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Evaluating the efficacy of management actions to control invasive species is crucial for maintaining funding and to provide feedback for the continual improvement of management efforts. However, it is often difficult to assess the efficacy of control methods due to limited resources for monitoring. Managers may view effort on monitoring as effort taken away from performing management actions. We developed a method to estimate invasive species abundance, evaluate management effectiveness, and evaluate population growth over time from a combination of removal activities (e.g., trapping, ground shooting) using only data collected during removal efforts (method of removal, date, location, number of animals removed, and effort). This dynamic approach allows for abundance estimation at discrete time points and the estimation of population growth between removal periods. To test this approach, we simulated over 1 million conditions, including varying the length of the study, the size of the area examined, the number of removal events, the capture rates, and the area impacted by removal efforts. Our estimates were unbiased (within 10% of truth) 81% of the time and were correlated with truth 91% of the time. This method performs well overall and, in particular, at monitoring trends in abundances over time. We applied this method to removal data from Mingo National Wildlife Refuge in Missouri from December 2015 to September 2019, where the management objective is elimination. Populations of feral swine on Mingo NWR have fluctuated over time but showed marked declines in the last 3–6 months of the time series corresponding to increased removal pressure. Our approach allows for the estimation of population growth across time (from both births and immigration) and therefore, provides a target removal rate (above that of the population growth) to ensure the population will decline. In Mingo NWR, the target monthly removal rate is 18% to cause a population decline. Our method provides advancement over traditional removal modeling approaches because it can be applied to evaluate management programs that use a broad range of removal techniques concurrently and whose management effort and spatial coverage vary across time.

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture–recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.

A necessary component of elimination programmes for wildlife disease is effective surveillance. The ability to distinguish between disease freedom and non‐detection can mean the difference between a successful elimination campaign and new epizootics. Understanding the contribution of different surveillance methods helps to optimize and better allocate effort and develop more effective surveillance programmes. We evaluated the probability of rabies virus elimination (disease freedom) in an enzootic area with active management using dynamic occupancy modelling of 10 years of raccoon rabies virus (RABV) surveillance data (2006–2015) collected from three states in the eastern United States. We estimated detection probability of RABV cases for each surveillance method (e.g. strange acting reports, roadkill, surveillance‐trapped animals, nuisance animals and public health samples) used by the USDA National Rabies Management Program. Strange acting, found dead and public health animals were the most likely to detect RABV when it was present, and generally detectability was higher in fall–winter compared to spring–summer. Found dead animals in fall–winter had the highest detection at 0.33 (95% CI: 0.20, 0.48). Nuisance animals had the lowest detection probabilities (~0.02). Areas with oral rabies vaccination (ORV) management had reduced occurrence probability compared to enzootic areas without ORV management. RABV occurrence was positively associated with deciduous and mixed forests and medium to high developed areas, which are also areas with higher raccoon (Procyon lotor) densities. By combining occupancy and detection estimates we can create a probability of elimination surface that can be updated seasonally to provide guidance on areas managed for wildlife disease. Synthesis and applications. Wildlife disease surveillance is often comprised of a combination of targeted and convenience‐based methods. Using a multi‐method analytical approach allows us to compare the relative strengths of these methods, providing guidance on resource allocation for surveillance actions. Applying this multi‐method approach in conjunction with dynamic occupancy analyses better informs management decisions by understanding ecological drivers of disease occurrence.

Facing growth pressures, firms attempt to target the large low-income consumer segment present in emerging markets. This multi-method study develops research insights regarding consumer-, retailer-, firm-, category- and country-level factors that enhance the acceptability, awareness, availability, and affordability of products that facilitate the low-income consumer adoption of and firms’ introduction of new products for low-income consumers. Study 1 uses a qualitative grounded-theory approach by interviewing company managers and low-income consumers in India and Chile. Study 2, empirically tests an integrated multi-level model of several category factors identified in Study 1, combined with country-level factors drawn from the literature, using a unique 12-year longitudinal panel dataset of new product introductions in 27 emerging market countries from Africa, Asia, Eastern Europe, and South America. The research identifies consumer aspirations, region-based versioning, visible packaging and the product demonstrations as critical motivating factors for adoption of products by low income consumers. Consumers’ knowledge of the product category, the concentration of branded products, availability of global brands, and the presence of traditional retail stores motivate firms to launch products for low income consumers in emerging markets.

Semiaquatic mammals serve as ecosystem engineers and indicator species in their environment. While they play important roles in both terrestrial and aquatic systems, the relative importance of each ecosystem in shaping semiaquatic mammal distributions remains unclear. Additionally, occupancy studies generally focus on a single type of waterbody (e.g., lentic or lotic systems), limiting a holistic understanding of factors impacting these species distribution. To address the relative importance of terrestrial and aquatic environments to semiaquatic mammal distributions, we surveyed 67 sites across four counties in southern Illinois, USA, from March to May 2023 for American beaver (Castor canadensis), muskrat (Ondatra zibethicus), river otter (Lontra canadensis), and American mink (Neogale vison). Sites were distributed evenly between waterbody type and size. Given the elusive nature of these species, we combined two detection methods, sign surveys and camera traps, to increase detection. We applied a Bayesian multi‐method occupancy model that incorporates both detection methods to estimate a single occupancy probability for each target species. To evaluate the relative importance of aquatic and terrestrial factors on occupancy, we built candidate models of aquatic and terrestrial covariates separately to identify the most important covariates of each category. The individual top model varied by species, but a combined model of the top aquatic and terrestrial models provided the best overall predictions for each species. Beaver, otter, and mink occupancy showed positive associations with large waterbodies, while muskrat occupancy was positively linked to lotic systems. Additionally, muskrat and mink occupancy were positively related to increasing distance from roads. Our results suggest that while aquatic and terrestrial factors have varying influences in predicting semiaquatic mammal occupancy, considering both yields the most accurate results. All four semiaquatic mammal species we studied were impacted differently by lentic and lotic waterbodies, highlighting the importance of considering both types to better understand their distributions. Our framework is applicable to numerous environments and has the potential to enhance efforts to sustain semiaquatic mammal populations and their habitats.

Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI) systems due to its portability and simplicity. In this paper, we provide a comprehensive review of research on EEG signal processing techniques since 2021, with a focus on preprocessing, feature extraction, and classification methods. We analyzed 61 research articles retrieved from academic search engines, including CNKI, PubMed, Nature, IEEE Xplore, and Science Direct. For preprocessing, we focus on innovatively proposed preprocessing methods, channel selection, and data augmentation. Data augmentation is classified into conventional methods (sliding windows, segmentation and recombination, and noise injection) and deep learning methods [Generative Adversarial Networks (GAN) and Variation AutoEncoder (VAE)]. We also pay attention to the application of deep learning, and multi-method fusion approaches, including both conventional algorithm fusion and fusion between conventional algorithms and deep learning. Our analysis identifies 35 (57.4%), 18 (29.5%), and 37 (60.7%) studies in the directions of preprocessing, feature extraction, and classification, respectively. We find that preprocessing methods have become widely used in EEG classification (96.7% of reviewed papers) and comparative experiments have been conducted in some studies to validate preprocessing. We also discussed the adoption of channel selection and data augmentation and concluded several mentionable matters about data augmentation. Furthermore, deep learning methods have shown great promise in EEG classification, with Convolutional Neural Networks (CNNs) being the main structure of deep neural networks (92.3% of deep learning papers). We summarize and analyze several innovative neural networks, including CNNs and multi-structure fusion. However, we also identified several problems and limitations of current deep learning techniques in EEG classification, including inappropriate input, low cross-subject accuracy, unbalanced between parameters and time costs, and a lack of interpretability. Finally, we highlight the emerging trend of multi-method fusion approaches (49.2% of reviewed papers) and analyze the data and some examples. We also provide insights into some challenges of multi-method fusion. Our review lays a foundation for future studies to improve EEG classification performance.

Supply chain disruptions, intensified by black swan events such as the COVID-19 pandemic and the Russia-Ukraine war, have increased the interest in resilient supply chains, which can be achieved by adopting sustainable Industry 4.0 (I4.0) practices. However, the critical success factors (CSFs) for sustainable I4.0 in operations and supply chain management (S-OSCM4.0) are unclear, and there is a lack of a holistic and empirically validated taxonomy of CSFs from multiple stakeholders' perspectives to guide organizations in this transition. Moreover, developing countries face specific challenges that require prioritizing the proper set of CSFs for sustainable digitalization. Therefore, this paper aims to develop a CSFs-based taxonomy for S-OSCM4.0 to help organizations stay current in I4.0 adoption and integrate sustainability in OSCM. We first conducted a systematic literature review (SLR) of 131 papers using bibliometric and content analyses and synthesized the theoretical findings into an alpha taxonomy of CSFs following an inductive approach. Then, we employed a Delphi survey technique combining fuzzy logic to solicit experts' perceptions from a developing country to analyze and validate the taxonomy and determine the most pertinent CSFs, resulting in a beta taxonomy of CSFs for S-OSCM4.0. The developed taxonomy represents a pioneering managerial artefact that can guide sustainable development through an inclusive digital transformation with less environmental impact, contributing to decision-making in S-OSCM4.0, especially for operations in developing countries.

Ensemble learning aggregates several models’ outputs to improve the overall model’s performance. Ensemble feature selection separating the appropriate features from the extra and non-essential features. In this paper, the main focus will be to expand the scope of Ensemble Learning to include Feature Selection. We will propose an Ensemble Feature Selection Method called AdaptDiabfor Diabetes Prediction that is Model-Agnostic. Our approach combines diverse feature selection techniques, such as filter and wrapper methods, harnessing their complementary strengths. We have used an adaptive combiner function, which dynamically selects the most informative features based on the characteristics of the ensemble members. We demonstrate the effectiveness of our proposed AdaptDiab method through empirical studies using various classification models. Empirical Results of Our Proposed Ensemble Feature Selection Model outperforms traditional methods. This paper contributes to Ensemble Learning Methods and provides a Practical and Better Framework for Feature selection.

PurposeDespite the growing interest in the role of relation-specific investments (RSIs) in superior firm performance, their impact on sustainability performance remains unexplored, as do the underlying mechanisms of such effects. Drawing on the relational view and resource orchestration theory (ROT), the authors propose that supply chain learning (SCL) mediates the link between RSIs and sustainability performance.Design/methodology/approachA multi-method approach was adopted, combining a case study and survey. An exploratory case study of four Chinese manufacturing firms was first conducted to develop research hypotheses. A quantitative survey of data collected from 269 firms was then undertaken to test hypotheses.FindingsProperty-based, knowledge-based and personal-based RSIs positively impact firm sustainability performance and SCL. SCL fully mediates the relationship between knowledge-as well as personal-based RSIs and sustainability performance, and partially mediates the relationship between property-based RSIs and sustainability performance.Practical implicationsThe study unveils important practical insights and approaches for firms endeavouring to achieve sustainability performance through RSIs and SCL.Originality/valueThe study extends the RSIs literature by linking RSIs and sustainability performance and differentiating the effects of different types of RSIs on sustainability performance. The theorized underlying mechanism advances the understanding of SCL in the link between RSIs and sustainability performance.

Under the background of engineering education professional certification, the Outcome-Based Education (OBE) education concept of “output-oriented” has been paid more and more attention. The traditional experimental teaching of programming course often focuses on the teaching of theoretical knowledge, and lacks the cultivation of students’ practical ability and innovative spirit. Engineering education puts forward new requirements for the teaching mode of program design course. The experimental teaching of programming courses requires further reform and innovation to cultivate high-quality technical engineering talents with good social responsibility, teamwork ability, and innovative thinking ability. Guided by the theory of engineering education combined with the educational philosophy of Conceive–Design–Implement–Operate (CDIO) and OBE, this paper carried out the reform of experimental teaching of programming course among students majoring in computer science and technology and information security. This teaching reform aimed to better cultivate students’ practical ability, innovation ability, and knowledge-integrated application ability, considering the course concept, course design, course implementation, and course operation, and exploring the practice of teaching process reconfiguration, teaching content organization, and teaching method integration. This multi-integration experimental teaching reform was found to fully mobilize students’ learning enthusiasm, tap into students’ potential, greatly improve students’ comprehensive practical ability, effectively achieve course goals, and lay a solid foundation for subsequent professional course learning. This teaching mode has been practically applied in current experimental teaching and is widely recognized by students, providing a reference for improving teaching quality.