Montana State University System

Abstract The generally accepted 2% ratio of transmitter weight to body weight constrains or precludes telemetry studies examining the timing and location of spawning of small adult westslope cutthroat trout Oncorhynchus clarkii lewisi in headwater streams. We empirically determined effects of surgically implanted dummy transmitters ranging in weight from 1 to 5 g on the swimming stamina and growth of small (81.3–206.9 g) adult westslope cutthroat trout in the laboratory to establish acceptable transmitter weights for field studies on this species. Mean growth rates and swimming stamina were not significantly different among treatments, including controls. No precipitous decline or threshold beyond which performance deteriorated markedly was observed. Data collected using telemetered westslope cutthroat trout implanted with transmitters less than about 4% of body weight should therefore approximate information about untelemetered individuals without significant bias. However, we also detected subtle effects on growth within this transmitter weight range related to individual transmitter–body weight ratios (0.5–5.3% initially), and there were indications that swimming stamina was affected similarly. Each 1% increase in transmitter– body weight ratio elicited an 11.6% decrease in growth and a possible 5.6% decrease in swimming stamina at 6 weeks postimplantation. Therefore, transmitter selection should weigh the costs of increased transmitter weight on fish performance against the benefits of longer transmission durations. In the case in which transmitter weights approaching 4% of body weight are necessary to complete a study and slight decreases in performance are not expected to affect findings materially, such weights may be acceptable. In other cases, researchers should choose the lightest possible transmitters that allow study goals to be achieved and not automatically select transmitters weighing 4% of body weight.

Purpose Food waste represents a major sustainability challenge with environmental, economic, social and health implications. Institutions of higher education contribute to generating food waste while serving as models in championing sustainability solutions. An experiential learning project was implemented as part of two university courses in a buffet-style university dining hall with the objective to reduce food waste while building student capacity to contribute to transformational food system change. Design/methodology/approach Partnerships were developed with university dining services. Students were trained to conduct a needs assessment in a university dining hall through food waste measurements. Students were facilitated through the process of applying baseline data on food waste to design, implement and evaluate a multi-component food waste intervention that consisted of offering reduced portion sizes, use of smaller serving utensils and educational messaging. Participant reflections were elicited to evaluate the effectiveness of the experiential learning experience. Findings The food waste intervention led to a 17 per cent reduction in total food waste, with a large portion of waste attributed to post-consumer plate waste. While the reduction in food waste was not statistically significant, it highlights the potential for food service operations to address food waste through reduction techniques while providing students an experiential opportunity that meets multiple learning objectives including systems thinking, collaboration and motivation for leading change in the food system. Originality/value This study highlights the opportunity of building student capacity to address sustainability challenges through an experiential learning model for reducing food waste in an institutional setting that other educators can adapt.

We read with interest the work by Pascal et al published recently in Gut .1 Here, they report the volatile microbial signatures of patients with Crohn’s disease (CD), a quality that greatly hinders our ability to classify healthy from affected subjects using 16S rRNA profiles from stool. Nonetheless, their work overcame these and other complications,2 producing a decision tree that classifies subjects with CD, UC, irritable bowel syndrome and anorexia. Although the authors note that both subtypes of IBD, particularly CD, have increased microbial community instability, this information is not used as a feature to improve classifier accuracy. Could microbiome instability become actionable by creating a new classifier that benefits from repeated measurements? If so, how many samples per individual are needed to assess instability? We collected daily stool samples for up to 6 weeks from 19 CD subjects and 12 controls (see the analysis notebook for cohort description, methods and data, https://github.com/knightlab-analyses/longitudinal-ibd) over two separate periods of 2 or 4 weeks spread over 2 and 5 months, for a total of 960 samples. We believe that this is the most densely sampled longitudinal study …

A technique for remote query monitoring of environmental parameters such as pressure, humidity, complex permittivity, temperature, strain, and gases such as carbon dioxide, oxygen, and ammonia is presented. Resonant peak passive telemetry is used for wireless remote monitoring. The resonance frequency of an inductor-capacitor sensor circuit changes with the surrounding environmental parameter, and a detector circuitry, which employs a loop antenna, is used to remotely identify the resonance frequency. Mutual coupling between the antenna and the sensor inductor enables wireless monitoring. Various detection techniques available for monitoring the sensor resonance frequency are examined, and a new method is presented for automated and continuous wireless detection of sensor resonance frequency. Results are presented for different sensor resonance frequencies using various sensor capacitance values. The designed system can effectively detect sensor resonance frequency variation in the range of 20 kHz-10 MHz with the highest achievable resolution of 0.01 MHz. Sensor resonance frequency changes that occur faster than 1 s cannot be detected. The automated continuous wireless remote sensor platform design provides significant advantage over past systems, and the entire design is simple, easy to use, and widely applicable for in vivo, in vitro, and in situ monitoring.

Internet of Vehicles (IoV) is regarded as an emerging paradigm for connected vehicles to exchange their information with other vehicles using vehicle-to-vehicle (V2V) communications by forming a vehicular ad hoc networks (VANETs), with roadside units using vehicle-to-roadside (V2R) communications. IoV offers several benefits such as road safety, traffic efficiency, and infotainment by forwarding up-to-date traffic information about upcoming traffic. For instance, IoV is regarded as a technology that could help reduce the number of deaths caused by road accidents, and reduce fuel costs and travel time on the road. Vehicles could rapidly learn about the road condition and promptly respond and notify drivers for making informed decisions. However, malicious users in IoV may mislead the whole communications and create chaos on the road. Data falsification attack is one of the main security issues in IoV where vehicles rely on information received from other peers/vehicles. In this paper, we present data falsification attack detection using hashes for enhancing network security and performance by adapting contention window size to forward accurate information to the neighboring vehicles in a timely manner (to improve throughput while reducing end-to-end delay). We also present clustering approach to reduce travel time in case of traffic congestion. Performance of the proposed approach is evaluated using numerical results obtained from simulations. We found that the proposed adaptive approach prevents IoV from data falsification attacks and provides higher throughput with lower delay.

This paper presents combined power system identification and controller design methods to dampen low-frequency oscillations in multimachine power systems. An iterative closed-loop identification method is used to find a linear model for the power system. Linear quadratic Gaussian controller design with loop transfer recovery (LQG/LTR), based on a generalized technique for the nonminimum phase (NMP) power system model, is used to design the controllers. Simulation results are presented to demonstrate the robustness of these controllers based on closed-loop identified plant models and the amount of loop transfer recovery that is possible for NMP plant models.

Testing scientific software is a difficult task due to their inherent complexity and the lack of test oracles. In addition, these software systems are usually developed by end-user developers who are not normally trained as professional software developers nor testers. These factors often lead to inadequate testing. Metamorphic testing (MT) is a simple yet effective testing technique for testing such applications. Even though MT is a wellknown technique in the software testing community, it is not very well utilized by the scientific software developers. The objective of this paper is to present MT as an effective technique for testing scientific software. To this end, we discuss why MT is an appropriate testing technique for scientists and engineers who are not primarily trained as software developers. Specifically, how it can be used to conduct systematic and effective testing on programs that do not have test oracles without requiring additional testing tools.

Climate change and other global change drivers threaten plant diversity in mountains worldwide. A widely documented response to such environmental modifications is for plant species to change their elevational ranges. Range shifts are often idiosyncratic and difficult to generalize, partly due to variation in sampling methods. There is thus a need for a standardized monitoring strategy that can be applied across mountain regions to assess distribution changes and community turnover of native and non-native plant species over space and time. Here, we present a conceptually intuitive and standardized protocol developed by the Mountain Invasion Research Network (MIREN) to systematically quantify global patterns of native and non-native species distributions along elevation gradients and shifts arising from interactive effects of climate change and human disturbance. Usually repeated every five years, surveys consist of 20 sample sites located at equal elevation increments along three replicate roads per sampling region. At each site, three plots extend from the side of a mountain road into surrounding natural vegetation. The protocol has been successfully used in 18 regions worldwide from 2007 to present. Analyses of one point in time already generated some salient results, and revealed region-specific elevational patterns of native plant species richness, but a globally consistent elevational decline in non-native species richness. Non-native plants were also more abundant directly adjacent to road edges, suggesting that disturbed roadsides serve as a vector for invasions into mountains. From the upcoming analyses of time series even more exciting results especially about range shifts can be expected. Implementing the protocol in more mountain regions globally would help to generate a more complete picture of how global change alters species distributions. This would inform conservation policy in mountain ecosystems, where some conservation policies remain poorly implemented.

A microwave spectrum analyzer capable of capturing multi-GHz spectra with sub-MHz resolution and unity probability of intercept based on optical spectral hole burning materials is proposed and initial demonstrations presented.

Nodes in a cognitive radio mesh network may select from a set of available channels to use provided they do not interfere with primary users. This ability can improve overall network performance but introduces the question of how best to use these channels. This paper addresses the following specific problem: given a routing path P, choose which channels each link in P should use and their transmission schedule so as to maximize the end-to-end data flow rate (throughput) supported by the entire path. This problem is relevant to applications such as streaming video or data where a connection may be long lasting and require a high constant throughput. The problem is hard to due the presence of both intraflow and inter-flow interference. We have developed a new constant-factor approximation algorithm for this problem. If certain natural conditions on the path are met, the performance guarantee is ¼ of optimal. It has been shown by simulation results that the end-to-end throughput given by the proposed algorithm is often within 90% or better of optimal.

Objective Intra-articular drug delivery holds great promise for the treatment of joint diseases such as osteoarthritis. The objective of this study was to evaluate the TAT peptide transduction domain (TAT-PTD) as a potential intra-articular drug delivery technology for synovial joints. Design Experiments examined the ability of TAT conjugates to associate with primary chondrocytes and alter cellular function both in vitro and in vivo. Further experiments examined the ability of the TAT-PTD to bind to human osteoarthritic cartilage. Results The results show that the TAT-PTD associates with chondrocytes, is capable of delivering siRNA for chondrocyte gene knockdown, and that the recombinant enzyme TAT-Cre is capable of inducing in vivo genetic recombination within the knee joint in a reporter mouse model. Last, binding studies show that osteoarthritic cartilage preferentially uptakes the TAT-PTD from solution. Conclusions The results suggest that the TAT-PTD is a promising delivery strategy for intra-articular therapeutics.

While the use of writing exercises in gateway STEM courses that focus on solving numeric problems is not widespread, there is evidence that students could benefit from the addition of such exercises [1].Writing exercises may be effective in both uncovering student misconceptions that are not necessarily apparent with typical computation problems, and as tools to foster conceptual change and metacognitive skill.In this paper, pilot studies of the use of two Natural Language Processing (NLP) techniques to identify common misconceptions in the writing of students in a course on electric circuit analysis are described.Performance on the writing exercise in question has been shown to correlate with a student's performance in the course [2].This is of particular interest as the writing exercise has been administered during the fifth class period, sufficiently early to direct additional resources to the success of students appearing to be at-risk for failing the course.Realizing an automated software solution to analyze the responses to this exercise would remove burden on instructor time and open the door to immediate and personalized feedback to the student.The first pilot study was run to determine how successful a simplistic rule-based approach would be in identifying the most common misconceptions found in a writing exercise requiring a student to speculate on the change in the power in the elements of a resistive circuit with a change to a single resistor value.An open-source NLP rule-based matching engine within spaCy [3] was used.The corpus consisted of one hundred and eighty-five unique responses to the question.Precision, recall, were used to assess the effectiveness of the rule-based NLP pipeline in comparison to that of a subject matter expert in identifying responses exemplifying seven misconceptions.Should this NLP pipeline be used in a system in which feedback is to be given to the student, a Directed Line of Reasoning (DLR) approach [5] would be beneficial in cases in which identification of a given misconception is in doubt.Considering this pilot study employed an extremely simplistic purely lexical-level rule-based classifier, the results are very promising and suggest the planned approach of developing a highly accurate, advanced rule-based classifier encompassing lexical/syntax/semantic driven rules is viable.As a compliment to the rule-based approach, this paper also describes a pilot study of the use of BERT (Bidirectional Encoder Representations from Transformers) [6], a machine learning approach that has shown tremendous promise in short-answer grading [7].

White-nose syndrome (WNS) has decimated hibernating bat populations across eastern and central North America for over a decade. Disease severity is driven by the interaction between bat characteristics, the cold-loving fungal agent, and the hibernation environment. While we further improve hibernation energetics models, we have yet to examine how spatial heterogeneity in host traits is linked to survival in this disease system. Here we develop predictive spatial models of body mass for the little brown myotis (Myotis lucifugus) and reassess previous definitions of the duration of hibernation of this species. Using data from published literature, public databases, local experts, and our own fieldwork, we fit a series of generalized linear models with hypothesized abiotic drivers to create distribution-wide predictions of pre-hibernation body fat and hibernation duration. Our results provide improved estimations of hibernation duration and identify a scaling relationship between body mass and body fat; this relationship allows for the first continuous estimates of pre-hibernation body mass and fat across the species’ distribution. We used these results to inform a hibernation energetic model to create spatially-varying fat use estimates for M. lucifugus. These results predict that WNS mortality of newly and soon-to-be infected M. lucifugus populations in western North America may be comparable to the substantial die-off observed in eastern and central populations.

Purpose This paper aims to explore the important role boundaries play in back-office framing of environmental engagement. This is of particular interest because it is not clear how organizations in an industry without standardized environmental reporting navigate their boundaries behind the scenes and why they engage with the environment the way they do. This element of their environmental identity offers important insights into the emergence of sustainability reporting. Design/methodology/approach Guided by Miles and Ringham (2019) the authors conduct an ethnography of the Montana ski industry. The ethnography includes extensive on-site observations at nine Montana ski areas and interviews with 16 ski area executives, two regulators and a land development executive. Findings The authors find three key boundaries – accountability structure, degree of regulatory burden and impact measurement approach – that shape the back-office economic and environmental framing of ski executives (Goffman, 1959, 1974). From these back-office frames the authors identify four front-office cultural performances – community ecosystem, quantitative ownership, approval seeking and advocacy platform – that represent the environmental engagement strategies at these resorts. Practical implications Understanding the relationships between boundaries and environmental engagement is an important step in developing appropriate industry-wide environmental accountability and sustainability expectations. The study’s findings extend to other industries that are both highly dependent on the environment and are in the early stages of developing environmental reporting standards. Originality/value Ski resorts operate in an industry that is impacted by changes in the natural environment. The authors chronicle the process by which boundaries lead to framing which leads to environmental engagement in this weather-dependent industry. The authors explain the process of environmental identity building, the result of which both precedes environmental reporting and puts such reporting into context. In this sense, the authors show how boundaries are set and maintained in the ski resort industry, and how fundamental these boundaries are to the development of individual companies' environmental engagement strategies.

Although customer engagement has become a prominent issue in the tourism industry, it has not been defined nor uniformly measured due to a variety of factors. While meeting planners are important customers to DMOs, their engagement with DMOs has rarely been studied. This study tests a model of meeting planners’ engagement in DMOs. The two-step data analysis using 305 meeting planners’ responses supported the proposed hypotheses and confirmed the moderating effect of reputation on meeting planners’ engagement. This study will help DMOs to achieve effective marketing strategies in the event destination industry.

There is a need for comprehensive solutions to address the challenges of spatio-temporal data quality assessment. Emphasis is often placed on the quality assessment of individual observations from sensors but not on the sensors themselves nor upon site metadata such as location and timestamps. The focus of this paper is on the development and evaluation of a representative and comprehensive, interpolation-based methodology for assessment of spatio-temporal data quality. We call our method the SMART method, short for Simple Mappings for the Approximation and Regression of Time series. When applied to a real-world, meteorological data set, we show that our method outperforms standard interpolators and we identify numerous problematic sites that otherwise would not have been flagged as bad. We further identify sites for which metadata is incorrect. We believe that there are many problems with real data sets like these and, in the absence of an approach like ours, these problems have largely gone unidentified. Our results bring into question the validity of provider-based quality control indicators. In addition to providing a comprehensive solution, our approach is novel for the simple but effective way that it accounts for spatial and temporal variation.

The latest performance results for a novel radio frequency (RF) sensor technology are presented for continuous monitoring of RF spectrum with large instantaneous bandwidth. The photonic approach relies on a spatial spectral (S2) holographic light absorbing crystal. The S2 spectrum analyzer as presently demonstrated operates on instantaneous bandwidths (IBW) over 10 GHz, in this case from 0.5–10.5 GHz, and is being extended to IBW of 40 GHz. The device presently outputs 100,000,000 frequency energy/time measurements per second, each measurement being the result of continuous monitoring spectrum analysis with energy amplitude assigned on a frequency and time grid from a continuous digital data stream at 500 MegaBytes per second. For these measurements, we have observed 61 dBc spur free dynamic range (SFDR) over the full IBW of 10 GHz. With low-noise RF gain we have observed RF sensitivity levels of −110 dBm for continuous RF tones with >59 dB two-tone SFDR. The resolution bandwidth (RBW) for the signals over a 10 GHz IBW can be as low as 100 kHz (100,000 frequencies per frame) at a 1 kHz frame rate (FR), and then increases with increased FRs that can vary from 1–200 kHz. All parameters of IBW, RBW and FR are fully reconfigurable on-the-fly for adaptive spectrum analysis, and the S2 device is also upgradeable to direction finding and other signal processing functions.

Abstract Relative to their limited areal extent, riparian ecosystems are disproportionately important in regulating inorganic solute export from agricultural landscapes. We investigated spatial patterns of solute concentrations in surface and ground waters of stream corridors to infer the dominant hydrologic transport and biogeochemical pathways that influence riparian nitrate and sulfate processing from uplands to streams. We selected three reaches of stream corridors draining an agricultural landscape that vary in hydrologic connection with upland aquifers. Non‐irrigated crop production dominates land use in the study area and influences the quality of upland groundwater draining to the stream corridors. We interpret patterns in solute concentrations of riparian groundwater and stream water relative to upland groundwater to infer the influences of biogeochemical processing and hydrologic connectivity. Excess nitrate from cultivated soils is evident in upland groundwater concentrations that consistently exceed the U.S. Environmental Protection Agency public drinking water standard. Nitrate and oxygen concentrations in riparian groundwaters were consistently lower than in terrace groundwater and adjacent stream waters, suggesting rapid consumption of oxygen and influence of anaerobic metabolic reduction processes in subsurface flow. Sulfate concentrations in streams were higher than in terrace groundwater, likely due to weathering of shale‐derived substrate in riparian aquifers. The degree of solute mitigation or augmentation by riparian biogeochemical processes depended on the geomorphic context that controlled the fraction of upland water passing through the riparian substrate. Observed net nitrate losses with net sulfate gains from uplands to stream channels reflect flow paths through a complex distribution of redox conditions throughout the riparian areas, emphasizing the importance of considering riparian area heterogeneity in predicting solute export in streams. This research contributes to understanding how stream corridor substrate and geomorphic context controls the biogeochemical and hydrologic processes influencing the quality of water exported from agricultural landscapes.

The use of phase-type distributions is an established method for extending the representational power of continuous time Bayesian networks beyond exponentially-distributed state transitions. In this paper, we propose a method for learning phase-type distributions from known parametric distributions. We find that by using particle swarm optimization to minimize a modified KL-divergence value, we are able to efficiently obtain good phase-type approximations for a variety of parametric distributions. Our experiments show that particle swarm optimization outperforms genetic algorithms and hill climbing with simulated annealing. In addition, we investigate the trade-off between accuracy and complexity with respect to the number of phases in the phase-type distribution. Finally, we propose and evaluate an extension that uses informed starting locations during optimization, which we found to improve convergence rates when compared to random initialization.

Flow network decomposition is a natural model for problems where we are given a flow network arising from superimposing a set of weighted paths and would like to recover the underlying data, i.e., decompose the flow into the original paths and their weights. Thus, variations on flow decomposition are often used as subroutines in multiassembly problems such as RNA transcript assembly. In practice, we frequently have access to information beyond flow values in the form of subpaths, and many tools incorporate these heuristically. But despite acknowledging their utility in practice, previous work has not formally addressed the effect of subpath constraints on the accuracy of flow network decomposition approaches. We formalize the flow decomposition with subpath constraints problem, give the first algorithms for it, and study its usefulness for recovering ground truth decompositions. For finding a minimum decomposition, we propose both a heuristic and an FPT algorithm. Experiments on RNA transcript datasets show that for instances with larger solution path sets, the addition of subpath constraints finds 13% more ground truth solutions when minimal decompositions are found exactly, and 30% more ground truth solutions when minimal decompositions are found heuristically.