Selection Of Sampling Locations Research Articles

A multi-branch dual attention segmentation network for epiphyte drone images

Acquiring images of epiphytes growing on trees using Unmanned Aerial Vehicles (UAVs) enables botanists to efficiently collect data on these important plant species. Despite the advantages offered by UAVs, challenges such as complex backgrounds, uneven lighting inside the tree canopy, and accessibility issues hinder the acquisition of quality images, resulting in acquiring images datasets of heterogenous quality. AI/Deep Learning algorithms can be used to segment target plants in these images for selecting sampling locations. Existing DL models require large volume of data for training, and they tend to prioritize local features over global ones, impacting segmentation accuracy, particularly on smaller, heterogeneous quality image datasets. To overcome these limitations, we propose a multi-branch dual attention segmentation network designed to effectively handle small datasets with heterogeneous quality. The proposed network incorporates dedicated branches for extracting both global and local features, utilizing spatial and channel attention mechanisms to focus on important regions. Through a fusion process and a decoder with crossed fusion technique, this network effectively combines and enhances features from multiple branches, resulting in improved segmentation performance. Output obtained from the trained model demonstrated major improvements in predicting the boundary regions and class labels, even in close-range, low-light, and zoomed/cropped images. The average Intersection over Union (IoU) scores of the trained model was 5% higher for images acquired close range, 48% higher for images in low-light conditions, and 68% higher for zoomed/cropped images when compared to those obtained from TransUnet, a state-of-the-art vision transformer model trained on epiphyte dataset. The proposed network can be used for segmenting epiphytes in images of heterogeneous quality as well as identifying targets in images acquired in domains such as agriculture and forestry.

Path to autonomous soil sampling and analysis by ground-based robots

Good site characterization is essential for the selection of remediation alternatives for impacted soils. The value of site characterization is critically dependent on the quality and quantity of the data collected. Current methods for characterizing impacted soils rely on expensive manual sample collection and off-site analysis. However, recent advances in terrestrial robotics and artificial intelligence offer a potentially revolutionary set of tools and methods that will help to autonomously explore natural environments, select sample locations with the highest value of information, extract samples, and analyze the data in real-time without exposing humans to potentially hazardous conditions. A fundamental challenge to realizing this potential is determining how to design an autonomous system for a given investigation with many, and often conflicting design criteria. This work presents a novel design methodology to navigate these criteria. Specifically, this methodology breaks the system into four components – sensing, sampling, mobility, and autonomy – and connects design variables to the investigation objectives and constraints. These connections are established for each component through a survey of existing technology, discussion of key technical challenges, and highlighting conditions where generality can promote multi-application deployment. An illustrative example of this design process is presented for the development and deployment of a robotic platform characterizing salt-impacted oil & gas reserve pits. After calibration, the relationship between the in situ robot chloride measurements and laboratory-based chloride measurements had a good linear relationship (R2-value = 0.861) and statistical significance (p-value = 0.003).

Optimal sampling using Conditioned Latin Hypercube for digital soil mapping: An approach using Bhattacharyya distance

Soil properties are important because they determine the soil’s suitability for different types of plant growth, ecosystems and biota functioning. Soil properties influence nutrient cycling, carbon sequestration and soil management. Digital Soil Mapping (DSM) is a procedure to map soil properties. Soil sampling for DSM is a foundational step in building prediction accuracy and essential for incorporating variability in terms of environmental covariates (ancillary variables). Conditioned Latin Hypercube (CLH) sampling is a method for generating a sample of points from a multivariate distribution that has been conditioned on one or more covariates. It is an extension of Latin Hypercube sampling, which is a popular technique for generating samples from a multivariate distribution in a way that ensures that each dimension is sampled uniformly. CLH sampling carries the benefit of selecting sampling locations covering the feature space and forming a Hypercube of the original sample. However, determining the optimum sample size is crucial in soil survey exercises constrained by budget and time limits. For this purpose, a study was carried out on Scotland's Finzean Estate (44.8 km2) location. A dataset of 21 independent features (16 continuous and five categorical) and 17,932 sampling locations was created using Digital Elevation Model (DEM) derivatives, soil classes map, land cover map, the peat depth map and parent material map to further generate sub-samples and compare the generated sub-samples with the original population. Two hundred CLH sampling datasets were extracted from the original population (17932 data points) with different sizes (5, 10, 15, 20, …, 100) and each size was given 10 repetitions e.g. (5_1, 5_2, …, 5_10). The sample datasets were analysed by comparing the mean, standard deviation, boxplot and estimates of the probability density function (pdf) for all the 16 continuous independent features. All the mentioned comparisons suggest that the impact of increasing sample size on the distribution of covariates can be observed up to a certain point, beyond which further increases in sample size may not yield noticeable differences. Bhattacharyya distance, a statistical measurement that quantifies the similarity between two probability distributions, was calculated between every quantitative and qualitative element of respective sampling size and for the original population. In contrast, as the CLH sample dataset size increased, the Bhattacharyya distance value decreased and became constant.. The optimum number of samples based on the study was determined for the spatial extent of the Finzean Estate in Scotland and a range of 25–50 CLH samples was suggested based on the study. This work, therefore, achieved both reductions in sampling location numbers compared to classical approaches and identification of the precise location of these sample location to achieve optimal DSM.

EXPLORING THE DRIVERS FOR HOUSEHOLD COOKING ENERGY TRANSITION IN GOMBE METROPOLIS, NIGERIA

Transition to sustainable energy in the household sector is a one-way solution to significantly reducing the contending problem of forest exploitation on one hand and on the other hand an important effort to minimizing the challenging subject of climate change. This study aimed at exploring the drivers for household cooking energy transition in Gombe metropolis with a view to providing detailed information on the transition pathways in the area for policy implications. The study used quantitative research method with structured questionnaire as the instrument for data collection, while secondary data was obtained from the Gombe State Bureau of Statistics. For data collection, systematic sampling strategy was used to select sampling locations and the households to be administered the questionnaire. The quantitative data was analysed using descriptive statistics and multinomial logistic regression analysis. Result from descriptive analysis revealed that household size (69.5%), households’ level of income (65.5%) and public influence (65.5%) were the major factors influencing modern energy choice in the study area on one hand and on the hand environmental consequences (35.8%), health risks (34%) and seasonal changes (49.5%) has no significant influence on household modern energy choice for cooking. Multinomial logistic regression analysis showed the likelihood of respondent’s socio-demographics and modern energy adoption (p-value <0.05). The study recommends conducting public awareness campaigns on the benefits of modern energy services for cooking through media and public gatherings. It also suggests providing adequate access to affordable modern energy to encourage households to adopt and shift to modern energy consumption for cooking.

A call for strategic water-quality monitoring to advance assessment and prediction of wildfire impacts on water supplies

Wildfires pose a risk to water supplies in the western U.S. and many other parts of the world, due to the potential for degradation of water quality. However, a lack of adequate data hinders prediction and assessment of post-wildfire impacts and recovery. The dearth of such data is related to lack of funding for monitoring extreme events and the challenge of measuring the outsized hydrologic and erosive response after wildfire. Assessment and prediction of post-wildfire surface water quality would be strengthened by the strategic monitoring of key parameters, and the selection of sampling locations based on the following criteria: (1) streamgage with pre-wildfire data; (2) ability to install equipment that can measure water quality at high temporal resolution, with a focus on storm sampling; (3) minimum of 10% drainage area burned at moderate to high severity; (4) lack of major water management; (5) high-frequency precipitation; and (6) availability of pre-wildfire water-quality data and (or) water-quality data from a comparable unburned basin. Water-quality data focused on parameters that are critical to human and (or) ecosystem health, relevant to water-treatment processes and drinking-water quality, and (or) inform the role of precipitation and discharge on flow paths and water quality are most useful. We discuss strategic post-wildfire water-quality monitoring and identify opportunities for advancing assessment and prediction. Improved estimates of the magnitude, timing, and duration of post-wildfire effects on water quality would aid the water resources community prepare for and mitigate against impacts to water supplies.

Distribution of Lead and Cadmium in Soils on Koko Seaport, Delta State, Nigeria

Soil contamination by Lead and Cadmium is a prevalent ecological problem that requires attention because these two metals are toxic and could accumulate and translocate in soil over time, which poses a risk of entering the food chain and affecting the living organisms in the food web. The objective of this study is to evaluate the levels of Pb and Cd in soil from selected sampling locations of the Koko Seaport in Delta State, Nigeria. Five areas near Koko Seaport were investigated for the transport of these heavy metals in the soil. Statistical analysis using ANOVA and chart models was used to determine the concentration of these metals. Cadmium and lead levels that were significant were found in the studied areas. The data did not show any differences between the two metals due to the significance of the *p>0.05 value. The variations showed a surge in comparisons of the amounts of cadmium in stations 2 and 3, respectively, as well as significant values for cadmium compared to lead. The outcome was directly influenced by the type and quantity of hazardous wastes present in the research area. The recommended remedial actions for these metals include soil cleansing, immobilization, and the choice of plants with hyper bio accumulation for phytoremediation methods that allow the use of genetic engineering.

An adaptive uncertainty-guided sampling method for geospatial prediction and its application in digital soil mapping

Sampling design can significantly reduce the uncertainty in geospatial predictions. In this paper, we developed an adaptive uncertainty-guided stepwise sampling (AUGSS) method to select sampling locations to supplement existing legacy sample points whose representation should be improved. The proposed method selects supplemental samples in a stepwise manner as guided by an objective function with two weighted sub-objectives. One reduces the area with high prediction uncertainty, and the other minimizes the overall prediction uncertainty for the entire area. The method takes an adaptive approach to adjust weights for the two sub-objectives and to tune an uncertainty threshold controlling whether a location can be reliably predicted during the sampling procedure. A case study on soil property prediction shows that AUGSS outperforms the stratified random sampling (SRS) and the non-adaptive uncertainty guided sampling method (UGSS) in terms of RMSE and Lin’s concordance correlation coefficient with different sample sizes. This study shows that the AUGSS method offers a potential for effectively adding supplemental samples to existing samples which are insufficient for spatial prediction. The adaptive strategy guided by predicted uncertainty provides an efficient support to improve the spatial pattern of samples, which plays a key role in the result accuracy of geospatial predictive mapping.

Spatial and Temporal Variation of Particulate Matter Around Lagos Environs

Air monitoring is very essential for characterizing health risks, formulating cost-effective abatement strategies, creating and enforcing appropriate air quality standards, and as well as healthy urban planning. This study investigates the spatial and temporal variation of aerosol pollution in Lagos environs, Nigeria. Selected sampling locations were Computer Village (CV), Oshodi Garage (OG), Magodo Estate (MG), and Caleb International College (CIC). Associated anthropogenic activities were the basis for the selection of the sampling location. Measurements of particulate matters were undertaken by employing the AEROCET 531S device in these selected residential, academic, and commercial environments across Lagos State. The measurements were taken at approximately 2 m from the ground in each sampling location. Data analysis and visualization were executed with python to enhance the visualization of the spatial and temporal distribution of the different particulate matter within and across the locations. A pronounced gradient of the particulate matter concentrations (µg/m3) was observed across the study area. The study outcome demonstrates that particulate matter distribution is dominated by PM10 in all locations under consideration. The proportions of fine particles to coarse particles in these environments were low ranging from 0.027 – 0.277 in CV, 0.018 – 0.395 in OG, 0.080 – 0.291 in MG, and 0.028 – 0.272 in CIC. Findings from this study established the dominance of coarse particles assumed to be contributed by cement dust, coal dust, flour, paint spray, pollen, cloud droplets, etc.

Cantigi (Vaccinium varingifolium (Blume) Miq.) Di Jalur Pendakian Gunung Lawu Kabupaten Karanganyar Jawa Tengah

Cantigi is one of the shrubs that grows at the height of the mountain. Camtigi is a plant endemic to the mountains. Among climbers, Cantigi is one of the fruits on the mountain that can be consumed. This study aims to determine the diversity of the Cantigi plant population that grows in the Mount Lawu area, Karanganyar Regency, Central Java. The research method is exploratory through exploration. The sampling technique was purpossive sampling. The selection of sampling locations was divided into 3 stations taken based on different altitudes. At an altitude of 2.819 – 3.025 m above sea level (Station I), an altitude of 3.025 – 3.192 (Station II) and an altitude of 3.192 – 3.265 (Station III). These three stations are the Mount Lawu Peak Area. The results of the study found that only 1 species of Cantigi was found in the top area of Mount Lawu, namely the species Vaccinium varingiaefolium Miq. The number of individuals encountered at each station were 67 individuals at an altitude of 2,819 – 3,025 m asl (Station I), 160 individuals at an altitude of 3.025 – 3.192 m asl (Station II) and 154 individuals at an altitude of 3.192 – 3.265 m asl (Station III). The highest number found was in the altitude range of 3.025 – 3.192 m above sea level. The results of the Diversity Index (H') obtained are 3,084 categorized as moderate and tend to be low. The conclusion of this study is that Cantigi is mostly found in areas with an altitude range of 3.025 – 3.192 m above sea level, which is about 160 individuals.

CCME-WQI and TM-WQI based Assessment for Groundwater Quality in Garividi region of Vizianagaram District, Andhra Pradesh, India

The Canadian Council of Ministers of the Environment method (CCME) and the Logarithmic Aggregation method proposed by Tiwari and Mishra (TM) were used to assess the groundwater quality for drinking in Garividi region (Gurla, Gajapatinagaram, Dattirajeru, Merakamuidam, Cheepurupalli and Garividi mandals) which occupy some central part of Vizianagaram district of Andhra Pradesh, India. The groundwater samples were collected during every month from November 2018 to October 2019 from 38 bore wells of selected sampling locations in the study area. Fourteen parameters such as pH, EC, TDS, TH, TA, Ca2+, Mg2+, Na+, CO32-, HCO3-, Cl-, SO42-, NO3-, F- of samples were analyzed using standard laboratory procedures. This paper describes the assessment of ground water quality for drinking purposes using CCME and TM water quality index methods and water quality parameters variation with regression correlation analysis during Post and Pre monsoons and monsoon. From CCME-WQI analysis, it is observed that quality of about 2.63% of the water samples is Excellent, about 28.95% of the water samples is Good, about 52.63% of the water samples is Fair, about 5.26% is Marginal and remaining 10.53% is Poor and from TM-WQI analysis, it is observed that quality of about 2.63% of the water samples is Excellent, quality of about 42.1% of the water samples is good, about 39.5% is Medium, about 13.2% is poor and remaining 2.63% is unsuitable for drinking purpose in the study area. From the correlation analysis, it is observed that EC and TDS are the important parameters, as they are significantly correlated with other parameters.

Efficient Sample Location Selection for Query Zone in Geo-Social Networks

While promoting a business or activity in geo-social networks, the geographical distance between its location and users is critical. Therefore, the problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency of DAIM heavily relies on the sample location selection. Specifically, the online seeding performance is sensitive to the distance between the promoted location and its nearest sample location, and the offline precomputation performance is sensitive to the number of sample locations. However, there is no work to fully study the problem of sample location selection for DAIM in geo-social networks. To do this, we first formalize the problem under a reasonable assumption that a promoted location always adheres to the distribution of users (query zone). Then, we propose two efficient location sampling approaches based on facility location analysis, which is one of the most well-studied areas of operations research, and these two approaches are denoted by Facility Location based Sampling (FLS) and Conditional Facility Location Based Sampling (CFLS), respectively. FLS conducts one-time sample location selection, and CFLS extends the one-time sample location selection to a continuous process, so that an online advertising service can be started immediately without sampling a lot of locations. Our experimental results on two real datasets demonstrate the effectiveness and efficiency of the proposed methods. Specifically, both FLS and CFLS can achieve better performance than the existing sampling methods for the DAIM problem, and CFLS can initialize the online advertising service in a matter of seconds and achieve better objective distance than FLS after sampling a large number of sample locations.

Optimal selection of monitoring sites in cities for SARS-CoV-2 surveillance in sewage networks.

Selecting sampling points to monitor traces of SARS-CoV-2 in sewage at the intra-urban scale is no trivial task given the complexity of the networks and the multiple technical, economic and socio-environmental constraints involved. This paper proposes two algorithms for the automatic selection of sampling locations in sewage networks. The first algorithm, is for the optimal selection of a predefined number of sampling locations ensuring maximum coverage of inhabitants and minimum overlapping amongst selected sites (static approach). The second is for establishing a strategy of iterations of sample&analysis to identify patient zero and hot spots of COVID-19 infected inhabitants in cities (dynamic approach). The algorithms are based on graph-theory and are coupled to a greedy optimization algorithm. The usefulness of the algorithms is illustrated in the case study of Girona (NE Iberian Peninsula, 148,504 inhabitants). The results show that the algorithms are able to automatically propose locations for a given number of stations. In the case of Girona, always covering more than 60% of the manholes and with less than 3% of them overlapping amongst stations. Deploying 5, 6 or 7 stations results in more than 80% coverage in manholes and more than 85% of the inhabitants. For the dynamic sensor placement, we demonstrate that assigning infection probabilities to each manhole as a function of the number of inhabitants connected reduces the number of iterations required to detect the zero patient and the hot spot areas.

Effects of ignoring survey design information for data reuse.

Data are currently being used, and reused, in ecological research at an unprecedented rate. To ensure appropriate reuse however, we need to ask the question: "Are aggregated databases currently providing the right information to enable effective and unbiased reuse?" We investigate this question, with a focus on designs that purposefully favor the selection of sampling locations (upweighting the probability of selection of some locations). These designs are common and examples are those designs that have uneven inclusion probabilities or are stratified. We perform a simulation experiment by creating data sets with progressively more uneven inclusion probabilities and examine the resulting estimates of the average number of individuals per unit area (density). The effect of ignoring the survey design can be profound, with biases of up to 250% in density estimates when naive analytical methods are used. This density estimation bias is not reduced by adding more data. Fortunately, the estimation bias can be mitigated by using an appropriate estimator or an appropriate model that incorporates the design information. These are only available however, when essential information about the survey design is available: the sample location selection process (e.g., inclusion probabilities), and/or covariates used in their specification. The results suggest that such information must be stored and served with the data to support meaningful inference and data reuse.

Model uncertainties of a storm and their influence on microplastics and sediment transport in the Baltic Sea

Abstract. Microplastics (MPs) are omnipresent in the aquatic environment where they pose a risk to ecosystem health and functioning. However, little is known about the concentration and transport patterns of this particulate contaminant. Measurement campaigns remain expensive, and assessments of regional MP distributions need to rely on a limited number of samples. Thus, the prediction of potential MP sink regions in the sea would be beneficial for a better estimation of MP concentration levels and a better sampling design. Based on a sediment transport model, this study investigates the transport of different MP model particles, polyethylene-terephthalate (PET) and polyvinyl chloride (PVC) particles with simplified spherical sizes of 10 and 330 µm, under storm conditions. A storm event was chosen because extreme wave heights cause intense sediment erosion down to depths that are otherwise unaffected; therefore, these events are critical for determining accumulation regions. The calculation of metocean parameters for such extreme weather events is subject to uncertainties. These uncertainties originate from the imperfect knowledge of the initial conditions and lateral boundary conditions for regional models, which are necessary to be able to run a numerical model. Processes, which can be resolved by the model, are limited by the model's resolution. For the processes for which the model resolution is too coarse, parameterizations are used. This leads to additional uncertainty based on the model physics. This sensitivity study targets the propagation of uncertainty from the atmospheric conditions to MP erosion and deposition, on the basis of freely available models and data. We find that atmospheric conditions have a strong impact on the quantity of eroded and deposited material. Thus, even if the settling and resuspension properties of MP were known, a quantitative transport estimation by ocean models would still show considerable uncertainty due to the imperfect knowledge of atmospheric conditions. The uncertainty in the transport depends on the particle size and density, as transport of the larger and denser plastic particles only takes place under storm conditions. Less uncertainty exists in the location of erosional and depositional areas, which seems to be mainly influenced by the bathymetry. We conclude that while quantitative model predictions of sedimentary MP concentrations in marine sediments are hampered by the uncertainty in the wind fields during storms, models can be a valuable tool to select sampling locations for sedimentary MP concentrations to support their empirical quantification. The purpose of this study is to support the strategic planning of measurement campaigns, as the model predictions can be used to identify regions with larger net deposition after a specific storm event.

Mapping Surface Moisture of a Gold Heap Leach Pad at the El Gallo Mine Using a UAV and Thermal Imaging

An understanding of the spatial and temporal variations of surface moisture content over a heap leach pad (HLP) is essential for leaching production and to achieve a high ore recovery. The current practice of leach pad monitoring and data collection remains highly manual and labor-intensive, and exposes technical staff to hazardous material (i.e., cyanide solution). To address these challenges, we propose using unmanned aerial vehicles (UAVs) equipped with thermal imaging sensors to remotely obtain high temporal and spatial resolution image data for monitoring the surface moisture distribution over HLPs. A field study was conducted over a sprinkler-irrigated HLP at El Gallo gold mine in Sinaloa State, Mexico, and the acquired data were used to derive an empirical relationship between the surface moisture content and the remotely sensed surface temperature using linear regression. Moreover, the data were used to generate moisture distribution maps of the entire HLP surface. In situ samples were taken manually during the field experiments to measure the ground-truth material moisture at selected sampling locations. The results show a good agreement between the remote sensing method and the measured ground-truth samples.

Assessment of the Physico-chemico and Microbiological changes during Nipa (Nypa fruticans) Sap Fermentation collected from different sites in Cagayan province, Philippines

Background/Objectives: Sap from Nypa fruticans are used as feedstock for a bioethanol national program to establish a sustainable and productive village-scale bioethanol industry. The study aimed to establish the physicochemical, microbiological, and molecular characteristics of nipa sap. Methods/Statistical Analysis: Nipa sap samples were collected from three sampling sites at different fermentation time. Physico-chemical parameters such as pH, titratable acidity, and total soluble solids were determined and statistically analyzed using One-way Analysis of Variance and mean ± SD values. Molecular approaches were used to profile and identify yeast species naturally residing in the sap during fermentation and assess the genetic diversity of nipa palm in selected sampling locations. Findings: Sampling site and fermentation time significantly affect the levels of pH, titratable acids, and total soluble solids in the sap samples. Sugar concentration in nipa sap is affected by sampling site: as the sampling site nears the river, the amount of sugars become significantly greater. Molecular studies revealed that among the 12 isolates obtained, five of them (Y24S1, Y48S1, Y72S1, Y24S2, and Y24S3) showed 100% similarity to strain of Pichia occidentalis, three (Y48S2, Y48S3, and Y72S3) were similar (100%) to a strain of Saccharomyces cerevisiae (YPDW9), and the other two isolates (Y72S1 and Y48S3A) were identical to Naganishia randhawae strain HBUMO6885 and Tremellales sp. LM523 respectively with a 100% similarity. PCR amplification of their trnL–trnF cpDNA genome, BLAST and phylogenetic analyses reveals that all nipa plant samples were similar to Nypa fruticans. Applications: The generated information on the effect of sampling site on sugar content of nipa sap samples could be good input in selecting expansion sites for the establishment of nipa plantations. Furthermore, the identified yeast strains can be used as inoculant or starter culture to improve fermentation. Keywords: Physico-chemical parameter; yeasts molecular profiling; nipa sap fermentation; phylogenetic analysis; genetic diversity

Characteristics of Pergau Reservoir Water Quality Profile

Water quality profile of Pergau Reservoir or also known as Sultan Ismail Petra Hydroelectric Reservoir has been conducted from 28th October to 3rd November 2019 during Pergau scientific expedation 2019. Three (3) lentic sampling stations (P1, P2 and P3) were selected with eleven (11) water parameters such as water temperature, pH, biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solid (TSS), total dissolved solid (TDS), dissolved oxygen (DO), turbidity, ammonical-nitrogen, oil and grease (O&G), and total coliform were analyzed for water quality status of Pergau Lake. Stratification profile of DO, pH and water temperature were recorded by 10m depth at all lentic stations. Generally DO, temperature and pH reading at the surface lentic sampling stations ranging between 7.11 to 9.21 mg/L, 27 to 27.5 °C and 7.50 to 8.72, respectively. Anoxic layer was detected at depth of 9 m at P1 whereas 3 m at P2 and P3. Profiling of DO, pH and water temperature shown decreased pattern when the water depth increased. Others parameters such as COD at the subsurface were ranged from 1.3 to 1.4 mg/L, TSS reading recorded ranged from 4.00 to 6.80 mg/L, ammoniacal-nitrogen ranged from 0.05 to 0.07 mg/L whereas BOD5 were ranged from 1.3 to 1.4 mg/L. The Department of Environment – Water Quality Index (DOE-WQI) classification was used to assess the water quality status of the selected sampling locations. The results showed that all locations fall under Class I ranged between 92.66 to 94.55% and in summary the area can be classified as clean and suitable for sensitive aquatic organisms.

Ore-Waste Discrimination with Adaptive Sampling Strategy

Grade control and short-term planning determine the performance of a mining project. Improving this decision, by collecting the most informative samples (data) may have significant financial impact on the project. In this paper, a method to select sampling locations is proposed in an advanced drilling grid for short-term planning and grade control in order to improve the correct assessment (ore-waste discrimination) of blocks. The sampling strategy is based on a regularized maximization of the conditional entropy of the field, functional that formally combines global characterization of the field with the principle of maximizing information extraction for ore-waste discrimination. This sampling strategy is applied to three real cases, where dense blast-hole data is available for validation from several benches. Remarkably, results show relevant and systematic improvement with respect to the standard regular grid strategy, where for deeper benches in the field the gains in ore-waste discrimination are more prominent.

A Deseda-tó környékének éjjeli nagylepke faunája

In this paper, the author publishes the results of his 2 years long research of the Heterocera-fauna around the Lake Deseda, Somogy county, Hungary. Altogether 386 Het-erocera species were found by both lamping and light trapping from 9 selected sampling locations. Also, the biogeographical and ecological analyses are given of these species. 7 pro-tected and 1 strictly protected taxa were found. 3 of them are considered endangered species (Chariaspilates formosaria, Polypogon gryphalis, Dioszeghyana schmidtii).

Public awareness of zoonotic diseases and public health implications in Makurdi Metropolis, Benue State, Nigeria.

This study was conducted with the objectives of assessing the level of awareness on zoonotic diseases among residents of Makurdi Metropolis. Questionnaire was structured into 3 sections namely, socio- demographic characteristics of residents, Awareness on domestic animals, and wild animals transmission of zoonotic diseases. Simple and systematic random sampling was used to select sample locations in which a total of 250 questionnaires were administered; Descriptive statistics, Students t-test and chi-square analysis were used to analyzed data. Field result showed that 144 respondents male were interviewed while 106 respondents were female. Residents of Makurdi Metropolis were quite aware of zoonotic diseases in both domestic and wild animals. The commonest domestic animal zoonotic diseases known to residents were: the Mad Dog disease, Bird Flu and Swain Flu. While the commonest wild animal zoonotic diseases known to residents were: Monkey Pox disease, Ebola disease, and Lassa fever, the awareness of residents on these zoonotic diseases could be associated with the recent outbreak of these diseases and in the area . It was therefore recommended that more awareness campaigns on other types of zoonotic diseases and they preventive measures should be given to residents of Makurdi metropolis to forestall future public health hazards.Keywords: Makurdi metropolis, zoonotic diseases transmission, Awareness, domestic and wild animals.