A data-driven microscopic on-ramp model based on macroscopic network flows

While macroscopic traffic flow models consider traffic as a fluid, microscopic traffic flow models describe the dynamics of individual vehicles. Capturing macroscopic traffic phenomena remains a challenge for microscopic models, especially in complex road sections such as on-ramps. In this paper, we propose a microscopic model for on-ramps derived from a macroscopic network flow model calibrated to real traffic data. The microscopic flow-based model requires additional assumptions regarding the acceleration and the merging behavior on the on-ramp to maintain consistency with the mean speeds, traffic flow and density predicted by the macroscopic model. To evaluate the model's performance, we conduct traffic simulations assessing speeds, accelerations, lane change positions, and risky behavior. Our results show that, although the proposed model may not fully capture all traffic phenomena of on-ramps accurately, it performs better than the Intelligent Driver Model (IDM) in most evaluated aspects. While the IDM is almost completely free of conflicts, the proposed model evokes a realistic amount and severity of conflicts and can therefore be used for safety analysis.Comment: 15 pages, 5 figure

Acetic acid hydroconversion over mono-and bimetallic indium doped catalysts supported on alumina and silicas of various textures

Abstract Consecutive hydroconversion of acetic acid (AA) to ethanol was compared over monometallic and novel bimetallic (containing In as guest metal) catalysts on alumina and silica supports (inter alia highly ordered SBA-15) of different porosity and pore structure. The transformation was studied in a fixed bed, flow-through reactor in the temperature range of 220–380°C using hydrogen flow at 21 bar total pressure. AA hydroconversion activity of Cu and Pt catalysts and the yield of selectively produced alcohol were increased drastically by applying SBA-15 as highly ordered, mesoporous silica support instead of alumina. The most active nickel catalysts do not allow the selective addition of hydrogen to carbon-oxygen bonds independently of supports producing mainly CH4; however, indium doping can completely eliminate the hydrodecarbonylation activity as found in earlier studies. The textural properties of studied silica supports of various textures such as SBA-15, CAB-O-SIL, and Grace Sylobead have a profound impact on the catalytic performance of Ni and Ni2In particles.</jats:p

Diabetic retinopathy: Proteomic approaches to help the differential diagnosis and to understand the underlying molecular mechanisms

Diabetic retinopathy is the most common diabetic eye disease and a leading cause of blindness among patients with diabetes. The appearance and the severity of the symptoms correlate with the duration of diabetes and poor blood glucose level management. Diabetic retinopathy is also categorized as a chronic low-level inflammatory disease; the high blood glucose level promotes the accumulation of the advanced glycation end products and leads to the stimulation of monocytes and macrophages. Examination of protein level alterations in tears using state-of the art proteomics techniques have identified several proteins as possible biomarkers for the different stages of the diabetic retinopathy. Some of the differentially expressed tear proteins have a role in the barrier function of tears linking the diabetic retinopathy with another eye complication of diabetes, namely the diabetic keratopathy resulting in impaired wound healing. Understanding the molecular events leading to the eye complications caused by hyperglycemia may help the identification of novel biomarkers as well as therapeutic targets in order to improve quality of life of diabetic patients. BIOLOGICAL SIGNIFICANCE: Diabetic retinopathy (DR), the leading cause of blindness among diabetic patients can develop without any serious symptoms therefore the early detection is crucial. Because of the increasing prevalence there is a high need for improved screening methods able to diagnose DR as soon as possible. The non-invasive collection and the relatively high protein concentration make the tear fluid a good source for biomarker discovery helping the early diagnosis. In this work we have reviewed the administration of advanced proteomics techniques used in tear biomarker studies and the identified biomarkers with potential to improve the already existing screening methods for DR detection

Quantitative body fluid proteomics in medicine - A focus on minimal invasiveness

Identification of new biomarkers specific for various pathological conditions is an important field in medical sciences. Body fluids have emerging potential in biomarker studies especially those which are continuously available and can be collected by non-invasive means. Changes in the protein composition of body fluids such as tears, saliva, sweat, etc. may provide information on both local and systemic conditions of medical relevance. In this review, our aim is to discuss the quantitative proteomics techniques used in biomarker studies, and to present advances in quantitative body fluid proteomics of non-invasively collectable body fluids with relevance to biomarker identification. The advantages and limitations of the widely used quantitative proteomics techniques are also presented. Based on the reviewed literature, we suggest an ideal pipeline for body fluid analyses aiming at biomarkers discoveries: starting from identification of biomarker candidates by shotgun quantitative proteomics or protein arrays, through verification of potential biomarkers by targeted mass spectrometry, to the antibody-based validation of biomarkers. The importance of body fluids as a rich source of biomarkers is discussed. SIGNIFICANCE: Quantitative proteomics is a challenging part of proteomics applications. The body fluids collected by non-invasive means have high relevance in medicine; they are good sources for biomarkers used in establishing the diagnosis, follow up of disease progression and predicting high risk groups. The review presents the most widely used quantitative proteomics techniques in body fluid analysis and lists the potential biomarkers identified in tears, saliva, sweat, nasal mucus and urine for local and systemic diseases

Application of Markov Chains for Modeling and Managing Industrial Electronic Repair Processes

This paper presents a research of Markov chain based modeling possibilities of electronic repair processes provided by electronics manufacturing service (EMS) companies. These stochastic processes are considered as business-like, industrialized activities that are typically complex with a high number of process states and many possible paths from the start state to the absorbing end states. Two models based on absorbing and acyclic absorbing Markov chains are introduced in order to model these processes. The presented method provides a quick tool for determining the most important operational and statistical parameters of the process and mapping the paths that contribute the most to the total load of the process. These results support several managerial applications concerning e.g. process improvement, quality control and resource allocation. The proposed model is illustrated with an industrial application

A Novel Surrogate Safety Indicator Based on Constant Initial Acceleration and Reaction Time Assumption

The development of surrogate safety measures has drawn significant research interest in the field of traffic safety analysis. Innovative data sources such as video-based traffic surveillance systems have made it possible to collect large amounts of microscopic traffic data. By deriving traffic safety indicators such as the Deceleration Rate to Avoid a Crash (DRAC) statements concerning traffic safety over a determined road section can be made. This work presents the derivation of a novel surrogate safety indicator based on a Constant Initial Acceleration and reaction time assumption which considers the interaction between vehicles and describes the traffic safety of a road section. The evaluation is based on a video-based microscopic traffic data collection. To examine the efficiency, the new developed indicator is compared to the original Deceleration Rate to Avoid a Crash (DRAC) and the modified indicator (MDRAC) which includes the reaction time. The results showed that the new indicator is more sensitive in detecting critical situations than the other indicators and in addition describes the conflict situations more realistically