Adoption of Precision Technologies by Brazilian Dairy Farms: The Farmer’s Perception

The use of precision farming technologies, such as milking robots, automated calf feeders, wearable sensors, and others, has significantly increased in dairy operations over the last few years. The growing interest in farming technologies to reduce labor, maximize productivity, and increase profitability is becoming noticeable in several countries, including Brazil. Information regarding technology adoption, perception, and effectiveness in dairy farms could shed light on challenges that need to be addressed by scientific research and extension programs. The objective of this study was to characterize Brazilian dairy farms based on technology usage. Factors such as willingness to invest in precision technologies, adoption of sensor systems, farmer profile, farm characteristics, and production indexes were investigated in 378 dairy farms located in Brazil. A survey with 22 questions was developed and distributed via Google Forms from July 2018 to July 2020. The farms were then classified into seven clusters: (1) top yield farms; (2) medium–high yield, medium-tech; (3) medium yield and top high-tech; (4) medium yield and medium-tech; (5) young medium–low yield and low-tech; (6) elderly medium–low yield and low-tech; and (7) low-tech grazing. The most frequent technologies adopted by producers were milk meters systems (31.7%), milking parlor smart gate (14.5%), sensor systems to detect mastitis (8.4%), cow activity meter (7.1%), and body temperature (7.9%). Based on a scale containing numerical values (1–5), producers indicated “available technical support” (mean; σ2) (4.55; 0.80) as the most important decision criterion involved in adopting technology, followed by “return on investment—ROI” (4.48; 0.80), “user-friendliness” (4.39; 0.88), “upfront investment cost” (4.36; 0.81), and “compatibility with farm management software” (4.2; 1.02). The most important factors precluding investment in precision dairy technologies were the need for investment in other sectors of the farm (36%), the uncertainty of ROI (24%), and lack of integration with other farm systems and software (11%). Farmers indicated that the most useful technologies were automatic milk meters systems (mean; σ2) (4.05; 1.66), sensor systems for mastitis detection (4.00; 1.57), automatic feeding systems (3.50; 2.05), cow activity meter (3.45; 1.95), and in-line milk analyzers (3.45; 1.95). Overall, the concerns related to data integration, ROI, and user-friendliness of technologies are similar to those of dairy farms located in other countries. Increasing available technical support for sensing technology can have a positive impact on technology adoption.

An Approach for Generating Weights Using the Pairwise Comparison Matrix

Data envelopment analysis (DEA) has been used for obtaining weights for the analytic hierarchy process (AHP), an approach known as DEAHP. This method sometimes identifies more than one decision criterion or alternative as DEAHP-efficient. To overcome this problem, this paper proposes a new approach that not only generates appropriate weights for the decision criteria or alternatives, but also differentiates between DEAHP-efficient decision criteria or alternatives. To this end, we propose a DEA model with an assurance region and a cross-weight model that prioritizes decision criteria or alternatives by considering their most unfavorable weights. Two numerical examples are also provided to illustrate the advantages and potential applications of the proposed model.

Hypothermia Outcome Prediction after Extracorporeal Life Support for Hypothermic Cardiac Arrest Patients: Assessing the Performance of the HOPE Score in Case Reports from the Literature

Aims: The hypothermia outcome prediction after extracorporeal life support (ECLS) score, or HOPE score, provides an estimate of the survival probability in hypothermic cardiac arrest patients undergoing ECLS rewarming. The aim of this study was to assess the performance of the HOPE score in case reports from the literature. Methods: Cases were identified through a systematic review of the literature. We included cases of hypothermic cardiac arrest patients rewarmed with ECLS and not included in the HOPE derivation and validation studies. We calculated the survival probability of each patient according to the HOPE score. Results: A total of 70 patients were included. Most of them (62/70 = 89%) survived. The discrimination using the HOPE score was good (Area Under the Receiver Operating Characteristic Curve = 0.78). The calibration was poor, with HOPE survival probabilities averaging 54%. Using a HOPE survival probability threshold of at least 10% as a decision criterion for rewarming a patient would have resulted in only five false positives and a single false negative, i.e., 64 (or 91%) correct decisions. Conclusions: In this highly selected sample, the HOPE score still had a good practical performance. The selection bias most likely explains the poor calibration found in the present study, with survivors being more often described in the literature than non-survivors. Our finding underscores the importance of working with a representative sample of patients when deriving and validating a score, as was the case in the HOPE studies that included only consecutive patients in order to minimize the risk of publication bias and lower the risk of overly optimistic outcomes.

BIM-Based Co-Simulation of Fire and Occupants’ Behavior for Safe Construction Rehabilitation Planning

Construction renovation projects increase the risk of structural fire, mostly due to the accumulation of combustible construction materials and waste. In particular, when the building remains operational during such projects, the redistribution of occupants and interruptions with access corridors/exit egress can exponentially increase the risk for the occupants. Most construction projects are, however, planned and scheduled merely based on the time and budget criteria. While safety is considered paramount and is meant to be applied as a hard constraint in the scheduling stage, in practice, safe evacuation considerations are reduced to rules of thumb and general code guidelines. In this paper, we propose simulation as a tool to introduce safety under structural fire, as a decision criterion, to be mixed with time and budget for selecting the best construction schedule alternative. We have used the BIM (building information model) to extract the building’s spatial and physical properties; and have applied co-simulation of fire, through computational fluid dynamics (CFD), and occupants’ evacuation behavior, through agent-based modeling (ABM) to estimate the average and maximum required safe egress time for various construction sequencing alternatives. This parameter is then used as a third decision criterion, combined with the project’s cost and duration, to evaluate construction schedule alternatives. We applied our method to a three-floor fire zone in a high-rise educational building in Montreal, and our results show that considering the fire safety criterion can make a difference in the final construction schedule. Our proposed method suggests an additional metric for evaluating renovation projects’ construction plans, particularly in congested buildings which need to remain fully or partially operational during the renovation. Thus, this method can be employed by safety officers and facility managers, as well as construction project planners to guide accounting for fire incidents while planning for these types of projects.

Limitations of a proposed correction for slow drifts in decision criterion

Trial history biases in decision-making tasks are thought to reflect systematic updates of decision variables, therefore their precise nature informs conclusions about underlying heuristic strategies and learning processes. However, random drifts in decision variables can corrupt this inference by mimicking the signatures of systematic updates. Hence, identifying the trial-by-trial evolution of decision variables requires methods that can robustly account for such drifts. Recent studies (Lak 20, Mendonça 20) have made important advances in this direction, by proposing a convenient method to correct for the influence of slow drifts in decision criterion, a key decision variable. Here we apply this correction to a variety of updating scenarios, and evaluate its performance. We show that the correction fails for a wide range of commonly assumed systematic updating strategies, distorting one's inference away from the veridical strategies towards a narrow subset. To address these limitations, we propose a model-based approach for disambiguating systematic updates from random drifts, and demonstrate its success on real and synthetic datasets. We show that this approach accurately recovers the latent trajectory of drifts in decision criterion as well as the generative systematic updates from simulated data. Our results offer recommendations for methods to account for the interactions between history biases and slow drifts, and highlight the advantages of incorporating assumptions about the generative process directly into models of decision-making.

Oculomotor freezing tracks perception and is immune to decision bias

The appearance of a salient stimulus rapidly inhibits saccadic eye movements. Curiously, this "oculomotor freezing" reflex is triggered only by stimuli that the participant reports seeing (White & Rolfs, 2016). But is oculomotor freezing linked to the participant's sensory experience, or their decision that a stimulus was present? If it were decision-related, oculomotor freezing should become less prevalent when the participant is induced to have a conservative decision criterion and reports seeing a stimulus less often. Here we manipulated decision criterion in two ways: by adjusting monetary payoffs and stimulus probability in a detection task. These bias manipulations greatly affected participants' explicit reports but did not affect the degree to which microsaccades were inhibited by stimulus presence. In addition, the link between oculomotor freezing and explicit reports was stronger when the decision criterion was conservative rather than liberal. The simplest explanation is that conservative reports of stimulus presence are more often based on a strong sensory signal that also inhibits microsaccades. We conclude that the sensory threshold for oculomotor freezing is independent of decision bias. To the extent that conscious experience is also unaffected by such bias, oculomotor freezing provides an involuntary, implicit indication that a stimulus has entered awareness.

DataStorm: Coupled, Continuous Simulations for Complex Urban Environments

Urban systems are characterized by complexity and dynamicity. Data-driven simulations represent a promising approach in understanding and predicting complex dynamic processes in the presence of shifting demands of urban systems. Yet, today’s silo-based, de-coupled simulation engines fail to provide an end-to-end view of the complex urban system, preventing informed decision-making. In this article, we present DataStorm to support integration of existing simulation, analysis and visualization components into integrated workflows. DataStorm provides a flow engine, DataStorm-FE , for coordinating data and decision flows among multiple actors (each representing a model, analytic operation, or a decision criterion) and enables ensemble planning and optimization across cloud resources. DataStorm provides native support for simulation ensemble creation through parameter space sampling to decide which simulations to run, as well as distributed instantiation and parallel execution of simulation instances on cluster resources. Recognizing that simulation ensembles are inherently sparse relative to the potential parameter space, we also present a density-boosting partition-stitch sampling scheme to increase the effective density of the simulation ensemble through a sub-space partitioning scheme, complemented with an efficient stitching mechanism that leverages partial and imperfect knowledge from partial dynamical systems to effectively obtain a global view of the complex urban process being simulated.