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Abstract Rapid and precise detection of waterborne pathogens is critical for effective public health management and environmental safety. Traditional methods for water quality monitoring, considered the “Gold Standard,” are time-consuming, costly, and rely on centralized laboratories and expertise. These methods are impractical for on-site, real-time monitoring, and requiring further improvements. This review explores various waterborne pathogens and cutting-edge molecular detection techniques. It highlights the growing importance of point-of-care and point-of-application methods to expedite results and improve health risk management. Loop-mediated isothermal amplification emerges as a reliable, rapid, and accessible tool in the realms of on-site diagnostics and surveillance. Moreover, the review emphasizes the crucial role of water sample preparation and in-field nucleic acid isolation in augmenting pathogen detection to enable precise assessments of water quality. The advancement of these techniques will guarantee access to safe water and improve the management of waterborne diseases.

Abstract We present a novel method for analysing socio-spatial segregation in cities by considering constraints imposed by transportation networks. Using a multilayered network approach, we model the interaction probabilities of socio-economic groups with random walks and Lévy flights. This method allows for evaluation of new transport infrastructure’s impact on segregation while quantifying each network’s contribution to interaction opportunities. The proposed random walk segregation index measures the probability of individuals encountering diverse social groups based on their available means of transit via random walks. The index incorporates temporal constraints in urban mobility with a parameter, $$\alpha \in [0,1)$$ α ∈ [ 0 , 1 ) , of the probability of the random walk continuing at each time step. By applying this to a toy model and conducting a sensitivity analysis, we explore how the index changes dependent on this temporal constraint. When the parameter equals zero, the measure simplifies to an isolation index. When the parameter approaches one it represents the city’s overall socio-economic distribution by mirroring the steady-state of the random walk process. Using Cuenca, Ecuador as a case study, we illustrate the method’s applicability in transportation planning as a valuable tool for policymakers, addressing spatial distribution of socio-economic groups and the connectivity of existing transport networks, thus promoting equitable interactions throughout the city.

There is increasing use of digital technologies in urban planning, including in the generation of designs and the participative side of planning. We examine this digital planning by reporting on the application of an experimental online participatory platform in the regeneration of a London housing estate, enabling reflection on participation processes and outcomes. Drawing on lessons learned, the paper synthesises a conceptual representation of online participation and a relational framework for understanding the participatory platform and its context. We subsequently develop a ‘matrix of participative space’, building on Arnstein’s ‘ladder of participation’, to present a two-dimensional framework of online participation, identifying cases of ‘participative deficit’ and ‘democratic deficit’. We conclude with implications for future digital participation in urban planning and design.

Abstract This paper presents a data processing pipeline designed to extract information from the hyperspectral signature of unknown space objects. The methodology proposed in this paper determines the material composition of space objects from single pixel images. Two techniques are used for material identification and classification: one based on machine learning and the other based on a least square match with a library of known spectra. From this information, a supervised machine learning algorithm is used to classify the object into one of several categories based on the detection of materials on the object. The behaviour of the material classification methods is investigated under non-ideal circumstances, to determine the effect of weathered materials, and the behaviour when the training library is missing a material that is present in the object being observed. Finally the paper will present some preliminary results on the identification and classification of space objects.

Hyperspectral imaging is a crucial tool in remote sensing, which captures far more spectral information than standard color images. However, the increase in spectral information comes at the cost of spatial resolution. Super-resolution is a popular technique where the goal is to generate a high-resolution version of a given low-resolution input. The majority of modern super-resolution approaches use convolutional neural networks (CNNs). However, convolution itself is a linear operation and the networks rely on the nonlinear activation functions after each layer to provide the necessary nonlinearity to learn the complex underlying function. This means that CNNs tend to be very deep to achieve the desired results. Recently, self-organized operational neural networks (ONNs) have been proposed that aim to overcome this limitation by replacing the convolutional filters with learnable nonlinear functions through the use of MacLaurin series expansions. This work focuses on extending the convolutional filters of a popular super-resolution model to more powerful operational filters to enhance the model performance on hyperspectral images (HSIs). We also investigate the effects that residual connections and different normalization types have on this type of enhanced network. Despite having fewer parameters than their convolutional network equivalents, our results show that ONNs achieve superior super-resolution performance on small HSI datasets.

Research on immersive virtual reality's (VR) impact on K-12 education, particularly experiential learning, has increased. However, there is a paucity of research providing insight into teacher and student perspectives on integrating virtually situated learning experiences (VSLE) into curricula and classrooms. This article reports on the pedagogical and practical benefits and challenges of using an immersive VSLE to support experiential learning across three K-12 schools. Focus groups with teachers and students revealed pedagogical benefits but also constraints related to classroom environments, teacher demands, and the VSLE's scale and interactivity. The findings were synthesized into an expanded conceptualisation of Situated Experiential Education Environments, incorporating nuanced pedagogical and practical considerations raised by participants.

Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is hyperspectral imaging (HSI). Unlike multispectral images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or super resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This article reviews and discusses the most important algorithms relevant to this area of research between 2002 and 2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Metaanalysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions.

Abstract Despite increasing scholarly interest in tertiary student perceptions of plagiarism, very little is known about those held by postgraduate (PG) students, although differences between undergraduate (UG) and PG students relate to both their characteristics and the demands of their studies. Furthermore, there is a dearth of research within the context of international education, where managing plagiarism is seen as a major challenge. This paper reports on a recent online survey with 207 Vietnamese (n = 72) and local (n = 135) PG students at a New Zealand university regarding their perceptions of plagiarism. The findings showed significant differences both between and within the two groups. Perception variations arose from a range of influences and prior experiences, not just the culture in which the students were initially educated. Differences related to participants’ age, gender, academic levels, disciplines, and teaching experience. This study’s findings contribute knowledge about under-researched PG students and problematize prevalent stereotypes of international students regarding plagiarism. They generate implications for higher education institutions to accommodate international and domestic PG student needs in ways that respect their diversities in detail as individuals, not as members of a homogeneous group. Further qualitative research to explore PG student perceptions in greater depth is recommended.

Hyperspectral images (HSI) feature rich spectral information in many narrow bands but at a cost of a relatively low spatial resolution. As such, various methods have been developed for enhancing the spatial resolution of the low-resolution HSI (Lr-HSI) by fusing it with high-resolution multispectral images (Hr-MSI). The difference in spectrum range and spatial dimensions between the Lr-HSI and Hr-MSI has been fundamental but challenging for multispectral/hyperspectral (MS/HS) fusion. In this article, a multiscale spatial fusion and regularization induced auxiliary task based convolutional neural network model is proposed for deep super-resolution of HSI, where an Lr-HSI is fused with an Hr-MSI to reconstruct a high-resolution HSI (Hr-HSI) counterpart. The multiscale fusion is used to efficiently address the discrepancy in spatial resolutions between the two inputs. Based on the general assumption that the acquired Hr-MSI and the reconstructed Hr-HSI share similar underlying characteristics, the auxiliary task is proposed to learn a representation for improved generality of the model and reduced overfitting. Experimental results on five public datasets have validated the effectiveness of our approach in comparison with several state-of-the-art methods.

Aims:: The aim of this study is to report the relationship between bladder compliance and nocturia in men presenting with a variety of lower urinary tract symptoms, and to clarify the level at which bladder compliance becomes an important contributor to nocturia. Methods:: We report the results of a retrospective analysis of 104 men who presented to a single center with lower urinary tract symptoms and subsequently underwent urodynamic studies and completed a 24-hour bladder diary. Regression analyses to predict the determinants of nocturia and bladder compliance were completed, and the difference in actual number of nightly voids (ANV) was determined for those above and below 3 different cut-offs for bladder compliance: 20, 40, and 60 mL/cm H2O. Results:: Bladder compliance is inversely related to nocturia, likely due to the relationships between age, maximum voided volume, and bladder compliance. Significant differences in ANV exist between the men above and below the bladder compliance cut-offs of 40 and 60 mL/cm H2O, with the greatest difference seen at the cut-off of 60. Conclusions:: Bladder compliance < 60 mL/cm H2O appears to be a minor contributing factor to nocturia severity that is overshadowed by other bladder diary variables.

As an essential tool for secure communications, adaptive steganography aims to communicate secret information with the least security cost. Inspired by the Ranking Priority Profile (RPP), we propose a novel two-step cost function for adaptive steganography in this paper. The RPP mainly includes three rules, i.e. Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. We use the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in designing the two-step cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, we deploy the Spreading rule to smooth the resulting image produced by 2D-SSA with WMF. Extensive experiments have shown the efficacy of the proposed method, which has improved performance over four benchmarking approaches against non-shared selection channel attack. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. Besides, the proposed approach is much faster than other model-based methods.

Inspection of rice seeds is a crucial task for plant nurseries and farmers since it ensures seed quality when growing seedlings. Conventionally, this process is performed by expert inspectors who manually screen large samples of rice seeds to identify their species and assess the cleanness of the batch. In the quest to automate the screening process through machine vision, a variety of approaches utilise appearance-based features extracted from RGB images while others utilise the spectral information acquired using Hyperspectral Imaging (HSI) systems. Most of the literature on this topic benchmarks the performance of new discrimination models using only a small number of species. Hence, it is unclear whether or not model performance variance confirms the effectiveness of proposed algorithms and features, or if it can be simply attributed to the inter-class/intra-class variations of the dataset itself. In this paper, a novel method to automatically screen and classify rice seed samples is proposed using a combination of spatial and spectral features, extracted from high resolution RGB and hyperspectral images. The proposed system is evaluated using a large dataset of 8,640 rice seeds sampled from a variety of 90 different species. The dataset is made publicly available to facilitate robust comparison and benchmarking of other existing and newly proposed techniques going forward. The proposed algorithm is evaluated on this large dataset and the experimental results show the effectiveness of the algorithm to eliminate impure species by combining spatial features extracted from high spatial resolution images and spectral features from hyperspectral data cubes.

Hyperspectral Image (HSI) classification methods that use Deep Learning (DL) have proven to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have demonstrated extremely powerful performance in such tasks. However, the lack of training samples is one of the main contributors to low classification performance. Traditional CNN-based techniques under-utilize the inter-band correlations of HSI because they primarily use 2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial information using the same operation. While this overcomes the limitation of 2D-CNNs, it may lead to insufficient extraction of features. In order to overcome this issue, we propose an HSI classification approach named Tri-CNN which is based on a multi-scale 3D-CNN and three-branch feature fusion. We first extract HSI features using 3D-CNN at various scales. The three different features are then flattened and concatenated. To obtain the classification results, the fused features then traverse a number of fully connected layers and eventually a softmax layer. Experimental results are conducted on three datasets, Pavia University (PU), Salinas scene (SA) and GulfPort (GP) datasets, respectively. Classification results indicate that our proposed methodology shows remarkable performance in terms of the Overall Accuracy (OA), Average Accuracy (AA), and Kappa metrics when compared against existing methods.

Industrial baking of sponge cakes requires various quality indicators to be measured during production such as moisture content and sponge hardness. Existing techniques for measuring these properties require randomly selected sponges to be removed from the production line before samples are manually cut out of each sponge in a destructive way for testing. These samples are subsequently processed manually using dedicated analysers to measure moisture and texture properties in a lengthy process, which can take a skilled operator around 20 min to complete per sponge. In this study, the authors present a new, single sensor hyperspectral imaging approach, which has the potential to measure both sponge moisture content and hardness simultaneously. In the last decade, hyperspectral imaging systems have reduced in cost and size and, as a result, they are becoming widely used in a number of industries and research areas. Recently, there has been an increased use of this technology in the food industry and in food science applications and research. The application of this technology in the cake production environment, empowered by sophisticated signal and image processing techniques and prediction algorithms as presented in this study has the potential to provide on‐line, real‐time, stand‐off cake quality monitoring.

Thermal and mechanical processes alter the microstructure of materials, which determines their mechanical properties. This makes reliable microstructural analysis important to the design and manufacture of components. However, the analysis of complex microstructures, such as Ti6Al4V, is difficult and typically requires expert materials scientists to manually identify and measure microstructural features. This process is often slow, labour intensive and suffers from poor repeatability. This paper overcomes these challenges by proposing a new set of automated techniques for 2D microstructural analysis. Digital image processing algorithms are developed to isolate individual microstructural features, such as grains and alpha lath colonies. A segmentation of the image is produced, where regions represent grains and colonies, from which morphological features such as; grain size, volume fraction of globular alpha grains and alpha colony size can be measured. The proposed measurement techniques are shown to obtain similar results to existing manual methods while drastically improving speed and repeatability. The benefits of the proposed approach when measuring complex microstructures are demonstrated by comparing it with existing analysis software. Using a few parameter changes, the proposed techniques are effective on a variety of microstructure types and both SEM and optical microscopy images. Keywords: Microstructure analysis, Segmentation, Watershed algorithm, Titanium alloy

This study evaluated the reasons for not taking antiretroviral treatment (ART) among women of reproductive age who are disengaged from HIV care (have missed pharmacy pickups and physician visits), with the goal of identifying strategies for reengagement in HIV care. Participants were cisgender women (n = 162), 18 to 49 years of age, and who completed sociodemographic, medical history, reasons why they were not taking ART, mental health, motivation, and self-efficacy assessments. Latent class analysis was used for analysis. Women who reported avoidance-based coping (avoid thinking about HIV) had higher depression ( U = 608.5, z = −2.7, P = .007), lower motivation ( U = 601, z = −2.8, P = .006), and lower self-efficacy ( U = 644.5, z = −2.4, P = .017) than those not using this maladaptive strategy. As women living with HIV experience a disproportionate burden of poor health outcomes, interventions focused on the management of depression may improve HIV outcomes and prevent HIV transmission.

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

Target detection and classification is an important application of hyperspectral imaging in remote sensing. A wide range of algorithms for target detection in hyperspectral images have been developed in the last few decades. Given the nature of hyperspectral images, they exhibit large quantities of redundant information and are therefore compressible. Dimensionality reduction is an effective means of both compressing and denoising data. Although spectral dimensionality reduction is prevalent in hyperspectral target detection applications, the spatial redundancy of a scene is rarely exploited. By applying simple spatial masking techniques as a preprocessing step to disregard pixels of definite disinterest, the subsequent spectral dimensionality reduction process is simpler, less costly and more informative. This paper proposes a processing pipeline to compress hyperspectral images both spatially and spectrally before applying target detection algorithms to the resultant scene. The combination of several different spectral dimensionality reduction methods and target detection algorithms, within the proposed pipeline, are evaluated. We find that the Adaptive Cosine Estimator produces an improved F1 score and Matthews Correlation Coefficient when compared to unprocessed data. We also show that by using the proposed pipeline the data can be compressed by over 90% and target detection performance is maintained.

An oriented bounding box (OBB) is preferable over a horizontal bounding box (HBB) in accurate object detection. Most of existing works utilize a two-stage detector for locating the HBB and OBB, respectively, which have suffered from the misaligned horizontal proposals and the interference from complex backgrounds. To tackle these issues, region of interest transformer and attention models were proposed, yet they are extremely computationally intensive. To this end, we propose a semi-anchor-free detector (SAFDet) for object detection in aerial images, where a rotation-anchor-free-branch (RAFB) is used to enhance the foreground features via precisely regressing the OBB. Meanwhile, a center-prediction-module (CPM) is introduced for enhancing object localization and suppressing the background noise. Both RAFB and CPM are deployed during training, avoiding increased computational cost of inference. By evaluating on DOTA and HRSC2016 datasets, the efficacy of our approach has been fully validated for a good balance between the accuracy and computational cost.

Trace organic compounds associated with human activity are now ubiquitous in the environment. As the population becomes more urbanised and the use of pesticides and person care products continues to increase, urban waterways are likely to receive higher loads of trace organic contaminants with unknown ecological consequences. To establish the extent of trace organic contamination in urban runoff, concentrations of emerging chemicals of concern were determined in sediments from 99 urban wetlands in and around Melbourne, Australia between February and April, 2015. As a preliminary estimation of potential risks to aquatic biota, we compared measured concentrations with thresholds for acute and chronic toxicity, and modelled toxic units as a function of demographic and land use trends. The synthetic pyrethroid insecticide bifenthrin was common and widespread, and frequently occurred at concentrations likely to cause toxicity to aquatic life. Personal care products DEET and triclosan were common and widely distributed, while the herbicides diuron and prometryn, and the fungicides pyrimethanil and trifloxystrobin occurred less frequently. Toxic unit modelling using random forests found complex and unexpected associations between urban land uses and trace organic concentrations. Synthetic pyrethroid insecticides were identified as emerging compounds of concern, particularly bifenthrin. In contrast with previous surveys, the highest bifenthrin concentrations were associated with lower housing and population density, implicating low-density residential land use in bifenthrin contamination. We discuss the implications for pesticide regulation and urban wetland management in a global context.

The Canadian Arctic Archipelago (CAA) presents unique challenges to the determination of melt onset (MO) using remote sensing data. High spatial resolution data is required to discern melt onset among the islands and narrow waterways of the region. Current passive microwave retrievals use daily averaged 19 GHz and 37 GHz data from the multi-channel microwave radiometer (SMMR) and/or the special sensor microwave/imager (SSM/I). The development of a new passive microwave melt onset method capable of using higher resolution data is desirable. The new passive microwave melt onset method described here, named the Dynamic Threshold Variability Method (DTVM), uses higher resolution data from the 37 GHz vertically-polarized channel from the advanced microwave scanning radiometers (AMSR-E and AMSR-2). The DTVM MO detection methodology differs from previously presented passive microwave Arctic MO methods in that it does not use a fixed threshold of a brightness temperature parameter. Instead, the DTVM determines MO dates based on the distribution of dates corresponding to the exceedance of a range of brightness temperature variability thresholds. The method also uses swath data instead of daily averaged brightness temperatures, which is found to lead to improved melt detection. Two current passive microwave MO methods are compared and evaluated for applicability in the CAA alongside the DTVM. The DTVM provides MO dates at a higher spatial resolution than earlier methods in addition to higher correlation with MO dates from surface air temperature (SAT) reanalyses. It is found that, for some years, MO dates in the CAA exhibit a latitudinal dependence, while in other years the MO dates in the CAA are relatively uniform across the domain.

To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel level. First, the HSI is divided into different spatial regions, each region is shape- and size-adapted and considered as a superpixel. For each superpixel, it contains a number of pixels with similar spectral characteristic. Since the utilization of multiple features in HSI classification has been proved to be an effective strategy, we have generated both spatial and spectral features for each superpixel. By assuming that all the pixels in a superpixel belongs to one certain class, a kernel SRC is introduced to the classification of HSI. In the SRC framework, we have employed a metric learning strategy to exploit the commonalities of different features. Experimental results on two popular HSI datasets have demonstrated the efficacy of our proposed methodology.

The Aleocharinae (Coleoptera: Staphylinidae) of Ontario were reviewed in the context of recently studied material, primarily from insect surveys conducted by the University of Guelph Insect Collection (Ontario, Canada). Aleochara daviesi Klimaszewski & Brunke sp. n., Agaricomorpha websteri Klimaszewski & Brunke sp. n., Atheta (Microdota) alesi Klimaszewski & Brunke sp. n., Dinaraea backusensis Klimaszewski & Brunke sp. n., and Strigota obscurata Klimaszewski & Brunke sp. n. are described as new to science. We also report 47 new Ontario records and 24 new Canadian records. Callicerus rigidicornis (Erichson) and Alevonota gracilenta (Erichson) are newly reported from North America as adventive species. A checklist, with Canadian distributions by province, of the 224 species of Aleocharinae known from Ontario is given. The following species are placed in subjective synonymy with Dexiogyia angustiventris (Casey): (De. asperata (Casey) syn. n., De. abscissa (Casey) syn. n., De. tenuicauda (Casey) syn. n., De. intenta (Casey) syn. n., De. alticola (Casey) syn. n.). The following species are placed in subjective synonymy with Acrotona subpygmaea (Bernhauer): (Ac. avia (Casey) syn. n., Ac. puritana (Casey) syn. n.). Lectotypes are designated for Thiasophila angustiventris Casey, Th. asperata Casey, Ischnoglossa intenta Casey, Oxypoda rubescans Casey, Chilopora americana Casey, Chilopora fuliginosa Casey, Coprothassa smithi Casey, Atheta subpygmaea Bernhauer, Colpodota puritana Casey, Strigota seducens Casey, Trichiusa compacta Casey, Trichiusa hirsuta Casey and Trichiusa robustula Casey.

Visual inspection of fuel channels is important for assessing the health of the UK’s fleet of Advanced Gas-Cooled Reactor (AGR) power plants. For each fuel channel inspected, any defects found must be classified and assessed by a panel of experts and documented before the plant can return to service. Part of the current inspection process involves extracting relevant frames from visual inspection videos and manually assembling them to form a “crack montage” image. As the plants age, there is increasing pressure to inspect more fuel channels. Dealing with this increase in inspection demand requires new techniques to support the analysis of an increased volume of gathered video data so that crack montages can be made within the tight timescales of the outages. Recent work by the authors has created a technique for automatically processing inspection videos to extract the relevant frames and produce so called chanoramas from which any required defect montages can be cropped. Chanoramas are 360° panoramic images, which show the entire inside surface of the fuel channel inspected, and this provides completely a new way for plant operators to view their visual inspection data and analyse the condition of AGR fuel channels. In this paper we present an industrial case study which first introduces the concept of a chanorama and summarises some initial findings of testing the techniques used to create them. Then, based on the initial testing results, new and advanced image processing techniques which have been designed to improve the quality of the final chanoramas are presented. The paper then expands upon the use of the raw data and describes techniques for rendering it to allow 3D visualisations of the fuel channels which allow inspection engineers to view features of interest from a range of different angles.

Staphylinidae (Rove Beetles) from northeastern North America deposited in the University of Guelph Insect Collection (Ontario, Canada) were curated from 2008-2010 by the first author. The identification of this material has resulted in the recognition of thirty-five new provincial or state records, six new Canadian records, one record for the United States and two new records for eastern Canada. All records are for subfamilies other than Aleocharinae and Pselaphinae, which will be treated in future publications as collaborative projects. Range expansions of ten exotic species to additional provinces and states are reported. The known distributions of each species in northeastern North America are summarized as maps and those species with a distinctive habitus are illustrated with color photographs. Genitalia and/or secondary sexual characters are illustrated for those species currently only identifiable on the basis of dissected males. The majority of the new records are in groups that have been recently revised, demonstrating the importance of curation and local insect surveys to the understanding of biodiversity, even for taxa and areas considered ‘relatively well-known’.

Although sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.

In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear. The performance results indicate that our chosen image features from the histogram and length-scale parameter were able to classify with up to 100% accuracy; particularly, at CNNs and CNNs-SVM, by employing 80% of the data sample for training and 20% for testing. Thus, in the assessment of corneal epithelium injuries, HSI has high potential as a method that could surpass current technologies regarding speed, objectivity, and reliability.

Road hierarchy and network structure are intimately linked; however, there is not a consistent basis for representing and analyzing the particular hierarchical nature of road network structure. This paper introduces the line structure—identified mathematically as a kind of linearly ordered incidence structure—as a means of representing road network structure and demonstrates its relation to existing representations of road networks: the “primal” graph, the “dual” graph, and the route structure. In doing so, the paper shows how properties of continuity, junction type, and hierarchy relating to differential continuity and termination are necessarily absent from primal and dual graph representations but intrinsically present in line structure representations. A new property indicative of hierarchical status—“cardinality”—is introduced and illustrated with application to example networks. The paper concludes by highlighting newly explicit relationships between different kinds of road network structure representation.

A significant challenge facing most tertiary institutions is identifying organisational strategic and operational priorities for investment in e-learning capability-in short, containing the rising cost of e-learning infrastructure while maximising the educational outcomes for students. This project is engaging in a detailed e-learning capability determination exercise over a number of New Zealand Tertiary Education Organisations (TEOs). This will assess their current strategic and operational capability for delivering and maintaining e-learning in an educationally effective but also organisationally effective manner. This process is intended to validate a framework for conducting such capability determinations and provide a documented standard methodology for determining e-learning strengths and weaknesses institutionally and by sector.

Over an 18 month period four educational institutions, a New Zealand University, PTE (Private Tertiary Enterprise), Wānanga, and ITP (Institute of Technology or Polytechnic), have engaged in a process of change influenced by technology. Their e-learning capability was benchmarked using the e-learning Maturity Model (eMM) and this information used to stimulate change activities. The project has worked with the staff of participating institutions to understand and support the explicit and implicit processes used to change and improve the e-learning experiences of students and staff. The resulting case studies, discussed in this paper, illustrate the issues that face tertiary organisations and leaders engaging with the opportunities and challenges of e-learning. Five factors have been identified as significant influences on the ability of organisations to change in response to technology in the current political and educational landscape of the New Zealand tertiary sector.

Technology and change are so closely related that the use of the word innovation seems synonymous with technology in many contexts, including that of higher education. This paper contends that university culture and existing capability constrain such innovation and to a large extent determine the nature and extent of organizational change. In the absence of strong leadership, technologies are simply used as vehicles to enable changes that are already intended or which reinforce the current identity. These contentions are supported by evidence from e-learning benchmarking activities carried out over the past five years in universities in the United States, the United Kingdom, Australia and New Zealand.

Technology and change are so closely related that the use of the word innovation seems synonymous with technology in many contexts, including that of higher education. This paper contends that university culture and existing capability constrain such innovation and to a large extent determine the nature and extent of organisational change. In the absence of strong leadership, technologies are simply used as vehicles to enable changes that are already intended or which reinforce the current identity. These contentions are supported by evidence from e-learning benchmarking activities carried out over the past five years in universities in the United States, the United Kingdom, Australia and New Zealand.

There is a growing body of research concerned with the relationship between urban form and travel patterns. Studies originate from a diversity of sources, and encompass a variety of geographic scales and locations. To add to this diversity, many different characteristics of urban form to have been examined in these studies, and travel patterns have been measured in a number of different ways. This paper brings together in a systematic way the results of many recent studies on urban form and travel patterns over the last 20 years. As well as this systematic approach, other key aspects of this review paper include the identification of where research has been concentrated (and where there are gaps in research), and in the critique of the studies, which includes issues of data accuracy, reliability and quality, the applicability of research methods and data interpretation. The critique focuses in some detail on the interaction of socio-economic factors with urban form and travel patterns.