Binary Model Research Articles

Overtaking risk assessment based on extreme value theory and intelligent network information

In order to evaluate the overtaking risk level of intelligent connected vehicles when providing different connected information, and to make up for the neglect of driver factors in traditional risk assessment and the inadequate evaluation of complex traffic scenarios by a single traffic conflict indicator, this paper introduces the Block maxima (BM) and Peak over threshold (POT) methods to fit the extreme value distribution for two conflict scenarios involved in overtaking events (following vehicle conflict and frontal vehicle conflict), so as to evaluate the risk of following vehicle accidents and frontal collision accidents during overtaking. In each conflict scenario, a non-stationary extreme value model considering driver factors and a binary extreme value model considering different traffic conflict indicators are constructed, and the model is verified by the overtaking test data of intelligent connected vehicles in a two-way two-lane vehicle. Overtaking events were extracted from the original test data and conflict indicators were calculated: including the collision time GAP with the front vehicle at the beginning of the overtaking event, the collision time TTC_t1 with the oncoming vehicle, the deceleration DRAC, and the collision time TTC with the oncoming vehicle and the headway time THW with the front vehicle at the end of the overtaking event. The probability of the event with a negative time conflict indicator or DRAC greater than MADR was used to characterize the degree of collision risk. The results show that in the following vehicle conflict scenario, the binary extreme value model constructed with different conflict indicators has different error results, among which the binary extreme value model constructed with THW&DRAC has the most accurate evaluation result (standard error MAE = 0.00028); in the front vehicle conflict scenario, the binary extreme value model constructed with TTC&DRAC has the most accurate evaluation result ( MAE = 0.006 ). In different conflict scenarios, the non- stationary extreme value model considering the driver factor significantly improves the risk assessment accuracy compared with the model without considering the driver factor (small AIC and BIC values). In addition, different intelligent network information (real-time distance, overtaking advice, speed advice) brings different overtaking risks, and when the intelligent network information is speed advice, the vehicle's overtaking risk is the lowest. Therefore, the non- stationary extreme value model and binary extreme value model considering driver factors proposed in this paper can effectively evaluate driving risks through traffic conflict indicators. Secondly, the experimental data of intelligent networked vehicles show that the extreme value theory model proposed in this paper can accurately evaluate the overtaking risk level of intelligent networked vehicles when providing different network information.

A machine learning comparison of transportation mode changes from high-speed railway promotion in Thailand

Thailand's collaboration with China to develop High-Speed Rail (HSR) represents a crucial step in enhancing transportation infrastructure and promoting regional economic growth. While most research has focused on existing travel modes such as cars, buses, and planes, there is a notable lack of analysis on future transport choices, particularly in developing countries like Thailand. This study addresses this gap by analyzing the factors influencing High-Speed Railways adoption to inform future traveler decisions. The comparison of models for predicting High-Speed Railways usage revealed that CatBoost consistently outperformed the other models, with cross-validation confirming its superior performance across all key metrics. The Binary Logit Model (BL) demonstrated moderate effectiveness, achieving an accuracy of 0.7404 ± 0.006, sensitivity of 0.7655 ± 0.006, and a relatively lower AUC of 0.8161 ± 0.0067. Its specificity (0.7140 ± 0.0129), precision (0.7376 ± 0.0085), and F1 score (0.7513 ± 0.0055) were moderate, but it struggled with handling imbalanced data. In contrast, XGBoost delivered significantly stronger results, with an accuracy of 0.8846 ± 0.0068, sensitivity of 0.9210 ± 0.0094, and an AUC of 0.9583 ± 0.0036. XGBoost also achieved high specificity (0.8464 ± 0.0062), precision (0.8630 ± 0.0055), and an F1 score (0.8910 ± 0.0067). LightGBM also performed well, achieving an accuracy of 0.8763 ± 0.0077 and a relatively high sensitivity of 0.9358 ± 0.0071. However, it exhibited lower specificity (0.8139 ± 0.0107) and precision (0.8408 ± 0.0083), resulting in a slightly reduced F1 score of 0.8857 ± 0.0069 and an AUC of 0.9506 ± 0.0034. CatBoost demonstrated the highest overall performance, achieving an accuracy of 0.8853 ± 0.0061, sensitivity of 0.9245 ± 0.0072, specificity of 0.8441 ± 0.0068, precision of 0.8616 ± 0.0058, an F1 score of 0.8920 ± 0.0058, and an AUC of 0.9584 ± 0.0034. The decision to use High-Speed Railways in Thailand is influenced by several key factors that reflect the country's unique transportation context. These factors include travel time, access time, service frequency, cost, waiting time, household income, automobile ownership, gender, and the purpose of travel. This information is valuable for shaping policies to support High-Speed Railways adoption in Thailand.

A decision rule for identifying patients at high risk for impaired lung diffusion capacity after COVID-19

Aim. To elaborate a decision rule for identifying the main predictors of impaired lung diffusion capacity after COVID-19. Materials and methods. The retrospective study included 341 patients without underlying lung diseases (median age 48 years) who experienced COVID-19 with bilateral pneumonia. The median extent of parenchymal lesion in the acute phase of COVID-19 (CTmax) was 50%. Spirometry, body plethysmography, and lung diffusion capacity for carbon monoxide (DLCO) test were performed. The data were analyzed by descriptive statistics, correlation analysis, one-dimensional logistic regression analysis with an assessment of odds ratios (OR), and multivariate logistic regression analysis. Receiver operating characteristic (ROC) analysis was used to assess the quality of the binary classifier model.Results. The initial model for predicting reduced DLCO (< 80% of predicted) included the following predictors: CTmax, time interval from the COVID-19 onset, gender, age, body mass index. Backward stepwise regression was applied, and a binary classifier model that includes CTmax was obtained. The sensitivity and specificity of the model for the training sample were 80 and 67%, respectively, for the test sample – 79 and 70%, respectively. The analysis of OR showed that OR > 1 was observed at СTmax > 40%.Conclusion. The decision rule was obtained for predicting impaired lung diffusion capacity after COVID-19 with virus-associated lung damage in patients without underlying bronchopulmonary diseases. Patients with CTmax > 40% require more thorough clinical follow-up with DLCO monitoring after the acute phase of COVID-19

Monitoring small-scale bioreactor studies for media development using polarized total synchronous fluorescence spectroscopy (pTSFS) and synchronous light scattering (SyLS)

Biopharmaceutical process development often involves the use of small-scale bioreactors (SSBR) for optimizing media formulations and process conditions during scale up to commercial scale production. Two key process parameters (CPP) used in SSBR studies are protein titre and viable cell density (VCD). Here, we explore the efficacy of parallel polarized total synchronous fluorescence spectroscopy (TSFS||) and Synchronous Light Scattering (SyLS||) to qualitatively monitor these CPPs and quantitatively predict titre and VCD for a large-scale cell culture media optimization SSBR study. The study involved 71 different media formulations (50+ components each), and the bioprocess was run for 13 days or more. Samples were extracted at set times (Day 0, 3, 9, and 13) and clarified by centrifugation. TSFS|| spectra showed significant emission changes along with increased light scatter over the course of the bioprocess. SyLS|| measurements strongly correlated with particle size data obtained from Dynamic Light Scattering but did not correlate well with VCD probably because of the centrifugation-based sample preparation. Statistical and principal component analysis (PCA) of the pTSFS data showed that spectral variation was greater between media formulations than due to the evolving bioprocess. This prevented the development of accurate global prediction models for media performance (e.g., predicting product titre at day 9 from media spectra measured at day 0). However, classification methods were successfully used to select media subsets with better quantitative prediction accuracy based on spectral similarities. A practical binary (high/low performance) classification model based on Support Vector Machines was generated for media formulation screening. Combining emission and scatter measurements with multivariate data analysis provides a more holistic, multi-attribute bioprocess monitoring method that minimizes the need to use different offline analytical methods. This methodology can be used to monitor process trajectories and deviations, and ultimately be used to predict bioprocess CPPs when implemented on production scale processes where there is much less compositional variation in the media. We believe this SSBR-pTSFS/SyLS approach will provide a valuable resource to develop the design/parameter space for in-process monitoring at production scale from early-stage process/media development studies.

Incomplete ordinal panel responses with initial condition and skew-normal random effect

This paper presents a model for the ordinal categorical panel data with skew-normal random effects, endogenous initial condition, and non ignorable (NI) non monotone missing data. We assumed three separate models for the initial condition, subsequent responses, and missing procedure. The baseline or initial response variable and subsequent response variables are modeled as ordinal logistic, whereas the logistic binary model is considered for the missing process where the maximum likelihood estimates are obtained using Monte Carlo methods. The results show that in the presence of missing data, the skew-normal random effects produce better results compared to the normal one, and considering the endogenous initial conditions is effective in modeling the results of the observed and missing responses such that the patients whose severity of schizophrenia is higher at the beginning of the process have a higher chance of missing on the subsequence times. In this research, schizophrenia data and then simulation studies have been used to check the performance of the proposed model. The results show that the skew-normal random effect using the NI non monotone missing mechanism by increasing the skewness value produces better results in the analysis of simulation studies.

Determinants of nutrition security status of women in rural households in Northwest Ethiopia

The nutrition security of women is critically important for society’s well-being. Therefore, this study was conducted to determine the determinants of the nutrition security status of rural women in Northwest Ethiopia. Data collected from rural women in 197 randomly selected households were analyzed using a binary logit regression model. The results show that 72.6% of the women are nutritionally insecure. The determinants that significantly affect nutrition security status are family size (a negative effect), women’s daily feed frequency, milk consumption, feeding habits of fruits and vegetables, feeding habits of animal products, and women empowerment (all positive effects). The weight, height, and BMI of nutritionally insecure women were significantly lower than those of nutritionally secure women. These findings reveal the need for government and other stakeholders’ interventions to increase access to nutritious food products and to provide training on feeding culture and dietary diversity to women. The findings of this study can help the Government of Ethiopia to focus on the significant determinants to achieve its National Development Priorities focusing on the Sustainable Development Goals (SDGs) of the UN, particularly Goals 2, 3, and 5.

Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs

BackgroundHistopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slides, known as whole slide images (WSIs). Artifacts are diagnostically irrelevant areas and may result in wrong predictions from deep learning (DL) algorithms. Therefore, detecting and excluding artifacts in the computational pathology (CPATH) system is essential for reliable automated diagnosis.MethodsIn this paper, we propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, folded tissue, air bubbles, and histologically irrelevant blood from WSIs. First, we train independent binary DL models as experts to capture particular artifact morphology. Then, we ensemble their predictions using a fusion mechanism. We apply probabilistic thresholding over the final probability distribution to improve the sensitivity of the MoE. We developed four DL pipelines to evaluate computational and performance trade-offs. These include two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs). These DL pipelines are quantitatively and qualitatively evaluated on external and out-of-distribution (OoD) data to assess generalizability and robustness for artifact detection application.ResultsWe extensively evaluated the proposed MoE and multiclass models. DCNNs-based MoE and ViTs-based MoE schemes outperformed simpler multiclass models and were tested on datasets from different hospitals and cancer types, where MoE using (MobileNet) DCNNs yielded the best results. The proposed MoE yields 86.15 % F1 and 97.93% sensitivity scores on unseen data, retaining less computational cost for inference than MoE using ViTs. This best performance of MoEs comes with relatively higher computational trade-offs than multiclass models. Furthermore, we apply post-processing to create an artifact segmentation mask, a potential artifact-free RoI map, a quality report, and an artifact-refined WSI for further computational analysis. During the qualitative evaluation, field experts assessed the predictive performance of MoEs over OoD WSIs. They rated artifact detection and artifact-free area preservation, where the highest agreement translated to a Cohen Kappa of 0.82, indicating substantial agreement for the overall diagnostic usability of the DCNN-based MoE scheme.ConclusionsThe proposed artifact detection pipeline will not only ensure reliable CPATH predictions but may also provide quality control. In this work, the best-performing pipeline for artifact detection is MoE with DCNNs. Our detailed experiments show that there is always a trade-off between performance and computational complexity, and no straightforward DL solution equally suits all types of data and applications. The code and HistoArtifacts dataset can be found online at Github and Zenodo, respectively.

Combined Method Comprising Low Burden Physiological Measurements with Dry Electrodes and Machine Learning for Classification of Visually Induced Motion Sickness in Remote-Controlled Excavator

The construction industry is actively developing remote-controlled excavators to address labor shortages and improve work safety. However, visually induced motion sickness (VIMS) remains a concern in the remote operation of construction machinery. To predict the occurrence and severity of VIMS, we developed a prototype system that acquires multiple physiological signals with different mechanisms under a low burden and detects VIMS from the collected data. Signals during VIMS were recorded from nine healthy adult males operating excavator simulators equipped with multiple displays and a head-mounted display. Light gradient-boosting machine-based VIMS detection binary classification models were constructed using approximately 30,000 s of time-series data, comprising 23 features derived from the physiological signals. These models were validated using leave-one-out cross-validation on seven participants who experienced severe VIMS and evaluated through area under the curve (AUC) scores. The mean receiver operating characteristic curve AUC score was 0.84, and the mean precision–recall curve AUC score was 0.71. All features were incorporated into the models, with saccade frequency and skin conductance response identified as particularly important. These trends aligned with subjective assessments of VIMS severity. This study contributes to advancing the use of remote-controlled machinery by addressing a critical challenge to operator performance and safety.

Enhanced patient journey associated with improved overall survival in colon cancer patients: A study by the Ligurian Oncology Network.

Colon cancer imposes a significant burden on global healthcare systems, necessitating efforts to improve oncology care quality and patient outcomes. We studied the correlation between care quality and survival outcomes among colon cancer patients within the Ligurian Oncology Network (Italy). We developed an Overall Quality Score (OQS) to evaluate the impact of oncology care quality on survival outcomes within the Ligurian Oncology Network. OQS indicators were selected through expert consensus, covering screening, diagnosis, treatment, and follow-up. A sample of colon cancer patients diagnosed in 2012 was randomly selected from administrative healthcare data. Analyses were performed using two models: a binary model (High and Low OQS) and a stratified model (Low, Medium, and High OQS). Statistical analysis involved survival curves, log-rank tests, and Cox proportional hazards models using SAS 9.4. Of 175 eligible colon cancer patients, 150 were included. Following a median follow-up of 7.6 years, a correlation between High-OQS (⩾ 65%) and prolonged disease-free survival was observed (unadjusted HR 0.57, 95%CI 0.33-0.99, log-rank p=0.041). The five-year disease-free survival rate for High-OQS patients was 70% (95%CI 57-80%), compared to 53% (95%CI 41-64%) for Low-OQS patients. Similarly, the five-year overall survival rate was 78% (95%CI 65-86%) for High-OQS patients, compared to 58% (95%CI 45-68%) for Low-OQS patients (unadjusted HR 0.56, 95%CI 0.31-1.00, log-rank p=0.048). Our findings highlight the potential impact of the patient journey on colon cancer survival outcomes. Optimising care pathways might improve patient outcomes in colon cancer management.

A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses.

Natural selection often acts on multiple traits simultaneously. For example, the virus HIV-1 faces pressure to evade host immunity while also preserving replicative fitness. While past work has studied selection during HIV-1 evolution, as in other examples where selection acts on multiple traits, it is challenging to quantitatively separate different contributions to fitness. This task is made more difficult because a single mutation can affect both immune escape and replication. Here, we develop an evolutionary model that disentangles the effects of escaping CD8+ T cell-mediated immunity, which we model as a binary trait, from other contributions to fitness. After validation in simulations, we applied this model to study within-host HIV-1 evolution in a clinical data set. We observed strong selection for immune escape, sometimes greatly exceeding past estimates, especially early in infection. Conservative estimates suggest that roughly half of HIV-1 fitness gains during the first months to years of infection can be attributed to T cell escape. Our approach is not limited to HIV-1 or viruses, and could be adapted to study the evolution of quantitative traits in other contexts.

The Effects of Health Insurance on Demand for Healthcare Services in Tanzania

ABSTRACT: Consistent healthcare financing systems within a particular nation or community have been regarded as vital for making universal healthcare access. This study intended to examine the influence of health insurance on the demand for healthcare services and the influence of socio-economic, demographic and hospital-related factors on demand for health care services in Tanzania context. The study employed secondary data, which were analysed by descriptive statistics and the results were presented in tables and percentages. Econometric analysis was also done using binary probit regression model to determine the factors influencing demand for health care services in Tanzania. The instrumental variable was applied to address the problem of endogeneity of health insurance. The regression results revealed that gender, education level, household size, income, treatment cost, perceived health quality and health insurance type were significant and positively linked with demand for health care services. However, marital status was also significant but negatively associated with the demand for healthcare services. The study concluded that, despite of the efforts made by the government and other stakeholders on emphasis over having health insurance, the enrolment to health insurance schemes is still low although it has been noted to significantly influence the demand for health care services. Lastly, the paper recommends that the government and other stakeholders are required to formulate or strengthen the formulated policy in practice, purposively to improve access, affordability and the quality of health care services. Governmental and non-governmental organizations (NGO`s) and other stakeholders should put more effective strategies to improve coverage but also reduce the financial burden caused by illness. Also, the government needs to subsidize the healthcare sector, especially to the poor and provide subsidies to private healthcare facilities, improvement of insurance type, specifically, the NHIF which needs to increase coverage. Furthermore, health care managers should educate other people and make a clear observation on factors that make individuals demand for health care because they have a great role in the health care sector as well as strategies to improve household`s income to meet the health needs in Tanzania.

Clustering Based on Laboratory Data in Patients With Heart Failure Admitted to the Intensive Care Unit.

Heart failure (HF) is a common condition that imposes a significant burden on healthcare systems. We aimed to identify subgroups of patients with heart failure admitted to the ICU using routinely measured laboratory biomarkers. A large dataset (N = 1176) of patients with heart failure admitted to the ICU at the Beth Israel Deaconess Medical Center in Boston, USA, between June 1, 2001, and October 31, 2012, was analyzed. We clustered patients to identify laboratory phenotypes. Cluster profiling was then performed to characterize each cluster, using a binary logistic model. Two distinct clusters of patients were identified (N = 679 and 497). There was a significant difference in the mortality rate between Clusters 1 and 2 (50 [7.4%] vs. 109 [21.9%], respectively, p < 0.001). Patients in the Cluster 2 were significantly older (mean [SD] age = 72.35 [14.40] and 76.37 [11.61] years, p < 0.001) with a higher percentage of chronic kidney disease (167 [24.6%] vs. 262 [52.7%], respectively, p < 0.001). The logistic model was significant (Log-likelihood ratio p < 0.001, pseudo R2 = 0.746) with an area under the curve of 0.905. The odds ratio for leucocyte count, mean corpuscular volume (MCV), red blood cell (RBC) distribution width, hematocrit (HcT), lactic acid, blood urea nitrogen (BUN), serum potassium, magnesium, and sodium were significant (all p < 0.05). Laboratory data revealed two phenotypes of ICU-admitted patients with heart failure. The two phenotypes are of prognostic importance in terms of mortality rate. They can be differentiated using blood cell count, kidney function status, and serum electrolyte concentrations.

Hybrid quantum annealing decomposition framework for unit commitment

Quantum computing is an emerging and promising technology that has overwhelming quantum advantages compared to its classical counterparts. Unit commitment (UC) is a critical issue in the power system, and it becomes more challenging with the integration of intermittent renewable energy. Therefore, this paper proposes an innovative decomposition and coordination optimization framework to accelerate the solution of UC, in which the interaction between an adiabatic quantum computer and a classical computer is designed to harness the immense computational power of quantum computers effectively. First, decomposition methods considering the requirements of quantum computers are introduced to decompose UC into small-scale models. Then, the paper presents a quadratic unconstrained binary optimization modeling method to transform UC problems into the form of quantum computing. Furthermore, due to the limitations of quantum computing resources, a reductive variable technique is proposed to reduce the number of slack variables in the optimization model and ensure that it remains feasible for quantum computers. Case studies conducted in test systems with a quantum annealing simulator and a real quantum annealing computer illustrate the feasibility and effectiveness of the method and demonstrate its potential in the era of quantum computing.

A trifecta of modelling tools: a Bayesian binary black hole model selection combining population synthesis and galaxy formation models

ABSTRACT Gravitational waves (GWs) have revealed surprising properties of binary black hole (BBH) populations, but there is still mystery surrounding how these compact objects evolve. We apply Bayesian inference and an efficient method to calculate the BBH merger rates in the Shark host galaxies, to determine the combination of COMPAS parameters that outputs a population most like the GW sources from the LIGO, Virgo, and KAGRA (LVK) transient catalogue. For our COMPAS models, we calculate the likelihood with and without the dependence on the predicted number of BBH merger events. We find strong correlations between hyper-parameters governing the specific angular momentum (AM) of mass lost during mass transfer, the mass-loss rates of Wolf–Rayet stars via winds and the chemically homogeneous evolution (CHE) formation channel. We conclude that analysing the marginalized and unmarginalized likelihood is a good indicator of whether the population parameters distribution and number of observed events reflect the LVK data. In doing so, we see that the majority of the models preferred in terms of the population-level parameters of the BBHs greatly overpredict the number of events we should have observed to date. Looking at the smaller number of models that perform well with both likelihoods, we find that those with no CHE, AM loss occurring closer to the donor during the first mass-transfer event, and/or higher rates of mass-loss from Wolf–Rayet winds are generally preferred by current data. We find these conclusions to be robust to our choice of selection criteria.

Enhanced intrusion detection framework for securing IoT network using principal component analysis and CNN

ABSTRACT The Internet of Things (IoT) has transformed our world by connecting smart devices and enabling seamless interactions. This reliance, however, has led to new security issues and types of attacks. It is of the utmost importance to safeguard the security of IoT networks, with network intrusion detection systems (NIDS) having a significant impact. This paper proposes a novel approach integrating Principal Component Analysis (PCA), Pearson Correlation Coefficient (PCC), and Convolutional Neural Network (CNN) to overcome these security issues. Our innovative method reduces data dimensionality and selects highly correlated features using PCC and PCA, addressing overfitting and improving model performance while maintaining high computational speed and low costs. Our approach uniquely distinguishes between benign and threat packets by employing 1D-CNN, 2D-CNN, and 3D-CNN algorithms trained on Edge-IIoTset and NSL-KDD benchmark datasets. The findings from our experiments indicate that the proposed framework significantly enhances accuracy, precision, recall, and F1-score compared to existing models for both binary and multiclass classifications. Our binary classification models achieved exceptional performance, with an average accuracy of 99.76%, 99.79% precision, 99.89% recall, and 99.85% F1-score on the Edge-IIoTset dataset. On the NSL-KDD dataset, the models attained 99.20% accuracy, 98.07% precision, 97.95% recall, and 97.71% F1-score. For multiclass classification, the proposed model demonstrated an average accuracy of 99.41%, precision of 98.61%, recall of 98.49%, and an F1-score of 98.56% on the Edge-IIoTset dataset. On the NSL-KDD dataset, the model achieved 92.43% accuracy, 93.21% precision, 93.60% recall, and a 93.7% F1-score. Our research introduces a significant advancement that substantially improves NIDS capabilities, making IoT networks safer and more connected.

A formal goodness-of-fit test for spatial binary Markov random field models.

Binary spatial observations arise in environmental and ecological studies, where Markov random field (MRF) models are often applied. Despite the prevalence and the long history of MRF models for spatial binary data, appropriate model diagnostics have remained an unresolved issue in practice. A complicating factor is that such models involve neighborhood specifications, which are difficult to assess for binary data. To address this, we propose a formal goodness-of-fit (GOF) test for diagnosing an MRF model for spatial binary values. The test statistic involves a type of conditional Moran's I based on the fitted conditional probabilities, which can detect departures in model form, including neighborhood structure. Numerical studies show that the GOF test can perform well in detecting deviations from a null model, with a focus on neighborhoods as a difficult issue. We illustrate the spatial test with an application to Besag's historical endive data as well as the breeding pattern of grasshopper sparrows across Iowa.

Modal Shift Potential of Different Mode Users Due to Introduction of the First MRT in Dhaka: A Prelaunch Study

Dhaka city is experiencing tremendous growth in traffic. Until recently, the city’s demand for traffic was entirely served by public buses, a mix of motorized and non-motorized paratransit, and private personalized transport. The first ever rail-based metro, mass rapid transit (MRT), namely MRT Line 6, was partially inaugurated on 28 December 2022. Authority expects that there will be visible modal shift. However, MRT systems in many Asian and European countries are attracting much lower private motorists than what is expected. Moreover, in Dhaka, a unique mix in road-based transport with public transit and varieties of paratransit and private personalized vehicles intensifies the uncertainties involved in modal shift. Therefore, based on a field survey done before four (04) months of the partial inauguration of MRT Line 6, this study intends to explore the modal shift potentials of different mode users to a completely new mode and the modal choice factors. The analysis of the results finds overall, 75% of all mode users are willing to shift, while para and public transit users are comparatively more willing than private personalized vehicle users. However, such willingness comes up with one or more conditions: comfort, reasonable fare, reduced total travel time, less crowd, etc. The study finds that affordability, availability, and accessibility factors have an influence on their modal shift behavior. Also, results from binary logistic model identify significant impact of several sociodemographic, trip- and accessibility-related factors influencing modal shift choice. Findings from this study explain the optimism regarding MRT by different mode users and inform the decision-makers about their course of actions including different interventions, strict and carrot approaches to hold on to the potential shifters and attract more.

Fostering adaptation to climate change among farmers in Pakistan: the influential role of farmers’ climate change knowledge and adaptive capacity

IntroductionAdaptation to climate change (ACC) is imperative to avoid deleterious consequences of climate change in agriculture. However, the uptake of adaptation measures has been slow among farmers because of low adaptive capacity (AC) in developing countries, particularly in Pakistan. Farmers and their supporting institutions have been successful in introducing technological innovations to respond and adapt to environmental challenges. The present study intended to determine the impact of farming technologies, along with human, financial, social, physical, natural, and climate information resources that support AC and hence ACC.MethodsThe study collected data from 360 farmers in Punjab through a multi-stage random sampling technique. A binary logit model and odds ratio were used to identify the factors affecting ACC. The study also utilized correlation tests to show the correlation between each pair of variables included in the analysis.ResultsThe results indicated that physical capital such as ownership of tube wells, transportation, and sowing and harvesting tools by the farmers builds farmers’ AC and consequently determines the ACC such as change crop variety (CCV), change crop type (CCT), change planting date (CPD), soil conservation (SC), water conservation (WC), and diversification strategies (DSs). The findings also revealed that human capital (age, education, family size, and labor), financial capital (off-farm employment, access to the marketing of produce, and agricultural credit), social capital (farmers-to-farmers extensions, access to extension services, and the farm association membership), and natural capital (land ownership, tenancy status, and the location of the farm) were importantly related to farm households’ ACC strategies. The odds (likelihood) of adaptation were higher for the users of farm technology as compared to non-users.DiscussionThe analysis conducted in this study showed that climate information resources amplify the adaptation to climate change: technology allows farming to be much more efficient, while climate change knowledge (CCK) self-motivates farmers to adopt more ACC measures. Our findings provide evidence that suggests the need to provide credits and financial support for farming technologies that speed up the ACC in the long run, while in the short run, climate information should be spread among farming communities.

Performance of agricultural extension agents in Indonesia: evidence from a nationally representative survey

ABSTRACT Purpose This study analyses the performance of field extension agents in Indonesia using a multi-dimensional performance indicator. Methodology The study uses data from an online survey of a nationally representative sample of 1,974 field extension agents. Binary and ordinal probit models are utilised to analyse the performance of extension agents and the factors influencing it based on a multi-dimensional measurement of performance. Findings and practical implications Improved performance is associated with increased career progression, availability of external funds, the opportunity to collaborate, involvement in government programs, implementation of disciplinary measures and a diversified use of advisory methods. To promote performance, the government should encourage and improve the institutional arrangements. Theoretical implications This study introduces a multidimensional measurement that covers timeliness, targeting and content dimensions while capturing both the institutional arrangement and demographic characteristics in explaining performance. Originality/value This study introduces an online survey for extension agents that encompasses a nationally representative sample, thereby demonstrating this cost effective approach designed for rapid data collection and facilitating the measurement of performance during unforeseen events, such as the Covid-19 pandemic. Its broad applicability allows for a thorough analysis of agricultural extension agents’ performance in a variety of contexts.

B-232 One Year Post-Implementation Experience Using a Custom Designed DNA Contamination Detection Module for a Clinical Myeloid Neoplasm Next Generation Sequencing Assay

Abstract Background In complex automated clinical laboratory Next Generation Sequencing (NGS) workflows, numerous opportunities exist for contamination events to occur. For oncology testing applications, detection of these events is crucial for accurate results reporting. A contamination detection module (MICon) using microhaplotype (MH) regions and an in-house designed analysis model was developed for use in a NGS assay for myeloid neoplasms (NGSHM) [1]. Variant detection for NGSHM has a validated analytical sensitivity of 2% variant allele frequency (VAF). Gross contamination events can potentially cause erroneous false positive variant detection whereas true low VAF mutations may be masked and evade detection. The following study was performed to evaluate MICon module performance on samples processed over the course of a year. Methods MH regions contain highly conserved tandem single nucleotide polymorphisms (SNPs) with high global heterogeneity occurring within a 300-nucleotide span. From the work of Kidd et al [2], 27 MH regions covering 92 SNPs and spanning 16 chromosomes were selected for inclusion in the 47 gene NGSHM target-capture panel. Sequencing was performed on a NovaSeq 6000 and analyzed through an internal bioinformatics pipeline, which incorporates the MICon module. MICon uses a binary classification model incorporating VAFs from MH regions, number of MH genotypes, and the contamination estimation score from verifyBamID to compute a single value on a 0-100 scale. Scores &amp;lt;50 are classified as non-contaminated and scores ≥50 require further investigation. Results The dataset was comprised of 10,990 samples. A total of 10,232 patient cases (93.1%) had non-contaminated MICon scores &amp;lt;50. Control replicates accounted for 261 samples (2.4%). There were 497 patient cases (4.5%) with MICon scores ≥50. Of the 497 patient cases, 137 (27.6%) had undergone hematopoietic stem cell transplantation (HSCT) and 114 (22.9%) contained gross chromosomal abnormalities, representing iatrogenic or tumor biological factors. An additional 135 (27.2%) of cases were contaminated from laboratory processing errors, 45 (9.1%) occurred due to chemistry failures during run processing, and 66 (13.2%) had an undetermined cause for a high score, representing laboratory-related and unknown factors. Overall, the 135 laboratory processing errors detected accounted for 1.3% of patient cases analyzed by the laboratory. These cases were repeated, and the non-contaminated results were accurately reported. Conclusions Herein it was shown the implementation of MICon has improved patient safety over the course of a year. The 1.3% of patient cases identified as arising from laboratory processing errors were successfully reprocessed. While the overall rate of laboratory processing errors appears low, the absolute number can be significant in a high-volume test setting, despite reliance on highly automated processes. Value is added by MICon through identifying errors that could result in the release of inaccurate patient results. By detecting previously unrealized errors, MICon has become an invaluable asset to the NGSHM assay by enhancing patient laboratory testing safety.