Search results for: PieceWise Affine Auto Regression with eXogenous input

Authors: Yixin Zhu, Lishengsa Yue, Jian Sun, Lanyue Tang

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Modeling driving distraction in microscopic traffic simulation is crucial for enhancing simulation accuracy. Current driving distraction models are mainly derived from physical motion constraints under distracted states, in which distraction-related error terms are added to existing microscopic driver models. However, the model accuracy is not very satisfying, due to a lack of modeling the cognitive mechanism underlying the distraction. This study models driving distraction based on the Queueing Network Human Processor model (QN-MHP). This study utilizes the queuing structure of the model to perform task invocation and switching for distracted operation and control of the vehicle under driver distraction. Based on the assumption of the QN-MHP model about the cognitive sub-network, server F is a structural bottleneck. The latter information must wait for the previous information to leave server F before it can be processed in server F. Therefore, the waiting time for task switching needs to be calculated. Since the QN-MHP model has different information processing paths for auditory information and visual information, this study divides driving distraction into two types: auditory distraction and visual distraction. For visual distraction, both the visual distraction task and the driving task need to go through the visual perception sub-network, and the stimuli of the two are asynchronous, which is called stimulus on asynchrony (SOA), so when calculating the waiting time for switching tasks, it is necessary to consider it. In the case of auditory distraction, the auditory distraction task and the driving task do not need to compete for the server resources of the perceptual sub-network, and their stimuli can be synchronized without considering the time difference in receiving the stimuli. According to the Theory of Planned Behavior for drivers (TPB), this study uses risk entropy as the decision criterion for driver task switching. A logistic regression model is used with risk entropy as the independent variable to determine whether the driver performs a distraction task, to explain the relationship between perceived risk and distraction. Furthermore, to model a driver’s perception characteristics, a neurophysiological model of visual distraction tasks is incorporated into the QN-MHP, and executes the classical Intelligent Driver Model. The proposed driving distraction model integrates the psychological cognitive process of a driver with the physical motion characteristics, resulting in both high accuracy and interpretability. This paper uses 773 segments of distracted car-following in Shanghai Naturalistic Driving Study data (SH-NDS) to classify the patterns of distracted behavior on different road facilities and obtains three types of distraction patterns: numbness, delay, and aggressiveness. The model was calibrated and verified by simulation. The results indicate that the model can effectively simulate the distracted car-following behavior of different patterns on various roadway facilities, and its performance is better than the traditional IDM model with distraction-related error terms. The proposed model overcomes the limitations of physical-constraints-based models in replicating dangerous driving behaviors, and internal characteristics of an individual. Moreover, the model is demonstrated to effectively generate more dangerous distracted driving scenarios, which can be used to construct high-value automated driving test scenarios.

Keywords: computational cognitive model, driving distraction, microscopic traffic simulation, psychological-physical constraints

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Authors: Diana Reckien

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Vulnerability assessments are increasingly used to support policy-making in complex environments, like urban areas. Usually, vulnerability studies include the construction of aggregate (sub-) indices and the subsequent mapping of indices across an area of interest. Vulnerability studies show a couple of advantages: they are great communication tools, can inform a wider general debate about environmental issues, and can help allocating and efficiently targeting scarce resources for adaptation policy and planning. However, they also have a number of challenges: Vulnerability assessments are constructed on the basis of a wide range of methodologies and there is no single framework or methodology that has proven to serve best in certain environments, indicators vary highly according to the spatial scale used, different variables and metrics produce different results, and aggregate or composite vulnerability indicators that are mapped easily distort or bias the picture of vulnerability as they hide the underlying causes of vulnerability and level out conflicting reasons of vulnerability in space. So, there is urgent need to further develop the methodology of vulnerability studies towards a common framework, which is one reason of the paper. We introduce a social vulnerability approach, which is compared with other approaches of bio-physical or sectoral vulnerability studies relatively developed in terms of a common methodology for index construction, guidelines for mapping, assessment of sensitivity, and verification of variables. Two approaches are commonly pursued in the literature. The first one is an additive approach, in which all potentially influential variables are weighted according to their importance for the vulnerability aspect, and then added to form a composite vulnerability index per unit area. The second approach includes variable reduction, mostly Principal Component Analysis (PCA) that reduces the number of variables that are interrelated into a smaller number of less correlating components, which are also added to form a composite index. We test these two approaches of constructing indices on the area of New York City as well as two different metrics of variables used as input and compare the outcome for the 5 boroughs of NY. Our analysis yields that the mapping exercise yields particularly different results in the outer regions and parts of the boroughs, such as Outer Queens and Staten Island. However, some of these parts, particularly the coastal areas receive the highest attention in the current adaptation policy. We imply from this that the current adaptation policy and practice in NY might need to be discussed, as these outer urban areas show relatively low social vulnerability as compared with the more central parts, i.e. the high dense areas of Manhattan, Central Brooklyn, Central Queens and the Southern Bronx. The inner urban parts receive lesser adaptation attention, but bear a higher risk of damage in case of hazards in those areas. This is conceivable, e.g., during large heatwaves, which would more affect more the inner and poorer parts of the city as compared with the outer urban areas. In light of the recent planning practice of NY one needs to question and discuss who in NY makes adaptation policy for whom, but the presented analyses points towards an under representation of the needs of the socially vulnerable population, such as the poor, the elderly, and ethnic minorities, in the current adaptation practice in New York City.

Keywords: vulnerability mapping, social vulnerability, additive approach, Principal Component Analysis (PCA), New York City, United States, adaptation, social sensitivity

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Authors: N. Manjunatha, K. T. Rangswamy, N. Nagaraju, H. A. Prameela, P. Rudraswamy, M. Krishnareddy

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The identification of virus in cowpea especially potyviruses is confusing. Even though there are several studies on viruses causing diseases in cowpea, difficult to distinguish based on symptoms and serological detection. The differentiation of potyviruses considering as a constraint, the present study is initiated for molecular characterization, host plant resistance and epidemiology of the BCMV infecting cowpea. The etiological agent causing cowpea mosaic was identified as Bean Common Mosaic Virus (BCMV) on the basis of RT-PCR and electron microscopy. An approximately 750bp PCR product corresponding to coat protein (CP) region of the virus and the presence of long flexuous filamentous particles measuring about 952 nm in size typical to genus potyvirus were observed under electron microscope. The characterized virus isolate genome had 10054 nucleotides, excluding the 3’ terminal poly (A) tail. Comparison of polyprotein of the virus with other potyviruses showed similar genome organization with 9 cleavage sites resulted in 10 functional proteins. The pairwise sequence comparison of individual genes, P1 showed most divergent, but CP gene was less divergent at nucleotide and amino acid level. A phylogenetic tree constructed based on multiple sequence alignments of the polyprotein nucleotide and amino acid sequences of cowpea BCMV and potyviruses showed virus is closely related to BCMV-HB. Whereas, Soybean variant of china (KJ807806) and NL1 isolate (AY112735) showed 93.8 % (5’UTR) and 94.9 % (3’UTR) homology respectively with other BCMV isolates. This virus transmitted to different leguminous plant species and produced systemic symptoms under greenhouse conditions. Out of 100 cowpea genotypes screened, three genotypes viz., IC 8966, V 5 and IC 202806 showed immune reaction in both field and greenhouse conditions. Single marker analysis (SMA) was revealed out of 4 SSR markers linked to BCMV resistance, M135 marker explains 28.2 % of phenotypic variation (R2) and Polymorphic information content (PIC) value of these markers was ranged from 0.23 to 0.37. The correlation and regression analysis showed rainfall, and minimum temperature had significant negative impact and strong relationship with aphid population, whereas weak correlation was observed with disease incidence. Path coefficient analysis revealed most of the weather parameters exerted their indirect contributions to the aphid population and disease incidence except minimum temperature. This study helps to identify specific gaps in knowledge for researchers who may wish to further analyse the science behind complex interactions between vector-virus and host in relation to the environment. The resistant genotypes identified are could be effectively used in resistance breeding programme.

Keywords: cowpea, epidemiology, genotypes, virus

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Authors: Tim Bolle, Franziska Kreimendahl, Thomas Gries, Stefan Jockenhoevel

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The therapeutic treatment of extensive, deep wounds is limited. Autologous split-skin grafts are used as a so-called ‘gold standard’. Most common deficits are the defects at the donor site, the risk of scarring as well as the limited availability and quality of the autologous grafts. The aim of this project is a tissue engineered dermal-epidermal skin replacement to overcome the limitations of the gold standard. A key requirement for the development of such a three-dimensional implant is the formation of a functional capillary-like network inside the implant to ensure a sufficient nutrient and gas supply. Tailored three-dimensional warp knitted spacer fabrics are used to reinforce the mechanically week fibrin gel-based scaffold and further to create a directed in vitro pre-vascularization along the parallel-oriented pile yarns within a co-culture. In this study various three-dimensional warp knitted spacer fabrics were developed in a factorial design to analyze the influence of the machine parameters such as the stitch density and the pattern of the fabric on the scaffold performance and further to determine suitable parameters for a successful fibrin gel-incorporation and a physiological performance of the scaffold. The fabrics were manufactured on a Karl Mayer double-bar raschel machine DR 16 EEC/EAC. A fine machine gauge of E30 was used to ensure a high pile yarn density for sufficient nutrient, gas and waste exchange. In order to ensure a high mechanical stability of the graft, the fabrics were made of biocompatible PVDF yarns. Key parameters such as the pore size, porosity and stress/strain behavior were investigated under standardized, controlled climate conditions. The influence of the input parameters on the mechanical and morphological properties as well as the ability of fibrin gel incorporation into the spacer fabric was analyzed. Subsequently, the pile yarns of the spacer fabrics were colonized with Human Umbilical Vein Endothelial Cells (HUVEC) to analyze the ability of the fabric to further function as a guiding structure for a directed vascularization. The cells were stained with DAPI and investigated using fluorescence microscopy. The analysis revealed that the stitch density and the binding pattern have a strong influence on both the mechanical and morphological properties of the fabric. As expected, the incorporation of the fibrin gel was significantly improved with higher pore sizes and porosities, whereas the mechanical strength decreases. Furthermore, the colonization trials revealed a high cell distribution and density on the pile yarns of the spacer fabrics. For a tailored reinforcing structure, the minimum porosity and pore size needs to be evaluated which still ensures a complete incorporation of the reinforcing structure into the fibrin gel matrix. That will enable a mechanically stable dermal graft with a dense vascular network for a sufficient nutrient and oxygen supply of the cells. The results are promising for subsequent research in the field of reinforcing mechanically weak biological scaffolds and develop functional three-dimensional scaffolds with an oriented pre-vascularization.

Keywords: fibrin-gel, skin replacement, spacer fabric, pre-vascularization

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Authors: Haftay Abraha Tadesse

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Background: Staphylococcus is a genus of worldwide distributed bacteria correlated to several infectious of different sites in humans and animals. They are among the most important causes of infection that are associated with the consumption of contaminated food. Objective: The objective of this study was to determine the isolates, antimicrobial susceptibility patterns and Public Health Significance of Staphylococcus aureus in raw meat from butchery and abattoir houses of Mekelle, Northern Ethiopia. Methodology: A cross-sectional study was conducted from April to October 2019. Socio-demographic data and Public Health Significance were collected using a predesigned questionnaire. The raw meat samples were collected aseptically in the butchery and abattoir houses and transported using an ice box to Mekelle University, College of Veterinary Sciences, for isolating and identification of Staphylococcus aureus. Antimicrobial susceptibility tests were determined by the disc diffusion method. Data obtained were cleaned and entered into STATA 22.0 and a logistic regression model with odds ratio was calculated to assess the association of risk factors with bacterial contamination. A P-value < 0.05 was considered statistically significant. Results: In the present study, 88 out of 250 (35.2%) were found to be contaminated with Staphylococcus aureus. Among the raw meat specimens, the positivity rate of Staphylococcus aureus was 37.6% (n=47) and (32.8% (n=41), butchery and abattoir houses, respectively. Among the associated risks, factories not using gloves reduces risk was found to (AOR=0.222; 95% CI: 0.104-0.473), Strict Separation b/n clean & dirty (AOR= 1.37; 95% CI: 0.66-2.86) and poor habit of hand washing (AOR=1.08; 95%CI: 0.35 3.35) was found to be statistically significant and have associated with Staphylococcus aureus contamination. All isolates of thirty-seven of Staphylococcus aureus were checked and displayed (100%) sensitive to doxycycline, trimethoprim, gentamicin, sulphamethoxazole, amikacin, CN, Co trimoxazole and nitrofurantoi. Whereas the showed resistance to cefotaxime (100%), ampicillin (87.5%), Penicillin (75%), B (75%), and nalidixic acid (50%) from butchery houses. On the other hand, all isolates of Staphylococcus aureus isolate 100% (n= 10) showed sensitive chloramphenicol, gentamicin and nitrofurantoin, whereas they showed 100% resistance of Penicillin, B, AMX, ceftriaxone, ampicillin and cefotaxime from abattoirs houses. The overall multi-drug resistance pattern for Staphylococcus aureus was 90% and 100% of butchery and abattoir houses, respectively. Conclusion: 35.3% Staphylococcus aureus isolated were recovered from the raw meat samples collected from the butchery and abattoirs houses. More has to be done in the development of hand washing behavior and availability of safe water in the butchery houses to reduce the burden of bacterial contamination. The results of the present finding highlight the need to implement protective measures against the levels of food contamination and alternative drug options. The development of antimicrobial resistance is nearly always a result of repeated therapeutic and/or indiscriminate use of them. Regular antimicrobial sensitivity testing helps to select effective antibiotics and to reduce the problems of drug resistance development towards commonly used antibiotics.

Keywords: abattoir house, AMR, butchery house, S. aureus

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Authors: N. K. Ambujam, V. Sudha

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Reservoirs are being contaminated all around the world with point source and Non-Point Source (NPS) pollution. The most common NPS pollutants are sediments and nutrients. Krishnagiri Reservoir (KR) has been chosen for the present case study, which is located in the tropical semi-arid climatic zone of Tamil Nadu, South India. It is the main source of surface water in Krishnagiri district to meet the freshwater demands. The reservoir has lost about 40% of its water holding capacity due to sedimentation over the period of 50 years. Hence, from the research and management perspective, there is a need for a sound knowledge on the spatial and seasonal variations of KR water quality. The present study encompasses the specific objectives as (i) to investigate the longitudinal heterogeneity and seasonal variations of physicochemical parameters, nutrients and biological characteristics of KR water and (ii) to examine the extent of degradation of water quality in KR. 15 sampling points were identified by uniform stratified method and a systematic monthly sampling strategy was selected due to high dynamic nature in its hydrological characteristics. The physicochemical parameters, major ions, nutrients and Chlorophyll a (Chl a) were analysed. Trophic status of KR was classified by using Carlson's Trophic State Index (TSI). All statistical analyses were performed by using Statistical Package for Social Sciences programme, version-16.0. Spatial maps were prepared for Chl a using Arc GIS. Observations in KR pointed out that electrical conductivity and major ions are highly variable factors as it receives inflow from the catchment with different land use activities. The study of major ions in KR exhibited different trends in their values and it could be concluded that as the monsoon progresses the major ions in the water decreases or water quality stabilizes. The inflow point of KR showed comparatively higher concentration of nutrients including nitrate, soluble reactive phosphorus (SRP), total phosphors (TP), total suspended phosphorus (TSP) and total dissolved phosphorus (TDP) during monsoon seasons. This evidently showed the input of significant amount of nutrients from the catchment side through agricultural runoff. High concentration of TDP and TSP at the lacustrine zone of the reservoir during summer season evidently revealed that there was a significant release of phosphorus from the bottom sediments. Carlson’s TSI of KR ranged between 81 and 92 during northeast monsoon and summer seasons. High and permanent Cyanobacterial bloom in KR could be mainly due to the internal loading of phosphorus from the bottom sediments. According to Carlson’s TSI classification Krishnagiri reservoir was ranked in the hyper-eutrophic category. This study provides necessary basic data on the spatio-temporal variations of water quality in KR and also proves the impact of point and NPS pollution from the catchment area. High TSI warrants a greater threat for the recovery of internal P loading and hyper-eutrophic condition of KR. Several expensive internal measures for the reduction of internal loading of P were introduced by many scientists. However, the outcome of the present research suggests for the innovative algae harvesting technique for the removal of sediment nutrients.

Keywords: NPS pollution, nutrients, hyper-eutrophication, krishnagiri reservoir

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Authors: Mustafa Metin Donma

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Metabolic syndrome (MetS) is characterized by a series of biochemical, physiological and anthropometric indicators and is a life-threatening health problem due to its close association with chronic diseases such as diabetes mellitus, hypertension, cancer and cardiovascular diseases. The syndrome deserves great interest both in adults and children. Central obesity is the indispensable component of MetS. Particularly, children, who are morbidly obese have a great tendency to develop the disease, because they are under the threat in their future lives. Preventive measures at this stage should be considered. For this, investigators seek for an informative scale or an index for the purpose. So far, several, but not many suggestions come into the stage. However, the diagnostic decision is not so easy and may not be complete particularly in the pediatric population. The aim of the study was to develop a MetS index capable of predicting MetS, while children are at the morbid obesity stage. This study was performed on morbid obese (MO) children, which were divided into two groups. Morbid obese children, who do not possess MetS criteria comprised the first group (n=44). The second group was composed of children (n=42) with MetS diagnosis. Parents were informed about the signed consent forms, which are required for the participation of their children in the study. The approval of the study protocol was taken from the institutional ethics committee of Tekirdag Namik Kemal University. Helsinki Declaration was accepted prior to and during the study. Anthropometric measurements including weight, height, waist circumference (WC), hip C, head C, neck C, biochemical tests including fasting blood glucose (FBG), insulin (INS), triglycerides (TRG), high density lipoprotein cholesterol (HDL-C) and blood pressure measurements (systolic (SBP) and diastolic (DBP)) were performed. Body fat percentage (BFP) values were determined by TANITA’s Bioelectrical Impedance Analysis technology. Body mass index and MetS indices were calculated. The equations for MetS index (MetSI) and advanced Donma MetS index (ADMI) were [(INS/FBG)/(HDL-C/TRG)]*100 and MetSI*[(SBP+DBP/Height)], respectively. Descriptive statistics including median values, compare means tests, correlation-regression analysis were performed within the scope of data evaluation using the statistical package program, SPSS. Statistically significant mean differences were determined by a p value smaller than 0.05. Median values for MetSI and ADMI in MO (MetS-) and MO (MetS+) groups were calculated as (25.9 and 36.5) and (74.0 and 106.1), respectively. Corresponding mean±SD values for BFPs were 35.9±7.1 and 38.2±7.7 in groups. Correlation analysis of these two indices with corresponding general BFP values exhibited significant association with ADMI, close to significance with MetSI in MO group. Any significant correlation was found with neither of the indices in MetS group. In conclusion, important associations observed with MetS indices in MO group were quite meaningful. The presence of these associations in MO group was important for showing the tendency towards the development of MetS in MO (MetS-) participants. The other index, ADMI, was more helpful for predictive purpose.

Keywords: body fat percentage, child, index, metabolic syndrome, obesity

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Alua Massalimova

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Situation analysis of Blood Service revealed that the strengths dominated over the weak 1.4 times. The possibilities dominate over the threats by 1.1 times. It follows that by using timely the possibility the Service, it is possible to strengthen its strengths and avoid threats. Priority directions of the resulting analysis are the use of subjective factors, such as personal management capacity managers of the Blood Center in the field of possibilities of legal activity of administrative decisions and the mobilization of stable staff in general market conditions. We have studied for the period 2011-2015 retrospectively indicators of Blood Service of Kazakhstan. Strengths of Blood Service of RK(Ps4,5): 1) indicators of donations for 1000 people is higher than in some countries of the CIS (in Russia 14, Kazakhstan - 17); 2) the functioning science centre of transfusiology; 3) the legal possibility of additional financing blood centers in the form of paid services; 4) the absence of competitors; 5) training on specialty Transfusiology; 6) the stable management staff of blood centers, a high level of competence; 7) increase in the incidence requiring transfusion therapy (oncohematology); 8) equipment upgrades; 9) the opening of a reference laboratory; 10) growth of the proportion of issued high-quality blood components; 11) governmental organization 'Drop of Life'; 12) the functioning bone marrow register; 13) equipped with modern equipment HLA-laboratory; 14) High categorization of average medical workers; 15) availability of own specialized scientific journal; 16) vivarium. The weaknesses (Ps = 3.5): 1) the incomplete equipping of blood centers and blood transfusion cabinets according to standards; 2) low specific weight of paid services of the CC; 3) low categorization of doctors; 4) high staff turnover; 5) the low scientific potential of industrial and clinical of transfusiology; 6) the low wages paid; 7) slight growth of harvested donor blood; 8) the weak continuity with offices blood transfusion; 9) lack of agitation work; 10) the formally functioning of Transfusion Association; 11) the absence of scientific laboratories; 12) high standard deviation from the average for donations in the republic. The possibilities (Ps = 2,7): 1): international grants; 2) organization of international seminars on clinical of transfusiology; 3) cross-sectoral cooperation; 4) to increase scientific research in the field of clinical of transfusiology; 5) reduce the share of donation unsuitable for transfusion and processing; 6) strengthening marketing management in the development of fee-based services; 7) advertising paid services; 8) strengthening the publishing of teaching aids; 9) team-building staff. The threats (Ps = 2.1): 1) an increase of staff turnover; 2) the risk of litigation; 3) reduction gemoprodukts based on evidence-based medicine; 4) regression of scientific capacity; 5) organization of marketing; 6) transfusiologist marketing; 7) reduction in the quality of the evidence base transfusions.

Keywords: blood service, healthcare, Kazakhstan, quantative swot analysis

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Authors: Ousmane Sarr

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The Senegalese coasts have high productivity of fishery resources due to the frequency of intense up-welling system that occurs along its coast, caused by the maritime trade winds making its waters nutrients rich. Fishing plays a primordial role in Senegal's socioeconomic plans and food security. However, a global diagnosis of the Senegalese maritime fishing sector has highlighted the challenges this sector encounters. Among these concerns, some significant stocks, a priority target for artisanal fishing, need further assessment. If no efforts are made in this direction, most stock will be overexploited or even in decline. It is in this context that this research was initiated. This investigation aimed to apply a multi-modal approach (LBB, Catch-only-based CMSY model and its most recent version (CMSY++); JABBA, and JABBA-Select) to assess the stock of Atlantic bonito, Sarda sarda (Bloch, 1793) in the Senegalese Exclusive Economic Zone (SEEZ). Available catch, effort, and size data from Atlantic bonito over 15 years (2004-2018) were used to calculate the nominal and standardized CPUE, size-frequency distribution, and length at retentions (50 % and 95 % selectivity) of the species. These relevant results were employed as input parameters for stock assessment models mentioned above to define the stock status of this species in this region of the Atlantic Ocean. The LBB model indicated an Atlantic bonito healthy stock status with B/BMSY values ranging from 1.3 to 1.6 and B/B0 values varying from 0.47 to 0.61 of the main scenarios performed (BON_AFG_CL, BON_GN_Length, and BON_PS_Length). The results estimated by LBB are consistent with those obtained by CMSY. The CMSY model results demonstrate that the SEEZ Atlantic bonito stock is in a sound condition in the final year of the main scenarios analyzed (BON, BON-bt, BON-GN-bt, and BON-PS-bt) with sustainable relative stock biomass (B2018/BMSY = 1.13 to 1.3) and fishing pressure levels (F2018/FMSY= 0.52 to 1.43). The B/BMSY and F/FMSY results for the JABBA model ranged between 2.01 to 2.14 and 0.47 to 0.33, respectively. In contrast, The estimated B/BMSY and F/FMSY for JABBA-Select ranged from 1.91 to 1.92 and 0.52 to 0.54. The Kobe plots results of the base case scenarios ranged from 75% to 89% probability in the green area, indicating sustainable fishing pressure and an Atlantic bonito healthy stock size capable of producing high yields close to the MSY. Based on the stock assessment results, this study highlighted scientific advice for temporary management measures. This study suggests an improvement of the selectivity parameters of longlines and purse seines and a temporary prohibition of the use of sleeping nets in the fishery for the Atlantic bonito stock in the SEEZ based on the results of the length-base models. Although these actions are temporary, they can be essential to reduce or avoid intense pressure on the Atlantic bonito stock in the SEEZ. However, it is necessary to establish harvest control rules to provide coherent and solid scientific information that leads to appropriate decision-making for rational and sustainable exploitation of Atlantic bonito in the SEEZ and the Eastern Atlantic Ocean.

Keywords: multi-model approach, stock assessment, atlantic bonito, SEEZ

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Authors: Chuyu Huang, Itsuki Minemi, Kuanlin Chen, Yuki Hirose

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It remains unclear how language speakers are able to perceive phonological contrasts that do not exist on their own. This experiment uses the vowel-length distinction in Japanese, which is phonologically contrastive and co-occurs with tonal change in some cases. For speakers whose first language does not distinguish vowel length, contrastive duration is usually misperceived, e.g., Mandarin speakers. Two alternative hypotheses for how Mandarin speakers would perceive a phonological contrast that does not exist in their language make different predictions. The stress parameter model does not have a clear prediction about the impact of tonal type. Mandarin speakers will likely be not able to perceive vowel length as well as Japanese native speakers do, but the performance might not correlate to tonal type because the prosody of their language is distinctive, which requires users to encode lexical prosody and notice subtle differences in word prosody. By contrast, cue-based phonetic models predict that Mandarin speakers may rely on pitch differences, a secondary cue, to perceive vowel length. Two groups of Mandarin speakers, including naive non-Japanese speakers and beginner learners, were recruited to participate in an AX discrimination task involving two Japanese sound stimuli that contain a phonologically contrastive environment. Participants were asked to indicate whether the two stimuli containing a vowel-length contrast (e.g., maapero vs. mapero) sound the same. The experiment was bifactorial. The first factor contrasted three syllabic positions (syllable position; initial/medial/final), as it would be likely to affect the perceptual difficulty, as seen in previous studies, and the second factor contrasted two pitch types (accent type): one with accentual change that could be distinguished with the lexical tones in Mandarin (the different condition), with the other group having no tonal distinction but only differing in vowel length (the same condition). The overall results showed that a significant main effect of accent type by applying a linear mixed-effects model (β = 1.48, SE = 0.35, p < 0.05), which implies that Mandarin speakers tend to more successfully recognize vowel-length differences when the long vowel counterpart takes on a tone that exists in Mandarin. The interaction between the accent type and the syllabic position is also significant (β = 2.30, SE = 0.91, p < 0.05), showing that vowel lengths in the different conditions are more difficult to recognize in the word-final case relative to the initial condition. The second statistical model, which compares naive speakers to beginners, was conducted with logistic regression to test the effects of the participant group. A significant difference was found between the two groups (β = 1.06, 95% CI = [0.36, 2.03], p < 0.05). This study shows that: (1) Mandarin speakers are likely to use pitch cues to perceive vowel length in a non-native language, which is consistent with the cue-based approaches; (2) an exposure effect was observed: the beginner group achieved a higher accuracy for long vowel perception, which implied the exposure effect despite the short period of language learning experience.

Keywords: cue-based perception, exposure effect, prosodic perception, vowel duration

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Authors: Massimo Ferrari, Maria Sole Pagliari

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Pollution is a classic example of economic externality: agents who produce it do not face direct costs from emissions. Therefore, there are no direct economic incentives for reducing pollution. One way to address this market failure would be directly taxing emissions. However, because emissions are global, governments might as well find it optimal to wait let foreign countries to tax emissions so that they can enjoy the benefits of lower pollution without facing its direct costs. In this paper, we first document the empirical relation between pollution and economic output with static and dynamic regression methods. We show that there is a negative relation between aggregate output and the stock of pollution (measured as the stock of CO₂ emissions). This relationship is also highly non-linear, increasing at an exponential rate. In the second part of the paper, we develop and estimate a two-country, two-sector model for the US and the euro area. With this model, we aim at analyzing how the public sector should respond to higher emissions and what are the direct costs that these policies might have. In the model, there are two types of firms, brown firms (which produce a polluting technology) and green firms. Brown firms also produce an externality, CO₂ emissions, which has detrimental effects on aggregate output. As brown firms do not face direct costs from polluting, they do not have incentives to reduce emissions. Notably, emissions in our model are global: the stock of CO₂ in the economy affects all countries, independently from where it is produced. This simplified economy captures the main trade-off between emissions and production, generating a classic market failure. According to our results, the current level of emission reduces output by between 0.4 and 0.75%. Notably, these estimates lay in the upper bound of the distribution of those delivered by studies in the early 2000s. To address market failure, governments should step in introducing taxes on emissions. With the tax, brown firms pay a cost for polluting hence facing the incentive to move to green technologies. Governments, however, might also adopt a beggar-thy-neighbour strategy. Reducing emissions is costly, as moves production away from the 'optimal' production mix of brown and green technology. Because emissions are global, a government could just wait for the other country to tackle climate change, ripping the benefits without facing any costs. We study how this strategic game unfolds and show three important results: first, cooperation is first-best optimal from a global prospective; second, countries face incentives to deviate from the cooperating equilibria; third, tariffs on imported brown goods (the only retaliation policy in case of deviation from the cooperation equilibrium) are ineffective because the exchange rate would move to compensate. We finally study monetary policy under when costs for climate change rise and show that the monetary authority should react stronger to deviations of inflation from its target.

Keywords: climate change, general equilibrium, optimal taxation, monetary policy

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Authors: S. D. Zalepugin, A. E. Murzich, D. G. Satskevich, A. B. Palivanov

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Introduction. The incidence of spinal injuries in patients under 18 years of age has increased significantly in recent years, which represents a significant economic, social and medical problem. The most common method of surgical stabilization of spinal fractures in pediatric patients is transpedicular posterior spinal fusion, which is widely used by spinal neurosurgeons in adult patients. Purpose of the study: This study evaluates the results of treatment of thoracolumbar spine lesions in children using the transpedicular screw fixation method. Materials and methods. From 2019 to 2024, 35 children with injuries to the thoracic and lumbar spine underwent surgical treatment using the transpedicular screw fixation method. Among the injured, girls prevailed (21 cases, 60%). The age of the victims ranged from 9 to 17 years. The main causes of damage were: catatrauma (19 cases), road accident (5 cases), sports injury (6 cases), and other reasons - 5 cases. In 5 cases, suicidal attempts occurred. Co-injury was observed in most cases (20 patients, or 57%), which is natural for high-energy injury. Vertebral-spinal injury with neurological disorders was observed in 13 patients, the disorders ranged from mild inferior (4 children) to moderate/severe paraparesis (5 patients) and inferior paraplegia (4 children). 6 children had pelvic organ dysfunction in the form of urinary and fecal retention or incontinence. All thirty-five patients, within a period of 1 to 57 days after the injury, underwent several surgical interventions from the posterior surgical access using a screw fixation method (posterior decompression + spinal fusion). In 12 cases, it was necessary to perform the second stage of surgical treatment - anterior decompression of the spinal cord or its roots. Verticalization of patients was carried out within 1 to 5 days after surgery. Results. In all patients, the nearest, up to 1 year, results were evaluated. In children operated in 2019-2021, the results were studied in terms of 3 to 5 years. The procedures used, clinical results and the quality of the fixative installation were assessed. All patients managed to achieve positive results. The use of internal fixation made it possible to carry out early verticalization of children, eliminate pain syndrome and achieve a regression of neurological disorders in most patients (especially in cases when the operation was performed early after injury - from 1 to 3 days). Within the first month, the ability to self-care was fully restored. Bone fusion was observed within 6-12 months after surgery. There were no complications after surgery. The analysis of postoperative radiographs, CT and MRI images revealed the correct standing of the screws in all cases. Conclusion. The posterior spinal fusion using the new method of screw fixation in pediatric patients allows to achieve durable stabilization of damage, begins early rehabilitation of patients and reduces the duration of hospital treatment by 2-3 times. Thus, we recommend the use of a transpedicular fixator in children as a reliable, technically feasible method for restoring spinal stability with a low risk of intra- and postoperative complications.

Keywords: pediatric patients, spinal injuries, transpedicular stabilization, operative treatment

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Authors: K. S. Vimal, D. Bodhini, K. Ramya, N. Lakshmipriya, R. M. Anjana, V. Sudha, J. A. Lovegrove, V. Mohan, V. Radha

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Gene-lifestyle interaction studies have been carried out in various populations. However, to date there are no studies in an Asian Indian population. Hence, we examined whether lifestyle factors such as diet and physical activity modify the association between fat mass and obesity–associated (FTO) gene variants and obesity and type 2 diabetes (T2D) in an Asian Indian population. We studied 734 unrelated T2D and 884 normal glucose-tolerant (NGT) participants randomly selected from the Chennai Urban Rural Epidemiology Study (CURES) in Southern India. Obesity was defined according to the World Health Organization Asia Pacific Guidelines (non-obese, BMI < 25 kg/m2; obese, BMI ≥ 25 kg/m2). Six single nucleotide polymorphisms (SNPs) in the FTO gene (rs9940128, rs7193144, rs8050136, rs918031, rs1588413 and rs11076023) identified from recent genome-wide association studies for T2D were genotyped by polymerase chain reaction-restriction fragment length polymorphism and direct sequencing. Dietary assessment was carried out using a validated food frequency questionnaire and physical activity was based upon the self-report. Interaction analyses were performed by including the interaction terms in the model. A joint likelihood ratio test of the main SNP effects and the SNP-diet/physical activity interaction effects was used in the linear regression analyses to maximize statistical power. Statistical analyses were performed using STATA version 13. There was a significant interaction between FTO SNP rs8050136 and carbohydrate energy percentage (Pinteraction=0.04) on obesity, where the ‘A’ allele carriers of the SNP rs8050136 had 2.46 times higher risk of obesity than those with ‘CC’ genotype (P=3.0x10-5) among individuals in the highest tertile of carbohydrate energy percentage. Furthermore, among those who had lower levels of physical activity, the ‘A’ allele carriers of the SNP rs8050136 had 1.89 times higher risk of obesity than those with ‘CC’ genotype (P=4.0x10-5). We also found a borderline interaction between SNP rs11076023 and carbohydrate energy percentage (Pinteraction=0.08) on T2D, where the ‘A’ allele carriers in the highest tertile of carbohydrate energy percentage, had 1.57 times higher risk of T2D than those with ‘TT’ genotype (P=0.002). There was also a significant interaction between SNP rs11076023 and physical activity (Pinteraction=0.03) on T2D. No further significant interactions between SNPs and macronutrient intake or physical activity on obesity and T2D were observed. In conclusion, this is the first study to provide evidence for a gene-diet and gene-physical activity interaction on obesity and T2D in an Asian Indian population. These findings suggest that the association between FTO gene variants and obesity and T2D is influenced by carbohydrate intake and physical activity levels. Greater understanding of how FTO gene influences obesity and T2D through dietary and exercise interventions will advance the development of behavioral intervention and personalised lifestyle strategies predicted to reduce the development of metabolic diseases in ‘A’ allele carriers of both SNPs in this Asian Indian population.

Keywords: dietary intake, FTO, obesity, physical activity, type 2 diabetes, Asian Indian.

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: Sachil Kumar, Anoop Kumar Verma, Wahid Ali, Uma Shankar Singh

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Globally approximately 55.3 million people die each year. In the India there were 95 lakh annual deaths in 2013. The number of deaths resulted from homicides, suicides and unintentional injuries in the same period was about 5.7 lakh. The ever-increasing crime rate necessitated the development of methods for determining time since death. An erroneous time of death window can lead investigators down the wrong path or possibly focus a case on an innocent suspect. In this regard a research was carried out by analyzing the temperature dependent degradation of a Cardiac Troponin-T protein (cTnT) in the myocardium postmortem as a marker for time since death. Cardiac tissue samples were collected from (n=6) medico-legal autopsies, (in the Department of Forensic Medicine and Toxicology, King George’s Medical University, Lucknow India) after informed consent from the relatives and studied post-mortem degradation by incubation of the cardiac tissue at room temperature (20±2 OC), 12 0C, 25 0C and 37 0C for different time periods ((~5, 26, 50, 84, 132, 157, 180, 205, and 230 hours). The cases included were the subjects of road traffic accidents (RTA) without any prior history of disease who died in the hospital and their exact time of death was known. The analysis involved extraction of the protein, separation by denaturing gel electrophoresis (SDS-PAGE) and visualization by Western blot using cTnT specific monoclonal antibodies. The area of the bands within a lane was quantified by scanning and digitizing the image using Gel Doc. The data shows a distinct temporal profile corresponding to the degradation of cTnT by proteases found in cardiac muscle. The disappearance of intact cTnT and the appearance of lower molecular weight bands are easily observed. Western blot data clearly showed the intact protein at 42 kDa, two major (27 kDa, 10kDa) fragments, two additional minor fragments (32 kDa) and formation of low molecular weight fragments as time increases. At 12 0C the intensity of band (intact cTnT) decreased steadily as compared to RT, 25 0C and 37 0C. Overall, both PMI and temperature had a statistically significant effect where the greatest amount of protein breakdown was observed within the first 38 h and at the highest temperature, 37 0C. The combination of high temperature (37 0C) and long Postmortem interval (105.15 hrs) had the most drastic effect on the breakdown of cTnT. If the percent intact cTnT is calculated from the total area integrated within a Western blot lane, then the percent intact cTnT shows a pseudo-first order relationship when plotted against the log of the time postmortem. These plots show a good coefficient of correlation of r = 0.95 (p=0.003) for the regression of the human heart at different temperature conditions. The data presented demonstrates that this technique can provide an extended time range during which Postmortem interval can be more accurately estimated.

Keywords: degradation, postmortem interval, proteolysis, temperature, troponin

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Authors: Mercedes Bertotto, Marcelo Bello, Hector Goicoechea, Veronica Fusca

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The National Service of Agri-Food Health and Quality (SENASA), controls honey to detect contamination by synthetic or natural chemical substances and establishes and controls the traceability of the product. The utility of near-infrared spectroscopy for the detection of adulteration of honey with high fructose corn syrup (HFCS) was investigated. First of all, a mixture of different authentic artisanal Argentinian honey was prepared to cover as much heterogeneity as possible. Then, mixtures were prepared by adding different concentrations of high fructose corn syrup (HFCS) to samples of the honey pool. 237 samples were used, 108 of them were authentic honey and 129 samples corresponded to honey adulterated with HFCS between 1 and 10%. They were stored unrefrigerated from time of production until scanning and were not filtered after receipt in the laboratory. Immediately prior to spectral collection, honey was incubated at 40°C overnight to dissolve any crystalline material, manually stirred to achieve homogeneity and adjusted to a standard solids content (70° Brix) with distilled water. Adulterant solutions were also adjusted to 70° Brix. Samples were measured by NIR spectroscopy in the range of 650 to 7000 cm⁻¹. The technique of specular reflectance was used, with a lens aperture range of 150 mm. Pretreatment of the spectra was performed by Standard Normal Variate (SNV). The ant colony optimization genetic algorithm sample selection (ACOGASS) graphical interface was used, using MATLAB version 5.3, to select the variables with the greatest discriminating power. The data set was divided into a validation set and a calibration set, using the Kennard-Stone (KS) algorithm. A combined method of Potential Functions (PF) was chosen together with Partial Least Square Linear Discriminant Analysis (PLS-DA). Different estimators of the predictive capacity of the model were compared, which were obtained using a decreasing number of groups, which implies more demanding validation conditions. The optimal number of latent variables was selected as the number associated with the minimum error and the smallest number of unassigned samples. Once the optimal number of latent variables was defined, we proceeded to apply the model to the training samples. With the calibrated model for the training samples, we proceeded to study the validation samples. The calibrated model that combines the potential function methods and PLSDA can be considered reliable and stable since its performance in future samples is expected to be comparable to that achieved for the training samples. By use of Potential Functions (PF) and Partial Least Square Linear Discriminant Analysis (PLS-DA) classification, authentic honey and honey adulterated with HFCS could be identified with a correct classification rate of 97.9%. The results showed that NIR in combination with the PT and PLS-DS methods can be a simple, fast and low-cost technique for the detection of HFCS in honey with high sensitivity and power of discrimination.

Keywords: adulteration, multivariate analysis, potential functions, regression

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Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

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Authors: Md. Nuru Miah, A. F. M. Akhter Uddin

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Medicinal plants are grown naturally in a tropical environment in Bangladesh and used as drug and therapeutic agents in the health care system. According to Bangladesh Agricultural Research Institute (BARI), there are 722 species of medicinal plants in the country. Of them, 255 plants are utilized by the manufacturers of Ayurvedic and Unani medicines. Medicinal plants like Aloevera, Ashwagonda, shotomul,Tulsi, Vuikumra, Misridana are extensively cultivated in some selected areas as well; where Aloevera scored the highest position in production. In the early 1980, Ayurvedic and Unani companies procured 80 percent of medicinal plants from natural forests, and the rest 20 percent was imported. Now the scenario has changed; 80 percent is imported, and the rest 20 percent is collected from local products(Source: Astudy on sectorbased need assessment of Business promotion council-Herbal products and medicinal plants, page-4). Uttara Development Program Society, a leading Non- Government development organization in Bangladesh, has been implementing a value chain development project under promoting Agricultural commercialization and Enterprises of Pally Karma Sahayak Foundation (PKSF) funded by the International Fund for Agricultural Development (IFAD) in Natore Sadar Upazila from April 2017 to sustainably develop the technological interventions for products and market development. The ultimate goal of the project is to increase income, generate employment and develop this sector as a sustainable business enterprise. Altogether 10,000 farmers (Nursery owners, growers, input supplier, processors, whole sellers, and retailers) are engaged in different activities of the project. The entrepreneurs engaged in medicinal plant cultivation did not know and follow environmental and good agricultural practices. They used to adopt traditional methodology in production and processing. Locally the farmers didn’t have any positive initiative to expand their business as well as developvalue added products. A lot of diversified products could be possible to develop and marketed with the introduction of post-harvest processing technology and market linkage with the local and global buyer. Training is imparted to the nursery owners and herbal growers on production technologies, sowing method, use of organic fertilizers/compost/pesticides, harvesting procedures, and storage facilities. Different types of herbal tea like Rosella, Moringa, Tulshi, and Basak are being produced and packed locally with a good scope of its marketing in different cities of the country. The project has been able to achieve a significant impact in the development of production technologies, but still, there is room for further improvement in processing, packaging, and marketing level. The core intervention of the current project to develop some entrepreneurs for branding, packaging, promotion, and marketing while considering environment friendly practices. The present strategies will strengthen the knowledge and skills of the entrepreneurs for the production and marketing of their products, maintaining worldwide accepted compliance system for easy access to the global market.

Keywords: aloe vera, herbs and shrubs, market, interventions

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Authors: Elena Chernyak, Ryan Ceresola

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Intimate partner violence is a serious social problem that affects a million women worldwide and impacts their health and wellbeing. Some risk factors for intimate partner violence against women (e.g., disobeying or arguing with a partner, women’s age, education, and employment) are similar in many countries, both developed and developing. However, one of the principal and most significant contributors to women’s vulnerability to violence perpetrated by their intimate partners is the witnessing of interparental aggression in the family of origin. Witnessing interparental violence may lead to acceptance of intimate partner violence as a normal way to resolve conflicts. Thus, utilization of violence becomes the behavioral model: men who witnessed the parental violence are more likely to employ physical violence against their female partners whereas women who observed their fathers beating their mothers learn to tolerate aggressive behavior and become victims of domestic violence themselves. Taking into consideration the importance of this subject matter, the association between witnessing intimate partner violence in family-of-origin and experience of intimate partner violence in adulthood requires further attention. The objective of this research is to analyze and compare the prevalence of intimate partner violence in ten developing countries in different regions, namely: Mali, Haiti, Jordan, Peru, the Philippines, Pakistan, Cambodia, Egypt, the Dominican Republic and Nigeria. Specifically, this research asks whether witnessing interparental violence in a family of origin is associated with the woman’s experience of intimate partner violence during adulthood and to what extent this factor varies among the countries under investigation. This study contributes to the literature on domestic violence against women, prevalence and experience of intimate partner violence against women in developing countries, and the risk factors, using recently collected, nationally representative population-based data from above-mentioned countries. The data used in this research are derived from the demographic and health surveys conducted in the ten mentioned above countries from 2013-2016. These surveys are cross-sectional, nationally representative surveys of ever-married or cohabitating women of reproductive age and the good source of high quality and comprehensive information about women, their children, partners, and households. To complete this analysis, multivariate logistic regression was run for each of the countries, and the results are presented with odds ratios, in order to highlight the effect of witnessing intimate partner violence controlling for other factors. The results of this study indicated that having witnessed partner violence in a family of origin significantly (by 50-500%) increases the likelihood of experiencing later abuse for respondents in all countries. This finding provides robust support for the intergenerational transmission of violence theory that explains the link between interparental aggression and intimate partner violence in subsequent relationships in adulthood as a result of a learned model of behavior observed in childhood. Furthermore, it was found that some of the control variables (e.g., education, number of children, and wealth) are associated with intimate partner violence in some countries under investigation while are not associated with male partner’s abusive behavior in some other, which may be explained by specific cultural and economic factors.

Keywords: intimate partner violence, domestic violence against women, developing countries, demographic and health surveys, risk factors

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Authors: Kaaryn M. Cater

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Students with sensory processing sensitivity (SPS), Meares-Irlen Syndrome (MIS) and dyslexia can become overwhelmed and struggle to thrive in traditional tertiary learning environments. An estimated 50% of tertiary students who disclose learning related issues are dyslexic. This study explores the relationship between SPS, MIS and dyslexia. Baseline measures will be analysed to establish any correlation between these three minority methods of information processing. SPS is an innate sensitivity trait found in 15-20% of the population and has been identified in over 100 species of animals. Humans with SPS are referred to as Highly Sensitive People (HSP) and the measure of HSP is a 27 point self-test known as the Highly Sensitive Person Scale (HSPS). A 2016 study conducted by the author established base-line data for HSP students in a tertiary institution in New Zealand. The results of the study showed that all participating HSP students believed the knowledge of SPS to be life-changing and useful in managing life and study, in addition, they believed that all tutors and in-coming students should be given information on SPS. MIS is a visual processing and perception disorder that is found in approximately 10% of the population and has a variety of symptoms including visual fatigue, headaches and nausea. One way to ease some of these symptoms is through the use of colored lenses or overlays. Dyslexia is a complex phonological based information processing variation present in approximately 10% of the population. An estimated 50% of dyslexics are thought to have MIS. The study exploring possible correlations between these minority forms of information processing is due to begin in February 2017. An invitation will be extended to all first year students enrolled in degree programmes across all faculties and schools within the institution. An estimated 900 students will be eligible to participate in the study. Participants will be asked to complete a battery of on-line questionnaires including the Highly Sensitive Person Scale, the International Dyslexia Association adult self-assessment and the adapted Irlen indicator. All three scales have been used extensively in literature and have been validated among many populations. All participants whose score on any (or some) of the three questionnaires suggest a minority method of information processing will receive an invitation to meet with a learning advisor, and given access to counselling services if they choose. Meeting with a learning advisor is not mandatory, and some participants may choose not to receive help. Data will be collected using the Question Pro platform and base-line data will be analysed using correlation and regression analysis to identify relationships and predictors between SPS, MIS and dyslexia. This study forms part of a larger three year longitudinal study and participants will be required to complete questionnaires at annual intervals in subsequent years of the study until completion of (or withdrawal from) their degree. At these data collection points, participants will be questioned on any additional support received relating to their minority method(s) of information processing. Data from this study will be available by April 2017.

Keywords: dyslexia, highly sensitive person (HSP), Meares-Irlen Syndrome (MIS), minority forms of information processing, sensory processing sensitivity (SPS)

Procedia PDF Downloads 248

Authors: Pawel Kiper, Alfonc Baba, Mahmoud Alhelou, Giorgia Pregnolato, Michela Agostini, Andrea Turolla

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Impairment of cervical spine mobility is often related to pain triggered by musculoskeletal disorders or direct traumatic injuries of the spine. To date, these disorders are assessed with goniometers and inclinometers, which are the most popular devices used in clinical settings. Nevertheless, these technologies usually allow measurement of no more than two-dimensional range of motion (ROM) quotes in static conditions. Conversely, the wide use of motion tracking systems able to measure 3 to 6 degrees of freedom dynamically, while performing standard ROM assessment, are limited due to technical complexities in preparing the setup and high costs. Thus, motion tracking systems are primarily used in research. These systems are an integral part of virtual reality (VR) technologies, which can be used for measuring spine mobility. To our knowledge, the accuracy of VR measure has not yet been studied within virtual environments. Thus, the aim of this study was to test the reliability of a protocol for the assessment of sensorimotor function of the cervical spine in a population of healthy subjects and to compare whether using immersive or non-immersive VR for visualization affects the performance. Both VR assessments consisted of the same five exercises and random sequence determined which of the environments (i.e. immersive or non-immersive) was used as first assessment. Subjects were asked to perform head rotation (right and left), flexion, extension and lateral flexion (right and left side bending). Each movement was executed five times. Moreover, the participants were invited to perform head reaching movements i.e. head movements toward 8 targets placed along a circular perimeter each 45°, visualized one-by-one in random order. Finally, head repositioning movement was obtained by head movement toward the same 8 targets as for reaching and following reposition to the start point. Thus, each participant performed 46 tasks during assessment. Main measures were: ROM of rotation, flexion, extension, lateral flexion and complete kinematics of the cervical spine (i.e. number of completed targets, time of execution (seconds), spatial length (cm), angle distance (°), jerk). Thirty-five healthy participants (i.e. 14 males and 21 females, mean age 28.4±6.47) were recruited for the cervical spine assessment with immersive and non-immersive VR environments. Comparison analysis demonstrated that: head right rotation (p=0.027), extension (p=0.047), flexion (p=0.000), time (p=0.001), spatial length (p=0.004), jerk target (p=0.032), trajectory repositioning (p=0.003), and jerk target repositioning (p=0.007) were significantly better in immersive than non-immersive VR. A regression model showed that assessment in immersive VR was influenced by height, trajectory repositioning (p<0.05), and handedness (p<0.05), whereas in non-immersive VR performance was influenced by height, jerk target (p=0.002), head extension, jerk target repositioning (p=0.002), and by age, head flex/ext, trajectory repositioning, and weight (p=0.040). The results of this study showed higher accuracy of cervical spine assessment when executed in immersive VR. The assessment of ROM and kinematics of the cervical spine can be affected by independent and dependent variables in both immersive and non-immersive VR settings.

Keywords: virtual reality, cervical spine, motion analysis, range of motion, measurement validity

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Authors: Ricardo Alberto Chiong Zevallos, Eduardo Moraes Rego Reis

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Pancreatic adenocarcinoma (PDAC) patients have a less than 10% 5-year survival rate. PDAC has no defined diagnostic and prognostic biomarkers. Gemcitabine is the first-line drug in PDAC and several other cancers. Long non-coding RNAs (lncRNAs) contribute to the tumorigenesis and are potential biomarkers for PDAC. Although lncRNAs aren’t translated into proteins, they have important functions. LncRNAs can decoy or recruit proteins from the epigenetic machinery, act as microRNA sponges, participate in protein translocation through different cellular compartments, and even promote chemoresistance. The chromatin remodeling enzyme EZH2 is a histone methyltransferase that catalyzes the methylation of histone 3 at lysine 27, silencing local expression. EZH2 is ambivalent, it can also activate gene expression independently of its histone methyltransferase activity. EZH2 is overexpressed in several cancers and interacts with lncRNAs, being recruited to a specific locus. EZH2 can be recruited to activate an oncogene or silence a tumor suppressor. The lncRNAs misregulation in cancer can result in the differential recruitment of EZH2 and in a distinct epigenetic landscape, promoting chemoresistance. The relevance of the EZH2-lncRNAs interaction to chemoresistant PDAC was assessed by Real Time quantitative PCR (RT-qPCR) and RNA Immunoprecipitation (RIP) experiments with naïve and gemcitabine-resistant PDAC cells. The expression of several lncRNAs and EZH2 gene targets was evaluated contrasting naïve and resistant cells. Selection of candidate genes was made by bioinformatic analysis and literature curation. Indeed, the resistant cell line showed higher expression of chemoresistant-associated lncRNAs and protein coding genes. RIP detected lncRNAs interacting with EZH2 with varying intensity levels in the cell lines. During RIP, the nuclear fraction of the cells was incubated with an antibody for EZH2 and with magnetic beads. The RNA precipitated with the beads-antibody-EZH2 complex was isolated and reverse transcribed. The presence of candidate lncRNAs was detected by RT-qPCR, and the enrichment was calculated relative to INPUT (total lysate control sample collected before RIP). The enrichment levels varied across the several lncRNAs and cell lines. The EZH2-lncRNA interaction might be responsible for the regulation of chemoresistance-associated genes in multiple cancers. The relevance of the lncRNA-EZH2 interaction to PDAC was assessed by siRNA knockdown of a lncRNA, followed by the analysis of the EZH2 target expression by RT-qPCR. The chromatin immunoprecipitation (ChIP) of EZH2 and H3K27me3 followed by RT-qPCR with primers for EZH2 targets also assess the specificity of the EZH2 recruitment by the lncRNA. This is the first report of the interaction of EZH2 and lncRNAs HOTTIP and PVT1 in chemoresistant PDAC. HOTTIP and PVT1 were described as promoting chemoresistance in several cancers, but the role of EZH2 is not clarified. For the first time, the lncRNA LINC01133 was detected in a chemoresistant cancer. The interaction of EZH2 with LINC02577, LINC00920, LINC00941, and LINC01559 have never been reported in any context. The novel lncRNAs-EZH2 interactions regulate chemoresistant-associated genes in PDAC and might be relevant to other cancers. Therapies targeting EZH2 alone weren’t successful, and a combinatorial approach also targeting the lncRNAs interacting with it might be key to overcome chemoresistance in several cancers.

Keywords: epigenetics, chemoresistance, long non-coding RNAs, pancreatic cancer, histone modification

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Authors: Irina Mizeva, Elena Potapova, Viktor Dremin, Mikhail Mezentsev, Valeri Shupletsov

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The local tissue perfusion is regulated by the microvascular tone which is under the control of a number of physiological mechanisms. Laser Doppler flowmetry (LDF) together with wavelet analyses is the most commonly used technique to study the regulatory mechanisms of cutaneous microcirculation. External factors such as temperature, local pressure of the probe on the skin, etc. influence on the blood flow characteristics and are used as physiological tests to evaluate microvascular regulatory mechanisms. Local probe pressure influences on the microcirculation parameters measured by optical methods: diffuse reflectance spectroscopy, fluorescence spectroscopy, and LDF. Therefore, further study of probe pressure effects can be useful to improve the reliability of optical measurement. During pressure tests variation of the mean perfusion measured by means of LDF usually is estimated. An additional information concerning the physiological mechanisms of the vascular tone regulation system in response to local pressure can be obtained using spectral analyses of LDF samples. The aim of the present work was to develop protocol and algorithm of data processing appropriate for study physiological response to the local pressure test. Involving 6 subjects (20±2 years) and providing 5 measurements for every subject we estimated intersubject and-inter group variability of response of both averaged and oscillating parts of the LDF sample on external surface pressure. The final purpose of the work was to find special features which further can be used in wider clinic studies. The cutaneous perfusion measurements were carried out by LAKK-02 (SPE LAZMA Ltd., Russia), the skin loading was provided by the originally designed device which allows one to distribute the pressure around the LDF probe. The probe was installed on the dorsal part of the distal finger of the index figure. We collected measurements continuously for one hour and varied loading from 0 to 180mmHg stepwise with a step duration of 10 minutes. Further, we post-processed the samples using the wavelet transform and traced the energy of oscillations in five frequency bands over time. Weak loading leads to pressure-induced vasodilation, so one should take into account that the perfusion measured under pressure conditions will be overestimated. On the other hand, we revealed a decrease in endothelial associated fluctuations. Further loading (88 mmHg) induces amplification of pulsations in all frequency bands. We assume that such loading leads to a higher number of closed capillaries, higher input of arterioles in the LDF signal and as a consequence more vivid oscillations which mainly are formed in arterioles. External pressure higher than 144 mmHg leads to the decrease of oscillating components, after removing the loading very rapid restore of the tissue perfusion takes place. In this work, we have demonstrated that local skin loading influence on the microcirculation parameters measured by optic technique; this should be taken into account while developing portable electronic devices. The proposed protocol of local loading allows one to evaluate PIV as far as to trace dynamic of blood flow oscillations. This study was supported by the Russian Science Foundation under project N 18-15-00201.

Keywords: blood microcirculation, laser Doppler flowmetry, pressure-induced vasodilation, wavelet analyses blood

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Authors: Nilki Upathissa, Torin Wirasinghe

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This solution addresses the challenges faced by traditional bookstores and the limitations of digital media, striking a balance between the tactile experience of printed books and the convenience of modern technology. The book exchange system offers a sustainable alternative, empowering users to access a diverse range of books while promoting community engagement. The user-friendly interfaces incorporated into the book exchange system ensure a seamless and enjoyable experience for users. Intuitive features for book management, search, and messaging facilitate effortless exchanges and interactions between users. By streamlining the process, the system encourages readers to explore new books aligned with their interests, enhancing the overall reading experience. Central to the system's success is the hybrid recommendation engine, which leverages advanced technologies such as Long Short-Term Memory (LSTM) models. By analyzing user input, the engine accurately predicts genre preferences, enabling personalized book recommendations. The hybrid approach integrates multiple technologies, including user interfaces, machine learning models, and recommendation algorithms, to ensure the accuracy and diversity of the recommendations. The evaluation of the book exchange system with the hybrid recommendation engine demonstrated exceptional performance across key metrics. The high accuracy score of 0.97 highlights the system's ability to provide relevant recommendations, enhancing users' chances of discovering books that resonate with their interests. The commendable precision, recall, and F1score scores further validate the system's efficacy in offering appropriate book suggestions. Additionally, the curve classifications substantiate the system's effectiveness in distinguishing positive and negative recommendations. This metric provides confidence in the system's ability to navigate the vast landscape of book choices and deliver recommendations that align with users' preferences. Furthermore, the implementation of this book exchange system with a hybrid recommendation engine has the potential to revolutionize the way readers interact with printed books. By facilitating book exchanges and providing personalized recommendations, the system encourages a sense of community and exploration within the reading community. Moreover, the emphasis on sustainability aligns with the growing global consciousness towards eco-friendly practices. With its robust technical approach and promising evaluation results, this solution paves the way for a more inclusive, accessible, and enjoyable reading experience for book lovers worldwide. In conclusion, the developed book exchange system with a hybrid recommendation engine represents a progressive solution to the challenges faced by traditional bookstores and the limitations of digital media. By promoting sustainability, widening access to printed books, and fostering engagement with reading, this system addresses the evolving needs of book enthusiasts. The integration of user-friendly interfaces, advanced machine learning models, and recommendation algorithms ensure accurate and diverse book recommendations, enriching the reading experience for users.

Keywords: recommendation systems, hybrid recommendation systems, machine learning, data science, long short-term memory, recurrent neural network

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Authors: Preeti Virkar, V. P. Uniyal, Vinod Kumar Bhatt

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With 50% decline in managed honey bee hives in the continents of Europe and America, farmers and landscape managers are turning to native wild bees for their essential ecosystem services of pollination. Wild bees population are too under danger due to the rapid land use changes from anthropogenic activities. With an escalating population reaching 9.0 billion by 2050, human-induced land use changes are predicted to further deteriorate the habitats of numerous species by the turn of this century. The status of bees are uncertain, especially in the tropical regions of the world, which also questions the crisis of global pollinator decline and their essential services to wild and managed flora. Our investigation collectively compares wild native bee diversity and their status in forests and agroecosystems in Doon Valley landscape, situated at the foothills of Himalayan ranges, Uttarakhand, India. We seek to ask whether (1) natural habitat are refuge to richer and rarer bees communities than the agroecosystems, (2) Are agroecosystems closer to natural habitats similar to them than agroecosystems farther away; hence support richer bee communities and hence, (3) Do polyculture farms support richer bee communities than monoculture. The data was collected using observation and pantrap sampling form February to May, 2012 to 2014. We recorded 43 species of bees in Doon Valley. They belonged to 5 families; Megachilidae, Apidae, Andrenidae, Halictidae and Collitidae. A multinomial model approach was used to classify the bees into 2 habitats, in which forests demonstrated to support greater number of specialist (26%, n= 11) species than agroecosystems (7%, n= 3). The valley had many species categorized as the rare (58%, n= 25) and very few generalists (9%, n=4). A linear regression model run on our data demonstrated higher bee diversity in agro-ecosystems in close proximity to forests (H’ for < 200 m = 1.60) compared to those further away (H’ for > 600 m = 0.56) (R2=0.782, SE=0.148, p value=0.004). Organic agriculture supported significantly greater species richness in comparison to conventional farms (Mann-Whitney U test, n1 = 33, n2 = 35; P = 0.001). Forests ecosystems are refuge to rare specialist groups and support bee communities in nearby agroecosystems. The findings of our investigation demonstrate the importance of natural habitats as a potential refuge for rare native wild bee pollinators. Polyculture in the valley behaves similar to natural habitats and supports diverse bee communities in comparison to conventional monocultures. Our study suggests that the farming communities adopt diverse organic agriculture systems to attract wild pollinators beneficial for better crop production. Forests are sanctuaries for bees to nest, forage, and breed. Therefore, our outcome also suggests landscape managers not only preserve protected areas but also enhance the floral diversity in semi-natural and urban areas.

Keywords: native bees, pollinators, polyculture, agroecosystem, natural habitat, diversity, monoculture, specialists, generalists

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Authors: Richele P. Severino, Maria M. F. Alchaar, Lorena R. F. De Sousa, Patrik S. Vital, Ana G. Silva, Rosy I. M. A. Ribeiro

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Recognized as a global biodiversity hotspot, the Cerrado (Brazil) presents an extreme abundance of endemic species and it is considered to be one of the biologically richest tropical savanna regions in the world. Erythroxylum genus is found in Cerrado and chemically is characterized by the presence of tropane alkaloids, among them cocaine, a natural alkaloid produced by Erythroxylum coca Lam., which was used as a local anesthetic in small surgeries. However, cocaine gained notoriety due to its psychoactive activity in the Central Nervous System (CNS), becoming one of the major problems of public health today. Some species of Erythroxylum are referred to in the literature as having pharmacological potential, which provide alkaloids, terpenoids, and flavonoids. E. vacciniifolium Mart., commonly known as 'catuaba', is used as a central nervous system stimulant and has aphrodisiac properties and E. pelleterianum A. St.-Hil. in the treatment of stomach pains. Already E. myrsinites Mart. and E. suberosum A. St.-Hil. are used in the tannery industry. Species of Erythroxylum are also used in folk medicine for various diseases, against diabetes, antiviral, fungicidal, cytotoxicity, among others. The Cerrado is recognized as the richer savannah in the world in biodiversity but little explored from the chemical view. In our on-going study of the chemistry of Erythroxylum genus, we have investigated four specimens collected in central Cerrado of Brazil: E. campestre (EC), E. deciduum (ED), E. suberosum (ES) and E. tortuosum (ET). The cytotoxic activity of extracts was evaluated using HeLa cells, in vitro assays. The chemical investigation was performed preparing the extracts using n-hexane (H), dichloromethane (D), ethyl acetate (E) and methanol (M). The cells were treated with increasing concentrations of extracts (50, 75 and 100 μg/mL) diluted in DMSO (1%) and DMEM (0.5% FBS and 1% P/S). The IC₅₀ values were determined measured spectrophotometrically at 570 nm, after incubation of HeLa cell line for 48 hours using the MTT (SIGMA M5655), and calculated by nonlinear regression analysis using GraphPad Prism software. All the assays were done in triplicate and repeated at least two times. The cytotoxic assays showed some promising results with IC₅₀ values less than 100 μg/mL (ETD = 38.5 μg/mL; ETM = 92.3 μg/mL; ESM = 67.8 μg/mL; ECD = 24.0 μg/mL; ECM = 32.9; EDA = 44.2 μg/mL). The chemical profile study of ethyl acetate (E) and methanolic (M) extracts of E. tortuosum leaves was performed by LC-MS, and the structures of the compounds were determined by analysis of ¹H, HSQC and HMBC spectra, and confirmed by comparison with the literature data. The investigation led to six substances: α-amyrin, β-amyrin, campesterol, stigmastan-3,5-diene, β-sitosterol and 7,4’-di-O-methylquercetin-3-O-β-rutinoside, with flavonoid the major compound of extracts. By alkaline extraction of the methanolic extract, it was possible to identify three alkaloids: tropacocaine, cocaine and 6-methoxy-8-methyl-8-azabicyclo[3.2.1]octan-3-ol. The results obtained are important for the chemical knowledge of the Cerrado biodiversity and brought a contribution to the chemistry of Erythroxylum genus.

Keywords: cytotoxicity, Erythroxylum, chemical profile, secondary metabolites

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Authors: Supriya Majumder, Pabitra Banik

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Fates of Arsenic (As) on the soil-plant environment belong to the critical emerging issue, which in turn to appraises the threatening implications of a human health risk — assessing the dynamics of As in soil solid components are likely to impose its potential availability towards plant uptake. In the present context, we introduced an improved Sequential Extraction Procedure (SEP) questioning to identify solid-phase speciation of As in paddy soil under variable soil environmental conditions during two consecutive seasons of rice cultivation practices. We coupled gradients of water management practices with the addition of fertilizer amendments to assess the changes in a partition of As through a field experimental study during monsoon and post-monsoon season using two rice cultivars. Water management regimes were varied based on the methods of cultivation of rice by Conventional (waterlogged) vis-a-vis System of Rice Intensification-SRI (saturated). Fertilizer amendment through the nutrient treatment of absolute control, NPK-RD, NPK-RD + Calcium silicate, NPK-RD + Ferrous sulfate, Farmyard manure (FYM), FYM + Calcium silicate, FYM + Ferrous sulfate, Vermicompost (VC), VC + Calcium silicate, VC + Ferrous sulfate were selected to construct the study. After harvest, soil samples were sequentially extracted to estimate partition of As among the different fractions such as: exchangeable (F1), specifically sorbed (F2), As bound to amorphous Fe oxides (F3), crystalline Fe oxides (F4), organic matter (F5) and residual phase (F6). Results showed that the major proportions of As were found in F3, F4 and F6, whereas F1 exhibited the lowest proportion of total soil As. Among the nutrient treatment mediated changes on As fractions, the application of organic manure and ferrous sulfate were significantly found to restrict the release of As from exchangeable phase. Meanwhile, conventional practice produced much higher release of As from F1 as compared to SRI, which may substantially increase the environmental risk. In contrast, SRI practice was found to retain a significantly higher proportion of As in F2, F3, and F4 phase resulting restricted mobilization of As. This was critically reflected towards rice grain As bioavailability where the reduction in grain As concentration of 33% and 55% in SRI concerning conventional treatment (p <0.05) during monsoon and post-monsoon season respectively. Also, prediction assay for rice grain As bioavailability based on the linear regression model was performed. Results demonstrated that rice grain As concentration was positively correlated with As concentration in F1 and negatively correlated with F2, F3, and F4 with a satisfactory level of variation being explained (p <0.001). Finally, we conclude that F1, F2, F3 and F4 are the major soil. As fractions critically may govern the potential availability of As in soil and suggest that rice cultivation with the SRI treatment is particularly at less risk of As availability in soil. Such exhaustive information may be useful for adopting certain management practices for rice grown in contaminated soil concerning to the environmental issues in particular.

Keywords: arsenic, fractionation, paddy soil, potential availability

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Authors: Kamelia Hamza

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Background: Proton pump inhibitors (PPIs) are the most prescribed drug classes for pediatric gastroesophageal reflux disease (GERD).Many patients are treated with these drugs for atypical manifestations attributed to gastroesophageal reflux (GER), even in the absence of proved causal relationship. There is an impression of increase use of PPI's treatment for reflux in "clalit health services," the largest health organization in Israel. In the recent years, the medicine is given without restriction, it's not limited to pediatric gastroenterologists only, but pediatricians and family doctors. The objective of this study is to evaluate the hypothesis that exposure to PPIs during the first year of life is associated with an increased risk of developing late adverse diseases: pneumonia, asthma, AGE, IBD, celiac disease, allergic disorders, obesity, attention deficit hyperactivity disorders (ADHD), autism spectrum disorders (ASD). Methods: The study is a retrospective case-control cohort study based on a computerized database of Clalit Health Services (CHS). It includes 9844 children born between 2002-2018 and reported to complain of at least one of the symptoms (reflux/ spitting up, irritability, feeding difficulties, colics). The study population included the study group (n=4922) of children exposed to PPIs at any time prior to the first year of life and a control group (n=4922) child not exposed to PPIs who were matched to each case of the study group on age, race, socioeconomic status, and year of birth. The prevalence of late complications/diseases in the study group was compared with the prevalence of late complications/diseases diagnosis between 2002-2020 in the control group. Odds ratios and 95% confidence intervals were calculated by using logistic regression models. Results: We found that compared to the control group, children exposed to PPIs in the first year of life had an increased risk of developing several late complications/ disorders: pneumonia, asthma, various allergies (urticaria, allergic rhinitis, or allergic conjunctivitis) OR, inhalant allergies, and food allergies. In addition, they showed an increased risk of being diagnosed with ADHD or ASD, but children exposed to PPIs in the first year of life had decrease the risk of obesity by 17% (OR 0.825, 95%CI 0.697-0.976). Conclusions: We found significant associations between the use of PPIs during the first year of life and subsequent development of late complications/diseases such as respiratory diseases, allergy diseases, ADHD, and ASD. More studies are needed to prove causality and determine the mechanism behind the effect of PPIs and the development of late complications.

Keywords: acid suppressing medications, proton pump inhibitors, histamine 2 blocker, late complications, gastroesophageal reflux, gastroesophageal reflux disease, acute gastroenteritis, community acquired pneumonia, asthma, allergic diseases, obesity, inflammatory bowel diseases, ulcerative colitis, crohn disease, attention deficit hyperactivity disorders, autism spectrum disorders

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Authors: Soheila Sadeghi

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

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

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