Search results for: soil texture prediction

Authors: Chaiyaporn Yuksen

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Backgroud: Supraventricular tachycardia (SVT) is an abnormally fast atrial tachycardia characterized by narrow (≤ 120 ms) and constant QRS. Adenosine was the drug of choice; the first dose was 6 mg. It can be repeated with the second and third doses of 12 mg, with greater than 90% success. The study found that patients observed at 4 hours after normal sinus rhythm was no recurrence within 24 hours. The objective of this study was to investigate the factors that influence the recurrence of SVT after adenosine in the emergency department (ED). Method: The study was conducted retrospectively exploratory model, prognostic study at the Emergency Department (ED) in Faculty of Medicine, Ramathibodi Hospital, a university-affiliated super tertiary care hospital in Bangkok, Thailand. The study was conducted for ten years period between 2010 and 2020. The inclusion criteria were age > 15 years, visiting the ED with SVT, and treating with adenosine. Those patients were recorded with the recurrence SVT in ED. The multivariable logistic regression model developed the predictive model and prediction score for recurrence PSVT. Result: 264 patients met the study criteria. Of those, 24 patients (10%) had recurrence PSVT. Five independent factors were predictive of recurrence PSVT. There was age>65 years, heart rate (after adenosine) > 100 per min, structural heart disease, and dose of adenosine. The clinical risk score to predict recurrence PSVT is developed accuracy 74.41%. The score of >6 had the likelihood ratio of recurrence PSVT by 5.71 times Conclusion: The clinical predictive score of > 6 was associated with recurrence PSVT in ED.

Keywords: clinical prediction score, SVT, recurrence, emergency department

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Authors: Maria Manzoor, Iram Gul, Muhammad Arshad, Jean Kallerhoff

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The potential of fungal isolates to be used in phytoremediation of widespread lead contaminated soil has been evaluated in this study. Five different fungal isolates (Trichoderma harzianum, Penicillium simplicissimum, Aspergillus flavus, Aspergillus niger and Mucor spp.) were obtained and tested for their tolerance to increasing concentration of lead (Pb) i.e. 100, 200, 300, 400 and 500 mgL-1 on PDA and PDB culture experiment. All strains were tolerant up to 500 mgL-1 following sequence; A. flavus > A. niger > Mucor spp. > P. simplicissimum > T. harzianum. Further the isolates were then monitored for possible effect on Pb solubility/mobility through soil incubation experiments and characterized for essays including pathogenicity, germination and root elongation and plant growth promoting activities including IAA (indole acetic acid), phosphorus solubilization and gibberellic acid (GA3) production. Results revealed that fungal isolates have positive effect on Pb mobility in soil and plant biomass production. Pb solubility was significantly (P> 0.05) increased in soil upon application of Mucor spp. P. simplicissimum and T. harzianum. when compared to control. Among different strains three isolates (Mucor spp., P. simplicissimum and T. harzianum) were nonpathogenic because no inhibitory effect of fungus was observed to plant growth when exposed to these strains in root shoot elongation essay. Particularly T. harzianum and P. simplicissimum showed great ability to increase root length by 1.1 and 1.3 folds and shoot length by 1.47 and 1.5 folds respectively under Pb stress (500 mgL-1). Significantly high production of IAA was observed in A. niger (26.7 μg/ml), Phosphorus solubilization was observed in T. harzianum (9.15 μg/ml) and GA3 production was observed in P. simplicissimum (11.02 μg/ml). From results it is concluded that Mucor spp., P. simplicissimum and T. harzianum have potential to increase Pb mobility and improving plant growth under highy Pb contamination, therefore can be used in microbially assisted phytoremediation of Pb contaminated soil.

Keywords: Pb tolerant fungus, Pb mobility, plant growth promoting activities, indole acetic acid (IAA)

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Authors: Kirsten Techmer, Jan-Erik Rybak, Simon Rudolph

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Natural formed pyrites (FeS2) are frequent sulfides in sedimentary and metamorphic rocks. Growth textures of idiomorphic pyrite assemblages reflect the conditions during their formation in the geologic sequence, furtheron the local texture analyses of the growth patterns of pyrite assemblages by EBSD reveal the possibility to resolve the growth conditions during the formation of pyrite at the micron scale. The spatial resolution of local texture measurements in the Scanning Electron Microscope used can be in the nanomete scale. Orientation contrasts resulting from domains of smaller misorientations within larger pyrite crystals can be resolved as well. The electron optical studies have been carried out in a Field-Emission Scanning Electron Microscope (FEI Quanta 200) equipped with a CCD camera to study the orientation contrasts along the surfaces of pyrite. Idiomorphic cubic single crystals of pyrite, polycrystalline assemblages of pyrite, spherically grown spheres of pyrite as well as pyrite-bearing ammonites have been studied by EBSD in the Scanning Electron Microscope. Samples were chosen to show no or minor secondary deformation and an idiomorphic 3D crystal habit, so the local textures of pyrite result mainly from growth and minor from deformation. The samples studied derived from Navajun (Spain), Chalchidiki (Greece), Thüringen (Germany) and Unterkliem (Austria). Chemical analyses by EDAX show pyrite with minor inhomogeneities e.g., single crystals of galena and chalcopyrite along the grain boundaries of larger pyrite crystals. Intergrowth between marcasite and pyrite can be detected in one sample. Pyrite may form intense growth twinning lamellae on {011}. Twinning, e.g., contact twinning is abundant within the crystals studied and the individual twinning lamellaes can be resolved by EBSD. The ammonites studied show a replacement of the shale by newly formed pyrite resulting in an intense intergrowth of calcite and pyrite. EBSD measurements indicate a polycrystalline microfabric of both minerals, still reflecting primary surface structures of the ammonites e.g, the Septen. Discs of pyrite (“pyrite dollar”) as well as pyrite framboids show growth patterns comprising a typical microfabric. EBSD studies reveal an equigranular matrix in the inner part of the discs of pyrite and a fiber growth with larger misorientations in the outer regions between the individual segments. This typical microfabric derived from a formation of pyrite crystals starting at a higher nucleation rate and followed by directional crystal growth. EBSD studies show, that the growth texture of pyrite in the samples studied reveals a correlation between nucleation rate and following growth rate of the pyrites, thus leading to the characteristic crystal habits. Preferential directional growth at lower nucleation rates may lead to the formation of 3D framboids of pyrite. Crystallographic misorientations between the individual fibers are similar. In ammonites studied, primary anisotropies of the substrates like e.g., ammonitic sutures, influence the nucleation, crystal growth and habit of the newly formed pyrites along the surfaces.

Keywords: Electron Back Scatter Diffraction (EBSD), growth pattern, Fe-sulfides (pyrite), texture analyses

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Authors: Uvais Qidwai, Amor Moursi, Mohamed Tahar, Malek Hamad, Hamad Alansi

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This paper focuses on the development and implementation of an advanced plant health monitoring system with an AI backbone and IoT sensory network. Our approach involves addressing the critical environmental factors essential for preserving a plant’s well-being, including air temperature, soil moisture, soil temperature, soil conductivity, pH, water levels, and humidity, as well as the presence of essential nutrients like nitrogen, phosphorus, and potassium. Central to our methodology is the utilization of computer vision technology, particularly a night vision camera. The captured data is then compared against a reference database containing different health statuses. This comparative analysis is implemented using an AI deep learning model, which enables us to generate accurate assessments of plant health status. By combining the AI-based decision-making approach, our system aims to provide precise and timely insights into the overall health and well-being of plants, offering a valuable tool for effective plant care and management.

Keywords: deep learning image model, IoT sensing, cloud-based analysis, remote monitoring app, computer vision, fuzzy control

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Authors: S. Girish, N. Ajay

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Permeation properties have been widely used to quantify durability characteristics of concrete for assessing long term performance and sustainability. The processes of deterioration in concrete are mediated largely by water. There is a strong interest in finding a better way of assessing the material properties of concrete in terms of durability. Water sorptivity is a useful single material property which can be one of the measures of durability useful in service life planning and prediction, especially in severe environmental conditions. This paper presents the results of the comparative study of sorptivity of Self-Compacting Concrete (SCC) with conventionally vibrated concrete. SCC is a new, special type of concrete mixture, characterized by high resistance to segregation that can flow through intricate geometrical configuration in the presence of reinforcement, under its own mass, without vibration and compaction. SCC mixes were developed for the paste contents of 0.38, 0.41 and 0.43 with fly ash as the filler for different cement contents ranging from 300 to 450 kg/m3. The study shows better performance by SCC in terms of capillary absorption. The sorptivity value decreased as the volume of paste increased. The use of higher paste content in SCC can make the concrete robust with better densification of the micro-structure, improving the durability and making the concrete more sustainable with improved long term performance. The sorptivity based on secondary absorption can be effectively used as a durability index to predict the time duration required for the ingress of water to penetrate the concrete, which has practical significance.

Keywords: self-compacting concrete, service life prediction, sorptivity, volume of paste

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Authors: Saeed Bagherifam, Trevor Brown, Chris Fellows, Ravi Naidu

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Over the last century, rapid urbanization, industrial emissions, and mining activities have resulted in widespread contamination of the environment by heavy metal(loid)s. Arsenic (As) is a toxic metalloid belonging to group 15 of the periodic table, which occurs naturally at low concentrations in soils and the earth’s crust, although concentrations can be significantly elevated in natural systems as a result of dispersion from anthropogenic sources, e.g., mining activities. Bioavailability is the fraction of a contaminant in soils that is available for uptake by plants, food chains, and humans and therefore presents the greatest risk to terrestrial ecosystems. Numerous attempts have been made to establish in situ and ex-situ technologies of remedial action for remediation of arsenic-contaminated soils. In situ stabilization techniques are based on deactivation or chemical immobilization of metalloid(s) in soil by means of soil amendments, which consequently reduce the bioavailability (for biota) and bioaccessibility (for humans) of metalloids due to the formation of low-solubility products or precipitates. This study investigated the effectiveness of two different types of synthetic manganese and iron oxides (birnessite and goethite) for stabilization of As in a soil spiked with 1000 mg kg⁻¹ of As and treated with 10% dosages of soil amendments. Birnessite was made using HCl and KMnO₄, and goethite was synthesized by the dropwise addition of KOH into Fe(NO₃) solution. The resulting contaminated soils were subjected to a series of chemical extraction studies including sequential extraction (BCR method), single-step extraction with distilled (DI) water, 2M HNO₃ and simplified bioaccessibility extraction tests (SBET) for estimation of bioaccessible fractions of As in two different soil fractions ( < 250 µm and < 2 mm). Concentrations of As in samples were measured using inductively coupled plasma mass spectrometry (ICP-MS). The results showed that soil with birnessite reduced bioaccessibility of As by up to 92% in both soil fractions. Furthermore, the results of single-step extractions revealed that the application of both birnessite and Goethite reduced DI water and HNO₃ extractable amounts of arsenic by 75, 75, 91, and 57%, respectively. Moreover, the results of the sequential extraction studies showed that both birnessite and goethite dramatically reduced the exchangeable fraction of As in soils. However, the amounts of recalcitrant fractions were higher in birnessite, and Goethite amended soils. The results revealed that the application of both birnessite and goethite significantly reduced bioavailability and the exchangeable fraction of As in contaminated soils, and therefore birnessite and Goethite amendments might be considered as promising adsorbents for stabilization and remediation of As contaminated soils.

Keywords: arsenic, bioavailability, in situ stabilisation, metalloid(s) contaminated soils

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Authors: Kazushi Murata, Fabian Watermann, O. B. Herve Gonroudobou, Le Thuy Hang, Toshiro Yamanaka, M. Larry Lopez C.

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Aims: Black pine coastal forests play an important role as a windbreak and as a natural barrier to sand and salt spray inland in Japan. The recent invasion of N₂-fxing black locust (Robinia pseudoacacia) trees in these forests is expected to have a nutritional contribution to black pine trees growth. Thus, the effect of this new source of N on black pine trees' N assimilation needs to be assessed. Methods: In order to evaluate this contribution, tree-ring isotopic composition (δ¹⁵N) and nitrogen content (%N) of black pine (Pinus thunbergii) trees in a pure stand (BPP) and a mixed stand (BPM) with black locust (BL) trees were measured for the period 2000–2019 for BPP and BL and 1990–2019 for BPM. The same measurements were conducted in plant tissues and in soil samples. Results: The tree ring δ15N values showed that for the last 30 years, BPM trees gradually switched from BPP to BL-derived soil N starting in the 1990s, becoming the dominant N source from 2000 as no significant diference was found between BPM and BL tree ring δ¹⁵N values from 2000 to 2019. No difference in root and sapwood BPM and BL δ¹⁵N values were found, but BPM foliage (−2.1‰) was different to BPP (−4.4‰) and BL (−0.3‰), which is related to the different N assimilation pathways between BP and BL. Conclusions: Based on the results of this study, the assimilation of BL-derived N inferred from the BPM tissues' δ¹⁵N values is the result of an increase in soil bioavailable N with a higher δ¹⁵N value.

Keywords: nitrogen-15, N₂-fxing species, mixed stand, soil, tree rings

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Authors: William Fuentes, Melany Gil

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Numerical simulations of offshore wind turbines (OWTs) in shallow waters demand sophisticated models considering the cyclic nature of the environmental loads. For the case of an OWT founded on sands, rapid loading may cause a reduction of the effective stress of the soil surrounding the structure. This eventually leads to its settlement, tilting, or other issues affecting its serviceability. In this work, a 3D FE model of an OWT founded on sand is constructed and analyzed. Cyclic loading with different histories is applied at certain points of the tower to simulate some environmental forces. The mechanical behavior of the soil is simulated through the recently proposed ISA-hypoplastic model for sands. The Intergranular Strain Anisotropy ISA can be interpreted as an enhancement of the intergranular strain theory, often used to extend hypoplastic formulations for the simulation of cyclic loading. In contrast to previous formulations, the proposed constitutive model introduces an elastic range for small strain amplitudes, includes the cyclic mobility effect and is able to capture the cyclic behavior of sands under a larger number of cycles. The model performance is carefully evaluated on the FE dynamic analysis of the OWT.

Keywords: offshore wind turbine, monopile, ISA, hypoplasticity

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Authors: Saci Abdelhakim Ferkous, Khedoudja Soudani, Smail Haddadi

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This paper presents the results of a laboratory experimental study that explores the skid resistance of asphalt concrete mixtures made of local low-performance aggregates by partially replacing sand with olive mill waste (OMW). OMW was mixed with aggregates using a dry process by replacing sand with contents of 5%, 7%, 10% and 15%. The mechanical performances of the mixtures were evaluated using the Marshall and Duriez tests. A modified accelerated polishing machine was used as polishing equipment, and a British pendulum tester (BPT) was used to test the skid resistance of the samples. Finally, texture parameter analysis was performed using scanning electron microscopy (SEM) and Mountains Map software to assess the effect of OMW on the friction coefficient evolution. Using a distinct road wheel for a modified version of an accelerated polishing machine, which is normally used to determine the polished stone value of aggregates, the results showed that the addition of OMW up to 10% conferred a better skid resistance in comparison to normal asphalt concrete. The presence of olive mill waste in the mixture until 15% guarantees a gain of 22%-29% in skid resistance after polishing compared with the reference mix. Indeed, from texture parameter analysis, it was observed that there was differential wear of the lightweight aggregates (OMW) compared to the other aggregates during the polishing process, which created a new surface microtexture that had new peaks and led to a good level of friction compared to the mixtures without OMW. In general, it was found that OMW is a promising modifier for asphalt mixtures with both engineering and economic merits.

Keywords: skid resistance, olive mill waste, polishing resistance, accelerated polishing machine, local materials, sustainable development.

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Authors: Manendra Singh Parihar, Sandip Ghosh Chowdhury

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Austenitic stainless steels are widely used and give a good combination of properties. When this steel is plastically deformed, a phase transformation of the metastable Face Centred Cubic Austenite to the stable Body Centred Cubic (α’) or to the Hexagonal close packed (ԑ) martensite may occur, leading to the enhancement in the mechanical properties like strength. The work was based on variant selection and corresponding texture analysis for the strain induced martensitic transformation during deformation of the parent austenite FCC phase to form the product HCP and the BCC martensite phases separately, obeying their respective orientation relationships. The automated method for reconstruction of the parent phase orientation using the EBSD data of the product phase orientation is done using the MATLAB and TSL-OIM software. The method of triplets was used which involves the formation of a triplet of neighboring product grains having a common variant and linking them using a misorientation-based criterion. This led to the proper reconstruction of the pre-transformation phase orientation data and thus to its micro structure and texture. The computational speed of current method is better compared to the previously used methods of reconstruction. The reconstruction of austenite from ԑ and α’ martensite was carried out for multiple samples and their IPF images, pole figures, inverse pole figures and ODFs were compared. Similar type of results was observed for all samples. The comparison gives the idea for estimating the correct sequence of the transformation i.e. γ → ε → α’ or γ → α’, during deformation of AISI 304 austenitic stainless steel.

Keywords: variant selection, reconstruction, EBSD, austenitic stainless steel, martensitic transformation

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Authors: Esra Yaldız, Tugba Bulbul Bahtiyar, Dicle Aydın

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Humans meet their need for shelter via housing which they structure in line with habits and necessities. In housing culture, traditional dwelling has an important role as a social and cultural transmitter. It provides concrete data by being planned in parallel with users’ life style and habits, having their own dynamics and components as well as their designs in harmony with nature, environment and the context they exist. Textures of traditional dwelling create a healthy and cozy living environment by means of adaptation to natural conditions, topography, climate, and context; utilization of construction materials found nearby and usage of traditional techniques and forms; and natural isolation of construction materials used. One of the examples of traditional settlements in Anatolia is Kilistra (Gökyurt) settlement of Konya province. Being among the important centers of Christianity in the past, besides having distinctive architecture, culture, natural features, and geographical differences (climate, geological structure, material), Kilistra can also be identified as a traditional settlement consisting of family, religious and economic structures as well as cultural interaction. The foundation of this study is the traditional residential texture of Kilistra with its unique features. The objective of this study is to assess the conformity of traditional residential texture of Kilistra with present topography, climatic data, and geographical values within the context of human scale construction, usage of green space, indigenous construction materials, construction form, building envelope, and space organization in housing.

Keywords: traditional residential architecture, Kilistra, Anatolia, Konya

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Authors: Faghihnia Torshizi Mostafa, Saitoh Masato

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An efficient method is developed for the response of a group of vertical, cylindrical fixed-head, finite length piles embedded in a homogeneous elastic stratum, subjected to harmonic force atop the pile group cap. Pile to pile interaction is represented through simplified beam-on-dynamic-Winkler-foundation (BDWF) with realistic frequency-dependent springs and dashpots. Pile group effect is considered through interaction factors. New closed-form expressions for interaction factors and curvature ratios atop the pile are extended by considering different boundary conditions at the tip of the piles (fixed, hinged). In order to investigate the fundamental characteristics of inertial bending strains in pile groups, inertial bending strains at the head of each pile are expressed in terms of slenderness ratio. The results of parametric study give valuable insight in understanding the behavior of fixed head pile groups in fundamental natural frequency of soil stratum.

Keywords: Winkler-foundation, fundamental frequency of soil stratum, normalized inertial bending strain, harmonic excitation

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Authors: Arpit Patil, Atharav Bhagwat, Rajas Bhope, Pramod Bide

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This research integrates IoT and ML to predict heart failure risks, utilizing the Framingham dataset. IoT devices gather real-time physiological data, focusing on heart rate dynamics, while ML, specifically Random Forest, predicts heart failure. Rigorous feature selection enhances accuracy, achieving over 90% prediction rate. This amalgamation marks a transformative step in proactive healthcare, highlighting early detection's critical role in cardiovascular risk mitigation. Challenges persist, necessitating continual refinement for improved predictive capabilities.

Keywords: cardiovascular diseases, internet of things, machine learning, cardiac risk assessment, heart failure prediction, early detection, cardio data analysis

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Authors: Abdelouahab Diafat, Meribai Abdelmalek, Ahmed Bahloul

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phytoremediation is the use of plants to remove or degrade organic or inorganic contaminants from soil and water this work aims to study the potential effect of malva sylvestris for the phytoremediation of soils contaminated by Zn. plants were grown in pots containing soil artificially contaminated with Zn at concentrations of 100, 200, and 300 mg/kg. the results obtained show that the Zn concentrations used have a negative effect on the growth of this plant the search for the metal carried out by the technique of atomic absorption spectrometry shows that this plant accumulates a small quantity of this metal. it can be concluded that the malva sylvestris plant tolerates Zn contaminated soils but it is not considered as a zinc hyperaccumulator plant

Keywords: phytoremidiation, Zn-contaminated soils, Malva Sylvestris, phytoextraction

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Authors: Timothy L. Porter, T. Randy Dillingham

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Vegetation growth and decomposition, along with soil microbial activity play a complex role in the production of greenhouse gases originating in forest soils. The absorption or emission (respiration) of these gases is a function of many factors relating to the soils themselves, the plants, and the environment in which the plants are growing. For this study, we have constructed a battery-powered, portable field mass spectrometer for use in analyzing gases in the soils surrounding trees, plants, and other areas. We have used the instrument to sample in real-time the greenhouse gases carbon dioxide and methane in soils where plant life may be contributing to the production of gases such as methane. Gases such as isoprene, which may help correlate gas respiration to microbial activity have also been measured. The instrument is composed of a quadrupole mass spectrometer with part per billion or better sensitivity, coupled to battery-powered turbo and diaphragm pumps. A unique ambient air pressure differentially pumped intake apparatus allows for the real-time sampling of gases in the soils from the surface to several inches below the surface. Results show that this instrument is capable of instant, part-per-billion sensitivity measurement of carbon dioxide and methane in the near surface region of various forest soils. We have measured differences in soil respiration resulting from forest thinning, forest burning, and forest logging as compared to pristine, untouched forests. Further studies will include measurements of greenhouse gas respiration as a function of temperature, microbial activity as measured by isoprene production, and forest restoration after fire.

Keywords: forest, soil, greenhouse, quadrupole

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Authors: Gherbi Khadidja

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In the Mediterranean region, water resources are limited and very unevenly distributed in space and time. The main objective of this project is the development of a wireless network for the management of water resources in northern Algeria, the Mitidja plain, which helps farmers to irrigate in the most optimized way and solve the problem of water shortage in the region. Therefore, we will develop an aid tool that can modernize and replace some traditional techniques, according to the real needs of the crops and according to the soil conditions as well as the climatic conditions (soil moisture, precipitation, characteristics of the unsaturated zone), These data are collected in real-time by sensors and analyzed by an algorithm and displayed on a mobile application and the website. The results are essential information and alerts with recommendations for action to farmers to ensure the sustainability of the agricultural sector under water shortage conditions. In the first part: We want to set up a wireless sensor network, for precise management of water resources, by presenting another type of equipment that allows us to measure the water content of the soil, such as the Watermark probe connected to the sensor via the acquisition card and an Arduino Uno, which allows collecting the captured data and then program them transmitted via a GSM module that will send these data to a web site and store them in a database for a later study. In a second part: We want to display the results on a website or a mobile application using the database to remotely manage our smart irrigation system, which allows the farmer to use this technology and offers the possibility to the growers to access remotely via wireless communication to see the field conditions and the irrigation operation, at home or at the office. The tool to be developed will be based on satellite imagery as regards land use and soil moisture. These tools will make it possible to follow the evolution of the needs of the cultures in time, but also to time, and also to predict the impact on water resources. According to the references consulted, if such a tool is used, it can reduce irrigation volumes by up to up to 40%, which represents more than 100 million m3 of savings per year for the Mitidja. This volume is equivalent to a medium-size dam.

Keywords: optimal irrigation, soil moisture, smart irrigation, water management

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Authors: Zhang Hui, Wu Caiqiu, Yuan Xuyin, Qiu Jie, Zhang Hanpei

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The heavy metal contaminations were determined with a detailed soil survey in roadside soils along Shenyang-Dalian Highway of Liaoning Province (China) and Pb, Cu, Cd, Ni and Zn were analyzed using the atomic absorption spectrophotometric method. The average concentration of Pb, Cu, Cd, Ni and Zn in roadside soils was determined to be 43.8, 26.5, 0.119, 32.1, 71.3 mg/kg respectively, and all of the heavy metal contents were higher than the background values. Different heavy metal distribution regularity was found in different land use type of roadside soil, there was an obvious peak of heavy concentration at 25m from road edge in the farmland, while in the forest and orchard soil, all heavy metals gradually decreased with the increase of distance from road edge and conformed to the exponential model. Furthermore, the heavy metal contents of heavy metals except Cd were markedly increased compared with those in 1999 and 2007, and the heavy metals concentrations of Shenyang- Dalian Highway were considered medium or low in comparison with those in other cities around the world. The assessment of heavy metal contamination of roadside soils illustrated a common low pollution for all heavy metal and recommended that more attention should be paid to Pb contamination in roadside soils in Shenyang-Dalian Highway.

Keywords: heavy metal contamination, roadside, highway, Nemerow Pollution Index

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Authors: Caihong Sun, Xizi Zhang

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Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.

Keywords: factorization machines, feature engineering, negative ratings, recommendation systems

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Authors: Javad Saeidaskari, Nader Khalafian

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The undesirable impact of vibrations induced by road and railway traffic is an important concern in modern world. These vibrations are transmitted through soil and cause disturbances to the residence area and high-tech production facilities alongside the train/traffic lines. In this paper for the first time a new method of soil improvement with vibration absorber material, is used to increase the damping factor, in other word, to reduce the ability of wave transitions in sand. In this study standard Firoozkooh No. 161 sand is used as the host sand. The semi rigid polyurethane (PU) foam which used in this research is one of the common materials for vibration absorbing purposes. Series of cyclic triaxial tests were conducted on remolded samples with identical relative density of 70% of maximum dry density for different volume percentage of shredded PU foam. The frequency of tests was 0.1 Htz with shear strain of 0.37% and 0.75% and also the effective confining pressures during the tests were 100 kPa and 350 kPa. In order to find out the best soil-PU foam mixture, different volume percent of PU foam varying from 10% to 30% were examined. The results show that adding PU foam up to 20%, as its optimum content, causes notable enhancement in damping ratio for both shear strains of 0.37% (52.19% and 69% increase for effective confining pressures of 100 kPa and 350 kPa, respectively) and 0.75% (59.56% and 59.11% increase for effective confining pressures of 100 kPa and 350 kPa, respectively). The results related to shear modulus present significant reduction for both shear strains of 0.37% (82.22% and 56.03% decrease for effective confining pressures of 100 kPa and 350 kPa, respectively) and 0.75% (89.32% and 39.9% decrease for effective confining pressures of 100 kPa and 350 kPa, respectively). In conclusion, shredded PU foams effectively affect the dynamic properties of sand and act as vibration absorber in soil.

Keywords: polyurethane foam, sand, damping ratio, shear modulus

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Authors: Weishou Shen, Changxin Zou, Dong Liu

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High-mountain environments are experiencing more rapid warming than lowlands. The Tibetan (Qinghai-Xizang, TP) Plateau, known as the “Third Pole” of the Earth and the “Water Tower of Asia,” is the highest plateau in the world, however, ecological response to climate change has been hardly documented in high altitude regions. In this paper, we investigated climate warming induced ecological changes on the Tibetan Plateau over the past 50 years through combining remote sensing data with a large amount of in situ field observation. The results showed that climate warming up to 0.41 °C/10 a has greatly improved the heat conditions on the TP. Lake and river areas exhibit increased trend whereas swamp area decreased in the recent 35 years. The expansion in the area of the lake is directly related to the increase of precipitation as well as the climate warming up that makes the glacier shrink, the ice and snow melting water increase and the underground frozen soil melting water increase. Climate warming induced heat condition growth and reduced annual range of temperature, which will have a positive influence on vegetation, agriculture production and decreased freeze–thaw erosion on the TP. Terrestrial net primary production and farmland area on the TP have increased by 0.002 Pg C a⁻¹ and 46,000 ha, respectively. We also found that seasonal frozen soil depth decreased as the consequence of climate warming. In the long term, accelerated snow melting and thinned seasonal frozen soil induced by climate warming possibly will have a negative effect on alpine ecosystem stability and soil preservation.

Keywords: global warming, alpine ecosystem, ecological response, remote sensing

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Authors: Kharina Budiman, Adinda Syifa Azhari, Eleonora Agustine

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Industrial activities get increase in this globalization era, one of the major impacts of industrial activities is a problem to the environment. This can happen because at the industrial production term will bring out pollutant in the shape of solid, liquid or gas. Normally this pollutant came from some dangerous materials for environment. However not every industry produces the same amount of pollutant, every industry produces different kind of pollution. To compare the pollution impact of industrial activities, soil sample has been taken around the animal feed industry and the fertilizer industry. This study applied the rock magnetic method and used Bartington MS2B to measured magnetic susceptibility (χ) as the physical parameter. This study tested soil samples using the value of susceptibility low frequency (χ lf) and Frequency Dependent (χ FD). Samples only taken in the soil surface with 0-5 cm depth and sampling interval was 20 cm. The animal feed factory has susceptibility low frequency (χ lf) = 111,9 – 325,7 and Frequency Dependent (χ FD) = 0,8 – 3,57 %. And the fertilizer factory has susceptibility low frequency (χ lf) = 187,1 – 494,8 and Frequency Dependent (χ FD) = 1,37 – 2,46 %. Based on the results, the highest value of susceptibility low frequency (χ lf) is the fertilizer factory, but the highest value of Frequency Dependent (FD) is the animal feed factory.

Keywords: industrial, pollution, magnetic susceptibility, χlf, χfd, animal feed industry and fertilizer industry

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Authors: Angela Vacaro de Souza, Fernando Ferrari Putti

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One of the main plants cultivated in protected environment is tomato crop, which presents significant growth in its demand, because it is a tasty fruit, rich in nutrients and of high added value, however, poor management of fertilizers induces the process of soil salinization, causing several consequences, from reduced productivity to even soil infertility. These facts are derived from the increased concentration of salts, which hampers the process of water absorption by the plant, resulting in a biochemical and nutritional imbalance in the plant. Thus, this study aimed to investigate the effects of untreated and electromagnetically treated water in salinized soils on physical, physicochemical, and biochemical parameters in tomato fruits. The experiment was conducted at the Faculty of Science and Engineering, Tupã Campus (FCE/UNESP). A randomized complete block design with two types of treated water was adopted, with five different levels of initial salinity (0; 1.5; 2.5; 4; 5.5; 7 dS m⁻¹) by fertigation. Although the effects of salinity on fruit quality parameters are evident, no beneficial effects on increasing or maintaining postharvest quality of fruits whose plants were treated with electromagnetized water were evidenced.

Keywords: Solanum lycopersicum, soil salinization, protected environment, fertigation

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Authors: E. Osazee, L. U. Bashir

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This study was conducted at the Department of Biological Sciences, Usmanu Danfodiyo University, Sokoto, Nigeria, to determine the effects of different weights of cow dung on indigenous bacterial isolates in remediation of crude oil contaminated soils. The soil (1kg) was contaminated with 20g of crude oil and this was treated with three (40g, 80g and 120g) weights of cow dung. The soils were amended after two weeks of crude oil contamination. Soil samples were collected from the plastic bags for microbiological analyses. The isolates were cultured to test their ability to grow on crude oil. The ability of the isolates to utilize the crude oil was determined using media dilution technique. Bacteria such as Proteus mirabilis, Bacillus lacterosporus, Morganella morganii, Serratia marcescens and Bacillus alvei were isolated. The variables measured were heterotrophic bacterial populations, hydrocarbon utilizing bacterial populations and the percentage of crude oil degraded in the soils. Data collected were subjected to analysis of variance (ANOVA). Results obtained indicated that all the different weights of cow dung showed appreciable effect in crude oil decontamination. Based on the findings of the experiments, it could be deduced that 120g of cow dung promoted higher degradation of hydrocarbons. Thus, it should be recommended for remediation of crude oil contaminated soil in the study area.

Keywords: crude oil, cow dung, amendment, bioremediation, decontamination

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Authors: Mohammed T. Hayajneh

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Machining of Metal Matrix Composites (MMCs) is very significant process and has been a main problem that draws many researchers to investigate the characteristics of MMCs during different machining process. The poor machining properties of hard particles reinforced MMCs make drilling process a rather interesting task. Unlike drilling of conventional materials, many problems can be seriously encountered during drilling of MMCs, such as tool wear and cutting forces. Cutting tool wear is a very significant concern in industries. Cutting tool wear not only influences the quality of the drilled hole, but also affects the cutting tool life. Prediction the cutting tool life during drilling is essential for optimizing the cutting conditions. However, the relationship between tool life and cutting conditions, tool geometrical factors and workpiece material properties has not yet been established by any machining theory. In this research work, fuzzy subtractive clustering system has been used to model the cutting tool life in drilling of Al₂O₃ particle reinforced aluminum alloy composite to investigate of the effect of cutting conditions on cutting tool life. This investigation can help in controlling and optimizing of cutting conditions when the process parameters are adjusted. The built model for prediction the tool life is identified by using drill diameter, cutting speed, and cutting feed rate as input data. The validity of the model was confirmed by the examinations under various cutting conditions. Experimental results have shown the efficiency of the model to predict cutting tool life.

Keywords: composite, fuzzy, tool life, wear

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Authors: Paul Kenneth D. Luzon, Maria Antonia N. Tanchuling

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The Quezon City Controlled Dumping facility also known as Payatas produces leachate which can contaminate soil and water environment in the area. The goal of this study is to quantify the leachate produced by the QCCDF using the Hydrologic Evaluation of Landfill Performance (HELP) model. Results could be used as input for groundwater contaminant transport studies. The HELP model is based on a simple water budget and is an essential “model requirement” used by the US Environmental Protection Agency (EPA). Annual waste profile of the QCCDF was calculated. Based on topographical maps and estimation of settlement due to overburden pressure and degradation, a total of 10M m^3 of waste is contained in the landfill. The input necessary for the HELP model are weather data, soil properties, and landfill design. Results showed that from 1988 to 2011, an average of 50% of the total precipitation percolates through the bottom layer. Validation of the results is still needed due to the assumptions made in the study. The decrease in porosity of the top soil cover showed the best mitigation for minimizing percolation rate. This study concludes that there is a need for better leachate management system in the QCCDF.

Keywords: help model, landfill, payatas trash slide, quezon city controlled dumping facility

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: R. Burahee, B. Chassinat, T. de Laclos, A. Dépée, A. Sastim

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The purpose of this paper is to propose a solution to detect, predict and reconstitute rain drops in real time – during the night – using an embedded material with an infrared camera. To prevent the system from needing too high hardware resources, simple models are considered in a powerful image treatment algorithm reducing considerably calculation time in OpenCV software. Using a smart model – drops will be matched thanks to a process running through two consecutive pictures for implementing a sophisticated tracking system. With this system drops computed trajectory gives information for predicting their future location. Thanks to this technique, treatment part can be reduced. The hardware system composed by a Raspberry Pi is optimized to host efficiently this code for real time execution.

Keywords: reconstitution, prediction, detection, rain drop, real time, raspberry, infrared

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Authors: Sateesh Kumar Pisini, Swetha Priya Pisini

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Granular piles are becoming popular as a technique of deep ground improvement not only in soft cohesive soils but also in loose cohesionless deposits. The present experimental study has been carried out on granular piles in sand (loose sand and medium dense sand i.e. relative density at 15% and 30%) with geogrid reinforcement. In this experimental study, a group of five piles installed in sand (at different spacing i.e s = 2d, 3d and 4d) the length and diameter of the pile (L = 0.4 m and d= 50 mm) kept as same for all series of experiments. Geogrid reinforcement is provided on granular piles with a limited number of laboratory tests. It has been conducted in laboratory to study the behavior of a granular pile with reinforced geogrid layers supporting a square footing at different s/d ratios. The influence of geogrid layers providing on granular piles investigated through model tests. In this paper the experimental study carried out results in significant increase in load carrying capacity and decrease in settlement reduction of the weak cohesionless soil. Also, the behavior of load carrying capacity and settlement with changing the s/d ratio has been carried out through a parametric study.

Keywords: granular piles, cohesionless soil, geogrid reinforcement, load carrying capacity

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Authors: Ahmed M. Al-Mahbashi, Mosleh A. Al-Shamrani, Muawia Dafalla

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Mixtures of sand and clay are frequently used to serve for specific purposes in several engineering practices. In environmental engineering, liner layers and cover layers are common for controlling waste disposal facilities. These layers are exposed to moisture and temperature fluctuation specially when existing in unsaturated condition. The relationship between soil suction and water content for these materials is essential for understanding their unsaturated behavior and properties such as retention capacity and unsaturated follow (hydraulic conductivity). This study is aimed at investigating retention capacity for two sand-natural expansive clay mixtures (15% (C15) and 30% (C30) expansive clay) at two ambient temperatures within the range of 5 -50 °C. Soil water retention curves (SWRC) for these materials were determined at these two ambient temperatures using different salt solutions for a wide range of suction (up to 200MPa). The results indicate that retention capacity of C15 mixture underwent significant changes due to temperature variations. This effect tends to be less visible when the clay fraction is doubled (C30). In addition, the overall volume change is marginally affected by high temperature within the range considered in this study.

Keywords: soil water retention curve, sand-expansive clay liner, suction, temperature

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Authors: Iman A. Saad, Loay E. George

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Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.

Keywords: iris recognition, contrast stretching, gradient features, texture features, Euclidean metric

Procedia PDF Downloads 337