Graduate Student Papers

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Adaptive Crop Management under Climate Uncertainty: Changing the Game for Sustainable Water Use
Myint, Soe W.; Aggarwal, Rimjhim; Zheng, Baojuan; Wentz, Elizabeth A.; Holway, Jim; Fan, Chao; Selover, Nancy J.; Wang, Chuyuan; Fischer, Heather A. (MDPI, 2021-08-23)
Water supplies are projected to become increasingly scarce, driving farmers, energy producers, and urban dwellers towards an urgent and emerging need to improve the effectiveness and the efficiency of water use. Given that agricultural water use is the largest water consumer throughout the U.S. Southwest, this study sought to answer two specific research questions: (1) How does water consumption vary by crop type on a metropolitan spatial scale? (2) What is the impact of drought on agricultural water consumption? To answer the above research questions, 92 Landsat images were acquired to generate fine-resolution daily evapotranspiration (ET) maps at 30 m spatial resolution for both dry and wet years (a total of 1095 ET maps), and major crop types were identified for the Phoenix Active Management Area. The study area has a subtropical desert climate and relies almost completely on irrigation for farming. Results suggest that there are some factors that farmers and water managers can control. During dry years, crops of all types use more water. Practices that can offset this higher water use include double or multiple cropping practice, drought tolerant crop selection, and optimizing the total farmed area. Double and multiple cropping practices result in water savings because soil moisture is retained from one planting to another. Further water savings occur when drought tolerant crop types are selected, especially in dry years. Finally, disproportionately large area coverage of high water consuming crops can be balanced and/or reduced or replaced with more water efficient crops. This study provides strong evidence that water savings can be achieved through policies that create incentives for adopting smart cropping strategies, thus providing important guidelines for sustainable agriculture management and climate adaptation to improve future food security.
AFM Image Analysis of the Adsorption of Xanthate and Dialkyl Dithiophosphate on Chalcocite
Zhang, Jinhong; Zhang, Wei (MDPI, 2022-08-13)
Atomic force microscopy (AFM) has been applied to study the adsorption morphology of various collectors, i.e., potassium ethyl xanthate (KEX) and potassium amyl xanthate (PAX) and Cytec Aerofloat 238 (sodium dibutyl dithiophosphate), on chalcocite in situ in aqueous solutions. The AFM images show that all these collectors adsorb strongly on chalcocite. Xanthate adsorbs mainly in the form of insoluble cuprous xanthate (CuX), which binds strongly with the mineral surface without being removed by flushing with ethanol alcohol. This xanthate/chalcocite adsorption mechanism is very similar to the one obtained with the xanthate/bornite system; while it is different from the one of the xanthate/chalcopyrite systems, for which oily dixanthogen is the main adsorption product on chalcopyrite surface. On the other hand, dibutyl dithiophosphate adsorbs on chalcocite in the form of hydrophobic patches, which can be removed by rinsing with ethanol alcohol. AFM images show that the adsorption of collectors increases with increasing adsorption time and collectors’ concentration. In addition, increasing the solution pH to 10 does not prevent the adsorption of xanthate and Aerofloat 238 on chalcocite and the result is in line with the fact that chalcocite floats well in a wide pH range up to 12 with xanthate and dialkyl dithiophosphate being used as collectors. The blending collectors study shows that xanthate and dialkyl dithiophosphate can co-adsorb with both insoluble cuprous xanthate and oily Cu(DTP)₂ (Cu dibutyl dithiophosphate) on a chalcocite surface. The present study helps to clarify the flotation mechanism of chalcocite in industry practice using xanthate and dialkyl dithiophosphate as collectors.
AI-Based Pipeline for Classifying Pediatric Medulloblastoma Using Histopathological and Textural Images
Attallah, Omneya; Zaghlool, Shaza (MDPI, 2022-02-03)
Pediatric medulloblastomas (MBs) are the most common type of malignant brain tumors in children. They are among the most aggressive types of tumors due to their potential for metastasis. Although this disease was initially considered a single disease, pediatric MBs can be considerably heterogeneous. Current MB classification schemes are heavily reliant on histopathology. However, the classification of MB from histopathological images is a manual process that is expensive, time-consuming, and prone to error. Previous studies have classified MB subtypes using a single feature extraction method that was based on either deep learning or textural analysis. Here, we combine textural analysis with deep learning techniques to improve subtype identification using histopathological images from two medical centers. Three state-of-the-art deep learning models were trained with textural images created from two texture analysis methods in addition to the original histopathological images, enabling the proposed pipeline to benefit from both the spatial and textural information of the images. Using a relatively small number of features, we show that our automated pipeline can yield an increase in the accuracy of classification of pediatric MB compared with previously reported methods. A refined classification of pediatric MB subgroups may provide a powerful tool for individualized therapies and identification of children with increased risk of complications.
Application of Artificial Neural Networks for Virtual Energy Assessment
Mortazavigazar, Amir; Wahba, Nourehan; Newsham, Paul; Triharta, Maharti; Zheng, Pufan; Chen, Tracy; Rismanchi, Behzad (MDPI)
A Virtual energy assessment (VEA) refers to the assessment of the energy flow in a building without physical data collection. It has been occasionally conducted before the COVID-19 pandemic to residential and commercial buildings. However, there is no established framework method for conducting this type of energy assessment. The COVID-19 pandemic has catalysed the implementation of remote energy assessments and remote facility management. In this paper, a novel framework for VEA is developed and tested on case study buildings at the University of Melbourne. The proposed method is a hybrid of top-down and bottom-up approaches: gathering the general information of the building and the historical data, in addition to investigating and modelling the electrical consumption with artificial neural network (ANN) with a projection of the future consumption. Through sensitivity analysis, the outdoor temperature was found to be the most sensitive (influential) parameter to electrical consumption. The lockdown of the buildings provided invaluable opportunities to assess electrical baseload with zero occupancies and usage of the building. Furthermore, comparison of the baseload with the consumption projection through ANN modelling accurately quantifies the energy consumption attributed to occupation and operational use, referred to as ‘operational energy’ in this paper. Differentiation and quantification of the baseload and operational energy may aid in energy conservation measures that specifically target to minimise these two distinct energy consumptions.
Assessing the Reactionary Response of High School Engineering Teachers Offering a Novel Pre-College Engineering Curriculum: Lessons Learned from the COVID-19 Pandemic
Griesinger, Tina; Olawale, David; Saqib, Najmus; Reid, Kenneth (MDPI, 2023-04-22)
The coronavirus (COVID-19) pandemic forced a rapid transition of K-16 education to remote and online learning in the final quarter of the 2019–2020 school year. The disruption was extreme for all teachers in K-12 but particularly for teachers involved in pilot programs, such as the NSF-funded Engineering for Us All (e4usa) project. This paper reports the key findings obtained through systematic data collection from a pilot cohort of high school teachers who adapted a brand-new engineering curriculum during the COVID-19 pandemic, students who experienced the adapted curriculum, and a new cohort of teachers who were tasked with teaching the updated curriculum.
Assessment of Machine Learning Algorithms for Predicting Air Entrainment Rates in a Confined Plunging Liquid Jet Reactor
Alazmi, Asmaa; Al-Anzi, Bader S. (MDPI, 2023-09-15)
A confined plunging liquid jet reactor (CPLJR) is an unconventional efficient and feasible aerator, mixer and brine dispenser that operates under many operating conditions. Such operating conditions could be challenging, and hence, utilizing prediction models built on machine learning (ML) approaches could be very helpful in giving reliable tools to manage highly non-linear problems related to experimental hydrodynamics such as CPLJRs. CPLJRs are vital in protecting the environment through preserving and sustaining the quality of water resources. In the current study, the effects of the main parameters on the air entrainment rate, Q_a, were investigated experimentally in a confined plunging liquid jet reactor (CPLJR). Various downcomer diameters (D_c), jet lengths (L_j), liquid volumetric flow rates (Qj), nozzle diameters (d_n), and jet velocities (V_j) were used to measure the air entrainment rate, Q_a. The non-linear relationship between the air entrainment ratio and confined plunging jet reactor parameters suggests that applying unconventional regression algorithms to predict the air entrainment ratio is appropriate. In addition to the experimental work, machine learning (ML) algorithms were applied to the confined plunging jet reactor parameters to determine the parameter that predicts Q_a the best. The results obtained from ML showed that K-Nearest Neighbour (KNN) gave the best prediction abilities, the proportion of variance in the Q_a that can be explained by the CPLJR parameter was 90%, the root mean square error (RMSE) = 0.069, and the mean absolute error (MAE) = 0.052. Sensitivity analysis was applied to determine the most effective predictor in predicting Q_a. The Qj and V_j were the most influential among all the input variables. The sensitivity analysis shows that the lasso algorithm can create an effective air entrainment rate model with just two of the most crucial variables, Qj and V_j. The coefficient of determination (R²) was 82%. The present findings support using machine learning algorithms to accurately forecast the CPLJR system’s experimental results.
Beyond the regulatory radar: knowledge and practices of rural medical practitioners in Bangladesh
Sujon, Hasnat; Sarker, Mohammad H. R.; Uddin, Aftab; Banu, Shakila; Islam, Mohammod R.; Amin, Md. R.; Hossain, Md. S.; Alahi, Md. F.; Asaduzzaman, Mohammad; Rizvi, Syed J. R.; Islam, Mohammad Z.; Uzzaman, Md. N. (2023-11-30)
Background: Informal and unregulated rural medical practitioners (RMPs) provide healthcare services to about two-thirds of people in Bangladesh, although their service is assumed to be substandard by qualified providers. As the RMPs are embedded in the local community and provide low-cost services, their practice pattern demands investigation to identify the shortfalls and design effective strategies to ameliorate the service. Methods: We conducted a cross-sectional study in 2015–16 using a convenient sample from all 64 districts of Bangladesh. Personnel practising modern medicine, without any recognized training, or with recognized training but practising outside their defined roles, and without any regulatory oversight were invited to take part in the study. Appropriateness of the diagnosis and the rationality of antibiotic and other drug use were measured as per the Integrated Management of Childhood Illness guideline. Results: We invited 1004 RMPs, of whom 877 consented. Among them, 656 (74.8%) RMPs owned a drugstore, 706 (78.2%) had formal education below higher secondary level, and 844 (96.2%) had informal training outside regulatory oversight during or after induction into the profession. The most common diseases encountered by them were common cold, pneumonia, and diarrhoea. 583 (66.5%) RMPs did not dispense any antibiotic for common cold symptoms. 59 (6.7%) and 64 (7.3%) of them could identify all main symptoms of pneumonia and diarrhoea, respectively. In pneumonia, 28 (3.2%) RMPs dispensed amoxicillin as first-line treatment, 819 (93.4%) dispensed different antibiotics including ceftriaxone, 721 (82.2%) dispensed salbutamol, and 278 (31.7%) dispensed steroid. In diarrhoea, 824 (94.0%) RMPs dispensed antibiotic, 937 (95.4%) dispensed ORS, 709 (80.8%) dispensed antiprotozoal, and 15 (1.7%) refrained from dispensing antibiotic and antiprotozoal together. Conclusions: Inappropriate diagnoses, irrational use of antibiotics and other drugs, and polypharmacy were observed in the practising pattern of RMPs. The government and other stakeholders should acknowledge them as crucial partners in the healthcare sector and consider ways to incorporate them into curative and preventive care.
Bioremediation of Hexavalent Chromium by Chromium Resistant Bacteria Reduces Phytotoxicity
Hossan, Shanewaz; Hossain, Saddam; Islam, Mohammad Rafiqul; Kabir, Mir Himayet; Ali, Sobur; Islam, Md Shafiqul; Imran, Khan Mohammad; Moniruzzaman, M.; Mou, Taslin Jahan; Parvez, Anowar Khasru; Mahmud, Zahid Hayat (MDPI, 2020-08-19)
Chromium (Cr) (VI) has long been known as an environmental hazard that can be reduced from aqueous solutions through bioremediation by living cells. In this study, we investigated the efficiency of reduction and biosorption of Cr(VI) by chromate resistant bacteria isolated from tannery effluent. From 28 screened Cr(VI) resistant isolates, selected bacterial strain SH-1 was identified as Klebsiella sp. via 16S rRNA sequencing. In Luria–Bertani broth, the relative reduction level of Cr(VI) was 95%, but in tannery effluent, it was 63.08% after 72 h of incubation. The cell-free extract of SH-1 showed a 72.2% reduction of Cr(VI), which indicated a higher activity of Cr(VI) reducing enzyme than the control. Live and dead biomass of SH-1 adsorbed 51.25 mg and 29.03 mg Cr(VI) per gram of dry weight, respectively. Two adsorption isotherm models—Langmuir and Freundlich—were used for the illustration of Cr(VI) biosorption using SH-1 live biomass. Scanning electron microscopy (SEM) analysis showed an increased cell size of the treated biomass when compared to the controlled biomass, which supports the adsorption of reduced Cr on the biomass cell surface. Fourier-transform infrared analysis indicated that Cr(VI) had an effect on bacterial biomass, including quantitative and structural modifications. Moreover, the chickpea seed germination study showed beneficial environmental effects that suggest possible application of the isolate for the bioremediation of toxic Cr(VI).
A Camelid-Derived STAT-Specific Nanobody Inhibits Neuroinflammation and Ameliorates Experimental Autoimmune Encephalomyelitis (EAE)
Mbanefo, Evaristus C.; Seifert, Allison; Yadav, Manoj Kumar; Yu, Cheng-Rong; Nagarajan, Vijayaraj; Parihar, Ashutosh; Singh, Sunanda; Egwuagu, Charles E. (MDPI, 2024-06-16)
Proinflammatory T-lymphocytes recruited into the brain and spinal cord mediate multiple sclerosis (MS) and currently there is no cure for MS. IFN-γ-producing Th1 cells induce ascending paralysis in the spinal cord while IL-17-producing Th17 cells mediate cerebellar ataxia. STAT1 and STAT3 are required for Th1 and Th17 development, respectively, and the simultaneous targeting of STAT1 and STAT3 pathways is therefore a potential therapeutic strategy for suppressing disease in the spinal cord and brain. However, the pharmacological targeting of STAT1 and STAT3 presents significant challenges because of their intracellular localization. We have developed a STAT-specific single-domain nanobody (SBT-100) derived from camelids that targets conserved residues in Src homolog 2 (SH2) domains of STAT1 and STAT3. This study investigated whether SBT-100 could suppress experimental autoimmune encephalomyelitis (EAE), a mouse model of MS. We show that SBT-100 ameliorates encephalomyelitis through suppressing the expansion of Th17 and Th1 cells in the brain and spinal cord. Adoptive transfer experiments revealed that lymphocytes from SBT-100-treated EAE mice have reduced capacity to induce EAE, indicating that the immunosuppressive effects derived from the direct suppression of encephalitogenic T-cells. The small size of SBT-100 makes this STAT-specific nanobody a promising immunotherapy for CNS autoimmune diseases, including multiple sclerosis.
Campylobacter jejuni - An emerging foodborne pathogen
Altekruse, Sean Fitzgerald; Stern, N. J.; Fields, P. I.; Swerdlow, David L. (1999-01)
Campylobacter jejuni is the most commonly reported bacterial cause of foodborne infection in the United States. Adding to the human and economic costs are chronic sequelae associated with C. jejuni infection-Guillain-Barre syndrome and reactive arthritis. In addition, an increasing proportion of human infections caused by C. jejuni are resistant to antimicrobial therapy. Mishandling of raw poultry and consumption of undercooked poultry are the major risk factors for human campylobacteriosis. Efforts to prevent human illness are needed throughout each link in the food chain.
Changes Associated with the Peri-Ovulatory Period, Age and Pregnancy in ACTH, Cortisol, Glucose and Insulin Concentrations in Mares
Hicks, Gemma R.; Fraser, Natalie S.; Bertin, François-René (MDPI, 2021-03-20)
Although there are many hormonal changes associated with reproduction, the effects of ovulation and early pregnancy on adrenocorticotropic hormone (ACTH) and insulin concentrations are poorly described. We hypothesise that both ovulation and early pregnancy will alter ACTH and insulin concentrations in healthy mares. Eighteen mares showing no clinical signs suggestive of, or laboratory findings consistent with, pituitary pars intermedia dysfunction PPID and insulin dysregulation (ID) are enrolled. ACTH, cortisol, insulin and glucose concentrations are measured over their peri-ovulatory period, as determined via ultrasounds and progesterone concentrations. The mares are grouped by age and gestation status, and a two-way repeated-measures ANOVA is used to determine the effects of age and early pregnancy, along with the peri-ovulatory period, on analyte concentrations. No significant effect of age, ovulation or early pregnancy is detected on the mares’ cortisol, insulin or glucose concentrations; however, there is a significant effect of early pregnancy and ovulation on ACTH concentrations (p = 0.04 and p = 0.04 respectively). ACTH concentrations change around ovulation and with early pregnancy. Therefore, knowledge of a mare’s reproductive status might be beneficial when interpreting ACTH concentrations.
Characterization of Temperature-Dependent Kinetics of Oculocutaneous Albinism-Causing Mutants of Tyrosinase
Wachamo, Samuel A.; Patel, Milan H.; Varghese, Paul K.; Dolinska, Monika B.; Sergeev, Yuri V. (MDPI, 2021-07-21)
Human tyrosinase (Tyr) is a glycoenzyme that catalyzes the first and rate-limiting step in melanin production, and its gene (TYR) is mutated in many cases of oculocutaneous albinism type phenotype in patients with OCA1 have only began to be examined and remain to be delineated. Here, we analyze the temperature-dependent kinetics of wild-type Tyr (WT) and two OCA1B mutant variants (R422Q and P406L) using Michaelis–Menten and Van’t Hoff analyses. Recombinant truncated human Tyr proteins (residues 19–469) were produced in the whole insect Trichoplusia Ni larvae. Proteins were purified by a combination of affinity and size-exclusion chromatography. The temperature dependence of diphenol oxidase protein activities and kinetic parameters were measured by dopachrome absorption. Using the same experimental conditions, computational simulations were performed to assess the temperature-dependent association of L-DOPA and Tyr. Our results revealed, for the first time, that the association of L-DOPA with R422Q and P406L followed by dopachrome formation is a complex reaction supported by enthalpy and entropy forces. We show that the WT has a higher turnover number as compared with both R422Q and P406L. Elucidating the kinetics and thermodynamics of mutant variants of Tyr in OCA1B helps to understand the mechanisms by which they lower Tyr catalytic activity and to discover novel therapies for patients.
Complete Genome Sequence of the Streptomyces-Specific Bacteriophage BRock
Baron, Stephen F.; Crossman, Ashley N.; Malik, Shikha; Sidhu, Parveen; Nehra, Kiran; Jamdagni, Pragati; Erill, Ivan; Temple, Louise M. (2020-08)
The complete genome sequence of the unique virulent bacteriophage BRock, isolated from compost on Streptomyces sp. strain SFB5A, was determined. BRock is a myovirus with a 112,523-bp genome containing a GC content of 52.3%. There were 188 protein-coding genes predicted, including structural and enzymatic proteins, but none predicted for lysogeny. Twenty-nine tRNAs were predicted.
Computational Analysis of Shear Banding in Simple Shear Flow of Viscoelastic Fluid-Based Nanofluids Subject to Exothermic Reactions
Khan, Idrees; Chinyoka, Tiri; Gill, Andrew (MDPI, 2022-02-25)
We investigated the shear banding phenomena in the non-isothermal simple-shear flow of a viscoelastic-fluid-based nanofluid (VFBN) subject to exothermic reactions. The polymeric (viscoelastic) behavior of the VFBN was modeled via the Giesekus constitutive equation, with appropriate adjustments to incorporate both the non-isothermal and nanoparticle effects. Nahme-type laws were employed to describe the temperature dependence of the VFBN viscosities and relaxation times. The Arrhenius theory was used for the modeling and incorporation of exothermic reactions. The VFBN was modeled as a single-phase homogeneous-mixture and, hence, the effects of the nanoparticles were based on the volume fraction parameter. Efficient numerical schemes based on semi-implicit finite-difference-methods were employed in MATLAB for the computational solution of the governing systems of partial differential equations. The fundamental fluid-dynamical and thermodynamical phenomena, such as shear banding, thermal runaway, and heat transfer rate (HTR) enhancement, were explored under relevant conditions. Important novel results of industrial significance were observed and demonstrated. Firstly, under shear banding conditions of the Giesekus-type VFBN model, we observed remarkable HTR and Therm-C enhancement in the VFBN as compared to, say, NFBN. Specifically, the results demonstrate that the VFBN are less susceptible to thermal runaway than are NFBN. Additionally, the results illustrate that the reduced susceptibility of the Giesekus-type VFBN to the thermal runaway phenomena is further enhanced under shear banding conditions, in particular when the nanofluid becomes increasingly polymeric. Increased polymer viscosity is used as the most direct proxy for measuring the increase in the polymeric nature of the fluid.
Construction Supply Chain Management Practice and Impact on Project Performance: Perspective From Nigerian Construction Firms
Adegoke, Abiola; Dada, Joshua (Nigerian Institute of Quantity Surveyors, 2022-10-27)
This paper investigates the awareness and extent of construction supply chain management (CSCM) practice by construction firms in Nigeria. In addition, the impact of CSCM on project cost and time performance was evaluated. Design/methodology/approach – A well structure questionnaire survey was administered on the ninety-two construction firms registered with the Bureau of Public Procurement of Oyo State in Southwestern Geopolitical Zone of Nigeria. Descriptive and inferential statistics were used to analyse the elicited data. The results show that construction firms in Nigeria are generally aware of CSCM. While the experience of small, medium and large-scale construction firms on CSCM practice differs, information acquisition and sharing (among other ten significant variables) was found to be the most important element. In determining the impact of CSCM practice on project cost, the eleven identified significant elements were used to develop a multi linear regression model equation Y = 3.654-0.053X1 – 0.036X2 – 0.041X3 - 0.065X4- 0.024X5 - 0.013 X6 - 0.021 X7- 0.021X8- 0.013 X9 - 0.035 X10 - 0.011X11. (Where Y is the cost of construction project and X1…X11 are elements of CSCM practice). In the same vein a model equation showing the impact of CSCM practice on project duration was developed as Y = 5.189-0.022X1 – 0.014X2 – 0.034X3 - 0.025X4- 0.060X5 - 0.011X6 - 0.036 X7 - 0.016X8 - 0.034 X9 - 0.014 X10 - 0.023X11. The generated model equations show an inverse relationship between cost and duration of construction projects and elements of CSCM practice. This implies that adequate utilisation of the elements of CSCM practice will lead to an appreciable reduction in project cost and time. Apart from the general impact of CSCM practice; the quantum effect of each of the elements can be evaluated from the model equations.
Curriculum Design in an Agricultural Education Program in Nigeria: Towards Advancing Career Readiness
Ajao, H.; Alegbeleye, I.D.; Westfall-Rudd, Donna M. (Advancements in Agricultural Development, Inc., 2022)
This research explores the effective curriculum design for higher-ed in preparing agricultural education graduates for Nigeria’s labor market. The continuing professional education program planning theory serves as the framework guiding this study. The study involves a phenomenological inquiry into the conscientious meaning experience of the faculty and alumni in an agricultural education department. A purposeful sampling method of 14 participants (four professors and 10 alumni) was used to select participants since the study relied on individuals close to the phenomenon. Data was collected using a standardized open-ended questionnaire and the Department’s handbook. Three themes emerged: The Department's curriculum design/development; Stakeholder’s consultation; and Principles considered while designing the curriculum. Recommendations were made for the Department to continuously review and update the curriculum to reflect the current needs of the industry and students. Lastly, the current study was recommended to be replicated in other main agricultural institutions in Nigeria.
The Death of Neoliberal Realism?
Wheeler, Zachariah (2020-06)
Deep Learning for Forest Plantation Mapping in Godavari Districts of Andhra Pradesh, India
More, Snehal; Karpatne, Anuj; Wynne, Randolph H.; Thomas, Valerie A. (Virginia Tech, 2019-08)
Small-area forest plantations play a vital role in the socioeconomic well-being of farmers in Southeast Asia. Most plantations are established on former agricultural land, often on land less suitable for agriculture. Plantations that are converted from natural forest have adverse impacts on biodiversity. Mapping small-area plantations is thus important to understand the dynamics of forest cover in Southeast Asia and to study the social, economic, and ecological effects of this important land cover and land use change. While the small size of forest plantations makes it difficult to detect them using moderate resolution satellite sensors, the problem is exacerbated by the high degree of mixing between plantations, surrounding vegetation, and other land covers, which often show variegated responses in satellite signals across space and time. In this paper, we study the problem of mapping small-area forest plantations in East and West Godavari districts of Andhra Pradesh, India using deep learning methods. Remotely sensed cloud-free data from the Harmonized Landsat Sentinel-2 S10 product were classified using a pixel-level neural network and training data labeled using a field-based survey in concert with expert aerial photo interpretation. We compare the performance of deep learning methods with a baseline random forest classifier in our study region of 21543 sq. km over a period of 3 years and analyze the differences in the results across land cover classes and seasons.
Deer movement and resource selection during Hurricane Irma: implications for extreme climatic events and wildlife
Abernathy, Heather N.; Crawford, Daniel A.; Garrison, Elina P.; Chandler, R. B.; Conner, M. L.; Miller, K. B.; Cherry, Michael J. (Royal Society, 2019-11-19)
Extreme climatic events (ECEs) are increasing in frequency and intensity and this necessitates understanding their influence on organisms. Animal behaviour may mitigate the effects of ECEs, but field studies are rare because ECEs are infrequent and unpredictable. Hurricane Irma made landfall in southwestern Florida where we were monitoring white-tailed deer (Odocoileus virginianus seminolus) with GPS collars. We report on an opportunistic case study of behavioural responses exhibited by a large mammal during an ECE, mitigation strategies for reducing the severity of the ECE effects, and the demographic effect of the ECE based on known-fate of individual animals. Deer altered resource selection by selecting higher elevation pine and hardwood forests and avoiding marshes. Most deer left their home ranges during Hurricane Irma, and the probability of leaving was inversely related to home range area. Movement rates increased the day of the storm, and no mortality was attributed to Hurricane Irma. We suggest deer mobility and refuge habitat allowed deer to behaviourally mitigate the negative effects of the storm, and ultimately, aid in survival. Our work contributes to the small but growing body of literature linking behavioural responses exhibited during ECEs to survival, which cumulatively will provide insight for predictions of a species resilience to ECEs and improve our understanding of how behavioural traits offset the negative impacts of global climate change.
Deprivation Has Inconsistent Effects on Delay Discounting: A Review
Downey, Haylee; Haynes, Jeremy M.; Johnson, Hannah M.; Odum, Amy L. (Frontiers, 2022-02-10)
Delay discounting, the tendency for outcomes to be devalued as they are more temporally remote, has implications as a target for behavioral interventions. Because of these implications, it is important to understand how different states individuals may face, such as deprivation, influence the degree of delay discounting. Both dual systems models and state-trait views of delay discounting assume that deprivation may result in steeper delay discounting. Despite early inconsistencies and mixed results, researchers have sometimes asserted that deprivation increases delay discounting, with few qualifications. The aim of this review was to determine what empirical effect, if any, deprivation has on delay discounting. We considered many kinds of deprivation, such as deprivation from sleep, drugs, and food in humans and non-human animals. For 23 studies, we analyzed the effect of deprivation on delay discounting by computing effect sizes for the difference between delay discounting in a control, or baseline, condition and delay discounting in a deprived state. We discuss these 23 studies and other relevant studies found in our search in a narrative review. Overall, we found mixed effects of deprivation on delay discounting. The effect may depend on what type of deprivation participants faced. Effect sizes for deprivation types ranged from small for sleep deprivation (Hedge's gs between −0.21 and 0.07) to large for opiate deprivation (Hedge's gs between 0.42 and 1.72). We discuss possible reasons why the effect of deprivation on delay discounting may depend on deprivation type, including the use of imagined manipulations and deprivation intensity. The inconsistency in results across studies, even when comparing within the same type of deprivation, indicates that more experiments are needed to reach a consensus on the effects of deprivation on delay discounting. A basic understanding of how states affect delay discounting may inform translational efforts.

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