Indigenous territories and tropical forest management in Latin America

Using data from Latin America, the authors argue that fundamental changes must take place in the legal recognition and demarcation of indigenous territories if indigenous peoples are to fulfill their potential as resource managers for threatened tropical forest ecosystems. The authors compare different national land tenure models for forest-dwelling indigenous peoples (contained in national Indian, agrarian, and protected-area laws in Latin America) and a model proposed by indigenous organizations in Latin America. The conventional models emerged during an era when most governments were more concerned with the rapid occupation and exploitation of frontier zones and the assimilation of indigenous peoples. Recent attention to the environmental degradation of these areas and the need to create alternative models of land use and development have directed attention to the potential contribution of indigenous peoples to the conservation and management of the vast tropical forests of Latin America. The authors find that indigenous peoples must be given some degree of control over their territories and resources. They contend that for successful management of tropical forests there must be a new type of partnership between indigenous peoples, the scientific community, national governments, and international development agencies. This relationship should be a contractual one, in which indigenous peoples are provided with juridical recognition and control over large areas of forest in exchange for a commitment to conserve the ecosystem and preserve biodiversity.Municipal Financial Management,Agricultural Knowledge&Information Systems,Forestry,Environmental Economics&Policies,Banks&Banking Reform

The Steady State Chlorophyll a Fluorescence Exhibits in Vivo an Optimum as a Function of Light Intensity which Reflects the Physiological State of the Plant

Modulated (690 and 730 nm), as well as direct chlorophyll (Chl) a fluorescence and changes in the concentration of the oxidized P700 were measured under steady state conditions in leaves of higher plants adapted to different light intensities. All the leaf samples exhibit an optimum curve of steady state fluorescence yield (Fs) versus the light intensity but its position with respect to light intensity varies considerably from one species to another or from one sample to other even in the same plant or within the same leaf sample. However, the optimum level of Fs was always at a moderate light intensity. By using the modulated fluorescence technique, the system with all closed (Flm) or open reaction center (Flo) were measured in steady state conditions. Each experimentally measured fluorescence yield was separated into a fluorescence emission of open (Fopen = Flo,(1—Vs)) and closed (Fclosed = (Flm . Vs)) reaction center (RC) of photosystem II where Vs=(Fs - Flo)/(Flm - Flo) is the function of fraction of closed reaction centers. With increasing light intensity, the fraction of open RC decreased while the fraction of closed RC increased. Maximum quantum efficiency (ΦPo) and actual quantum efficiency (ΦP) decreased by increasing light intensity. An optimum level of Fs was observed, when the fraction of closed reaction centers Vs of each sample was about 0.2 showing a common quenching mechanism which determines the fluorescence properties under steady state condition. This explains the apparent phenomenological contradiction that the fluorescence yield under steady state conditions can increase or decrease upon an increase of actinic ligh

Possible transmission of HIV Infection due to human bite

The potential risk of HIV-1 infection following human bite although epidemiologically insignificant, but it is biologically possible. There are anecdotal reports of HIV transmission by human bites particularly if saliva is mixed with blood. The oral tissues support HIV replication and may serve as a previously unrecognized HIV reservoir. The HIV infected individuals have more viruses in blood than saliva, possibly due to the potent HIV-inhibitory properties of saliva. The case presented here is of a primary HIV infections following a human bite where in the saliva was not blood stained but it got smeared on a raw nail bed of a recipient. The blood and saliva of the source and blood of the recipient showed a detectable viral load with 91% sequence homology of C2-V3 region of HIV gp120 between the two individuals. The recipient did not receive PEP [post exposure prophylaxis] as his family physician was unaware of salivary transmission. The family physician should have taken PEP decision after proper evaluation of the severe and bleeding bite. Hence it is necessary to treat the HIV infected human bites with post exposure prophylaxis

Application of multi-layer extreme learning machine for efficient building energy prediction

Building energy efficiency is vital, due to the substantial amount of energy consumed in buildings and the associated adverse effects. A high-accuracy energy prediction model is considered as one of the most effective ways to understand building energy efficiency. In several studies, various machine learning models have been proposed for the prediction of building energy efficiency. However, the existing models are based on classical machine learning approaches and small datasets. Using a small dataset and inefficient models may lead to poor generalization. In addition, it is not common to see studies examining the suitability of machine learning methods for forecasting the energy consumption of buildings during the early design phase so that more energy-efficient buildings can be constructed. Hence, for these purposes, we propose a multilayer extreme learning machine (MLELM) for the prediction of annual building energy consumption. Our MLELM fuses stacks of autoencoders (AEs) with an extreme learning machine (ELM). We designed the autoencoder based on the ELM concept, and it is used for feature extraction. Moreover, the autoencoders were trained in a layer-wise manner, employed to extract efficient features from the input data, and the extreme learning machine model was trained using the least squares technique for a fast learning speed. In addition, the ELM was used for decision making. In this research, we used a large dataset of residential buildings to capture various building sizes. We compared the proposed MLELM with other machine learning models commonly used for predicting building energy consumption. From the results, we validated that the proposed MLELM outperformed other comparison methods commonly used in building energy consumption prediction. From several experiments in this study, the proposed MLELM was identified as the most efficient predictive model for energy use before construction, which can be used to make informed decisions about, manage, and optimize building design before construction

Critical success factors (CSFs) for motivating end-user stakeholder’s support for ensuring sustainability of PPP projects in Nigerian host communities

This is an accepted manuscript of an article published by Emerald in Journal of Engineering, Design and Technology on 06/09/2021, available online: https://doi.org/10.1108/JEDT-04-2021-0202 The accepted version of the publication may differ from the final published version.Purpose: This study aims to investigate two public private partnership (PPP) road projects in Nigeria for exploring factors that can motivate end-user stakeholders for contributing towards sustaining a PPP project in the long-term. Design/methodology/approach: Using a case study methodology approach, this study adopts two-way data collection strategies via in-depth interviews with PPP experts and end-user stakeholders in Nigeria host communities and a questionnaire survey to relevant stakeholders. Findings: The study identifies an eight-factor structure indicating critical success factors for ensuring end-user stakeholders support PPP projects on a long-term basis in their host communities. Originality/value: Results of the study have huge implications for policymakers and project companies by encouraging the early integration of far-sighted measures that will promote long-term support and sustainability for PPP projects amongst the end-user stakeholders.Published versio

Serological screening for tick-borne encephalitis virus in eight Norwegian herds of semi-domesticated reindeer

Tick-borne encephalitis virus (TBEV) is found in Ixodes ricinus ticks throughout the area where viable tick populations exist. In Norway, TBEV is found in I. ricinus from the south coast until Brønnøy municipality in Nordland County and the range of the vector is expanding due to changes in climate, vegetation, host animals and environmental conditions. TBEV might thus have the potential to establish in new areas when I. ricinus expand its geographical distribution. At present, there is little knowledge on the status of the virus in high-altitude areas of inland regions in Norway. It has previously been indicated that reindeer may be an important sentinel species and indicator of the spread of ticks and TBEV in high-altitude regions. In this study, 408 semi-domesticated Eurasian tundra reindeer (Rangifer tarandus tarandus) from eight herds, from Tana in Troms and Finnmark County in northern Norway to Filefjell in Innlandet and Viken Counties in southern Norway, were screened for TBEV antibodies using a commercial enzyme-linked immunosorbent assay (ELISA). We found 16 TBEV reactive reindeer samples by ELISA; however, these results could not be confirmed by the serum neutralization test (SNT). This could indicate that a flavivirusand not necessarily TBEV, may be circulating among Norwegian semi-domesticated reindeer. The results also indicate that TBEV was not enzootic in Norwegian semi-domesticated reindeer in 2013–2015. This knowledge is important as an information base for future TBEV and flavivirus surveillance in Norway

Sustainability Barriers in Nigeria Construction Practice

© 2022 IOP Publishing. Content from this work may be used under the terms of the Creative Commons Attribution 3.0. licence. https://creativecommons.org/licenses/by/3.0/The struggles to practise sustainable construction are not gaining the desired traction in Nigeria. This study established the likely barriers to successful application of sustainable construction in the Nigeria construction industry and factors to overcome the possible barriers. A quantitative approach was used for the study and a questionnaire survey was conducted among the professionals and other stakeholders. A descriptive method was used in analysing the collected data. Among the highly ranked sustainability barriers to construction practice are poor sustainability education in academic institutions, lack of incentives for designers to facilitate sustainable design, ignorance of lifecycle cost benefits, sustainable construction regarded as low priority and other issues take priority, and resistance to cultural change in the industry. The research recommends adequate sustainability education in academic institutions to positively impact the required cultural change in the industry. There is call for proper government policies that support implementation of sustainable construction practices. The study also advances the need for construction professionals and stakeholders to embrace the concept of sustainability education through continuing professional development and or postgraduate studies to improve the thinking and practicability of sustainable practice of construction in Nigeria.Peer reviewedFinal Published versio

A machine learning approach for predicting critical factors determining adoption of off-site construction in Nigeria

Purpose (limit 100 words) Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors. Design/methodology/approach (limit 100 words) The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI). Findings (limit 100 words) The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine, and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM), and belief in OSC as the main influencing factors. Research limitations/implications (limit 100 words) Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond. Practical implications (limit 100 words) The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered. Originality/value (limit 100 words) The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage

Quasiperiodic waves at the onset of zero Prandtl number convection with rotation

We show the possibility of quasiperiodic waves at the onset of thermal convection in a thin horizontal layer of slowly rotating zero-Prandtl number Boussinesq fluid confined between stress-free conducting boundaries. Two independent frequencies emerge due to an interaction between a stationary instability and a self-tuned wavy instability in presence of coriolis force, if Taylor number is raised above a critical value. Constructing a dynamical system for the hydrodynamical problem, the competition between the interacting instabilities is analyzed. The forward bifurcation from the conductive state is self-tuned.Comment: 9 pages of text (LaTex), 5 figures (Jpeg format

Assessment of Some Mango Species by Fruit Characters and Fingerprint

Abstract: Six local mango accessions; Zebda, Zaghloul, Gemela, Ganofia, El-Madam and ElKobbaneia were collected from private farm in Sharkia Governorate. Physical and chemical characteristics of fruits study besides of molecular characterization (as total proteins). The data showed that El-Kobbaneia fruit had the biggest fruit also El-Madam produced the smallest one. The lowest fiber percentage was clear in Ganofia fruit followed by Zebda fruit as compared with all mango fruits under study. The highest fruit Juice percentage was shown in El-Kobbaneia fruit, while Ganofia fruit had the lowest one. Also, the lowest titrable acidity was clear in Ganofia fruit, but the highest one was detected in Zebda fruit. Meanwhile, the highest total sugar was clear in Gemela fruit. However, El-Madam fruit had the lowest VC. The highest total number of variable bands (seven) was existed in Zebda species while the lowest number was presented in Ganofia species (2 bands). The percentage of polymorphism in all mango species ranged between 16.7% in both EL-Kobbaneia and Gemela species to 29.2 % in Zebda species