The scientist/practitioner in behavior analysis: A case study.

This article is a transcription of Murray Sidman's presentation at the annual meeting of the Society for the Quantitative Analysis of Behavior in May 1998. It describes the development (from 1965 to 1975) of behavior-change programs implemented outside the animal laboratory to benefit humans before such application was established formally as an entity derived from the experimental analysis of behavior. The presentation illustrates the use of an inductive method in practice, where working with a fluid behavior stream entails making intervention decisions on the spot. Examples include fading and backward-chaining procedures in the establishment and stimulus control of novel actions. Sidman also discusses the certification of practitioners and the interaction between client and therapist and between basic and applied endeavors. The latter define what is contemporaneously described as translational intervention. It is noteworthy that Sidman's presentation was at a meeting attended by both practitioners and scientists.

The frequency and cueing mechanisms of involuntary autobiographical memories while driving.

Involuntary autobiographical memories (IAMs) have been typically studied with paper diaries, kept for a week or longer. However, such studies are unable to capture the true frequency of IAMs, nor the level of detail that would give new insights into the mechanisms of IAMs. To address this gap, a new audio-recording method was developed and tested on the first author who recorded 674 IAMs while driving a car on a 30-40-minute-long habitual route on 20 occasions. Results revealed very high frequency of IAMs (almost 34 per journey) that were reported more often in response to dynamic (one-off) than static cues. Moreover, a substantial number of memory chains and long-term priming of IAMs by previously encountered incidental stimuli were also recorded. Based on these results, a new theoretical model is proposed in which the occurrence of IAMs is determined by an interplay of factors at the time of the IAM, such as the type of ongoing activity and internal or external triggers, as well as different types of long-term priming. The results also have practical implications for studying mind-wandering and safety issues in driving and aircraft-flying, where periods of concentration are followed by monotony and less demanding tasks.

The Dielectrophoretic Alignment of Biphasic Metal Fillers for Thermal Interface Materials.

Pad-type thermal interface materials (TIMs) with composite structures are required to exhibit high thermal conductivity while maintaining conformal contact with the heat sink, which is strongly influenced by the type and content of the thermally conductive filler. This study presents that biphasic metal particles can be effectively aligned using the dielectrophoretic chaining (DEP-C) mechanism, thereby enhancing the thermal conductivity of a pad-type TIM. A eutectic gallium-indium (EGaIn) alloy liquid metal and solid copper were used as the filler materials with two different phases. The biphasic metal particle mixture of EGaIn and Cu (EGaIn-Cu) were better aligned by DEP-C than when they presented individually because fusion between the two particles increased the effective size. As expected, the thermal conductivity of the TIM composites increased when DEP-C aligned the filler. Notably, TIMs with both EGaIn-Cu fillers showed the largest increase in thermal conductivity, of up to 64.6%, and the highest thermal conductivity values after DEP-C application compared to TIMs with only the EGaIn or Cu filler. Finally, the heat dissipation performance of the TIM composite on a lit light-emitting diode is shown, where the TIM with DEP-C-aligned fillers exhibits improved performance.

The NEAT Equating Via Chaining Random Forests in the Context of Small Sample Sizes: A Machine-Learning Method.

The part of responses that is absent in the nonequivalent groups with anchor test (NEAT) design can be managed to a planned missing scenario. In the context of small sample sizes, we present a machine learning (ML)-based imputation technique called chaining random forests (CRF) to perform equating tasks within the NEAT design. Specifically, seven CRF-based imputation equating methods are proposed based on different data augmentation methods. The equating performance of the proposed methods is examined through a simulation study. Five factors are considered: (a) test length (20, 30, 40, 50), (b) sample size per test form (50 versus 100), (c) ratio of common/anchor items (0.2 versus 0.3), and (d) equivalent versus nonequivalent groups taking the two forms (no mean difference versus a mean difference of 0.5), and (e) three different types of anchors (random, easy, and hard), resulting in 96 conditions. In addition, five traditional equating methods, (1) Tucker method; (2) Levine observed score method; (3) equipercentile equating method; (4) circle-arc method; and (5) concurrent calibration based on Rasch model, were also considered, plus seven CRF-based imputation equating methods for a total of 12 methods in this study. The findings suggest that benefiting from the advantages of ML techniques, CRF-based methods that incorporate the equating result of the Tucker method, such as IMP_total_Tucker, IMP_pair_Tucker, and IMP_Tucker_cirlce methods, can yield more robust and trustable estimates for the "missingness" in an equating task and therefore result in more accurate equated scores than other counterparts in short-length tests with small samples.

Gradual Change Procedures in Behavior Analysis.

A wide variety of procedures within behavior analysis use gradual change in stimuli, response requirements, reinforcement, or some combination thereof, to effectively change behavior. Such procedures include shaping, thinning, fading, and chaining. Collectively, gradual change procedures represent a conceptually systematic technology of behavior change with wide-ranging empirical support across diverse settings and contexts. However, navigating the gradual change literature can be challenging. Similar terms are used to describe functionally distinct procedures (e.g., stimulus fading, delay fading, demand fading), and distinct terms are used to describe functionally similar procedures (e.g., leaning, demand fading). I propose a taxonomy in which gradual change procedures are categorized according to the functional component of the contingency on which they act. Three broad categories are proposed: Gradual Changes in Discriminative Stimuli, Response Requirement, and Reinforcement. I provide examples of research in each category, across basic and applied settings, including terminology used by the author(s) to describe each procedure. Finally, I discuss benefits of this framework for consumers of the literature.

Accelerating Minimap2 for Accurate Long Read Alignment on GPUs.

Long read sequencing technology is becoming increasingly popular for Precision Medicine applications like Whole Genome Sequencing (WGS) and microbial abundance estimation. Minimap2 is the state-of-the-art aligner and mapper used by the leading long read sequencing technologies, today. However, Minimap2 on CPUs is very slow for long noisy reads. ~60-70% of the run-time on a CPU comes from the highly sequential chaining step in Minimap2. On the other hand, most Point-of-Care computational workflows in long read sequencing use Graphics Processing Units (GPUs). We present minimap2-accelerated (mm2-ax), a heterogeneous design for sequence mapping and alignment where minimap2's compute intensive chaining step is sped up on the GPU and demonstrate its time and cost benefits. We extract better intra-read parallelism from chaining without losing mapping accuracy by forward transforming Minimap2's chaining algorithm. Moreover, we better utilize the high memory available on modern cloud instances apart from better workload balancing, data locality and minimal branch divergence on the GPU. We show mm2-ax on an NVIDIA A100 GPU improves the chaining step with 5.41 - 2.57X speedup and 4.07 - 1.93X speedup : costup over the fastest version of Minimap2, mm2-fast, benchmarked on a Google Cloud Platform instance of 30 SIMD cores.

Task-Similarity-Based VNF Aggregation for Air-Ground Integrated Networks.

In a harsh environment, function aggregation of air-ground integrated network service function chaining (SFC) deployment can easily cause network load imbalance, which affects the network security and reliability. In this study, a task-similarity-based virtual network function (VNF) aggregation scheme was proposed. It considered air-ground network resource consumption and load balance before SFC mapping. A model for selecting VNFs to be aggregated based on task similarity was built. The tasks were classified based on their similarity. Furthermore, the VNFs to be aggregated were selected within the class under the constraints of the underlying physical resources. Load balancing was achieved by adjusting the similarity threshold. Moreover, an SFC mapping selection scheme based on network resource awareness was used to obtain the most suitable physical nodes for single-chain and multi-chain mapping according to various attributes of physical network nodes. The simulation results indicated that the proposed scheme with a better load balance design outperformed existing works on VNF aggregation. We also demonstrated that the task-similarity-based scheme was resource-consumption efficient and effective.

Comparison of Menstrual Care Skills Training Programs Provided to Girls with Intellectual Disabilities By a Healthcare Professional and Caregivers: A Randomized Controlled Trial.

Objective: To compare the effectiveness of the training provided by the professional and caregivers in developing menstrual care skills of girls with intellectual disabilities. Methods: A randomized controlled trial was conducted with 22 moderate girls with intellectual disabilities randomly assigned to G-I and G-II. Both groups received training program using chaining technique and modeling on a doll and on itself for 8 weeks. Assessments were made on the 1, 15, 30 and 60 days. Results: The girls' median age was 15 (14-18) years. Scores skill of menstrual care were significantly increased in both groups (p < .05; effect size = 0.61). G-I's a pad placement and changing dirty pad skill scores were higher than G-II (p = 0.05; effect size = 0.44). Conclusion: The training process performing by caregivers can be affected by the complexity of the menstrual self-care skills. School-parents cooperation-based reproductive health programs may be facilitated the teaching of complex menstrual care skills of girls with intellectual disabilities.

Thinking about order: a review of common processing of magnitude and learned orders in animals.

Rich behavioral and neurobiological evidence suggests cognitive and neural overlap in how quantitatively comparable dimensions such as quantity, time, and space are processed in humans and animals. While magnitude domains such as physical magnitude, time, and space represent information that can be quantitatively compared (4 "is half of" 8), they also represent information that can be organized ordinally (1→2→3→4). Recent evidence suggests that the common representations seen across physical magnitude, time, and space domains in humans may be due to their common ordinal features rather than their common quantitative features, as these common representations appear to extend beyond magnitude domains to include learned orders. In this review, we bring together separate lines of research on multiple ordinal domains including magnitude-based and learned orders in animals to explore the extent to which there is support for a common cognitive process underlying ordinal processing. Animals show similarities in performance patterns across natural quantitatively comparable ordered domains (physical magnitude, time, space, dominance) and learned orders (acquired through transitive inference or simultaneous chaining). Additionally, they show transfer and interference across tasks within and between ordinal domains that support the theory of a common ordinal representation across domains. This review provides some support for the development of a unified theory of ordinality and suggests areas for future research to better characterize the extent to which there are commonalities in cognitive processing of ordinal information generally.

Impaired executive functioning mediates the association between aging and deterministic sequence learning.

Sensitivity to the fixed ordering of actions and events, or deterministic sequence learning, is an important skill throughout adulthood. Yet, it remains unclear whether age deficits in sequencing exist, and we lack a firm understanding of which factors might contribute to age-related impairments when they arise. Though debated, executive functioning, governed by the frontal lobe, may underlie age-related sequence learning deficits in older adults. The present study asked if age predicts errors in deterministic sequence learning across the older adult lifespan (ages 55-89), and whether executive functioning accounts for any age-related declines. Healthy older adults completed a comprehensive measure of frontal-based executive abilities as well as a deterministic sequence learning task that required the step-by-step acquisition of associations through trial-and-error feedback. Among those who met a performance-based criterion, increasing age was positively correlated with higher sequencing errors; however, this relationship was no longer significant after controlling for executive functioning. Moreover, frontal-based executive abilities mediated the relationship between age and sequence learning performance. These findings suggest that executive or frontal functioning may underlie age deficits in learning judgment-based, deterministic serial operations.

Algorithms for Colinear Chaining with Overlaps and Gap Costs.

Colinear chaining has proven to be a powerful heuristic for finding near-optimal alignments of long DNA sequences (e.g., long reads or a genome assembly) to a reference. It is used as an intermediate step in several alignment tools that employ a seed-chain-extend strategy. Despite this popularity, efficient subquadratic time algorithms for the general case where chains support anchor overlaps and gap costs are not currently known. We present algorithms to solve the colinear chaining problem with anchor overlaps and gap costs in Õ(n) time, where n denotes the count of anchors. The degree of the polylogarithmic factor depends on the type of anchors used (e.g., fixed-length anchors) and the type of precedence an optimal anchor chain is required to satisfy. We also establish the first theoretical connection between colinear chaining cost and edit distance. Specifically, we prove that for a fixed set of anchors under a carefully designed chaining cost function, the optimal "anchored" edit distance equals the optimal colinear chaining cost. The anchored edit distance for two sequences and a set of anchors is only a slight generalization of the standard edit distance. It adds an additional cost of one to an alignment of two matching symbols that are not supported by any anchor. Finally, we demonstrate experimentally that optimal colinear chaining cost under the proposed cost function can be computed orders of magnitude faster than edit distance, and achieves correlation coefficient >0.9 with edit distance for closely as well as distantly related sequences.

Transfer of Test-Enhanced Learning.

Memory for paired-associate words is facilitated by interim testing relative to restudy. According to the mediator effectiveness hypothesis, the benefit of retrieval practice is a consequence of the activation of a mediator word linking the cue and target. Evidence for the activation of cue-related mediators stems from the finding that mediators are more effective at prompting recall of target words than are words not associated with the original cue, a pattern that is larger following testing than restudy. The benefit of testing for the unstudied cues at the final test is referred to as transfer of test-enhanced learning. One goal of the current study was to examine whether the activation of mediators leads to the recall of targets indirectly via the original cues in a process known as backward chaining. We indexed backward chaining with the probability of incorrectly recalling a trial-specific original cue in place of a target. The second goal was to explore whether testing would yield a transfer effect for cues associated with target words. In four experiments, following an initial study of weakly related word pairs (e.g., Mother-CHILD), participants either restudied the pairs or attempted to recall the target given the original cue (e.g., Mother). On a final cued-recall test, participants were presented with unstudied cues that were related to either the original cue (semantic mediators, e.g., Father) or the target (target-related cues, e.g., Baby). The type of new cue presented on the final test was varied either between subjects (Experiment 1) or mixed within a list (Experiments 2, 3, and 4). Mixing mediators and target-related cues reduced the transfer of test-enhanced learning and increased the likelihood of recalling the original cues when shown a mediator. These results challenge the assumptions of the mediator effectiveness hypothesis.

Carbon Footprint of Alternative Grocery Shopping and Transportation Options from Retail Distribution Centers to Customer.

The COVID-19 pandemic has accelerated the growth of e-commerce and automated warehouses, vehicles, and robots and has created new options for grocery supply chains. We report and compare the greenhouse gas (GHG) emissions for a 36-item grocery basket transported along 72 unique paths from a centralized warehouse to the customer, including impacts of micro-fulfillment centers, refrigeration, vehicle automation, and last-mile transportation. Our base case is in-store shopping with last-mile transportation using an internal combustion engine (ICE) SUV (6.0 kg CO2e). The results indicate that emissions reductions could be achieved by e-commerce with micro-fulfillment centers (16-54%), customer vehicle electrification (18-42%), or grocery delivery (22-65%) compared to the base case. In-store shopping with an ICE pick-up truck has the highest emissions of all paths investigated (6.9 kg CO2e) while delivery using a sidewalk automated robot has the least (1.0 kg CO2e). Shopping frequency is an important factor for households to consider, e.g. halving shopping frequency can reduce GHG emissions by 44%. Trip chaining also offers an opportunity to reduce emissions with approximately 50% savings compared to the base case. Opportunities for grocers and households to reduce grocery supply chain carbon footprints are identified and discussed.

Connecting animal trainers and behavior analysts through loopy training.

The communities of behavior analysts and animal trainers remain relatively disconnected despite potentially beneficial links between behavioral principles and the practices of animal training. Describing existing links between research by behavior analysts and practices used by animal trainers may foster connections. In this paper, we describe an approach used by many clicker trainers, referred to as loopy training. Loopy training is a teaching process built around the concept of movement cycles. Interactions between the animal learner and the handler are refined into predictable, cyclical patterns that can be expanded into complex sequences. These sequences include cues, target responses, conditioned reinforcers, and consummatory responses. We link the foundations of loopy training to existing work in the experimental analysis of behavior, compare loopy training to other shaping approaches, and describe areas for future research. We conclude with a series of recommendations for further developing connections between behavior analysts and animal trainers, using loopy training as the foundation for our suggestions.

Quantum Bridge Analytics II: QUBO-Plus, network optimization and combinatorial chaining for asset exchange.

Quantum Bridge Analytics relates to methods and systems for hybrid classical-quantum computing, and is devoted to developing tools for bridging classical and quantum computing to gain the benefits of their alliance in the present and enable enhanced practical application of quantum computing in the future. This is the second of a two-part tutorial that surveys key elements of Quantum Bridge Analytics and its applications. Part I focused on the Quadratic Unconstrained Binary Optimization (QUBO) model which is presently the most widely applied optimization model in the quantum computing area, and which unifies a rich variety of combinatorial optimization problems. Part II (the present paper) introduces the domain of QUBO-Plus models that enables a larger range of problems to be handled effectively. After illustrating the scope of these QUBO-Plus models with examples, we give special attention to an important instance of these models called the Asset Exchange Problem (AEP). Solutions to the AEP enable market players to identify exchanges of assets that benefit all participants. Such exchanges are generated by a combination of two optimization technologies for this class of QUBO-Plus models, one grounded in network optimization and one based on a new metaheuristic optimization approach called combinatorial chaining. This combination opens the door to expanding the links to quantum computing applications established by QUBO models through the Quantum Bridge Analytics perspective. We show how the modeling and solution capability for the AEP instance of QUBO-Plus models provides a framework for solving a broad range of problems arising in financial, industrial, scientific, and social settings.

Multiskilled personnel assignment problem under uncertain demand: A benchmarking analysis.

The personnel assignment problem in different service industries aims to minimize the staff surplus/shortage costs. However, uncertainty in the staff demand challenges the accomplishment of that objective. This research studies the personnel assignment problem considering uncertain demand and multiskilled workforce configured through a 2-chaining strategy. We develop a two-stage stochastic optimization (TSSO) approach to calculate the multiskilling requirements that minimize the training costs and the expected costs of staff surplus/shortage. Later, we evaluate and compare the performance of the TSSO approach solutions with the solutions of two alternative optimization approaches under uncertainty - robust optimization (RO) and closed-form equation (CF). These two alternative approaches were published in Henao et al. [1] and Henao et al. [2], respectively. In addition, we compare the performance of the TSSO approach solutions with the solution of the deterministic (DT) approach and the solutions of myopic multiskilling approaches. To make performance comparisons between the different approaches, we used both real and simulated data derived from a retail store operating in Chile. The results show that, for different demand variability levels, TSSO, RO, and CF always belong to the set of approaches with the lowest average total cost. That is, in this group, there are no statistical differences from one approach to another, so these approaches are the most cost-effective. We also provide insights to retail decision-makers for addressing two key aspects. First, the methodology allows to address two fundamental multiskilling issues: how much multiskilling to add and how it should be added. Second, it is provided understanding on how to select the most suitable approach according to the balance between the conservatism and the reliability associated with the solutions delivered by each approach. Finally, we identify some methodological challenges for future research, such as the evaluation of k-chaining strategies with k≥2.

A two-stage stochastic optimization model for the retail multiskilled personnel scheduling problem: a k-chaining policy with k≥2.

Considering an uncertain demand, this study evaluates the potential benefits of using a multiskilled workforce through a k-chaining policy with k≥2. For the service sector and, particularly for the retail industry, we initially propose a deterministic mixed-integer linear programming model that determines how many employees should be multiskilled, in which and how many departments they should be trained, and how their weekly working hours will be assigned. Then, the deterministic model is reformulated using a two-stage stochastic optimization (TSSO) model to explicitly incorporate the uncertain personnel demand. The methodology is tested for a case study using real and simulated data derived from a Chilean retail store. We also compare the TSSO approach solutions with the myopic approaches' solutions (i.e., zero and total multiskilling). The case study is oriented to answer two key questions: how much multiskilling to add and how to add it. Results show that TSSO approach solutions always report maximum reliability for all levels of demand variability considered. It was also observed that, for high levels of demand variability, a k-chaining policy with k≥2 is more cost-effective than a 2-chaining policy. Finally, to evaluate the conservatism level in the solutions reported by the TSSO approach, two truncation types in the probability density function (pdf) associated with the personnel demand were considered. Results show that, if the pdf is only truncated at zero (more conservative truncation) the levels of required multiskilling are higher than when the pdf is truncated at 5th and 95th percentiles (less conservative truncation).

Understanding the effects of prompting immediately after problem behavior occurs during functional communication training.

When reported, the methods for prompting functional communication responses (FCRs) following problem behavior during functional communication training (FCT) vary. Some researchers have prompted the FCR immediately following problem behavior but doing so may inadvertently strengthen problem behavior as the first link in an undesirable response chain. This study investigated the effects of prompting FCRs following problem behavior during FCT with 4 children who exhibited severe problem behavior. Problem behavior remained low and FCR rates were near optimal when prompts were delivered immediately following problem behavior for 2 participants. Delaying prompts following problem behavior was instrumental for FCR acquisition for 1 participant but led to escalation of problem behavior for a 2nd participant. The conditions under which immediate prompts following problem behavior may improve or worsen FCT are discussed.

Inter-droplet force between magnetically polarizable Pickering oil-in-water nanoemulsions stabilized with γ-Al2O3 nanoparticles: Role of electrostatic and electric dipolar interactions.

HYPOTHESIS: The presence of nanoparticles at oil-water interface influences the interaction forces between Pickering emulsions. When charged nanoparticles are at the oil-water interface of an electrostatically stabilized emulsion, in addition to the screened Coulombic interaction, electric dipolar force also influences the total inter-droplet force profiles. An in-depth understanding of the effects of such electric dipolar forces is essential for designing colloidally stable Pickering nanoemulsions for various applications. EXPERIMENTS: Inter-droplet forces between γ-Al2O3 nanoparticle stabilized oil-in-water nanoemulsion, containing superparamagnetic nanoparticles (magnetically polarizable) in the oil phase, are measured using the magnetic-chaining technique at different pH and salt concentrations. The role of mono-, di- and tri-valent salts on the inter-droplet force profiles are assessed. FINDINGS: Force measurement studies reveal a lowering of inter-droplet spacing, within the linear chains, for higher salt concentrations due to an increased screening. Strong interfacial attachment of the charged nanoparticles results in the formation of an asymmetric charge cloud leading to an electric dipolar interaction. Incorporating the contributions of electric dipolar and screened Coulombic interactions, the theoretically estimated total repulsive force magnitudes are in good agreement with the experimental data. The obtained results offer better insights into the nature of colloidal force between charged particle stabilized nanoemulsions.

Dynamic Service Function Chaining Orchestration in a Multi-Domain: A Heuristic Approach Based on SRv6.

With the emergence of virtualization technology, Network Function Virtualization (NFV) and Software Defined Networking (SDN) make the network function abstract from the hardware and allow it to be run on virtual machines. These technologies can help to provide more efficient services to users by Service Function Chaining (SFC). The sequence of multiple VNFs required by network operators to perform traffic steering is called SFC. Mapping and deploying SFC on the physical network can enable users to obtain customized services in time. At present, a key problem in deploying SFC is how to reduce network resource consumption and load pressure while ensuring the corresponding services for users. In this paper, we first introduce an NFV architecture for SFC deployment, and illustrate the SFC orchestration process which is based on SRv6 in multi-domain scenario. Then, we propose an effective SFC dynamic orchestration algorithm. First, we use Breadth-First Search algorithm to traverse network and find the shortest path for deploying VNFs. Next, we use the improved Ant Colony Optimization algorithm to generate the optimal deployment scheme. Finally, we conduct a series of experiments to verify the performance of our algorithm. Compared with other deployment algorithms, the results show that our solution effectively optimizes end-to-end delay, bandwidth resource consumption and load balancing.