Search results for: temporal modalities

Authors: Shotiros Protong

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The incidents of sudden landslides in Thailand during the past decade have occurred frequently and more severely. It is necessary to focus on the principal parameters used for analysis such as land cover land use, rainfall values, characteristic of soil and digital elevation model (DEM). The combination of intense rainfall and severe monsoons is increasing due to global climate change. Landslide occurrences rapidly increase during intense rainfall especially in the rainy season in Thailand which usually starts around mid-May and ends in the middle of October. The rain-triggered landslide hazard analysis is the focus of this research. The combination of geotechnical and hydrological data are used to determine permeability, conductivity, bedding orientation, overburden and presence of loose blocks. The regional landslide hazard mapping is developed using the Slope Stability Index SINMAP model supported on Arc GIS software version 10.1. Geological and land use data are used to define the probability of landslide occurrences in terms of geotechnical data. The geological data can indicate the shear strength and the angle of friction values for soils above given rock types, which leads to the general applicability of the approach for landslide hazard analysis. To address the research objectives, the methods are described in this study: setup and calibration of the SINMAP model, sensitivity of the SINMAP model, geotechnical laboratory, landslide assessment at present calibration and landslide assessment under future climate simulation scenario A2 and B2. In terms of hydrological data, the millimetres/twenty-four hours of average rainfall data are used to assess the rain triggered landslide hazard analysis in slope stability mapping. During 1954-2012 period, is used for the baseline of rainfall data at the present calibration. The climate change in Thailand, the future of climate scenarios are simulated by spatial and temporal scales. The precipitation impact is need to predict for the climate future, Statistical Downscaling Model (SDSM) version 4.2, is used to assess the simulation scenario of future change between latitude 16o 26’ and 18o 37’ north and between longitude 98o 52’ and 103o 05’ east by SDSM software. The research allows the mapping of risk parameters for landslide dynamics, and indicates the spatial and time trends of landslide occurrences. Thus, regional landslide hazard mapping under present-day climatic conditions from 1954 to 2012 and simulations of climate change based on GCM scenarios A2 and B2 from 2013 to 2099 related to the threshold rainfall values for the selected the study area in Uttaradit province in the northern part of Thailand. Finally, the landslide hazard mapping will be compared and shown by areas (km2 ) in both the present and the future under climate simulation scenarios A2 and B2 in Uttaradit province.

Keywords: landslide hazard, GIS, slope stability index (SINMAP), landslides, Thailand

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Authors: Prabhat Kashyap, Krishan Kumar

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Delhi, the capital of India, is one of the most populated and polluted city among the world. In terms of air quality, Delhi’s air is degrading day by day & becomes worst of any major city in the world. The role of biogenic volatile organic compounds (BVOCs) is not much studied in cities like Delhi as a culprit for degraded air quality. They not only play a critical role in rural areas but also determine the atmospheric chemistry of urban areas as well. Particularly, Isoprene (2-methyl 1,3-butadiene, C5H8) is the single largest emitted compound among other BVOCs globally, that influence the tropospheric ozone chemistry in urban environment as the ozone forming potential of isoprene is very high. It is mainly emitted by vegetation & a small but significant portion is also released by vehicular exhaust of petrol operated vehicles. This study investigates the spatial and temporal variations of quantitative measurements of isoprene emissions along with different traffic tracers in 2 different seasons (post-monsoon & winter) at four different locations of Delhi. For the quantification of anthropogenic and biogenic isoprene, two sites from traffic intersections (Punjabi Bagh & CRRI) and two sites from vegetative locations (JNU & Yamuna Biodiversity Park) were selected in the vicinity of isoprene emitting tree species like Ficus religiosa, Dalbergia sissoo, Eucalyptus species etc. The concentrations of traffic tracers like benzene, toluene were also determined & their robust ratios with isoprene were used to differentiate anthropogenic isoprene with biogenic portion at each site. The ozone forming potential (OFP) of all selected species along with isoprene was also estimated. For collection of intra-day samples (3 times a day) in each season, a pre-conditioned fenceline monitoring (FLM) carbopack X thermal desorption tubes were used and further analysis was done with Gas chromatography attached with mass spectrometry (GC-MS). The results of the study proposed that the ambient air isoprene is always higher in post-monsoon season as compared to winter season at all the sites because of high temperature & intense sunlight. The maximum isoprene emission flux was always observed during afternoon hours in both seasons at all sites. The maximum isoprene concentration was found to be 13.95 ppbv at Biodiversity Park during afternoon time in post monsoon season while the lower concentration was observed as low as 0.07 ppbv at the same location during morning hours in winter season. OFP of isoprene at vegetation sites is very high during post-monsoon because of high concentrations. However, OFP for other traffic tracers were high during winter seasons & at traffic locations. Furthermore, high correlation between isoprene emissions with traffic volume at traffic sites revealed that a noteworthy share of its emission also originates from road traffic.

Keywords: biogenic VOCs, isoprene emission, anthropogenic isoprene, urban vegetation

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Authors: Carlos Tapia Cortez, Serkan Saydam, Jeff Coulton, Claude Sammut

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Strategic mineral commodities (SMC) both energetic and metals have long been fundamental for human beings. There is a strong and long-run relation between the mineral resources industry and society's evolution, with the provision of primary raw materials, becoming one of the most significant drivers of economic growth. Due to mineral resources’ relevance for the entire economy and society, an understanding of the SMC market behaviour to simulate price fluctuations has become crucial for governments and firms. For any human activity, SMC price fluctuations are affected by economic, geopolitical, environmental, technological and psychological issues, where cognition has a major role. Cognition is defined as the capacity to store information in memory, processing and decision making for problem-solving or human adaptation. Thus, it has a significant role in those systems that exhibit dynamic equilibrium through time, such as economic growth. Cognition allows not only understanding past behaviours and trends in SCM markets but also supports future expectations of demand/supply levels and prices, although speculations are unavoidable. Technological developments may also be defined as a cognitive system. Since the Industrial Revolution, technological developments have had a significant influence on SMC production costs and prices, likewise allowing co-integration between commodities and market locations. It suggests a close relation between structural breaks, technology and prices evolution. SCM prices forecasting have been commonly addressed by econometrics and Gaussian-probabilistic models. Econometrics models may incorporate the relationship between variables; however, they are statics that leads to an incomplete approach of prices evolution through time. Gaussian-probabilistic models may evolve through time; however, price fluctuations are addressed by the assumption of random behaviour and normal distribution which seems to be far from the real behaviour of both market and prices. Random fluctuation ignores the evolution of market events and the technical and temporal relation between variables, giving the illusion of controlled future events. Normal distribution underestimates price fluctuations by using restricted ranges, curtailing decisions making into a pre-established space. A proper understanding of SMC's price dynamics taking into account the historical-cognitive relation between economic, technological and psychological factors over time is fundamental in attempting to simulate prices. The aim of this paper is to discuss the SMC market cognition hypothesis and empirically demonstrate its dynamic-cognitive capacity. Three of the largest and traded SMC's: oil, copper and gold, will be assessed to examine the economic, technological and psychological cognition respectively.

Keywords: commodity price simulation, commodity price uncertainties, dynamic-cognition, dynamic systems

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Authors: M. A. Akulov, V. O. Zaharov, A. A. Tomskij

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Introduction: Scapulohumeral periarthrosis, resulting as a reaction to mechanical injury of shoulder tendons and muscles, is associated with high incidence of temporal and permanent disability. There is a strong need for investigation of treatment of that patient group. Severe pain leads to limitation of movements range, which result in secondary alterations of joint capsule and ligamentous apparatus. Muscle tension and edema, swelling of fascial and fibrous structures result in nerve and vascular compression in intramuscular and osseo-muscular-fibrous spaces. Botulinum toxin injection leads to decrease of muscle tone, increase of movements range and associated pain alleviation. Study aim: Optimization of rehabilitation process in scapolohumeral periarthrosis using Xeomin. Patients and methods: 40 patients aged 37-56 years with scapulohumeral periarthrosis were evaluated. Patients were divided into two groups according to treatment regimen. The first (main) group included 21 patients, receiving intramuscular Xeomin 150-200 U in the area of brachio-scapular joint and trigger points (inducing motion range limitation and pain). Treatment procedures were combined with physical therapy and osteopathic procedures. The second (control) group included 19 patients, receiving conventional physical therapy and osteopathic procedures. The evaluation and efficacy comparison was carried out using McGill pain questionnaire, Clinical Global Impression scale (CGI), and patient-reported increase of brachio-scapular joint movement range and pain decrease at 1, 3 and 6 months of treatment. Results. The study demonstrated a significant improvement in the main group after one month of treatment, which persisted during months of treatment. At baseline, rank pain index on McGill pain questionnaire was 18,4±4,9 and 17,8±5,1 in the main and control group, respectively (p > 0,05). At 1 month of treatment we observed a significant decrease of pain syndrome (no pain or modest pain) and increase of movement range in angular degrees in the main group (р < 0,05). In the control group significant improvements were observed only on the 3 month of treatment (р < 0,05), but at 6 months of treatment the improvement in pain syndrome and motion range in brachio-scapular joint was significantly smaller, than in the main group. Rank pain index on McGill pain scale was 5,2±1,8 in the main group compared to 12,0±2,6 in the control group (р < 0,05). At 6 months of treatment patients in the first group reported a significant/highly significant improvement of general health on CGI, whereas in the second group most patients reported a minimal improvement. We observed a sustained and persistent improvement of motion range in brachio-scapular joint in the main group. Conclusion: Xeomin injections as a part of rehabilitation process in scapulohumeral periarthrosis lead to reduced time and increased quality of rehabilitation.

Keywords: botulinum toxin, rehabilitation, scapulohumeral periarthrosis

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Authors: A. Maghoul, A. Rostampouryasouri, MR. Maghami

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The development of an urban design and power network planning has been gaining momentum in recent years. The integration of renewable energy with urban design has been widely regarded as an increasingly important solution leading to climate change and energy security. Through the use of passive strategies and solar integration with Urban Building Energy Modeling (UBEM) tools, architects and designers can create high-quality designs that meet the needs of clients and stakeholders. To determine the most effective ways of combining renewable energy with urban development, we analyze the relationship between urban form and renewable energy production. The procedure involved in this practice include passive solar gain (in building design and urban design), solar integration, location strategy, and 3D models with a case study conducted in Tehran, Iran. The study emphasizes the importance of spatial and temporal considerations in the development of sector coupling strategies for solar power establishment in arid and semi-arid regions. The substation considered in the research consists of two parallel transformers, 13 lines, and 38 connection points. Each urban load connection point is equipped with 500 kW of solar PV capacity and 1 kWh of battery Energy Storage (BES) to store excess power generated from solar, injecting it into the urban network during peak periods. The simulations and analyses have occurred in EnergyPlus software. Passive solar gain involves maximizing the amount of sunlight that enters a building to reduce the need for artificial lighting and heating. Solar integration involves integrating solar photovoltaic (PV) power into smart grids to reduce emissions and increase energy efficiency. Location strategy is crucial to maximize the utilization of solar PV in an urban distribution feeder. Additionally, 3D models are made in Revit, and they are keys component of decision-making in areas including climate change mitigation, urban planning, and infrastructure. we applied these strategies in this research, and the results show that it is possible to create sustainable and energy-efficient urban environments. Furthermore, demand response programs can be used in conjunction with solar integration to optimize energy usage and reduce the strain on the power grid. This study highlights the influence of ancient Persian architecture on Iran's urban planning system, as well as the potential for reducing pollutants in building construction. Additionally, the paper explores the advances in eco-city planning and development and the emerging practices and strategies for integrating sustainability goals.

Keywords: energy-efficient urban planning, sustainable architecture, solar energy, sustainable urban design

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Authors: Jesús Horacio, Alfredo Ollero, Víctor Bouzas-Blanco, Augusto Pérez-Alberti

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Rivers have traditionally been classified, assessed and managed in terms of hydrological, chemical and / or biological criteria. Geomorphological classifications had in the past a secondary role, although proposals like River Styles Framework, Catchment Baseline Survey or Stroud Rural Sustainable Drainage Project did incorporate geomorphology for management decision-making. In recent years many studies have been attracted to the geomorphological component. The geomorphological processes and their associated forms determine the structure of a river system. Understanding these processes and forms is a critical component of the sustainable rehabilitation of aquatic ecosystems. The fluvial auto-classification approach suggests that a river is a self-built natural system, with processes and forms designed to effectively preserve their ecological function (hydrologic, sedimentological and biological regime). Fluvial systems are formed by a wide range of elements with multiple non-linear interactions on different spatial and temporal scales. Besides, the fluvial auto-classification concept is built using data from the river itself, so that each classification developed is peculiar to the river studied. The variables used in the classification are specific stream power and mean grain size. A discriminant analysis showed that these variables are the best characterized processes and forms. The statistical technique applied allows to get an individual discriminant equation for each geomorphological type. The geomorphological classification was developed using sites with high naturalness. Each site is a control point of high ecological and geomorphological quality. The changes in the conditions of the control points will be quickly recognizable, and easy to apply a right management measures to recover the geomorphological type. The study focused on Galicia (NW Spain) and the mapping was made analyzing 122 control points (sites) distributed over eight river basins. In sum, this study provides a method for fluvial geomorphological classification that works as an open and flexible tool underlying the fluvial auto-classification concept. The hydrographic mapping is the visual expression of the results, such that each river has a particular map according to its geomorphological characteristics. Each geomorphological type is represented by a particular type of hydraulic geometry (channel width, width-depth ratio, hydraulic radius, etc.). An alteration of this geometry is indicative of a geomorphological disturbance (whether natural or anthropogenic). Hydrographic mapping is also dynamic because its meaning changes if there is a modification in the specific stream power and/or the mean grain size, that is, in the value of their equations. The researcher has to check annually some of the control points. This procedure allows to monitor the geomorphology quality of the rivers and to see if there are any alterations. The maps are useful to researchers and managers, especially for conservation work and river restoration.

Keywords: fluvial auto-classification concept, mapping, geomorphology, river

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Authors: Caroline Lavratti, Pedro Dal Lago, Gustavo Reinaldo, Gilson Dorneles, Andreia Bard, Laira Fuhr, Daniela Pochmann, Alessandra Peres, Luciane Wagner, Viviane Elsner

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Approximately 1% of the world's population is affected by schizophrenia (SZ), a chronic and debilitating neurodevelopmental disorder. Among possible factors, reduced levels of Brain-derived neurotrophic factor (BDNF) has been recognized in physiopathogenesis and course of SZ. In this context, peripheral BDNF levels have been used as a biomarker in several clinical studies, since this neurotrophin is able to cross the blood-brain barrier in a bi-directional manner and seems to present a strong correlation with the central nervous system fluid levels. The patients with SZ usually adopts a sedentary lifestyle, which has been partly associated with the increase in obesity incidence rates, metabolic syndrome, type 2 diabetes and coronary heart disease. On the other hand, exercise, a non-invasive and low cost intervention, has been considered an important additional therapeutic option for this population, promoting benefits to physical and mental health. To our knowledge, few studies have been pointed out that the positive effects of exercise in SZ patients are mediated, at least in part, to enhanced levels of BDNF after training. However, these studies are focused on evaluating the effect of single bouts of exercise of chronic interventions, data concerning the short- and long-term exercise outcomes on BDNF are scarce. Therefore, this study aimed to evaluate the effect of a concurrent exercise protocol (CEP) on plasma BDNF levels of SZ patients in different time-points. Material and Methods: This study was approved by the Research Ethics Committee of the Centro Universitário Metodista do IPA (no 1.243.680/2015). The participants (n=15) were subbmited to the CEP during 90 days, 3 times a week for 60 minutes each session. In order to evaluate the short and long-term effects of exercise, blood samples were collected pre, 30, 60 and 90 days after the intervention began. Plasma BDNF levels were determined with the ELISA method, from Sigma-Aldrich commercial kit (catalog number RAB0026) according to manufacturer's instructions. Results: A remarkable increase on plasma BDNF levels at 90 days after training compared to baseline (p=0.006) and 30 days (p=0.007) values were observed. Conclusion: Our data are in agreement with several studies that show significant enhancement on BDNF levels in response to different exercise protocols in SZ individuals. We might suggest that BDNF upregulation after training in SZ patients acts in a dose-dependent manner, being more pronounced in response to chronic exposure. Acknowledgments: This work was supported by Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS)/Brazil.

Keywords: exercise, BDNF, schizophrenia, time-points

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Authors: Ibtissem Hosni, Lilia Bennaceur Farah, Saber Mohamed Naceur

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In order to assess the water availability in the soil, it is crucial to have information about soil distributed moisture content; this parameter helps to understand the effect of humidity on the exchange between soil, plant cover and atmosphere in addition to fully understanding the surface processes and the hydrological cycle. On the other hand, aerodynamic roughness length is a surface parameter that scales the vertical profile of the horizontal component of the wind speed and characterizes the surface ability to absorb the momentum of the airflow. In numerous applications of the surface hydrology and meteorology, aerodynamic roughness length is an important parameter for estimating momentum, heat and mass exchange between the soil surface and atmosphere. It is important on this side, to consider the atmosphere factors impact in general, and the natural erosion in particular, in the process of soil evolution and its characterization and prediction of its physical parameters. The study of the induced movements by the wind over soil vegetated surface, either spaced plants or plant cover, is motivated by significant research efforts in agronomy and biology. The known major problem in this side concerns crop damage by wind, which presents a booming field of research. Obviously, most models of soil surface require information about the aerodynamic roughness length and its temporal and spatial variability. We have used a bi-dimensional multi-scale (2D MLS) roughness description where the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each one having a spatial scale using the wavelet transform and the Mallat algorithm to describe natural surface roughness. We have introduced multi-layer aspect of the humidity of the soil surface, to take into account a volume component in the problem of backscattering radar signal. As humidity increases, the dielectric constant of the soil-water mixture increases and this change is detected by microwave sensors. Nevertheless, many existing models in the field of radar imagery, cannot be applied directly on areas covered with vegetation due to the vegetation backscattering. Thus, the radar response corresponds to the combined signature of the vegetation layer and the layer of soil surface. Therefore, the key issue of the numerical estimation of soil moisture is to separate the two contributions and calculate both scattering behaviors of the two layers by defining the scattering of the vegetation and the soil blow. This paper presents a synergistic methodology, and it is for estimating roughness and soil moisture from C-band radar measurements. The methodology adequately represents a microwave/optical model which has been used to calculate the scattering behavior of the aerodynamic vegetation-covered area by defining the scattering of the vegetation and the soil below.

Keywords: aerodynamic, bi-dimensional, vegetation, synergistic

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Authors: S. M. A. Ismail, M. I. Ibrahim, H. Masdar, F. M. Effendi, M. F. Suhaimi, A. Suun

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Introduction: It is generally known that the rehabilitation process is as important as the reconstruction surgery. Several literature has focused on how early the rehabilitation modalities can be initiated after the surgery to ensure a safe return of patients to sports or at least regaining the pre-injury level of function following an ACL reconstruction. Objectives: The main objective is to study and evaluate the outcome of early balance exercises and agility training in sports rehabilitation for patients post ACL reconstruction. To compare between early balance exercises and agility training as intervention and control. (material or non-material). All of them were recruited for material exercise (balance exercises and agility training with strengthening) and strengthening only rehabilitation protocol (non-material). Followed the prospective intervention trial. Materials and Methods: Post-operative ACL reconstruction patients performed in Selayang and Sg Buloh Hospitals from 2012 to 2014 were selected for this study. They were taken from Malaysian Knee Ligament Registry (MKLR) and all patients had single bundle reconstruction with autograft hamstring tendon (semitendinosus and gracilis). ACL injury from any type of sports were included. Subjects performed various type of physical activity for rehabilitation in every 18 week for a different type of rehab activity. All subject attended all 18 sessions of rehabilitation exercises and evaluation was done during the first, 9th and 18th session. Evaluation format were based on clinical assessment (anterior drawer, Lachmann, pivot shift, laxity with rolimeter, the end point and thigh circumference) and scoring (Lysholm Knee scoring and Tegner Activity Level scale). Rehabilitation protocol initiated from 24 week after the surgery. Evaluation format were based on clinical assessment (anterior drawer, Lachmann, pivot shift, laxity with rolimeter, the end point and thigh circumference) and scoring (Lysholm Knee scoring and Tegner Activity Level scale). Results and Discussion: 100 patients were selected of which 94 patients are male and 6 female. Age range is 18 to 54 year with the average of 28 years old for included 100 patients. All patients are evaluated after 24 week after the surgery. 50 of them were recruited for material exercise (balance exercises and agility training with strengthening) and 50 for strengthening only rehabilitation protocol (non-material). Demographically showed 85% suffering sports injury mainly from futsal and football. 39 % of them have abnormal BMI (26 – 38) and involving of the left knee. 100% of patient had the basic radiographic x-ray of knee and 98% had MRI. All patients had negative anterior drawer’s, Lachman test and Pivot shift test during the post ACL reconstruction after the complete rehabilitation. There was 95 subject sustained grade I injury, 5 of grade II and 0 of grade III with 90% of them had soft end-point. Overall they scored badly on presentation with 53% of Lysholm score (poor) and Tegner activity score level 3/10. After completing 9 weeks of exercises, of material group 90% had grade I laxity, 75% with firm end-point, Lysholm score 71% (fair) and Tegner activity level 5/10 comparing non-material group who had 62% of grade I laxity , 54% of firm end-point, Lyhslom score 62 % (poor) and Tegner activity level 4/10. After completed 18 weeks of exercises, of material group maintained 90% grade I laxity with 100 % with firm end-point, Lysholm score increase 91% (excellent) and Tegner activity level 7/10 comparing non-material group who had 69% of grade I laxity but maintained 54% of firm end-point, Lysholm score 76% (fair) and Tegner activity level 5/10. These showed the improvement were achieved fast on material group who have achieved satisfactory level after 9th cycle of exercises 75% (15/20) comparing non-material group who only achieved 54% (7/13) after completed 18th session. Most of them were grade I. These concepts are consolidated into our approach to prepare patients for return to play including field testing and maintenance training. Conclusions: The basic approach in ACL rehabilitation is to ensure return to sports at post-operative 6 month. Grade I and II laxity has favourable and early satisfactory outcome base on clinical assessment and Lysholm and Tegner scoring point. Reduction of laxity grading indicates satisfactory outcome. Firm end-point showed the adequacy of rehabilitation before starting previous sports game. Material exercise (balance exercises and agility training with strengthening) were beneficial and reliable in order to achieve favourable and early satisfactory outcome comparing strengthening only (non-material).We have identified that rehabilitation protocol varies between different patients. Therefore future post ACL reconstruction rehabilitation guidelines should look into focusing on rehabilitation techniques instead of time.

Keywords: post anterior cruciate ligament (ACL) reconstruction, single bundle, hamstring tendon, sports rehabilitation, balance exercises, agility balance

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Authors: Fabio N. Moreno, Letícia B. Marinho, Beatriz D. Ruiz, Maria Helena R. B. Martins

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Brazil is a top consumer of pesticides worldwide, and the São Paulo State is one of the highest consumers among the Brazilian federative states. However, representative data about the occurrence of pesticides in surface waters of the São Paulo State is scarce. This paper aims to present the results of pesticides monitoring executed within the Water Quality Monitoring Network of CETESB (The Environmental Agency of the São Paulo State) between the 2018-2022 period. Surface water sampling points (21 to 25) were selected within basins of predominantly agricultural land-use (5 to 85% of cultivated areas). The samples were collected throughout the year, including high-flow and low-flow conditions. The frequency of sampling varied between 6 to 4 times per year. Selection of pesticide molecules for monitoring followed a prioritizing process from EMBRAPA (Brazilian Agricultural Research Corporation) databases of pesticide use. Pesticides extractions in aqueous samples were performed according to USEPA 3510C and 3546 methods following quality assurance and quality control procedures. Determination of pesticides in water (ng L-1) extracts were performed by high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS) and by gas chromatography with nitrogen phosphorus (GC-NPD) and electron capture detectors (GC-ECD). The results showed higher frequencies (20- 65%) in surface water samples for Carbendazim (fungicide), Diuron/Tebuthiuron (herbicides) and Fipronil/Imidaclopride (insecticides). The frequency of observations for these pesticides were generally higher in monitoring points located in sugarcane cultivated areas. The following pesticides were most frequently quantified above the Aquatic life benchmarks for freshwater (USEPA Office of Pesticide Programs, 2023) or Brazilian Federal Regulatory Standards (CONAMA Resolution no. 357/2005): Atrazine, Imidaclopride, Carbendazim, 2,4D, Fipronil, and Chlorpiryfos. Higher median concentrations for Diuron and Tebuthiuron in the rainy months (october to march) indicated pesticide transport through surface runoff. However, measurable concentrations in the dry season (april to september) for Fipronil and Imidaclopride also indicates pathways related to subsurface or base flow discharge after pesticide soil infiltration and leaching or dry deposition following pesticide air spraying. With exception to Diuron, no temporal trends related to median concentrations of the most frequently quantified pesticides were observed. These results are important to assist policymakers in the development of strategies aiming at reducing pesticides migration to surface waters from agricultural areas. Further studies will be carried out in selected points to investigate potential risks as a result of pesticides exposure on aquatic biota.

Keywords: pesticides monitoring, são paulo state, water quality, surface waters

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Authors: Roberta Martino, Viviana Ventre

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Intertemporal choices are choices under conditions of uncertainty in which the consequences are distributed over time. The Discounted Utility Model is the essential reference for describing the individual in the context of intertemporal choice. The model is based on the idea that the individual selects the alternative with the highest utility, which is calculated by multiplying the cardinal utility of the outcome, as if the reception were instantaneous, by the discount function that determines a decrease in the utility value according to how the actual reception of the outcome is far away from the moment the choice is made. Initially, the discount function was assumed to have an exponential trend, whose decrease over time is constant, in line with a profile of a rational investor described by classical economics. Instead, empirical evidence called for the formulation of alternative, hyperbolic models that better represented the actual actions of the investor. Attitudes that do not comply with the principles of classical rationality are termed anomalous, i.e., difficult to rationalize and describe through normative models. The development of behavioral finance, which describes investor behavior through cognitive psychology, has shown that deviations from rationality are due to the limited rationality condition of human beings. What this means is that when a choice is made in a very difficult and information-rich environment, the brain does a compromise job between the cognitive effort required and the selection of an alternative. Moreover, the evaluation and selection phase of the alternative, the collection and processing of information, are dynamics conditioned by systematic distortions of the decision-making process that are the behavioral biases involving the individual's emotional and cognitive system. In this paper we present an original decomposition of the discount function to investigate the psychological principles of hyperbolic discounting. It is possible to decompose the curve into two components: the first component is responsible for the smaller decrease in the outcome as time increases and is related to the individual's impatience; the second component relates to the change in the direction of the tangent vector to the curve and indicates how much the individual perceives the indeterminacy of the future indicating his or her aversion to uncertainty. This decomposition allows interesting conclusions to be drawn with respect to the concept of impatience and the emotional drives involved in decision-making. The contribution that neuroscience can make to decision theory and inter-temporal choice theory is vast as it would allow the description of the decision-making process as the relationship between the individual's emotional and cognitive factors. Neurofinance is a discipline that uses a multidisciplinary approach to investigate how the brain influences decision-making. Indeed, considering that the decision-making process is linked to the activity of the prefrontal cortex and amygdala, neurofinance can help determine the extent to which abnormal attitudes respect the principles of rationality.

Keywords: impatience, intertemporal choice, neurofinance, rationality, uncertainty

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Authors: Zhang Xin

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Barrier lakes are lakes formed by storing water in valleys, river valleys or riverbeds after being blocked by landslide, earthquake, debris flow, and other factors. They have great potential safety hazards. When the water is stored to a certain extent, it may burst in case of strong earthquake or rainstorm, and the lake water overflows, resulting in large-scale flood disasters. In order to ensure the safety of people's lives and property in the downstream, it is very necessary to monitor the barrier lake. However, it is very difficult and time-consuming to manually monitor the barrier lake in high altitude areas due to the harsh climate and steep terrain. With the development of earth observation technology, remote sensing monitoring has become one of the main ways to obtain observation data. Compared with a single satellite, multi-satellite remote sensing cooperative observation has more advantages; its spatial coverage is extensive, observation time is continuous, imaging types and bands are abundant, it can monitor and respond quickly to emergencies, and complete complex monitoring tasks. Monitoring with multi-temporal and multi-platform remote sensing satellites can obtain a variety of observation data in time, acquire key information such as water level and water storage capacity of the barrier lake, scientifically judge the situation of the barrier lake and reasonably predict its future development trend. In this study, The Sarez Lake, which formed on February 18, 1911, in the central part of the Pamir as a result of blockage of the Murgab River valley by a landslide triggered by a strong earthquake with magnitude of 7.4 and intensity of 9, is selected as the research area. Since the formation of Lake Sarez, it has aroused widespread international concern about its safety. At present, the use of mechanical methods in the international analysis of the safety of Lake Sarez is more common, and remote sensing methods are seldom used. This study combines remote sensing data with field observation data, and uses the 'space-air-ground' joint observation technology to study the changes in water level and water storage capacity of Lake Sarez in recent decades, and evaluate its safety. The situation of the collapse is simulated, and the future development trend of Lake Sarez is predicted. The results show that: 1) in recent decades, the water level of Lake Sarez has not changed much and remained at a stable level; 2) unless there is a strong earthquake or heavy rain, it is less likely that the Lake Sarez will be broken under normal conditions, 3) lake Sarez will remain stable in the future, but it is necessary to establish an early warning system in the Lake Sarez area for remote sensing of the area, 4) the coordinative remote sensing observation technology is feasible for the high altitude barrier lake of Sarez.

Keywords: coordinative observation, disaster, remote sensing, geographic information system, GIS

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Authors: Nasser Salah Eldin Mohammed Salih Shebka

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Current problematic issues in AI fields are mainly due to those of knowledge representation conceptual theories, which in turn reflected on the entire scope of cognitive sciences. Knowledge representation methods and tools are driven from theoretical concepts regarding human scientific perception of the conception, nature, and process of knowledge acquisition, knowledge engineering and knowledge generation. And although, these theoretical conceptions were themselves driven from the study of the human knowledge representation process and related theories; some essential factors were overlooked or underestimated, thus causing critical methodological deficiencies in the conceptual theories of human knowledge and knowledge representation conceptions. The evaluation criteria of human cumulative knowledge from the perspectives of nature and theoretical aspects of knowledge representation conceptions are affected greatly by the very materialistic nature of cognitive sciences. This nature caused what we define as methodological deficiencies in the nature of theoretical aspects of knowledge representation concepts in AI. These methodological deficiencies are not confined to applications of knowledge representation theories throughout AI fields, but also exceeds to cover the scientific nature of cognitive sciences. The methodological deficiencies we investigated in our work are: - The Segregation between cognitive abilities in knowledge driven models.- Insufficiency of the two-value logic used to represent knowledge particularly on machine language level in relation to the problematic issues of semantics and meaning theories. - Deficient consideration of the parameters of (existence) and (time) in the structure of knowledge. The latter requires that we present a more detailed introduction of the manner in which the meanings of Existence and Time are to be considered in the structure of knowledge. This doesn’t imply that it’s easy to apply in structures of knowledge representation systems, but outlining a deficiency caused by the absence of such essential parameters, can be considered as an attempt to redefine knowledge representation conceptual approaches, or if proven impossible; constructs a perspective on the possibility of simulating human cognition on machines. Furthermore, a redirection of the aforementioned expressions is required in order to formulate the exact meaning under discussion. This redirection of meaning alters the role of Existence and time factors to the Frame Work Environment of knowledge structure; and therefore; knowledge representation conceptual theories. Findings of our work indicate the necessity to differentiate between two comparative concepts when addressing the relation between existence and time parameters, and between that of the structure of human knowledge. The topics presented throughout the paper can also be viewed as an evaluation criterion to determine AI’s capability to achieve its ultimate objectives. Ultimately, we argue some of the implications of our findings that suggests that; although scientific progress may have not reached its peak, or that human scientific evolution has reached a point where it’s not possible to discover evolutionary facts about the human Brain and detailed descriptions of how it represents knowledge, but it simply implies that; unless these methodological deficiencies are properly addressed; the future of AI’s qualitative progress remains questionable.

Keywords: cognitive sciences, knowledge representation, ontological reasoning, temporal logic

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Authors: D. Courault, L. Hossard, V. Demarez, E. Ndikumana, D. Ho Tong Minh, N. Baghdadi, F. Ruget

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In the global change context, efficient management of the available resources has become one of the most important topics, particularly for sustainable crop development. Timely assessment with high precision is crucial for water resource and pest management. Rice cultivated in Southern France in the Camargue region must face a challenge, reduction of the soil salinity by flooding and at the same time reduce the number of herbicides impacting negatively the environment. This context has lead farmers to diversify crop rotation and their agricultural practices. The objective of this study was to evaluate this crop diversity both in crop systems and in agricultural practices applied to rice paddy in order to quantify the impact on the environment and on the crop production. The proposed method is based on the combined use of crop models and multispectral data acquired from the recent Sentinel 2 satellite sensors launched by the European Space Agency (ESA) within the homework of the Copernicus program. More than 40 images at fine spatial resolution (10m in the optical range) were processed for 2016 and 2017 (with a revisit time of 5 days) to map crop types using random forest method and to estimate biophysical variables (LAI) retrieved by inversion of the PROSAIL canopy radiative transfer model. Thanks to the high revisit time of Sentinel 2 data, it was possible to monitor the soil labor before flooding and the second sowing made by some farmers to better control weeds. The temporal trajectories of remote sensing data were analyzed for various rice cultivars for defining the main parameters describing the phenological stages useful to calibrate two crop models (STICS and SAFY). Results were compared to surveys conducted with 10 farms. A large variability of LAI has been observed at farm scale (up to 2-3m²/m²) which induced a significant variability in the yields simulated (up to 2 ton/ha). Observations on more than 300 fields have also been collected on land use. Various maps were elaborated, land use, LAI, flooding and sowing, and harvest dates. All these maps allow proposing a new typology to classify these paddy crop systems. Key phenological dates can be estimated from inverse procedures and were validated against ground surveys. The proposed approach allowed to compare the years and to detect anomalies. The methods proposed here can be applied at different crops in various contexts and confirm the potential of remote sensing acquired at fine resolution such as the Sentinel2 system for agriculture applications and environment monitoring. This study was supported by the French national center of spatial studies (CNES, funded by the TOSCA).

Keywords: agricultural practices, remote sensing, rice, yield

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Authors: Roberta Martino, Viviana Ventre

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Every day we face choices whose consequences are deferred in time. These types of choices are the intertemporal choices and play an important role in the social, economic, and financial world. The Discounted Utility Model is the mathematical model of reference to calculate the utility of intertemporal prospects. The discount rate is the main element of the model as it describes how the individual perceives the indeterminacy of subsequent periods. Empirical evidence has shown a discrepancy between the behavior expected from the predictions of the model and the effective choices made from the decision makers. In particular, the term temporal inconsistency indicates those choices that do not remain optimal with the passage of time. This phenomenon has been described with hyperbolic models of the discount rate which, unlike the linear or exponential nature assumed by the discounted utility model, is not constant over time. This paper explores the problem of inconsistency by tracing the decision-making process through the concept of impatience. The degree of impatience and the degree of decrease of impatience are two parameters that allow to quantify the weight of emotional factors and cognitive limitations during the evaluation and selection of alternatives. In fact, although the theory assumes perfectly rational decision makers, behavioral finance and cognitive psychology have made it possible to understand that distortions in the decision-making process and emotional influence have an inevitable impact on the decision-making process. The degree to which impatience is diminished is the focus of the first part of the study. By comparing consistent and inconsistent preferences over time, it was possible to verify that some anomalies in the discounted utility model are a result of the combination of cognitive bias and emotional factors. In particular: the delay effect and the interval effect are compared through the concept of misperception of time; starting from psychological considerations, a criterion is proposed to identify the causes of the magnitude effect that considers the differences in outcomes rather than their ratio; the sign effect is analyzed by integrating in the evaluation of prospects with negative outcomes the psychological aspects of loss aversion provided by Prospect Theory. An experiment implemented confirms three findings: the greatest variation in the degree of decrease in impatience corresponds to shorter intervals close to the present; the greatest variation in the degree of impatience occurs for outcomes of lower magnitude; the variation in the degree of impatience is greatest for negative outcomes. The experimental phase was implemented with the construction of the hyperbolic factor through the administration of questionnaires constructed for each anomaly. This work formalizes the underlying causes of the discrepancy between the discounted utility model and the empirical evidence of preference reversal.

Keywords: decreasing impatience, discount utility model, hyperbolic discount, hyperbolic factor, impatience

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Authors: Tak Mau Simon Chan, Ying Chuen Lance Chan

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Homelessness has been a popular and controversial debate in Hong Kong, a city which is densely populated and well-known for very expensive housing. The constitution of the homeless as threats to the community and environmental hygiene is ambiguous and debatable in the Hong Kong context. The lack of an intervention model is the critical research gap thus far, aside from the tangible services delivered. The life story approach (LSA), with its unique humanistic orientation, has been well applied in recent decades to depict the needs of various target groups, but not the homeless. It is argued that the life story approach (LSA), which has been employed by health professionals in the landscape of dementia, and health and social care settings, can be used as a reference in the local Chinese context through indigenization. This study, therefore, captures the viewpoints of service providers and users by constructing an indigenous intervention model that refers to the LSA in serving the chronically homeless. By informing 13 social workers and 27 homeless individuals in 8 focus groups whilst 12 homeless individuals have participated in individual in-depth interviews, a framework of LSA in homeless people is proposed. Through thematic analysis, three main themes of their life stories was generated, namely, the family, negative experiences and identity transformation. The three domains solidified framework that not only can be applied to the homeless, but also other disadvantaged groups in the Chinese context. Based on the three domains of family, negative experiences and identity transformation, the model is applied in the daily practices of social workers who help the homeless. The domain of family encompasses familial relationships from the past to the present to the speculated future with ten sub-themes. The domain of negative experiences includes seven sub-themes, with reference to the deviant behavior committed. The last domain, identity transformation, incorporates the awareness and redefining of one’s identity and there are a total of seven sub-themes. The first two domains are important components of personal histories while the third is more of an unknown, exploratory and yet to-be-redefined territory which has a more positive and constructive orientation towards developing one’s identity and life meaning. The longitudinal temporal dimension of moving from the past – present - future enriches the meaning making process, facilitates the integration of life experiences and maintains a more hopeful dialogue. The model is tested and its effectiveness is measured by using qualitative and quantitative methods to affirm the extent that it is relevant to the local context. First, it contributes to providing a clear guideline for social workers who can use the approach as a reference source. Secondly, the framework acts as a new intervention means to address problem saturated stories and the intangible needs of the homeless. Thirdly, the model extends the application to beyond health related issues. Last but not least, the model is highly relevant to the local indigenous context.

Keywords: homeless, indigenous intervention, life story approach, social work practice

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Authors: Shanlong Lu, Jiming Jin, Xiaochun Wang

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The Tibetan Plateau has the largest number of inland lakes with the highest elevation on the planet. These massive and large lakes are mostly in natural state and are less affected by human activities. Their shrinking or expansion can truly reflect regional climate and environmental changes and are sensitive indicators of global climate change. However, due to the sparsely populated nature of the plateau and the poor natural conditions, it is difficult to effectively obtain the change data of the lake, which has affected people's understanding of the temporal and spatial processes of lake water changes and their influencing factors. By using the MODIS (Moderate Resolution Imaging Spectroradiometer) MOD09Q1 surface reflectance images as basic data, this study produced the 8-day lake water surface data set of the Tibetan Plateau from 2000 to 2012 at 250 m spatial resolution, with a lake water surface extraction method of combined with lake water surface boundary buffer analyzing and lake by lake segmentation threshold determining. Then based on the dataset, the lake water surface variations and their influencing factors were analyzed, by using 4 typical natural geographical zones of Eastern Qinghai and Qilian, Southern Qinghai, Qiangtang, and Southern Tibet, and the watersheds of the top 10 lakes of Qinghai, Siling Co, Namco, Zhari NamCo, Tangra Yumco, Ngoring, UlanUla, Yamdrok Tso, Har and Gyaring as the analysis units. The accuracy analysis indicate that compared with water surface data of the 134 sample lakes extracted from the 30 m Landsat TM (Thematic Mapper ) images, the average overall accuracy of the lake water surface data set is 91.81% with average commission and omission error of 3.26% and 5.38%; the results also show strong linear (R2=0.9991) correlation with the global MODIS water mask dataset with overall accuracy of 86.30%; and the lake area difference between the Second National Lake Survey and this study is only 4.74%, respectively. This study provides reliable dataset for the lake change research of the plateau in the recent decade. The change trends and influencing factors analysis indicate that the total water surface area of lakes in the plateau showed overall increases, but only lakes with areas larger than 10 km2 had statistically significant increases. Furthermore, lakes with area larger than 100 km2 experienced an abrupt change in 2005. In addition, the annual average precipitation of Southern Tibet and Southern Qinghai experienced significant increasing and decreasing trends, and corresponding abrupt changes in 2004 and 2006, respectively. The annual average temperature of Southern Tibet and Qiangtang showed a significant increasing trend with an abrupt change in 2004. The major reason for the lake water surface variation in Eastern Qinghai and Qilian, Southern Qinghai and Southern Tibet is the changes of precipitation, and that for Qiangtang is the temperature variations.

Keywords: lake water surface variation, MODIS MOD09Q1, remote sensing, Tibetan Plateau

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Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

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As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

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Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

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Authors: Daniel R. dos Santos, Mateus S. Maldonado, Estevão J. R. Batista

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The military commanders increasingly dependent on spatial awareness, as knowing where enemy are, understanding how war battle scenarios change over time, and visualizing these trends in ways that offer insights for decision-making. Thanks to advancements in geospatial technologies and artificial intelligence algorithms, the commanders are now able to modernize military operations on a universal scale. Thus, geovisualisation has become an essential asset in the defense sector. It has become indispensable for better decisionmaking in dynamic/temporal scenarios, operation planning and management for the war field, situational awareness, effective planning, monitoring, and others. For example, a 3D visualization of war field data contributes to intelligence analysis, evaluation of postmission outcomes, and creation of predictive models to enhance decision-making and strategic planning capabilities. However, old-school visualization methods are slow, expensive, and unscalable. Despite modern technologies in generating 3D point clouds, such as LIDAR and stereo sensors, monocular depth values based on deep learning can offer a faster and more detailed view of the environment, transforming single images into visual information for valuable insights. We propose a dedicated monocular depth reconstruction approach via deep learning techniques for 3D geovisualisation of satellite images. It introduces scalability in terrain reconstruction and data visualization. First, a dataset with more than 7,000 satellite images and associated digital elevation model (DEM) is created. It is based on high resolution optical and radar imageries collected from Planet and Copernicus, on which we fuse highresolution topographic data obtained using technologies such as LiDAR and the associated geographic coordinates. Second, we developed an imagery-DEM fusion strategy that combine feature maps from two encoder-decoder networks. One network is trained with radar and optical bands, while the other is trained with DEM features to compute dense 3D depth. Finally, we constructed a benchmark with sparse depth annotations to facilitate future research. To demonstrate the proposed method's versatility, we evaluated its performance on no annotated satellite images and implemented an enclosed environment useful for Geovisualisation applications. The algorithms were developed in Python 3.0, employing open-source computing libraries, i.e., Open3D, TensorFlow, and Pythorch3D. The proposed method provides fast and accurate decision-making with GIS for localization of troops, position of the enemy, terrain and climate conditions. This analysis enhances situational consciousness, enabling commanders to fine-tune the strategies and distribute the resources proficiently.

Keywords: depth, deep learning, geovisualisation, satellite images

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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Effective management of hypertension remains a critical public health challenge, particularly among racially and ethnically diverse populations. This study employs sophisticated statistical models to rigorously investigate the predictors of poor blood pressure (BP) control, with a specific focus on demographic, socioeconomic, and clinical risk factors. Leveraging a large sample of 19,253 adults drawn from the National Health and Nutrition Examination Survey (NHANES) across three distinct time periods (2013-2014, 2015-2016, and 2017-2020), we applied multivariable logistic regression and generalized estimating equations (GEE) to account for the clustered structure of the data and potential within-subject correlations. Our multivariable models identified significant associations between poor BP control and several key predictors, including race/ethnicity, age, gender, body mass index (BMI), prevalent diabetes, and chronic kidney disease (CKD). Non-Hispanic Black individuals consistently exhibited higher odds of poor BP control across all periods (OR = 1.99; 95% CI: 1.69, 2.36 for the overall sample; OR = 2.33; 95% CI: 1.79, 3.02 for 2017-2020). Younger age groups demonstrated substantially lower odds of poor BP control compared to individuals aged 75 and older (OR = 0.15; 95% CI: 0.11, 0.20 for ages 18-44). Men also had a higher likelihood of poor BP control relative to women (OR = 1.55; 95% CI: 1.31, 1.82), while BMI ≥35 kg/m² (OR = 1.76; 95% CI: 1.40, 2.20) and the presence of diabetes (OR = 2.20; 95% CI: 1.80, 2.68) were associated with increased odds of poor BP management. Further analysis using GEE models, accounting for temporal correlations and repeated measures, confirmed the robustness of these findings. Notably, individuals with chronic kidney disease displayed markedly elevated odds of poor BP control (OR = 3.72; 95% CI: 3.09, 4.48), with significant differences across the survey periods. Additionally, higher education levels and better self-reported diet quality were associated with improved BP control. College graduates exhibited a reduced likelihood of poor BP control (OR = 0.64; 95% CI: 0.46, 0.89), particularly in the 2015-2016 period (OR = 0.48; 95% CI: 0.28, 0.84). Similarly, excellent dietary habits were associated with significantly lower odds of poor BP control (OR = 0.64; 95% CI: 0.44, 0.94), underscoring the importance of lifestyle factors in hypertension management. In conclusion, our findings provide compelling evidence of the complex interplay between demographic, clinical, and socioeconomic factors in predicting poor BP control. The application of advanced statistical techniques such as GEE enhances the reliability of these results by addressing the correlated nature of repeated observations. This study highlights the need for targeted interventions that consider racial/ethnic disparities, clinical comorbidities, and lifestyle modifications in improving BP control outcomes.

Keywords: hypertension, blood pressure, NHANES, generalized estimating equations

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Authors: Delaram Hosseinioun

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In their collections, Shirin Neshat, Mitra Tabrizian, Gohar Dashti and Newsha Tavakolian adopt a hybrid form of narrative to confront the restrictions imposed on women in hegemonic public and private spaces. Focusing on motives such as social marginalisation, crisis of belonging, as well as lack of agency for women, the artists depict the regression of women’s rights in their respective generations. Based on the ideas of Michael Bakhtin, namely his concept of polyphony or the plurality of contradictory voices, the views of Judith Butler on giving an account to oneself and Henri Leverbre’s theories on social space, this study illustrates the artists’ concept of identity in crisis through time and space. The research explores how the artists took their art as a novel dimension to depict and confront the hardships imposed on Iranian women. Henri Lefebvre makes a distinction between complex social structures through which individuals situate, perceive and represent themselves. By adding Bakhtin’s polyphonic view to Lefebvre’s concepts of perceived and lived spaces, the study explores the sense of social fragmentation in the works of Dashti and Tavakolian. One argument is that as the representatives of the contemporary generation of female artists who spend their lives in Iran and faced a higher degree of restrictions, their hyperbolic and theatrical styles stand as a symbolic act of confrontation against restrictive socio-cultural norms imposed on women. Further, the research explores the possibility of reclaiming one's voice and sense of agency through art, corresponding with the Bakhtinian sense of polyphony and Butler’s concept of giving an account to oneself. Works of Neshat and Tabrizian as the representatives of the previous generation who faced exile and diaspora, encompass a higher degree of misplacement, violence and decay of women’s presence. In Their works, the women’s body encompasses Lefebvre’s dismantled temporal and special setting. Notably, the ongoing social conviction and gender-based dogma imposed on women frame some of the concurrent motives among the selected collections of the four artists. By applying an interdisciplinary lens and integrating the conducted interviews with the artists, the study illustrates how the artists seek a transcultural account for themselves and women in their generations. Further, the selected collections manifest the urgency for an authentic and liberal voice and setting for women, resonating with the concurrent Women, Life, Freedom movement in Iran.

Keywords: persian modern female photographers, transcultural studies, shirin neshat, mitra tabrizian, gohar dashti, newsha tavakolian, butler, bakhtin, lefebvre

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Authors: Men An Pan, Hong Ren Chen, Chih Heng Shih, Hsing Yuan Yen

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Pingtung County is located in the southernmost region of Taiwan. During the winter season, pollutants due to insufficient dispersion caused by the downwash of the northeast monsoon lead to the poor air quality of the County. Through the implementation of various control methods, including the application of permits of air pollution, fee collection of air pollution, control oil fume of catering sectors, smoke detection of diesel vehicles, regular inspection of locomotives, and subsidies for low-polluting vehicles. Moreover, to further mitigate the air pollution, additional alternative controlling strategies are also carried out, such as construction site control, prohibition of open-air agricultural waste burning, improvement of river dust, and strengthening of road cleaning operations. The combined efforts have significantly reduced air pollutants in the County. However, in order to effectively and promptly monitor the ambient air quality, the County has subsequently deployed micro-sensors, with a total of 400 IoTs (Internet of Things) micro-sensors for PM2.5 and VOC detection and 3 air quality monitoring stations of the Environmental Protection Agency (EPA), covering 33 townships of the County. The covered area has more than 1,300 listed factories and 5 major industrial parks; thus forming an Internet of Things (IoTs) based multi-level air quality monitoring system. The results demonstrate that the IoTs multi-level air quality sensors combined with other strategies such as “sand and gravel dredging area technology monitoring”, “banning open burning”, “intelligent management of construction sites”, “real-time notification of activation response”, “nighthawk early bird plan with micro-sensors”, “unmanned aircraft (UAV) combined with land and air to monitor abnormal emissions”, and “animal husbandry odour detection service” etc. The satisfaction improvement rate of air control, through a 2021 public survey, reached a high percentage of 81%, an increase of 46% as compared to 2018. For the air pollution complaints for the whole year of 2021, the total number was 4213 in contrast to 7088 in 2020, a reduction rate reached almost 41%. Because of the spatial-temporal features of the air quality monitoring IoTs system by the application of microsensors, the system does assist and strengthen the effectiveness of the existing air quality monitoring network of the EPA and can provide real-time control of the air quality. Therefore, the hot spots and potential pollution locations can be timely determined for law enforcement. Hence, remarkable results were obtained for the two years. That is, both reduction of public complaints and better air quality are successfully achieved through the implementation of the present IoTs system for real-time air quality monitoring throughout Pingtung County.

Keywords: IoT, PM, air quality sensor, air pollution, environmental monitoring

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Authors: Monkiz Khasreen

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In the context of the global trend towards reducing new buildings carbon footprint, the design team is required to make early decisions that have a major influence on embodied and operational carbon. Sustainability strategies should be clear during early stages of building design process, as changes made later can be extremely costly. Life-Cycle Assessment (LCA) could be used as the vehicle to carry other tools and processes towards achieving the requested improvement. Although LCA is the ‘golden standard’ to evaluate buildings from 'cradle to grave', lack of details available on the concept design makes LCA very difficult, if not impossible, to be used as an estimation tool at early stages. Issues related to transparency and accessibility of information in the building industry are affecting the credibility of LCA studies. A verified database derived from LCA case studies is required to be accessible to researchers, design professionals, and decision makers in order to offer guidance on specific areas of significant impact. This database could be the build-up of data from multiple sources within a pool of research held in this context. One of the most important factors that affects the reliability of such data is the temporal factor as building materials, components, and systems are rapidly changing with the advancement of technology making production more efficient and less environmentally harmful. Recent LCA studies on different building functions, types, and structures are always needed to update databases derived from research and to form case bases for comparison studies. There is also a need to make these studies transparent and accessible to designers. The work in this paper sets out to address this need. This paper also presents life-cycle case study of a new-build educational building in England. The building utilised very current construction methods and technologies and is rated as BREEAM excellent. Carbon emissions of different life-cycle stages and different building materials and components were modelled. Scenario and sensitivity analyses were used to estimate the future of new educational buildings in England. The study attempts to form an indicator during the early design stages of similar buildings. Carbon dioxide emissions of this case study building, when normalised according to floor area, lie towards the lower end of the range of worldwide data reported in the literature. Sensitivity analysis shows that life cycle assessment results are highly sensitive to future assumptions made at the design stage, such as future changes in electricity generation structure over time, refurbishment processes and recycling. The analyses also prove that large savings in carbon dioxide emissions can result from very small changes at the design stage.

Keywords: architecture, building, carbon dioxide, construction, educational buildings, England, environmental impact, life-cycle assessment

Procedia PDF Downloads 113

Authors: Tatiana N. Astakhova, Alexander E. Saprygin, Tatyana A. Golovko, Alexander N. Savostyanov, Mikhail S. Vlasov, Natalia V. Borisova, Alexandera G. Karpova, Urana N. Kavai-ool, Elena D. Mokur-ool, Nikolay A. Kolchanov, Lubomir I. Aftanas

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The study is dedicated to the research of brain activity in bilingual inhabitants of Siberia. We compared behavioral reactions and event-related potentials in Turkic-speaking inhabitants of Siberia (Tuvinians and Yakuts) and Russians. 63 healthy aboriginals of the Tyva Republic, 29 inhabitants of the Sakha (Yakutia) Republic, and 55 Russians from Novosibirsk participated in the study. All the healthy and right-handed participants, matched on age and sex, were students of different universities. EEG’s were recorded during the solving of linguistic tasks. In these tasks, participants had to find a syntax error in the written sentences. There were four groups of sentences: Russian, English, Tuvinian, and Yakut. All participants completed the tasks in Russian and English. Additionally, Tuvinians and Yakuts completed the tasks in Tuvinian or Yakut respectively. For Russians, EEG's were recorded using 128-channels according to the extended International 10-10 system, and the signals were amplified using “Neuroscan (USA)” amplifiers. For Tuvinians and Yakuts, EEG's were recorded using 64-channels and amplifiers Brain Products, Germany. In all groups, 0.3-100 Hz analog filtering and sampling rate 1000 Hz were used. As parameters of behavioral reactions, response speed and the accuracy of recognition were used. Event-related potentials (ERP) responses P300 and P600 were used as indicators of brain activity. The behavioral reactions showed that in Russians, the response speed for Russian was faster than for English. Also, the accuracy of solving tasks was higher for Russian than for English. The peak P300 in Russians were higher for English, the peak P600 in the left temporal cortex were higher for the Russian language. Both Tuvinians and Yakuts have no difference in accuracy of solving tasks in Russian and in their respective national languages. However, the response speed was faster for tasks in Russian than for tasks in their national language. Tuvinians and Yakuts showed bad accuracy in English, but the response speed was higher for English than for Russian and the national languages. This can be explained by the fact that they did not think carefully and gave a random answer for English. In Tuvinians, The P300 and P600 amplitudes and cortical topology were the same for Russian and Tuvinian and different for English. In Yakuts, the P300 and P600 amplitudes and topology of ERP for Russian were the same as what Russians had for Russian. In Yakuts, brain reactions during Yakut and English comprehension had no difference, and were reflected to foreign language comprehension - while the Russian language comprehension was reflected to native language comprehension. We found out that the Tuvinians recognized both Russian and Tuvinian as native languages, and English as a foreign language. The Yakuts recognized both English and Yakut as a foreign language, and only Russian as a native language. According to the inquirer, both Tuvinians and Yakuts use the national language as a spoken language, whereas they don’t use it for writing. It can well be a reason that Yakuts perceive the Yakut writing language as a foreign language while writing Russian as their native.

Keywords: EEG, ERP, native and foreign languages comprehension, Siberian inhabitants

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Authors: Tanya Kaur Bedi, Shankha Pratim Bhattacharya

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The US Environmental Protection Agency defines indoor air quality as “The air quality within and around buildings, especially as it relates to the health and comfort of building occupants”. According to the 2021 report by the Energy Policy Institute at Chicago, Indian residents, a country which is home to the highest levels of air pollution in the world, lose about 5.9 years from life expectancy due to poor air quality and yet has numerous dwellings dependent on natural ventilation. Currently the urban population spends 90% of the time indoors, this scenario raises a concern for occupant health and well-being. The built environment can affect health directly and indirectly through immediate or long-term exposure to indoor air pollutants. Health effects associated with indoor air pollutants include eye/nose/throat irritation, respiratory diseases, heart disease, and even cancer. This study attempts to demonstrate the causal relationship between the indoor air quality and its determining aspects. Detailed indoor air quality audits were conducted in residential buildings located in Kolkata, India in the months of December and January 2021. According to the air pollution knowledge assessment city program in India, Kolkata is also the second most polluted mega-city after Delhi. Although the air pollution levels are alarming year-long, the winter months are most crucial due to the unfavorable environmental conditions. While emissions remain typically constant throughout the year, cold air is denser and moves slower than warm air, trapping the pollution in place for much longer and consequently is breathed in at a higher rate than the summers. The air pollution monitoring period was selected considering environmental factors and major pollution contributors like traffic and road dust. This study focuses on the relationship between the built environment and the spatial-temporal distribution of air pollutants in and around it. The measured parameters include, temperature, relative humidity, air velocity, particulate matter, volatile organic compounds, formaldehyde, and benzene. A total of 56 rooms were audited, selectively targeting the most dominant middle-income group. The data-collection was conducted using a set of instruments positioned in the human breathing-zone. The study assesses indoor air quality based on factors determining natural ventilation and air pollution dispersion such as surrounding environment, dominant wind, openable window to floor area ratio, windward or leeward side openings, and natural ventilation type in the room: single side or cross-ventilation, floor height, residents cleaning habits, etc.

Keywords: indoor air quality, occupant health, urban housing, air pollution, natural ventilation, architecture, urban issues

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Authors: Chien-Ho Lin, Ho-Ching Yang, Barbara Knowlton, Shin-Leh Huang, Ming-Chang Chiang

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Practicing motor tasks arranged in an interleaved order (interleaved practice, or IP) generally leads to better learning than practicing tasks in a repetitive order (repetitive practice, or RP), an example of how desirable difficulty during practice benefits learning. Greater difficulty during practice, e.g. IP, is associated with greater brain activity measured by higher blood-oxygen-level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) in the sensorimotor areas of the brain. In this study resting-state fMRI was applied to investigate whether increase in resting-state brain connectivity immediately after practice predicts the benefit of desirable difficulty to motor learning. 26 healthy adults (11M/15F, age = 23.3±1.3 years) practiced two sets of three sequences arranged in a repetitive or an interleaved order over 2 days, followed by a retention test on Day 5 to evaluate learning. On each practice day, fMRI data were acquired in a resting state after practice. The resting-state fMRI data was decomposed using a group-level spatial independent component analysis (ICA), yielding 9 independent components (IC) matched to the precuneus network, primary visual networks (two ICs, denoted by I and II respectively), sensorimotor networks (two ICs, denoted by I and II respectively), the right and the left frontoparietal networks, occipito-temporal network, and the frontal network. A weighted resting-state functional connectivity (wRSFC) was then defined to incorporate information from within- and between-network brain connectivity. The within-network functional connectivity between a voxel and an IC was gauged by a z-score derived from the Fisher transformation of the IC map. The between-network connectivity was derived from the cross-correlation of time courses across all possible pairs of ICs, leading to a symmetric nc x nc matrix of cross-correlation coefficients, denoted by C = (pᵢⱼ). Here pᵢⱼ is the extremum of cross-correlation between ICs i and j; nc = 9 is the number of ICs. This component-wise cross-correlation matrix C was then projected to the voxel space, with the weights for each voxel set to the z-score that represents the above within-network functional connectivity. The wRSFC map incorporates the global characteristics of brain networks measured by the between-network connectivity, and the spatial information contained in the IC maps measured by the within-network connectivity. Pearson correlation analysis revealed that greater IP-minus-RP difference in wRSFC was positively correlated with the RP-minus-IP difference in the response time on Day 5, particularly in brain regions crucial for motor learning, such as the right dorsolateral prefrontal cortex (DLPFC), and the right premotor and supplementary motor cortices. This indicates that enhanced resting brain connectivity during the early phase of memory consolidation is associated with enhanced learning following interleaved practice, and as such wRSFC could be applied as a biomarker that measures the beneficial effects of desirable difficulty on motor sequence learning.

Keywords: desirable difficulty, functional magnetic resonance imaging, independent component analysis, resting-state networks

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Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

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Authors: Shaista Suhail

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As cancer populations is increasing sharply, the incidence of oral squamous cell carcinoma (OSCC) has also been expected to increase. Oral carcinogenesis is a highly complex, multistep process which involves accumulation of genetic alterations that lead to the induction of proteins promoting cell growth (encoded by oncogenes), increased enzymatic (telomerase) activity promoting cancer cell proliferation. The global increase in frequency and mortality, as well as the poor prognosis of oral squamous cell carcinoma, has intensified current research efforts in the field of prevention and early detection of this disease. The advances in the understanding of the molecular basis of oral cancer should help in the identification of new markers. The study of the carcinogenic process of the oral cancer, including continued analysis of new genetic alterations, along with their temporal sequencing during initiation, promotion and progression, will allow us to identify new diagnostic and prognostic factors, which will provide a promising basis for the application of more rational and efficient treatments. Telomerase activity has been readily found in most cancer biopsies, in premalignant lesions or germ cells. Activity of telomerase is generally absent in normal tissues. It is known to be induced upon immortalization or malignant transformation of human cells such as in oral cancer cells. Maintenance of telomeres plays an essential role during transformation of precancer to malignant stage. Mammalian telomeres, a specialized nucleoprotein structures are composed of large conctamers of the guanine-rich sequence 5_-TTAGGG-3_. The roles of telomeres in regulating both stability of genome and replicative immortality seem to contribute in essential ways in cancer initiation and progression. It is concluded that activity of telomerase can be used as a biomarker for diagnosis of malignant oral cancer and a target for inactivation in chemotherapy or gene therapy. Its expression will also prove to be an important diagnostic tool as well as a novel target for cancer therapy. The activation of telomerase may be an important step in tumorgenesis which can be controlled by inactivating its activity during chemotherapy. The expression and activity of telomerase are indispensable for cancer development. There are no drugs which can effect extremely to treat oral cancers. There is a general call for new emerging drugs or methods that are highly effective towards cancer treatment, possess low toxicity, and have a minor environment impact. Some novel natural products also offer opportunities for innovation in drug discovery. Natural compounds isolated from medicinal plants, as rich sources of novel anticancer drugs, have been of increasing interest with some enzyme (telomerase) blockage property. The alarming reports of cancer cases increase the awareness amongst the clinicians and researchers pertaining to investigate newer drug with low toxicity.

Keywords: oral carcinoma, telomere, telomerase, blockage

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Authors: Pantana Tor-Ngern

Abstract:

In the global water cycle, tree water use (Tr) largely contributes to evapotranspiration which is the total amount of water evaporated from terrestrial ecosystems to the atmosphere, regulating climates. Tree water use responds to environmental factors, including atmospheric humidity and sunlight (represented by vapor pressure deficit or VPD and photosynthetically active radiation or PAR, respectively) and soil moisture. In forests, Tr responses to such factors depend on species and their spatial and temporal variations. Tropical forests in Southeast Asia (SEA) have experienced land-use conversion from abandoned agricultural practices, resulting in patches of forests at different stages including old-growth and secondary forests. Because the inherent structures, such as canopy height and tree density, significantly vary among forests at different stages and can strongly affect their respective microclimate, Tr and its responses to changing environmental conditions in successional forests may differ. Daily and seasonal variations in the environmental factors may exert significant impacts on the respective Tr patterns. Extrapolating Tr data from short periods of days to longer periods of seasons or years can be complex and is important for estimating long-term ecosystem water use which often includes normal and abnormal climatic conditions. Thus, this study aims to investigate the diurnal variation of Tr, using measured sap flux density (JS) data, with changes in VPD in eight evergreen tree species in an old-growth forest (hereafter OF; >200 years old) and a young forest (hereafter YF, <10 years old) in Khao Yai National Park, Thailand. The studied species included Sysygium syzygoides, Aquilaria crassna, Cinnamomum subavenium, Nephelium melliferum, Altingia excelsa in OF, and Syzygium nervosum and Adinandra integerrima in YF. Only Sysygium antisepticum was found in both forest stages. Specifically, hysteresis, which indicates the asymmetrical changes of JS in response to changing VPD across daily timescale, was examined in these species. Results showed no hysteresis in all species in OF, except Altingia excelsa which exhibited a 3-hour delayed JS response to VPD. In contrast, JS of all species in YF displayed one-hour delayed responses to VPD. The OF species that showed no hysteresis indicated their well-coupling of their canopies with the atmosphere, facilitating the gas exchange which is essential for tree growth. The delayed responses in Altingia excelsa in OF and all species in YF were associated with higher JS in the morning than that in the afternoon. This implies that these species were sensitive to drying air, closing stomata relatively rapidly compared to the decreasing atmospheric humidity (VPD). Such behavior is often observed in trees growing in dry environments. This study suggests that detailed investigation of JS at sub-daily timescales is imperative for better understanding of mechanistic responses of trees to the changing climate, which will benefit the improvement of earth system models.

Keywords: sap flow, tropical forest, forest succession, thermal dissipcation probe

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