Determinant Factors in the Implementation of Information Technology Strategic Management to Academicians' Performance in Higher Education Institution

This study aimed to understand the determinant factors of information technology (IT) strategic management to individual (lecturer) performance using data samples from selected higher education institutions in Indonesia. Since the use of IT innovation in (HEI) is often considered a lens representing the strength of strategy, competitiveness, and quality within a corporate view, it is vague on its impact on individual performance. The investigation included data collection based on an online survey conducted on 325 respondents to investigate the relationship between strategic factors, elaborated into several relevant criteria. The results of statistical data processing showed that of all the strategic factors involved, the business model and strategic alignment categorized in high determinations in influencing academicians' performance at HEI

Towards new horizons: Climate trends in europe increase the environmental suitability for permanent populations of hyalomma marginatum (ixodidae)

Ticks and tick-borne pathogens are changing their current distribution, presumably due to the impact of the climate trends. On a large scale, these trends are changing the environmental suitability of Hyalomma marginatum, the main vector of several pathogens affecting human health. We generated annual models of environmental suitability for the tick in the period 1970–2018, using harmonic regression-derived data of the daily maximum and minimum temperature, soil moisture and water vapor deficit. The results demonstrate an expansion of the suitable area in Mediterranean countries, southeast central Europe and south of the Balkans. Also, the models allowed us to interpret the impact of the ecological variables on these changes. We deduced that (i) maximum temperature was significant for all of the biogeographical categories, (ii) soil humidity has an influence in the Mediterranean climate areas, and (iii) the minimum temperature and deficit water vapor did not influence the environmental suitability of the species. The conclusions clearly show that climate change could create new areas in Europe with suitable climates for H. marginatum, while keeping its “historical” distribution in the Mediterranean. Therefore, it is necessary to further explore possible risk areas for H. marginatum and its associated pathogens

How could climate change influence the distribution of the black soldier fly, Hermetia illucens (Linnaeus) (Diptera, Stratiomyidae)?

The black soldier fly, Hermetia illucens (Linnaeus, 1758), is a saprophagous species used to decompose organic matter. This study proposes a distribution model of H. illucens to illustrate its current and future distribution. The methodology includes data collection from the Global Biodiversity Information Facility (GBIF), complemented with iNaturalist, manual expert curation of occurrence records, six species distribution models algorithms and one ensemble model. The average temperature of the driest annual quarter and the precipitation of the coldest annual quarter were the key variables influencing the potential distribution of H. illucens. The distribution range is estimated to decrease progressively and their suitable habitats could change dramatically in the future due to global warming. On the other hand, current optimal habitats would become uninhabitable for the species, mainly at low latitudes. Under this scenario, the species is projected to move to higher latitudes and elevations in the future. The results of this study provide data on the distribution of H. illucens, facilitating its location, management and sustainable use in current and future scenarios

Species distribution modelling of the endangered Mahogany Glider (Petaurus gracilis) reveals key areas for targeted survey and conservation

The Mahogany Glider (Petaurus gracilis) is one of the most endangered marsupials in Australia. Its known distribution is an approximately 120 km strip of fragmented coastal woodland in north-east Queensland, from north of Townsville to the Tully area. Records are clustered in a number of well-surveyed areas, with significant areas of lowland habitat unsurveyed. Around 30% of historic records fall in areas that were subsequently cleared for farmland, and ongoing clearing and fragmentation of lowland sclerophyll forest continues within the potential distribution. Resolving the distribution is an urgent requirement to guide conservation but Mahogany Gliders are difficult to detect in the field. Species distribution modelling offers a technique for estimating the fine-scale distribution and for targeting further field survey and conservation efforts. We used known occurrence records (N = 481) to predict the distribution of Mahogany Gliders across the Wet Tropics bioregion. We used climatic, topographic and other environmental predictors to generate distribution models using Maxent and Random Forest algorithms, each with two bias correction methods. The predictions revealed that many unknown populations may exist within the currently defined distribution and in important areas beyond this (e.g. Hinchinbrook Island). There was reasonable congruence between models, and we include syntheses of the models to present the most likely current distribution. The most important predictor variables across the models were precipitation seasonality (high seasonality), elevation (generally <100 m), soil type (hydrosols) and vegetation type (including Eucalyptus and Melaleuca woodlands). Our results identify core habitat and reveal key areas that require targeted field surveys. Importantly, the predicted suitable habitat is highly fragmented and ongoing conservation efforts need to improve habitat connectivity and limit further fragmentation

Species distribution modeling of northern sea otters (Enhydra lutris kenyoni) in a data-limited ecosystem

Thesis (M.S.) University of Alaska Fairbanks, 2022Species distribution models are used to map and predict geographic distributions of animals based on environmental covariates. However, species distribution models often require high resolution habitat data and time-series data on animal locations. In data-limited regions with little animal survey data or habitat information, modeling is more challenging and often ignores important environmental attributes. For sea otters (Enhydra lutris), a federally protected keystone species with variable population trends across their range, predictive modeling of distributions has been successfully conducted in areas with an abundance of sea otter and habitat data. Here, we used open-access data across a single time step and leveraged a presence-only model, Maximum Entropy (MaxEnt), to investigate subtidal habitat associations (substrate and algal cover, bathymetry, and rugosity) of northern sea otters (E. lutris kenyoni) in a data-limited ecosystem, Kachemak Bay, Alaska. These habitat associations corroborated previous findings regarding the importance of bathymetry and understory kelp as predictors of sea otter presence. Novel associations were detected, as filamentous algae and shell litter were positively and negatively associated with sea otter presence, respectively. This study provides a quantitative framework for conducting species distribution modeling with limited temporal and spatial animal distribution and abundance data and utilized drop camera information as a novel approach to better understand habitat requirements of a stable sea otter population.National Park Service, U.S. Geological Survey, National Oceanic and Atmospheric Administration, U.S. Fish and Wildlife Service, Bureau of Ocean Energy Management, Alaska EPSCoR, Oil Spill Recovery Institute Graduate Research Fellowship, Oil Spill Recovery Institute Two Petes' Award, Coastal Marine Institute Graduate Student Initiative Award, North Pacific Research Board Graduate Student Research Award, Dieter Family Marine Science Research Scholarshi

Projeções das receitas líquidas de empresas do ramo têxtil durante o período de flexibilização das medidas sanitárias da pandemia do Covid-19: um comparativo entre as predições dos modelos de Redes Neurais, Sarima e Holt-Winters

TCC(graduação) - Universidade Federal de Santa Catarina, Centro Tecnológico, Engenharia de Produção.Previsões de demanda tem sua importância justificada por servirem de auxílio aos tomadores de decisão de todos os tipos de empresas, viabilizando o planejamento estratégico e operacional de suas organizações além de influenciar em diferentes horizontes de tempo. Ao longo dos anos diversas técnicas foram utilizadas para gerar previsões de demanda cada vez mais precisa. Nos dias atuais recursos computacionais ganharam destaque, por fornecerem uma forma mais rápida e simples de se obter previsões de curto e longo prazo, muitas vezes fornecendo melhor acuracidade que os métodos tradicionais, portanto, estão sendo mais utilizados por diversas empresas. O cenário instaurado em meados de 2019, o qual estabeleceu-se estado pandêmico devido a COVID-19, impactou fortemente a economia global, estabelecendo novas formas de consumo e mudando significativamente o perfil do consumidor. Para o setor têxtil tais mudanças foram fortemente sentidas e a queda percebida nas receitas durante período foi evidente. O presente trabalho se propõe a analisar projeções de receitas utilizando diferentes métodos de previsão de demanda para diferentes empresas do setor têxtil após o período de flexibilização da pandemia, analisou-se as projeções para o curto prazo pelas métricas de Machine Learning e estatísticas para séries temporais, considerando que os modelos fariam as ponderações necessárias frente as fortes mudanças nas receitas líquidas que o período pandêmico ocasionou no setor têxtil. Adotou-se como premissa que no momento da coleta dos dados o cenário de flexibilização estaria associado a presença ativa da Covid-19. Em sua conclusão será feito uma comparação utilizando testes de acuracidade, que serão fatores fundamentais para escolha do modelo que melhor se ajusta a empresa estudada

Mine evaluation optimisation

The definition of a mineral resource during exploration is a fundamental part of lease evaluation, which establishes the fair market value of the entire asset being explored in the open market. Since exact prediction of grades between sampled points is not currently possible by conventional methods, an exact agreement between predicted and actual grades will nearly always contain some error. These errors affect the evaluation of resources so impacting on characterisation of risks, financial projections and decisions about whether it is necessary to carry on with the further phases or not. The knowledge about minerals below the surface, even when it is based upon extensive geophysical analysis and drilling, is often too fragmentary to indicate with assurance where to drill, how deep to drill and what can be expected. Thus, the exploration team knows only the density of the rock and the grade along the core. The purpose of this study is to improve the process of resource evaluation in the exploration stage by increasing prediction accuracy and making an alternative assessment about the spatial characteristics of gold mineralisation. There is significant industrial interest in finding alternatives which may speed up the drilling phase, identify anomalies, worthwhile targets and help in establishing fair market value. Recent developments in nonconvex optimisation and high-dimensional statistics have led to the idea that some engineering problems such as predicting gold variability at the exploration stage can be solved with the application of clusterwise linear and penalised maximum likelihood regression techniques. This thesis attempts to solve the distribution of the mineralisation in the underlying geology using clusterwise linear regression and convex Least Absolute Shrinkage and Selection Operator (LASSO) techniques. The two presented optimisation techniques compute predictive solutions within a domain using physical data provided directly from drillholes. The decision-support techniques attempt a useful compromise between the traditional and recently introduced methods in optimisation and regression analysis that are developed to improve exploration targeting and to predict the gold occurrences at previously unsampled locations.Doctor of Philosoph

Drivers of Kelp Forest Responses to Global Change: From Populations to Policies

Kelps are large brown algae in the order Laminariales and are foundation species that form the basis of kelp forests. Present across a quarter of the world’s coastlines, kelp forests provide diverse services to coastal communities, as habitat for commercially and culturally important species, as a food source for humans as well as myriad animals, as raw material used in the pharmaceutical and cosmetics industries, as a place of recreation and tourism, and as a potential source of carbon sequestration. Like all ecosystems on the planet, kelp forests are deeply impacted by human-derived global change. The most comprehensive meta-analysis of global trends in kelp forest cover found that on average kelp forest cover has been declining globally over the past two decades. This negative trend is underlaid by various human impacts. For instance, kelp harvesting is thought to be the primary driver of kelp forest decline in northern Chile, invasive bryozoans in parts of Nova Scotia, and climate-change induced changes in water temperature in western Australia. Furthermore, human impacts can intersect to create cumulative impacts on kelp forests that prevent attribution of kelp forest decline to any one factor. For instance, in Northern California, a marine heat wave, loss of an urchin-eating predator, and unusually high urchin recruitment all contributed to the collapse of Nereocystis forests in 2015. While some kelp forests in North America are rigorously studied, covered by long-term monitoring, and relatively well-understood (such as Monterey Bay and Point Loma in California), the vast majority are not. In this dissertation, I utilize broad approaches to overcome these data limitations and provide insights into the drivers of kelp forest responses to global change that are relevant to decision-makers. In Chapter 2 I created a 35-year time series of Nereocystis canopy area in Oregon using remote sensing analysis of Landsat satellite data. This dataset showed that multiple-endmember spectral mixing analysis techniques previously utilized to track changes in Macrocystis pyrifera canopy in California can be used with different species and in different regions. Additionally, it retroactively provided rich insights into trends in kelp forest cover across the state and identified novel environmental correlates of kelp forest cover, such as a positive association with winter wave height that have deepened our understanding of the population biology and ecology of Nereocystis. Insights from this work has been shared with the Oregon Department of Fish and Wildlife, the Oregon Kelp Alliance, the Elakha Alliance, the Coquille Tribe, Senator Jeff Merkley and others in order to support policy-making, restoration, and science funding efforts related to kelp in Oregon. In Chapter 3, I used the contributed data of over 30 collaborators to provide range-wide insights into the impacts of disease on a Pycnopodia helianthoides, a urchin-eating predator whose loss has been tied to kelp forest overgrazing. This work found that disease-driven mortality had been so severe (~99%) in the southern half of the species range that recovery to ecologically-relevant population levels was unlikely to happen in a time frame relevant for kelp forests. In the northern half of the range however, populations were impacted less dramatically and could still be found in relatively high densities, although densities were patchy. Additionally, we found that the importance of temperature in predicting Pycnopodia distributions rose dramatically after the disease event, indicating that temperature-disease interactions were related to the latitudinal pattern in disease severity. Furthermore, through a partnership with The Nature Conservancy, this work has influenced global Pycnopodia conservation efforts through the successful listing of the species as Critically Endangered on the IUCN Red List, the formation of a Pycnopodia Recovery Working group, and incorporation into a Pycnopodia Recovery Roadmap. Finally, in Chapter 4, I brought together insights from the scholarly literature on kelp forest ecology, the framework of Ecosystem Based Management (EBM), and regional case studies of ongoing management to synthesize cross-regional principles for managing kelp forests sustainably. Our case studies focused on three regions, each with unique socioecological contexts around kelp forests: northern Chile, California (USA), and British Columbia (Canada). Drawing lessons from across disciplines and regions, we identified six principles for kelp forest EBM: 1) monitoring at biologically relevant temporal and spatial scales, 2) assessing and addressing cumulative impacts, 3) managing across spatial scales, 4) co-management with users, 5) employing rapid adaptive management and/or the precautionary principle, and 6) managing food web connections. We explore and illustrate these concepts using examples from multiple regions to provide concrete guidance on EBM-inspired strategies that are likely to improve kelp forest management outcomes. We hope this work can be a starting place for further discussion around and development of best practices in kelp forest management