Official Test Research Articles

Finnish parliament ASR corpus: Analysis, benchmarks and statistics.

Public sources like parliament meeting recordings and transcripts provide ever-growing material for the training and evaluation of automatic speech recognition (ASR) systems. In this paper, we publish and analyse the Finnish Parliament ASR Corpus, the most extensive publicly available collection of manually transcribed speech data for Finnish with over 3000h of speech and 449 speakers for which it provides rich demographic metadata. This corpus builds on earlier initial work, and as a result the corpus has a natural split into two training subsets from two periods of time. Similarly, there are two official, corrected test sets covering different times, setting an ASR task with longitudinal distribution-shift characteristics. An official development set is also provided. We developed a complete Kaldi-based data preparation pipeline and ASR recipes for hidden Markov models (HMM), hybrid deep neural networks (HMM-DNN), and attention-based encoder-decoders (AED). For HMM-DNN systems, we provide results with time-delay neural networks (TDNN) as well as state-of-the-art wav2vec 2.0 pretrained acoustic models. We set benchmarks on the official test sets and multiple other recently used test sets. Both temporal corpus subsets are already large, and we observe that beyond their scale, HMM-TDNN ASR performance on the official test sets has reached a plateau. In contrast, other domains and larger wav2vec 2.0 models benefit from added data. The HMM-DNN and AED approaches are compared in a carefully matched equal data setting, with the HMM-DNN system consistently performing better. Finally, the variation of the ASR accuracy is compared between the speaker categories available in the parliament metadata to detect potential biases based on factors such as gender, age, and education.

SIAEF/PoE: Accountability of Earnestness for encoding subjective information in Blockchain

Blockchain technology has the potential to be applied widely in supply chain operations. One such area is proactive supply chain risk management (SCRM). In this area, existing researchers have highlighted the fraudulent behaviour of supply chain partners who do not disclose information on the risks that impact their operations. Blockchain can address this problem by encoding each partner’s commitment to SCRM and achieve consensus. However, before this can be achieved, a key challenge to address is the inability of existing consensus mechanisms such as Proof of Work (PoW), Proof of Authority (PoA) and Proof of Stake (PoS) to deal with information that does not have a digital footprint. In this paper, we address this gap by proposing the Proof by Earnestness (PoE) consensus mechanism which accounts for the authenticity, legitimacy and trustworthiness of information that does not have a digital footprint. We also propose the Subjective Information Authenticity Earnestness Framework (SIAEF) as the overarching framework that assists PoE in achieving its aim. We test the applicability of SIAEF and PoE in a real-world blockchain environment by deploying it as a decentralized application (Dapp) and applying it in BscScan Testnet which is an official test blockchain network.

Non-invasive, plug-and-play pollution detector for vehicle on-board instantaneous CO2 emission monitoring

Autonomous driving has gained momentum in recent years. To help create a sustainable future, an autonomous vehicle should take into account its carbon footprint. This work proposes a new, portable, cost-effective carbon dioxide (CO2) emissions sensor installed in a Light Duty Vehicle (LDV), as a novel enabler towards carbon footprint aware autonomous driving. This work achieves the ability to report real-time metrics about the instantaneous CO2 emissions whilst satisfying non-invasive procedures (weight <1 kg) by means of Unified Diagnostic Services (UDS) compliant interrogators. The proposed solution is validated in compliance with European Union’s official emissions test procedures, and compared with other commercial off-the-shelf solutions. During the test, a 50 km trip divided in 3 driving cycles is performed to obtain the instantaneous CO2 emission, achieving a measuring rate similar to other commercial, non-portable and expensive alternatives.

K‐Go: An executable formal semantics of Go language in K framework

AbstractGo is a relatively new programming language and has become one of the most important programming languages used widely in blockchain. A formal semantics plays an important role in program synthesis, analysis and verification. The existed formal semantics of Go are not executable and do not cover the core features, hence, this paper presents an executable formal semantics for Go, called K‐Go, using rewriting logic in framework to make sure the semantics is both executable and applicable. K‐Go includes syntax, configuration and rules and covers the declaration and definition of basic types, basic expression, concurrency, main statement data reading and writing based on channel. K‐Go is validated by test set with 100% semantic coverage based on the official test suite. The evaluation experiment shows that K‐Go is correct. Using formal semantics of Go language, building Go program synthesis, analysis and verification tools is easy for analyzing more characteristics of Go programs.

Bioaccumulation and Biotransformation of BDE-47 Using Zebrafish Eleutheroembryos (Danio rerio).

Polybrominated diphenyl ethers (PBDEs) are well-known endocrine disrupting chemicals identified as organic persistent pollutants. Their metabolites OH-BDE and MeO-BDE have been reported to be potentially more toxic than the postulated precursor PBDEs. One of the most predominant congeners of PBDEs in the environmentisBDE-47, due to its high presence in industrially used mixtures. In the present study, the bioaccumulation and biotransformation of BDE-47 into its major metabolites is evaluated using zebrafish (Danio rerio) eleutheroembryos adapting a previously developed alternative method to bioconcentration official guideline Organisation for Economic Co-ordination and Development 305, which reduces the animal suffering, time, and cost. For the simultaneous determination of BDE-47 and its metabolites in larvae and exposure medium, and considering the polarity difference of the analytes and the small sample size, the development of a validated analytical method is a step to ensure quality results. In the present study, an ultrasound-assisted extraction followed by a solid phase extraction dispersive clean-up step and gas chromatography-mass spectrometry-microelectroncapturedetector(GC-MS-μECD) with a previous derivatization process was optimized and validated. Bioconcentration factors (BCFs) were calculated using a first-order one-compartment toxicokinetic model. The profiles found show rapid absorption in the first hours of larval development and great bioaccumulative capacity, finding BCFs of 7294 ± 899 and 36 363 ± 5702 at nominal concentrations of 10 and 1 μg L-1 , respectively. Metabolization studies show increasing concentrations of the metabolites BDE-28, 2'-OH-BDE-28, and 5-MeO-BDE-47 throughout the exposure time. The results obtained show the feasibility of the method for bioaccumulation and open up the possibility of metabolic studies with zebrafish eleutheroembryos, which is a very underdeveloped field without official testing or regulation. Environ Toxicol Chem 2023;42:835-845. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Knowing What it is: Semantic-Enhanced Dual Attention Transformer

Attention has become an indispensable component of the models of various multimedia tasks like Image Captioning (IC) and Visual Question Answering (VQA). However, most existing attention modules are designed for capturing the spatial dependency, and are still insufficient in semantic understanding, e.g.}, the categories of objects and their attributes, which is also critical for image captioning. To compensate for this defect, we propose a novel attention module termed Channel-wise Attention Block (CAB) to model channel-wise dependency for both visual modality and linguistic modality, thereby improving semantic learning and multi-modal reasoning simultaneously. Specifically, CAB has two novel designs to tickle with the high overhead of channel-wise attention, which are the reduction-reconstruction block structure and the gating-based attention prediction. Based on CAB, we further propose a novel Semantic-enhanced Dual Attention Transformer (termed SDATR), which combines the merits of spatial and channel-wise attentions. To validate SDATR, we conduct extensive experiments on the MS COCO dataset and yield new state-of-the-art performance of 134.5 CIDEr score on COCO Karpathy test split and 136.0 CIDEr score on the official online testing server. To examine the generalization of SDATR, we also apply it to the task of visual question answering, where superior performance gains are also witnessed.

Tuberculosis Vaccines for Cattle

Abstract Bovine tuberculosis (bTB) is a zoonotic disease caused by Mycobacterium bovis that mainly affects cattle. The traditional bTB control strategy is to slaughter animals positive for the official diagnostic test. This method, however, may not be economically feasible in developing countries and is less effective when cattle are in contact with wildlife reservoirs of infection. In these scenarios, cattle vaccination could be an important strategy to control bTB. In this review, we describe the diversified vaccine development against bTB and identify gaps in current knowledge. We discuss progress made in live attenuated (including BCG), inactivated and subunit vaccines tested in preclinical models and field trials. We also provide an overview of existing knowledge in the correlates and biomarkers of protection after vaccination against bTB, as well as the most recent advances in the understanding of the protective immune response against tuberculosis. The similarities between the mechanisms of the immune protection elicited by humans and cattle against TB and bTB, respectively, make any advance in human TB and bTB vaccines mutually beneficial. In accordance with the One Health concept, this mutual benefit is expected to progress in the future.

Experimental investigation of the effect of the use of nanoparticle additional biodiesel on fuel consumption and exhaust emissions in tractor using a coated engine

This study focuses on reducing the fuel consumption and exhaust gas emission values of the tractor used in the agricultural field. With the additive added to the fuel and the coating of the tractor engine, the fuel consumption values were reduced, and agricultural production costs were tried to be reduced. On the other hand, exhaust emission values were also tried to be reduced and thus a more environmentally friendly production method was aimed to be adopted. For this reason, the cylinders of the tractor engine were coated with Al2O3 + 13%TiO2 metal powders mixed by mass using the plasma spray method. All experiments were repeated by attaching these coated pistons to the engine. The fuel used in the experiments was purchased from a commercial company and the nanoparticle (molybdenum) additive was added to the biodiesel at the rates of 25 ppm, 50 ppm, 100 ppm, and 200 ppm by mass. The fuel mixtures obtained in the coated and uncoated engine, when the engine is in the full throttle position, using the PTO load test unit at 1000 rpm, 1200 rpm, 1400 rpm, 1600 rpm, 1800 rpm, and 2000 rpm. It has been tested by loading at 2200 rpm, 2400 rpm, and 2600 rpm engine speeds. Code for the Official Testing of Agricultural and Forestry Tractor Performance standards were adhered to during all engine tests. The results showed that coating the cylinders and adding nanoparticles into biodiesel reduces the fuel consumption value, increases the exhaust gas temperature, decreases HC, CO, and PM emissions, and increases NOx emissions.

Protein Microarray-Guided Development of a Highly Sensitive and Specific Dipstick Assay for Glanders Serodiagnostics.

Burkholderia mallei, the causative agent of glanders, is a clonal descendant of Burkholderia pseudomallei, the causative agent of melioidosis, which has lost its environmental reservoir and has a restricted host range. Despite limitations in terms of sensitivity and specificity, complement fixation is still the official diagnostic test for glanders. Therefore, new tools are needed for diagnostics and to study the B. mallei epidemiology. We recently developed a highly sensitive serodiagnostic microarray test for human melioidosis based on the multiplex detection of B. pseudomallei proteins. In this study, we modified our array tests by using anti-horse IgG conjugate and tested sera from B. mallei-infected horses (n = 30), negative controls (n = 39), and horses infected with other pathogens (n = 14). Our array results show a sensitivity of 96.7% (confidence interval [CI] 85.5 to 99.6%) and a specificity of 100.0% (CI, 95.4 to 100.0%). The reactivity pattern of the positive sera on our array test allowed us to identify a set of 12 highly reactive proteins of interest for glanders diagnosis. The B. mallei variants of the three best protein candidates were selected for the development of a novel dipstick assay. Our point-of-care test detected glanders cases in less than 15 min with a sensitivity of 90.0% (CI, 75.7 to 97.1%) and a specificity of 100.0% (CI, 95.4 to 100.0%). The microarray and dipstick can easily be adopted for the diagnosis of both B. mallei and B. pseudomallei infections in different animals. Future studies will show whether multiplex serological testing has the potential to differentiate between these pathogens.

FROM NATIONALIZATION TO SECURITIZATION: AN ANALYSIS OF THE USE OF GENOSE COVID-19 TEST KITS IN INDONESIA

This research was carried out starting from the contemplation of seeing the Indonesian Government Policy on handling COVID-19 which is often inconsistent in several ways. The consequence of these problems is the trend of an increase in the number of Indonesian people who were confirmed positive for COVID-19. One of the inconsistencies in Indonesian government Policy can be seen in using the GeNose C-19 as a legally used COVID-19 test kit in Indonesia. GeNose developed and produced by Gadjah Mada University is claimed to be the national product. The procedure of GeNose is different from Polymerase Chain Reaction (PCR) and Swab tests. GeNose uses a breathing technique, and its result can be seen in 80 seconds. Aside from that, GeNose is also relatively cheap in terms of cost, when compared to PCR tests and Swab tests. It was designated as an official COVID-19 test kit in early 2021 by the state and has officially started being used legally at various airports, stations, and bus terminals. But unfortunately, in the last few days, due to an increase in the number of Indonesians who are confirmed positive for COVID-19, GeNose has started to stop. Hence, this research will look at the dynamics of how the Indonesian government is carrying out the nationalization and securitization of the GeNose test kit The researcher uses Barry Buzan's securitization theory and public health policy approach as analytical tools. The research method used is a qualitative approach with content analysis techniques. The results of the study stated that the actor of the nationalization and securitization of the GeNose issue was the state. There is a shifting paradigm in threats from military security to human security. GeNose issue so that at first nationalized and then constructed into a security threat and carried out securitization.

Position-guided transformer for image captioning

Transformer-based frameworks have shown superiorities in image captioning. However, such frameworks are strenuous to consider geometric interrelations among visual contents in an image, as well as fail to prevent changes in the distribution of each layer’s input in self-attention. In this work, we firstly propose a Bi-Positional Attention (BPA) module, which incorporates absolute and relative position encoding to precisely explore internal relations between objects and their geometric information in an image. Additionally, we use a Group Normalization (GN) method inside BPA to relieve shifts of the distribution and better exploit the channel dependence of visual features. To validate our proposals, we apply BPA and GN into the original Transformer to constitute our Position-Guided Transformer (PGT) network, which learns a more comprehensive positional representations to augment spatial interactions among objects for image captioning. We conduct extensive experiments to verify the effectiveness of our model. Compared with non-pretraining state-of-the-art methods, experimental results on the MSCOCO benchmark dataset demonstrate that our PGT achieves competitive performance, reaching 134.2% CIDEr score on the Karpathy split with a single model, and 136.2% CIDEr score on the official testing server with an ensemble configuration.

Verification of Chisel Hardware Designs with ChiselVerify

With the current ever-increasing demand for performance, hardware developers find themselves turning ever-more towards the construction of application-specific accelerators to achieve higher performance and lower energy consumption. In order to meet the ever-shortening time constraints, both hardware development and verification tools need to be improved.Chisel, as a hardware construction language, tackles this problem by speeding up the development of digital designs. However, the Chisel infrastructure lacks tools for verification. This paper improves the efficiency of verification in Chisel by proposing methods to support both formal and dynamic verification of digital designs in Scala. It builds on top of ChiselTest, the official testing framework for Chisel. Our work supports functional coverage, constrained random verification, bus functional models, and transaction-level modeling in a verification library named ChiselVerify, while the formal methods are directly integrated into Chisel3.

A modified secure hash design to circumvent collision and length extension attacks

This paper introduces an improved version of the secure hash algorithms, SHA-1, and SHA-2. The proposed work produces a strengthened secure hash design using the fusion between the SHA-1 and SHA-2 hash standards. This design helps to protect the SHA-1 and SHA-2 against collision and length extension attacks. The proposed design is incorporated in the round steps of the hash functions using the function manipulators of the SHA-1 and SHA-2. Moreover, the proposed design employs a padding method that is different from the one used in the SHA-1 and SHA-2 construction. The proposed design is verified by the official test vectors that were confirmed by the National Institute of Standard and Technology. Moreover, the avalanche effect, hamming distance, and bit-hit properties were studied using different test cases. The proposed design gives over 52 percent of the avalanche effect and over 84 hamming distance. The proposed design was tested against collision attack and produced unique hash values for real collided examples and shows resistance against length extension attack.

Rapid evaluation of ergosterol to detect yeast contamination in fruit juices

Foods and beverages are nutrient-rich systems prone to a rapid development of microorganisms that hamper their long-period storage. Particularly, yeasts are strong fermenters of fresh and processed fruits and vegetables; hence, they are often accountable for their spoilage and production of off-flavor. This work provides a quick and easy tool to recognize and count the spoilage of juices with ergosterol as distinctive biomarker of molds and yeasts. Melon juice was reconstituted at natural physical–chemical parameters according to legislation, and Saccharomyces cerevisiae was selected as yeast to contaminate the juice. Chemical and enzyme tests were performed on the fresh juice to ensure its authentic properties. Ergosterol was then evaluated using a spectrophotometric method that was proven against the official plate count test. The study showed linear and consistent results and, therefore, the ergosterol molecule may be indicated for testing molds and yeasts in contaminated beverages, replacing the common and time-consuming analysis.

Automatic Localization and Identification of Thoracic Diseases from Chest X-rays with Deep Learning.

There are numerous difficulties in using deep learning to automatically locate and identify diseases in chest X-rays (CXR). The most prevailing two are the lack of labeled data of disease locations and poor model transferability between different datasets. This study aims to tackle these problems. We built a new form of bounding box dataset and developed a two-stage model for disease localization and identification of CXRs based on deep learning. The dataset marks anomalous regions in CXRs but not the corresponding diseases, different from all previous datasets. The advantages of this design are reduced labor of annotation and fewer possible errors associated with image labeling. The two-stage model combines the robustness of the region proposal network, feature pyramid network, and multi-instance learning techniques. We trained and validated our model with the new bounding box dataset and the CheXpert dataset. Then, we tested its classification and localization performance on an external dataset, which is the official split test set of ChestX-ray14. For classification result, the mean area under the receiver operating characteristic curve (AUC) metrics of our model on the CheXpert validation dataset was 0.912, which was 0.021, superior to the baseline model. The mean AUC of our model on an external testing set was 0.784, whereas the state-of-the-art model got 0.773. The localization results showed comparable performance to the stateof- the-art models. Our model exhibits a good transferability between datasets. The new bounding box dataset is proven to be useful and shows an alternative technique for compiling disease localization datasets.

Development and Validation of Creative and Critical Thinking Competency Diagnostic Scale Using Item Response Theory

The purpose of this study is to develop and validate a diagnostic scale in the form of a written test from which creative and critical thinking (two key competencies at university) can be objectively measured.The research method consisted of 40 questions, modified and supplemented to suit the undergraduate students of I University, from the TOCT questionnaire. This survey had been verified for its validity by education professionals. It was used in a preliminary test in January 2021, and narrowed down to 20 questions. Furthermore, in October 2021 the official test was conducted on 93 students enrolled in the ‘Logical Thinking’course for the unit of Thinking Education as a basic subject to verify the validity of the diagnostic scale developed in the preliminary test. This diagnostic scale comprises objective multiple choice questions with 5 possible answers in the areas of comprehensive understanding, creative composition, objective evaluation, and problem-solving. This study used a two-parameter logistic model of descriptive statistics, correlation analysis, reliability analysis, and item response theory to examine the correction rate, independence, reliability and question characteristics.The results of the study showed the following: First, the average score of the participants in the study displayed a low correction rate (46.3%) of 9.26 out of 20. Second, the result of the correlation analysis displayed a rather low positive (+) correlation analysis between comprehensive understanding and problem-solving between creative composition and problem-solving, as well as between creative composition and objective evaluation. However, the correlation coefficient was less than 0.4, which indicated that there is no multi-collinearity. Third, the result of the reliability analysis displayed a very low reliability due to a relatively small number of questions having a suitable item difficulty in each sub-category. Fourth, the result of the item difficulty analysis displayed 8 easy questions, 5 moderately difficult questions and 7 difficult questions. Fifth, the result of item discrimination analysis showed that there were 4 questions with item discrimination, and 6 questions with high discrimination, whereas 10 questions had no item discrimination. Based on these study results, the limitations and implications inherent in the creative and critical thinking diagnostic scale were discussed.

An assessment of the impact of formal preparation activities on performance in the University Clinical Aptitude Test (UCAT): a national study

BackgroundPrevious studies have shown performance in the University Clinical Aptitude Test (UCAT) to be associated with measures of candidate socio-economic advantage such as parental occupation and type of school attended. It is possible that access to preparation support and materials may in part explain these associations. In this paper we determine whether use of formal preparation resources is associated with higher UCAT scores and whether differences in use of preparation resources exist between socio-demographic groups.MethodsAfter completing the 2017 UCAT UK school-leaver candidates (n = 14,332) were asked to answer a questionnaire regarding their use of official UCAT and commercial resources, school-based support, and time spent preparing. Multiple linear and logistic regression models were used to evaluate the associations between preparedness, demographic characteristics and UCAT performance.ResultsFive thousand, four hundred thirty-nine (38%) candidates responded to the questionnaire. Use of freely available UCAT official practice tests, paid commercial materials, attendance at school-based preparation courses and spending more time preparing were significantly associated with higher UCAT scores. Candidates who were from less deprived backgrounds and attending independent or grammar schools were significantly more likely to use paid commercial materials and spend longer preparing.ConclusionsReported use of preparation resources varies between candidates from different socio-demographic backgrounds and is associated independently with performance in the UCAT. Increasing the availability of freely available resources may mitigate some of these differences.

A rapidDNAextraction method for the detection ofClavibacter sepedonicusandRalstonia solanacearumin potato samples in a multiplexPCRtest

AbstractA method is described that generates DNA extracts suitable for the detection ofClavibacter sepedonicusandRalstonia solanacearumin potato samples, without the need for laborious, time‐consuming extraction protocols or expensive kits and laboratory equipment. Furthermore, a multiplex test is presented that allows the simultaneous detection of both pathogens in one standard PCR reaction. The methods described here allowed the detection of both bacteria in cell densities of 103cells per mL of plant extract, thus fulfilling the demands of official testing schemes, e.g. in the European Union. High potato starch content did not affect analytical sensitivity, and high concentrations of one pathogen did not impede the detection of the other. This method might be useful for all laboratories worldwide carrying out medium‐ to large‐scale potato testing for bacteria, and also seems to be suitable for the detection of virus diseases relevant for seed potato certification.

A novel multi-scale 2D CNN with weighted focal loss for arrhythmias detection on varying-dimensional ECGs

Objective. The ECG is a standard diagnostic tool for identifying many arrhythmias. Accurate diagnosis and early intervention for arrhythmias are of great significance to the prevention and treatment of cardiovascular disease. Our objective is to develop an algorithm that can automatically identify 30 arrhythmias by using varying-dimensional ECG signals. Approach. In this paper, we firstly proposed a novel multi-scale 2D CNN that can effectively capture pathological information from small-scale to large-scale from ECG signals to identify 30 arrhythmias from 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs. Secondly, we explored the effects of varying convolution kernels sizes and branch subnetworks on the model’s performance for each arrhythmia. Thirdly, we introduced the weighted focal loss to alleviate the positive-negative class imbalance problem in the multi-label arrhythmias classification. Fourthly, we explored the utility of reduced-lead ECGs in detecting arrhythmias by comparing the performances of models on varying-dimensional ECGs. Main results. As a follow-up entry after the PhysioNet/Computing in Cardiology Challenge (2021), our proposed approach achieved the official test scores of 0.52, 0.47, 0.53, 0.51, and 0.50 for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs on the hidden test set (comparable to that of 6th, 11th, 4th, 5th, and 7th out of 39 teams in the Challenge). Significance. A multi-scale framework capable of detecting 30 arrhythmias from varying-dimensional ECGs was proposed in our work. We preliminarily verified that the multi-scale perception fields may be necessary to capture more comprehensive pathological information for arrhythmias detection. Besides, we also verified that the weighted focal loss may alleviate the positive–negative class imbalance and improve the model’s generalization performance on the cross-dataset. In addition, we observed that some reduced-lead models, such as the 4-lead and 3-lead models, can even achieve performance that is almost comparable to that of the 12-lead model. The excellent performance of our proposed framework demonstrates its great potential in detecting a wide range of arrhythmias.

Evaluation of Garden State Parkway Alternate Bus Routing Field Operational Test

This study evaluates the effectiveness of the New Jersey Garden State Parkway (GSP) Alternate Bus Routing (ABR) system, an Advanced Public Transportation System (APTS). The GSP ABR Field Operational Test (FOT), which was conducted on a small portion of the parkway, was mainly concerned with real-time routing of New Jersey Transit (NJT) buses traveling the GSP to a parallel alternate route to avoid congestion on the parkway.One of the most important features of this FOT is the use of NJT buses as probe vehicles that provide real-time travel-time information to the bus routing algorithm. The use of probe vehicles for network surveillance increases the attractiveness of similar systems that can be deployed over larger networks where infrastructure-based surveillance can be prohibitively expensive. The functional evaluation of GSP ABR includes the collection and analysis of data from various sources including traffic sensors, probe vehicles, and surveys conducted among transit bus operators and system users.During the official testing period in 1997, the GSP ABR system produced accurate diversion messages, which proved the reliability of the system. However, the relatively short length of the alternate route, U.S. Route 9, and similar traffic patterns observed on both routes did not give the evaluation team the opportunity to observe scenarios where statistically significant travel-time improvements exist. This result prevented the evaluation team from reaching conclusive recommendations in terms of the effectiveness of this FOT. However, the survey results show that both transit bus operators and ABR system operators believe the system can be very beneficial if implemented in a different network.From a policy point of view, the general conclusions of the functional tests presented along with a list of lessons learned can be used in the effective design of future FOTs in the area of APTS.