The effects of urbanisation on non-timber forest product dependencies : a case study of three settlements in the Chobe district of northern Botswana

The aim of this study was to investigate the impacts of urbanisation on the use of, and access to, NTFPs in three settlements in the Chobe District of northern Botswana. Specific objectives were to determine the extent of NTFP use occurring in these areas; the purposes of use; the factors that influence use and access in the rural/urban context, particularly government rules and regulations; and implications for future NTFP use in this region. Research was conducted in three settlements: Kasane, Kazungula and Lesoma. Kasane is an urban town, Kazungula is less urbanised and Lesoma is a rural village. All areas are surrounded by state-owned Forest Reserves and the Chobe National Park. The study employed both qualitative and quantitative data collection methods including household interviews (30 in Kasane, 30 in Kazungula and 25 in Lesoma), four key informant interviews, two focus groups with youth and the collection of other grey literature relating to government harvest permits and market data. Households in all three areas used NTFPs despite the different rural and urban contexts in which they exist. Kasane and Kazungula showed a less diverse range of resource use, with fuelwood and wild foods the most commonly used resources in all three areas. These resources were used mainly for subsistence purposes. Harvest locations varied but were most commonly in and around the settlements themselves. Households in Kasane and Kazungula expressed the desire to use fewer resources in the future, mainly for conservation reasons, while those in Lesoma wished to use more. The government rules and regulations, particularly the DFRR permit system, were found to restrict resource access. Despite this, households in the more urban areas felt that the laws were necessary while those households in Lesoma thought that the laws conflicted with community livelihood needs. The majority of respondents believed conservation management to be a barrier to resource access as the presence of wild animals and anti- poaching units in the harvesting areas compromised safety. The general absence of resource commercialisation and market opportunities in the settlements, especially the urban towns of Kasane and Kazungula, were other commonly cited barriers to resource access. The perceived degradation of traditional practices due to modernity and urbanisation was evident for most households in all three areas but the actual loss of indigenous knowledge was most apparent in the urban areas. Wider implications for this case study are the application of the findings to further research into the impacts of urbanisation. This study can add to the literature around the implementation of improved urban development strategies, including the reliance on NTFPs and declines in cultural and environmental degradation. Recommendations provided in this study include further investigations into resource use; the application of resource co-management; improved market infrastructure and the implementation of ecotourism and local craft-making projects

The effect of novel thiazole-derived small molecules on the neuronal differentiation of human neuroblastoma SH-SY5Y cells

Expected release date-April 202

Aliasing is Good for You: Joint Registration and Reconstruction for Super-Resolution

In many applications, the sampling frequency is limited by the physical characteristics of the components: the pixel pitch, the rate of the A/D converter, etc. A low-pass filter is then often applied before the sampling operation to avoid aliasing. However, when multiple copies are available, it is possible to use the information that is inherently present in the aliasing to reconstruct a higher resolution signal. If the different copies have unknown relative offsets, this is a non-linear problem in the offsets and the signal coefficients. They are not easily separable in the set of equations describing the super-resolution problem. Thus, we perform joint registration and reconstruction from multiple unregistered sets of samples. We give a mathematical formulation for the problem when there are M sets of N samples of a signal that is described by L expansion coefficients. We prove that the solution of the registration and reconstruction problem is generically unique if MN >= L+M-1. We describe two subspace-based methods to compute this solution. Their complexity is analyzed, and some heuristic methods are proposed. Finally, some numerical simulation results on one and two-dimensional signals are given to show the performance of these methods

An FPGA Implementation of a Montgomery Multiplier Over GF(2^m)

This paper describes an efficient FPGA implementation for modular multiplication in the finite field GF(2^m) that is suitable for implementing Elliptic Curve Cryptosystems. We have developed a systolic array implementation of a~Montgomery modular multiplication. Our solution is efficient for large finite fields (m=160-193), that offer a high security level, and it can be scaled easily to larger values of m. The clock frequency of the implementation is independent of the field size. In contrast to earlier work, the design is not restricted to field representations using irreducible trinomials, all one polynomials or equally spaced polynomials

Applying General Access Structure to Proactive Secret Sharing Schemes

Verifiable secret sharing schemes (VSS) are secret sharing schemes (SSS) dealing with possible cheating by participants. In this paper we use the VSS proposed by Cramer, Damgard and Maurer \cite{CDM99,CDM00,Cra00}. They introduced a purely linear algebraic method to transform monotone span program (MSP) based secret sharing schemes into VSS. In fact, the monotone span program model of Karchmer and Wigderson \cite{KW93} deals with arbitrary monotone access structures and not just threshold ones. Stinson and Wei \cite{SW99} proposed a proactive SSS based on threshold (polynomial) VSS. The purpose of this paper is to build unconditionally secure proactive SSS over any access structure, as long as it admits a linear secret sharing scheme (LSSS)

Benchmarking least squares support vector machine classifiers.

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a ( convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.least squares support vector machines; multiclass support vector machines; sparse approximation; discriminant-analysis; sparse approximation; learning algorithms; classification; framework; kernels; time; SISTA;

Privacy in times of internet, social media and big data

The current use of the internet, social media and big data severely affects the privacy of ordinary users. This positioning paper is primarily aimed at the private user young and old who did not have special education or training regarding ICT but still uses these services intensively and who, whether or not, rightly worries about the hazards to which his or her privacy is exposed. This requires not only a better and deeper understanding of the technological possibilities and limitations, but also the commercial interests, and their relation to the constraints and threats of our personal privacy when using the many often valuable services. The speci c aspects of privacy as patients, or the privacy regulations for companies and institutions that track and process les with data from individuals, employees, students, or customers, is not dealt with but is referred to other reports. This positioning paper has been conceived by a working group of members of KVAB and external experts covering the different aspects of this interdisciplinary subject, that have met regularly over a period of one year.Since the ICT world is often overwhelmed with “jargon” words, the scope of which does not penetrate or because the newspapers sometimes describe very frightening lowly-backed situations, we rst discuss the main concepts both at the level of the machine learning, data extraction and the big data, as well as the privacy issues that arise, and nally the ways in which a better privacy can be acquired.In order to make this more concrete for the modal reader, we discuss important privacy hazards in a number of concrete situations, such as the digital life of a family, the big data police in passenger pro les, the internet of things, the context of smart cities, distributed information versus central collection, autonomous vehicles, and location information. Although this digital revolution is not over yet, the modal user can already modify his behavior.There is extensive scienti c literature on this subject, but there are also many widely accessible texts available recently, including websites, to which the interested reader is referred to in the bibliography.The ten recommendations mainly focus on various target groups and situations

Real-time monitoring of 3T3-L1 preadipocyte differentiation using a commercially available electric cell-substrate impedance sensor system

Real-time analysis offers multiple benefits over traditional end point assays. Here, we present a method of monitoring the optimisation of the growth and differentiation of murine 3T3-L1 preadipocytes to adipocytes using the commercially available ACEA xCELLigence Real-Time Cell Analyser Single Plate (RTCA SP) system. Our findings indicate that the ACEA xCELLigence RTCA SP can reproducibly monitor the primary morphological changes in pre- and post-confluent 3T3-L1 fibroblasts induced to differentiate using insulin, dexamethasone, 3-isobutyl-1-methylxanthine and rosiglitazone; and may be a viable primary method of screening compounds for adipogenic factors

Guardian of the furnace: mitochondria, TRAP1, ROS and stem cell maintenance

Mitochondria are key to eukaryotic cell survival and their activity is linked to generation of reactive oxygen species (ROS) which in turn acts as both an intracellular signal and an effective executioner of cells with regards to cellular senescence. The mitochondrial molecular chaperone tumor necrosis factor receptor associated protein 1 (TRAP1) is often termed the cytoprotective chaperone for its role in cancer cell survival and protection from apoptosis. Here, we hypothesize that TRAP1 serves to modulate mitochondrial activity in stem cell maintenance, survival and differentiation