smartphone app could assist drivers to control their vehicles on expressways safely. For this research purpose, a 3-month driving experiment was conducted in Japan, where the different combination and utilization of app functions were investigated. During the experiment, a series of questionnaire surveys were also carried out in response to six different app versions. A bivariate ordered probit model was estimated to jointly represent speed limit compliance and acceleration/deceleration simultaneously. Model estimation results show that with the warning information provided via the smartphone app, it is more effective to improve drivers' performance on speed limit compliance than on acceleration/deceleration control. The app improves speed limit compliance and acceleration/deceleration control of careless drivers but only improves speed limit compliance of irritable and excessively confident drivers and acceleration/deceleration control of aggressive drivers. Finally, implications of analysis results to traffic safety measures are discussed. [ABSTRACT FROM AUTHOR]

Crash severity prediction has been raised as a key problem in traffic accident studies. Thus, to progress in this area, in this study, a thorough artificial neural network combined with an improved metaheuristic algorithm was developed and tested in terms of its structure, training function, factor analysis, and comparative results. Data from I5, an interstate highway in the Washington State during the period of 2011–2015, were used for fitting and prediction, and after setting the theoretical three-layer neural network (NN), an improved Particle Swarm Optimization (PSO) method with adaptive inertial weight was offered to optimize the NN, and finally, a comparison among different adaptive strategies was conducted. The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. The method developed in this study can be applied to a big data analysis of traffic accidents and be used as a fast-useful tool for policy makers and traffic safety researchers. [ABSTRACT FROM AUTHOR]

As an additional effort for exploring the unknowns about the ownership behavior of autonomous vehicles (AVs), we implemented an online stated preference (SP) survey in Japan and collected 1728 responses from car users across the whole country in 2016. The SP choice set included three types of AVs (conditional, high, and full automation) and a conventional vehicle. Each respondent answered three SP profiles. SP Attributes of AVs (additional purchase cost, reduction rate of insurance, reduction rate of permanent parking cost, penetration rate in the market, and release timing to the market) were defined relative to the conventional vehicle. Changes in future income were assumed based on each respondent's historical trend for assisting him/her to show more realistic responses. It was found that on average, respondents are willing to pay an additional 402,233 to 793,611 JPY to purchase an AV in the future. Estimating a mixed logit model with repeated choices and correlations between random-effect parameters following a normal distribution confirmed the significant influence of unobserved and correlated heterogeneities with respect to SP attributes. Both expected and unexpected results with respect to the random-effect variables are observed. Policy implications of analysis results are discussed. [ABSTRACT FROM AUTHOR]

Highlights • A car-following model was proposed based on deep reinforcement learning. • It uses speed deviations as reward function and considers a reaction delay of 1 s. • Deep deterministic policy gradient algorithm was used to optimize the model. • The model outperformed traditional and recent data-driven car-following models. • The model demonstrated good capability of generalization. Abstract This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study's results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The injury pattern in crashes is modeled from a multi-class classification perspective for accurate recognition. • A machine learning based analysis framework is proposed including preprocessing, classification, evaluation, and application. • The proposed framework is implemented and verified on three years of single vehicle ROR crash records. • The experimental results validate the effectiveness of the proposed framework. • This framework can be further applied for pattern recognition in traffic safety research. Abstract Detecting driver injury patterns is a typical classification problem. Crash data sets are highly skewed where fatalities and severe injuries are often less represented compared to other events. The severity prediction performance of the existing models is poor due to the highly imbalanced samples of different severity levels within a given dataset. This paper proposes a machine learning based analysis framework from a multi-class classification perspective for accurate recognition of the driver injury patterns. The proposed framework includes preprocessing, classification, evaluation and application of a given dataset. This framework is verified based on the three years single-vehicle ROR (run-off-road) crash records collected in Washington State from 2011 to 2013. At first, thirteen most important safety-related variables are recognized through random forests. Then, the four driver’s injury severity levels viz., fatal/serious injury, evident injury, possible injury, and no injury are predicted by integrating the decomposed binary neural network models to achieve better performance. Finally, a sensitivity analysis is carried out to interpret variables’ impacts on the decomposed injury severity levels. The study shows that lack of restraint, female drivers, truck usage, driver impairment, driver distraction, vehicle overturn (rollover), dawn/dusk, and overtaking are the leading factors contributing to the driver fatalities or severe injuries in a single-vehicle ROR crash. Most of the findings are consistent with the previous studies. The experimental results validate the effectiveness of the proposed framework which can be further applied for pattern recognition in traffic safety research. [ABSTRACT FROM AUTHOR]

The title phosphors (x = 1, 0.95, 0.9, 0.85, 0.8) are prepared by stoichiometric solid state reactions of ZnO and Ga2O3 (corundum crucible, 1300 °C, 5 h). [ABSTRACT FROM AUTHOR]

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis. [ABSTRACT FROM AUTHOR]

Transit signal priority (TSP) is a promising low-cost strategy that gives preferential treatments for the buses to go through intersections with minimum delay time. In this paper, a new TSP control model was presented for isolated intersections to minimize bus delay and to reduce the impact of TSP on other vehicles by optimizing signal control phase selection and compression. This paper starts with the phase selection and compression strategies to provide treatments to bus priority requests. Then, two new features on phase selection and compression aspects are applied to TSP, i.e. the time that a bus priority request needs is provided by the phase(s) with the lowest traffic volume, and multi-phases can be selected to serve a bus request. Field data are collected from a major traffic corridor in Changzhou (China) and applied for VISSIM simulation. The proposed TSP control model as well as the fixed-time control and the conventional TSP control models are tested and compared under different traffic demands, headways and maximum saturation degrees. The comparative results showed that the proposed model outperformed the conventional TSP control model in terms of reducing bus delay, minimizing the impact on other vehicles and reducing the stop rate for buses. This paper reveals that, the proposed TSP strategy can significantly optimize the phase compression process and improve transit efficiency. [ABSTRACT FROM AUTHOR]

Grade variation on interstate highways affects the roadway geometric design, vehicle performance and driver behavior, thus possibly exerting an unexpected effect on vehicular speed. Hence, determining the internal relationship between grade and speed is important and useful for drivers, traffic regulators and other traffic participants. However, the problem with performing this research is the lack of large-scale gradient and speed data. Google Earth (GE) provides an application programming interface for extracting elevation data worldwide. The elevation dataset from GE can be easily converted to grade data. In addition, our team has collected and stored speed series data for different freeways over several years. Based on the above obtainable grade and speed datasets, we conducted research on the effect of grades on free flow speeds from two perspectives. First, the influence of grades on speed was analyzed from both quantitative and qualitative aspects. The analysis of the distributions of four typical types of speeds demonstrated a decreasing tendency as the speed increased. Steeper grades generated a more intense speed fluctuation in terms of the four types of speeds. Second, a model based on the Student’s t-test was developed to evaluate the level of significant difference among speed series under neighboring grades. The Student’s t-test demonstrated that adjacent grades do not significantly influence the speeds. In summary, speeds under different grades showed obviously different tendencies. The findings of this study can help transport authorities set more reasonable speed limits and improve the geometric design of interstates with grade variation constraints. [ABSTRACT FROM AUTHOR]

Using a relatively small training set of ~16 thousand images from macromolecular crystallisation experiments, we compare classification results obtained with four of the most widely-used convolutional deep-learning network architectures that can be implemented without the need for extensive computational resources. We show that the classifiers have different strengths that can be combined to provide an ensemble classifier achieving a classification accuracy comparable to that obtained by a large consortium initiative. We use eight classes to effectively rank the experimental outcomes, thereby providing detailed information that can be used with routine crystallography experiments to automatically identify crystal formation for drug discovery and pave the way for further exploration of the relationship between crystal formation and crystallisation conditions. [ABSTRACT FROM AUTHOR]

As the COVID-19 pandemic fades, the aviation industry is entering a fast recovery period. To analyze airport networks' post-pandemic resilience during the recovery process, this paper proposes a Comprehensive Resilience Assessment (CRA) model approach using the airport networks of China, Europe, and the U.S.A as case studies. The impact of COVID-19 on the networks is analyzed after populating the models of these networks with real air traffic data. The results suggest that the pandemic has caused damage to all three networks, although the damages to the network structures of Europe and the U.S.A are more severe than the damage in China. The analysis suggests that China, as the airport network with less network performance change, has a more stable level of resilience. The analysis also shows that the different levels of stringency policy in prevention and control measures during the epidemic directly affected the recovery rate of the network. This paper provides new insights into the impact of the pandemic on airport network resilience. [ABSTRACT FROM AUTHOR]

To deal with the conflicts between left-turn and through traffic streams and increase the discharge capacity, this paper addresses the pre-signal which is implemented at a signalized intersection. Such an intersection with pre-signal is termed as a tandem intersection. For the tandem intersection, phase swap sorting strategy is deemed as the most effective phasing scheme in view of some exclusive merits, such as easier compliance of drivers, and shorter sorting area. However, a major limitation of the phase swap sorting strategy is not considered in previous studies: if one or more vehicle is left at the sorting area after the signal light turns to red, the capacity of the approach would be dramatically dropped. Besides, previous signal control studies deal with a fixed timing plan that is not adaptive with the fluctuation of traffic flows. Therefore, to cope with these two gaps, this paper firstly takes an in-depth analysis of the traffic flow operations at the tandem intersection. Secondly, three groups of loop detectors are placed to obtain the real-time vehicle information for adaptive signalization. The lane selection behavior in the sorting area is considered to set the green time for intersection signals. With the objective of minimizing the vehicle delay, the signal control parameters are then optimized based on a dynamic programming method. Finally, numerical experiments show that average vehicle delay and maximum queue length can be reduced under all scenarios. [ABSTRACT FROM AUTHOR]

Pedestrian movement data including volumes, walking speeds, and trajectories are essential in transportation engineering, planning, and research. Although traditional image-based pedestrian detectors provide very rich information, their performance degrades quickly with increased occurrence of occlusion. The three-dimensional sensing capabilities of Microsoft's Kinect present a potential cost-effective solution for occlusion-robust pedestrian detection. This article proposes an efficient pedestrian detection approach for crowded scenes by fusing RGB and depth images from the Kinect. More specifically, we first extract the pedestrian contour regions from RGB images using background subtraction. Then, we develop a region clustering algorithm to extract pedestrians from the contour regions using depth information. Finally, a tracking and counting algorithm is designed to acquire pedestrian volumes. The proposed approach was proven effective with an average detection accuracy of 93.1% at 20 frames per second. These results demonstrate the feasibility of using the low-cost Kinect device for real-world pedestrian detection in crowded scenes. [ABSTRACT FROM AUTHOR]

Understanding the relationships between influential factors and incident clearance time is crucial to make effective countermeasures for incident management agencies. Although there have been a certain number of achievements on incident clearance time modeling, limited effort is made to investigate the relative role of incident response time and its self-selection in influencing the clearance time. To fill this gap, this study uses the endogenous switching model to explore the influential factors in incident clearance time, and aims to disentangle causation from self-selection bias caused by response process. Under the joint two-stage model framework, the binary probit model and switching regression model are formulated for both incident response time and clearance time, respectively. Based on the freeway incident data collected in Washington State, full information maximum likelihood (FIML) method is utilized to estimate the endogenous switching model parameters. Significant factors affecting incident response time and clearance time can be identified, including incident, temporal, geographical, environmental, traffic and operational attributes. The estimate results reveal the influential effects of incident, temporal, geographical, environmental, traffic and operational factors on incident response time and clearance time. In addition, the causality of incident response time itself and its self-selection correction on incident clearance time are found to be indispensable. These findings suggest that the causal effect of response time on incident clearance time will be overestimated if the self-selection bias is not considered. [ABSTRACT FROM AUTHOR]

Taxi GPS trajectories data contain massive spatial and temporal information of urban human activity and mobility. Taking taxi as mobile sensors, the information derived from taxi trips benefits the city and transportation planning. The original data used in study are collected from more than 1100 taxi drivers in Harbin city. We firstly divide the city area into 400 different transportation districts and analyze the origin and destination distribution in urban area on weekday and weekend. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to cluster pick-up and drop-off locations. Furthermore, four spatial interaction models are calibrated and compared based on trajectories in shopping center of Harbin city to study the pick-up location searching behavior. By extracting taxi trips from GPS data, travel distance, time and average speed in occupied and non-occupied status are then used to investigate human mobility. Finally, we use observed OD matrix of center area in Harbin city to model the traffic distribution patterns based on entropy-maximizing method, and the estimation performance verify its effectiveness in case study. [ABSTRACT FROM AUTHOR]

Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, biological batch (bio-batch) effects, a significant issue stemming from the different times at which data is generated, lead to substantial shifts in the underlying data distribution, thus complicating reliable differentiation between cell lines from distinct batch cultures. To address this challenge, we introduce CLANet, a pioneering framework for cross-batch cell line identification using brightfield images, specifically designed to tackle three distinct bio-batch effects. We propose a cell cluster-level selection method to efficiently capture cell density variations, and a self-supervised learning strategy to manage image quality variations, thus producing reliable patch representations. Additionally, we adopt multiple instance learning(MIL) for effective aggregation of instance-level features for cell line identification. Our innovative time-series segment sampling module further enhances MIL's feature-learning capabilities, mitigating biases from varying incubation times across batches. We validate CLANet using data from 32 cell lines across 93 experimental bio-batches from the AstraZeneca Global Cell Bank. Our results show that CLANet outperforms related approaches (e.g. domain adaptation, MIL), demonstrating its effectiveness in addressing bio-batch effects in cell line identification. • A pioneering work for cross-batch cell line identification with brightfield images. • Categorizing batch effects into three forms, offering tailored solutions for each. • Constructing a large-scale cell image dataset with 32 cell lines across 93 batches. • Accurate cell line identification even with single-batch training per class. [ABSTRACT FROM AUTHOR]

Cell Painting is a high-content image-based assay applied in drug discovery to predict bioactivity, assess toxicity and understand mechanisms of action of chemical and genetic perturbations. We investigate label-free Cell Painting by predicting the five fluorescent Cell Painting channels from brightfield input. We train and validate two deep learning models with a dataset representing 17 batches, and we evaluate on batches treated with compounds from a phenotypic set. The mean Pearson correlation coefficient of the predicted images across all channels is 0.84. Without incorporating features into the model training, we achieved a mean correlation of 0.45 with ground truth features extracted using a segmentation-based feature extraction pipeline. Additionally, we identified 30 features which correlated greater than 0.8 to the ground truth. Toxicity analysis on the label-free Cell Painting resulted a sensitivity of 62.5% and specificity of 99.3% on images from unseen batches. We provide a breakdown of the feature profiles by channel and feature type to understand the potential and limitations of label-free morphological profiling. We demonstrate that label-free Cell Painting has the potential to be used for downstream analyses and could allow for repurposing imaging channels for other non-generic fluorescent stains of more targeted biological interest. [ABSTRACT FROM AUTHOR]

Cell line authentication is important in the biomedical field to ensure that researchers are not working with misidentified cells. Short tandem repeat is the gold standard method, but has its own limitations, including being expensive and time-consuming. Deep neural networks achieve great success in the analysis of cellular images in a cost-effective way. However, because of the lack of centralized available datasets, whether or not cell line authentication can be replaced or supported by cell image classification is still a question. Moreover, the relationship between the incubation times and cellular images has not been explored in previous studies. In this study, we automated the process of the cell line authentication by using deep learning analysis of brightfield cell line images. We proposed a novel multi-task framework to identify cell lines from cell images and predict the duration of how long cell lines have been incubated simultaneously. Using thirty cell lines' data from the AstraZeneca Cell Bank, we demonstrated that our proposed method can accurately identify cell lines from brightfield images with a 99.8% accuracy and predicts the incubation durations for cell images with the coefficient of determination score of 0.927. Considering that new cell lines are continually added to the AstraZeneca Cell Bank, we integrated the transfer learning technique with the proposed system to deal with data from new cell lines not included in the pre-trained model. Our method achieved excellent performance with a precision of 97.7% and recall of 95.8% in the detection of 14 new cell lines. These results demonstrated that our proposed framework can effectively identify cell lines using brightfield images. [ABSTRACT FROM AUTHOR]