Search results for: tomato yield prediction

Authors: Aid Abdelkrim

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A method of fatigue damage accumulation based upon application of energy parameters of the fatigue process is proposed in the paper. Using this model is simple, it has no parameter to be determined, it requires only the knowledge of the curve W–N (W: strain energy density N: number of cycles at failure) determined from the experimental Wöhler curve. To examine the performance of nonlinear models proposed in the estimation of fatigue damage and fatigue life of components under random loading, a batch of specimens made of 6082 T 6 aluminium alloy has been studied and some of the results are reported in the present paper. The paper describes an algorithm and suggests a fatigue cumulative damage model, especially when random loading is considered. This work contains the results of uni-axial random load fatigue tests with different mean and amplitude values performed on 6082T6 aluminium alloy specimens. The proposed model has been formulated to take into account the damage evolution at different load levels and it allows the effect of the loading sequence to be included by means of a recurrence formula derived for multilevel loading, considering complex load sequences. It is concluded that a ‘damaged stress interaction damage rule’ proposed here allows a better fatigue damage prediction than the widely used Palmgren–Miner rule, and a formula derived in random fatigue could be used to predict the fatigue damage and fatigue lifetime very easily. The results obtained by the model are compared with the experimental results and those calculated by the most fatigue damage model used in fatigue (Miner’s model). The comparison shows that the proposed model, presents a good estimation of the experimental results. Moreover, the error is minimized in comparison to the Miner’s model.

Keywords: damage accumulation, energy model, damage indicator, variable loading, random loading

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Authors: Muhammad A. Muhammad

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Butenafine (N-4-tert-butyl benzyl-N-methyl-1-naphthylene methylamine hydrochloride) is a benzylamine antimycotic (antifungal) agent that has a broad spectrum of action. The quest for improved antimycotic action brought about many research on the structure-activity properties of butenafine in relation to other antifungal agents. Of all those research, only little or no effort was recorded on the substituents attached to the aromatic systems in butenafine. In this research, N-(4-(tert-butyl) benzyl)-1-(4-tert-butyl) phenyl)-N-methyl methanaminium chloride, which is a butenafine analogue was synthesised from tert-butyl benzyl derivatives, by reductive amination using various solvents through a direct approach, where 1,2-dichloroethane gave the best solvent action at 40 °C (Yield: 75%) and of all the reducing agents used, sodium borohydride was found to give the best reducing action in the presence of silica chloride at room temperature (Yield: 50%). Characterization of the compound by 1H NMR showed a singlet peak of 18 hydrogen atoms with a chemical shift at 1.3-1.5 ppm for the presence of 6 methyl groups in the two tert-butyl substituents, the 13C NMR also indicated the presence of the two tert-butyl substituents by the peak with a chemical shift at 31-32 ppm for the six methyl carbon atoms, the IR indicated the presence of a tertiary ammonium ion by a strong band at 2460 cm-1 and finally the EIS-MS confirmed the molar mass of the compound by a mass to charge ratio of 324.2693. These results suggested that the target molecule was actually synthesised and therefore, 1,2-dichloroethane is a good solvent for this synthesis, and the most suitable reducing agent is sodium borohydride.

Keywords: antimicrobial agents, antimycotic agents, butenafine, chemotherapeutic agents, semisynthetic agents

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Authors: Tamara Romero, Manuel Romero-Huelva, Jose V. Segarra, Jose Castro, Carlos Fernandez

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Many foods resulting from processing and manufacturing end up as waste, most of which is burned, dumped into landfills or used as compost, which leads to wasted resources, and environmental problems due to unsuitable disposal. Using residues of the crop and food processing industries to feed livestock has the advantage to obviating the need for costly waste management programs. The main residue generated in citrus cultivations and rice crop are pruning waste and rice straw, respectively. Within Spain, the Valencian Community is one of the world's oldest citrus and rice production areas. The objective of this experiment found out the effects of including orange leaves and rice straw as ingredients in the concentrate diets of goats, on milk production and methane (CH₄) emissions. Ten Murciano-Granadina dairy goats (45 kg of body weight, on average) in mid-lactation were selected in a crossover design experiment, where each goat received two treatments in 2 periods. Both groups were fed with 1.7 kg pelleted mixed ration; one group (n= 5) was a control (C) and the other group (n= 5) used orange leaves and rice straw (OR). The forage was alfalfa hay, and it was the same for the two groups (1 kg of alfalfa was offered by goat and day). The diets employed to achieve the requirements during lactation period for caprine livestock. The goats were allocated to individual metabolism cages. After 14 days of adaptation, feed intake and milk yield were recorded daily over a 5 days period. Physico-chemical parameters and somatic cell count in milk samples were determined. Then, gas exchange measurements were recorded individually by an open-circuit indirect calorimetry system using a head box. The data were analyzed by mixed model with diet and digestibility as fixed effect and goat as random effect. No differences were found for dry matter intake (2.23 kg/d, on average). Higher milk yield was found for C diet than OR (2.3 vs. 2.1 kg/goat and day, respectively) and, greater milk fat content was observed for OR than C (6.5 vs. 5.5%, respectively). The cheese extract was also greater in OR than C (10.7 vs. 9.6%). Goats fed OR diet produced significantly fewer CH₄ emissions than C diet (27 vs. 30 g/d, respectively). These preliminary results (LIFE Project LOWCARBON FEED LIFE/CCM/ES/000088) suggested that the use of these waste by-products was effective in reducing CH₄ emission without detrimental effect on milk yield.

Keywords: agricultural waste, goat, milk production, methane emission

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Authors: Alireza Dadkhah

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A filed experiment was done to develop management practices to reduce the use of synthetic herbicides, in the arid and semi-arid agricultural ecosystems of north east of Iran. Five treatments including I: chopped residues of canola (Brasica vulgaris), II: chopped residues of wheat (Triticum aestivum) both were separately incorporated to 25 cm depth soil, 20 days before sowing, III: shoot aqueous extract of canola, IV: shoot aqueous extract of wheat which were separately sprayed at post emergence stage and V: without any residues and spraying as control. The weed control treatments reduced the total weed cover, weed density and biomass of weed. The reduction in weed density with canola and wheat residues incorporation were up to 67.5 and 62.2% respectively, at 40 days after sowing and 65.3% and 75.6%, respectively, at 90 days after sowing, compared to control. However, post emergence spraying of shoot aqueous extract of canola and wheat, suppressed weed density up to 41.8 and 36.6% at 40 days after sowing and 54.2% and 52.7% at 90 days after sowing respectively, compared to control. Weed control treatments reduced weed cover (%), weed biomass and weeds stem length. Incorporation of canola and wheat residues in soil reduced weed cover (%) by 62.5% and 63% respectively, while spraying of shoot water extract of canola and wheat suppressed weed cover (%) by 39.6% and 40.4% respectively at 90 days after sowing. Application of canola and wheat residues increased soybean yield by 45.4% and 69.5% respectively, compared to control while post emergence application of shoot aqueous extract of canola and wheat increased soybean yield by 22% and 29.8% respectively.

Keywords: allelopathy, Bio-herbicide, Brassica oleracea, plant residues, Triticum aestivum

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Authors: Mustafa Salman, Nurcan Cetinkaya, Zehra Selcuk, Bugra Genc

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The objectives of the present study were to estimate the microbial-N flow to the small intestine and to predict the digestible organic matter intake (DOMI) in grazing Karayaka sheep based on urinary excretion of purine derivatives (xanthine, hypoxanthine, uric acid, and allantoin) by the use of spot urine sampling under field conditions. In the trial, 10 Karayaka sheep from 2 to 3 years of age were used. The animals were grazed in a pasture for ten months and fed with concentrate and vetch plus oat hay for the other two months (January and February) indoors. Highly significant linear and cubic relationships (P<0.001) were found among months for purine derivatives index, purine derivatives excretion, purine derivatives absorption, microbial-N and DOMI. Through urine sampling and the determination of levels of excreted urinary PD and Purine Derivatives / Creatinine ratio (PDC index), microbial-N values were estimated and they indicated that the protein nutrition of the sheep was insufficient. In conclusion, the prediction of protein nutrition of sheep under the field conditions may be possible with the use of spot urine sampling, urinary excreted PD and PDC index. The mean purine derivative levels in spot urine samples from sheep were highest in June, July and October. Protein nutrition of pastured sheep may be affected by weather changes, including rainfall. Spot urine sampling may useful in modeling the feed consumption of pasturing sheep. However, further studies are required under different field conditions with different breeds of sheep to develop spot urine sampling as a model.

Keywords: Karayaka sheep, spot sampling, urinary purine derivatives, PDC index, microbial-N, feed intake

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Authors: Adel M. Zakri, Mohammed A. Al-Saleh, Judith. K. Brown, Ali M. Idris

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Betasatellites are small circular single stranded DNA molecules found associated with begomoviruses on field symptomatic plants. Their genome size is about half that of the helper begomovirus, ranging between 1.3 and 1.4 kb. The helper begomoviruses are usually members of the family Geminiviridae. Okra leaves showing vein thickening were collected from okra plants growing in Jazan, Saudi Arabia. Total DNA was extracted from leaves and used as a template to amplify circular DNA using rolling circle amplification (RCA) technology. Products were digested with PstI to linearize the helper viral genome(s), and associated DNA satellite(s), yielding a 2.8kbp and 1.4kbp fragment, respectively. The linearized fragments were cloned into the pGEM-5Zf (+) vector and subjected to DNA sequencing. The 2.8 kb fragment was identified as Cotton leaf curl Gezira virus genome, at 2780bp, an isolate closely related to strains reported previously from Saudi Arabia. A clone obtained from the 1.4 kb fragments he 1.4kb was blasted to GeneBank database found to be a betasatellite. The genome of betasatellite was 1357-bp in size. It was found to be a recombinant containing one fragment (877-bp) that shared 91% nt identity with Cotton leaf curl Gezira betasatellite [KM279620], and a smaller fragment [133--bp) that shared 86% nt identity with Tomato leaf curl Sudan virus [JX483708]. This satellite is thus a recombinant between a malvaceous-infecting satellite and a solanaceous-infecting begomovirus.

Keywords: begomovirus, betasatellites, cotton leaf curl Gezira virus, okra plants

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Authors: M. D. Haneef, R. B. Randall, Z. Peng

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Journal bearings used in IC engines are prone to premature failures and are likely to fail earlier than the rated life due to highly impulsive and unstable operating conditions and frequent starts/stops. Vibration signature extraction and wear debris analysis techniques are prevalent in the industry for condition monitoring of rotary machinery. However, both techniques involve a great deal of technical expertise, time and cost. Limited literature is available on the application of these techniques for fault detection in reciprocating machinery, due to the complex nature of impact forces that confounds the extraction of fault signals for vibration based analysis and wear prediction. This work is an extension of a previous study, in which an engine simulation model was developed using a MATLAB/SIMULINK program, whereby the engine parameters used in the simulation were obtained experimentally from a Toyota 3SFE 2.0 litre petrol engines. Simulated hydrodynamic bearing forces were used to estimate vibrations signals and envelope analysis was carried out to analyze the effect of speed, load and clearance on the vibration response. Three different loads 50/80/110 N-m, three different speeds 1500/2000/3000 rpm, and three different clearances, i.e., normal, 2 times and 4 times the normal clearance were simulated to examine the effect of wear on bearing forces. The magnitude of the squared envelope of the generated vibration signals though not affected by load, but was observed to rise significantly with increasing speed and clearance indicating the likelihood of augmented wear. In the present study, the simulation model was extended further to investigate the bearing wear behavior, resulting as a consequence of different operating conditions, to complement the vibration analysis. In the current simulation, the dynamics of the engine was established first, based on which the hydrodynamic journal bearing forces were evaluated by numerical solution of the Reynold’s equation. Also, the essential outputs of interest in this study, critical to determine wear rates are the tangential velocity and oil film thickness between the journal and bearing sleeve, which if not maintained appropriately, have a detrimental effect on the bearing performance. Archard’s wear prediction model was used in the simulation to calculate the wear rate of bearings with specific location information as all determinative parameters were obtained with reference to crank rotation. Oil film thickness obtained from the model was used as a criterion to determine if the lubrication is sufficient to prevent contact between the journal and bearing thus causing accelerated wear. A limiting value of 1 µm was used as the minimum oil film thickness needed to prevent contact. The increased wear rate with growing severity of operating conditions is analogous and comparable to the rise in amplitude of the squared envelope of the referenced vibration signals. Thus on one hand, the developed model demonstrated its capability to explain wear behavior and on the other hand it also helps to establish a correlation between wear based and vibration based analysis. Therefore, the model provides a cost-effective and quick approach to predict the impending wear in IC engine bearings under various operating conditions.

Keywords: condition monitoring, IC engine, journal bearings, vibration analysis, wear prediction

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Authors: Muhsin Kizhisseri, Jorg Schluter, Saleh Gharie

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Atherosclerosis is the main reason of stroke, which is one of the deadliest diseases in the world. The carotid artery in the brain is the prominent location for atherosclerotic progression, which hinders the blood flow into the brain. The inclusion of computational fluid dynamics (CFD) into the diagnosis cycle to understand the hemodynamics of the patient-specific carotid artery can give insights into stroke prediction. Realistic outlet boundary conditions are an inevitable part of the numerical simulations, which is one of the major factors in determining the accuracy of the CFD results. The Windkessel model-based outlet boundary conditions can give more realistic characteristics of the distal vascular branches of the carotid artery, such as the resistance to the blood flow and compliance of the distal arterial walls. This study aims to find the most influential distal branches of the carotid artery by using the Windkessel model parameters in the outlet boundary conditions. The parametric study approach to Windkessel model parameters can include the geometrical features of the distal branches, such as radius and length. The incorporation of the variations of the geometrical features of the major distal branches such as the middle cerebral artery, anterior cerebral artery, and ophthalmic artery through the Windkessel model can aid in identifying the most influential distal branch in the carotid artery. The results from this study can help physicians and stroke neurologists to have a more detailed and accurate judgment of the patient's condition.

Keywords: stroke, carotid artery, computational fluid dynamics, patient-specific, Windkessel model, distal vascular branches

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Maia Kukhaleishvili, Ekaterine Bulauri, Iveta Megrelishvili, Tamar Shamatava, Tamar Chipashvili

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Plant viruses can cause loss of yield and quality in a lot of important crops. Symptoms of pathogens are variable depending on the cultivars and virus strain. Selection of resistant potato varieties would reduce the risk of virus transmission and significant economic impact. Other way to avoid reduced harvest yields is regular potato seed production sampling and testing for viral infection. The aim of this study was to determine the occurrence and distribution of viral diseases according potato cultivars for further selection of virus-free material in Georgia. During the summer 2015- 2016, 5 potato cultivars (Sante, Laura, Jelly, Red Sonia, Anushka) at 5 different farms located in Akhalkalaki were tested for 6 different potato viruses: Potato virus A (PVA), Potato virus M (PVM), Potato virus S (PVS), Potato virus X (PVX), Potato virus Y (PVY) and potato leaf roll virus (PLRV). A serological method, Double Antibody Sandwich-Enzyme linked Immunosorbent Assay (DASELISA) was used at the laboratory to analyze the results. The result showed that PVY (21.4%) and PLRV (19.7%) virus presence in collected samples was relatively high compared to others. Researched potato cultivars except Jelly and Laura were infected by PVY with different concentrations. PLRV was found only in three potato cultivars (Sante, Jelly, Red Sonia) and PVM virus (3.12%) was characterized with low prevalence. PVX, PVA and PVS virus infection was not reported. It would be noted that 7.9% of samples were containing PVY/PLRV mix infection. Based on the results it can be concluded that PVY and PLRV infections are dominant in all research cultivars. Therefore significant yield losses are expected. Systematic, long-term control of potato viral infection, especially seed-potatoes, must be regarded as the most important factor to increase seed productivity.

Keywords: virus, potato, infection, diseases

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Authors: Asma Alhuqail, Nagwa Aref

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Programmed cell death resembles a real nature active defense in Datura metel against TMV after three days of virus infection. Physiological plant response was assessed for asymptomatic healthy and symptomatic infected detached leaves. The results indicated H2O2 and Chlorophyll-a as the most potential parameters. Chlorophyll-a was considered the only significant predictor variant for the H2O2 dependent variant with a P value of 0.001 and R-square of 0.900. The plant immune response was measured within three days of virus infection using the cutoff value of H2O2 (61.095 lmol/100 mg) and (63.201 units) for the tail moment in the Comet Assay. Their percentage changes were 255.12% and 522.40% respectively which reflects the stress of virus infection in the plant. Moreover, H2O2 showed 100% specificity and sensitivity in the symptomatic infected group using the receiver-operating characteristic (ROC). All tested parameters in the symptomatic infected group had significant correlations with twenty-five positive and thirty-one negative correlations where the P value was <0.05 and 0.01. Chlorophyll-a parameter had a crucial role of highly significant correlation between total protein and salicylic acid. Contrarily, this correlation with tail moment unit was (r = _0.930, P <0.01) where the P value was < 0.01. The strongest significant negative correlation was between Chlorophyll-a and H2O2 at P < 0.01, while moderate negative significant correlation was seen for Chlorophyll-b where the P value < 0.05. The present study discloses the secret of the three days of rapid transient production of activated oxygen species (AOS) that was enough for having potential quantitative physiological parameters for defensive plant response toward the virus.

Keywords: programmed cell death, plant–adaptive immune response, hydrogen peroxide (H2O2), physiological parameters

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Authors: Khuram Nawaz Sadozai, Sonia

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The agriculture sector is deemed more vulnerable to climate change as its variation can directly affect the crop’s productivity, but farmers’ adaptation strategies play a vital role in climate change-agriculture relationship. Therefore, this research has been undertaken to assess the Climate Change impact on wheat productivity and farmers’ adaptability strategies in Khyber Pakhtunkhwa province, Pakistan. The panel dataset was analyzed to gauge the impact of changing climate variables (i.e., temperature, rainfall, and humidity) on wheat productivity from 1985 to 2015. Amid the study period, the fixed effect estimates confirmed an inverse relationship of temperature and rainfall on the wheat yield. The impact of temperature is observed to be detrimental as compared to the rainfall, causing 0.07 units reduction in the production of wheat with 1C upsurge in temperature. On the flip side, humidity revealed a positive association with the wheat productivity by confirming that high humidity could be beneficial to the production of the crop over time. Thus, this study ensures significant nexus between agricultural production and climatic parameters. However, the farming community in the underlying study area has limited knowledge about the adaptation strategies to lessen the detrimental impact of changing climate on crop yield. It is recommended that farmers should be well equipped with training and advanced agricultural management practices under the realm of climate change. Moreover, innovative technologies pertinent to the agriculture system should be encouraged to handle the challenges arising due to variations in climate factors.

Keywords: climate change, fixed effect model, panel data, wheat productivity

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Authors: Derya Yucel, Nigar Angın, Dürdane Mart, Meltem Turkeri, Volkan Catalkaya, Celal Yucel

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Chickpea (Cicer arietinum L.) is one of the important legumes widely grown for dietery proteins in semi-arid Mediteranean climatic conditions. To evaluate the genetic diversity with improved heat and drought tolerance capacity in chickpea, thirty-four selected chickpea genotypes were tested under different field-growing conditions (rainfed winter sowing, irrigated-late sowing and rainfed-late sowing) in 2015 growing season. A factorial experiment in randomized complete block design with 3 reps was conducted at the Eastern Mediterranean Research Institute Adana, Turkey. Based on grain yields under different growing conditions, several indices were calculated to identify economically higher-yielding chickpea genotypes with greater heat and drought tolerance capacity. Average across chickpea genotypes, the values of tolerance index, mean productivity, yield index, yield stability index, stress tolerance index, stress susceptibility index, and geometric mean productivity were ranged between 1.1 to 218, 38 to 202, 0.3 to 1.7, 0.2 to 1, 0.1 to 1.2, 0.02 to 1.4, and 36 to 170 for drought stress and 3 to 54, 23 to 118, 0.3 to 1.7, 0.4 to 0.9, 0.2 to 2, 0.2to 2.3, and 23 to 118 for heat stress, respectively. There were highly significant differences observed among the tested chickpea genotypes response to drought and heat stresses. Among the chickpea genotypes, the Aksu, Arda, Çakır, F4 09 (X 05 TH 21-16189), FLIP 03-108 were identified with a higher drought and heat tolerance capacity. Based on our field studies, it is suggested that the drought and heat tolerance indicators of plants can be used by breeders to select stress-resistant economically productive chickpea genotypes suitable to grow under Mediteranean climatic conditions.

Keywords: irrigation, rainfed, stress susceptibility, tolerance indice

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Authors: Vadim A. Kuznetsov, Anna M. Soldatova, Tatyana N. Enina, Elena A. Gorbatenko, Dmitrii V. Krinochkin

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The aim of the study was to evaluate potential parameters related with super-response to CRT. Methods: 60 CRT patients (mean age 54.3 ± 9.8 years; 80% men) with congestive heart failure (CHF) II-IV NYHA functional class, left ventricular ejection fraction < 35% were enrolled. At baseline, 1 month, 3 months and each 6 months after implantation clinical, electrocardiographic and echocardiographic parameters, NT-proBNP level were evaluated. According to the best decrease of left ventricular end-systolic volume (LVESV) (mean follow-up period 33.7 ± 15.1 months) patients were classified as super-responders (SR) (n=28; reduction in LVESV ≥ 30%) and non-SR (n=32; reduction in LVESV < 30%). Results: At baseline groups differed in age (58.1 ± 5.8 years in SR vs 50.8 ± 11.4 years in non-SR; p=0.003), gender (female gender 32.1% vs 9.4% respectively; p=0.028), width of QRS complex (157.6 ± 40.6 ms in SR vs 137.6 ± 33.9 ms in non-SR; p=0.044). Percentage of LBBB was equal between groups (75% in SR vs 59.4% in non-SR; p=0.274). All parameters of mechanical dyssynchrony were higher in SR, but only difference in left ventricular pre-ejection period (LVPEP) was statistically significant (153.0 ± 35.9 ms vs. 129.3 ± 28.7 ms p=0.032). NT-proBNP level was lower in SR (1581 ± 1369 pg/ml vs 3024 ± 2431 pg/ml; p=0.006). The survival rates were 100% in SR and 90.6% in non-SR (log-rank test P=0.002). Multiple logistic regression analysis showed that LVPEP (HR 1.024; 95% CI 1.004–1.044; P = 0.017), baseline NT-proBNP level (HR 0.628; 95% CI 0.414–0.953; P=0.029) and age at baseline (HR 1.094; 95% CI 1.009-1.168; P=0.30) were independent predictors for CRT super-response. ROC curve analysis demonstrated sensitivity 71.9% and specificity 82.1% (AUC=0.827; p < 0.001) of this model in prediction of super-response to CRT. Conclusion: Super-response to CRT is associated with better survival in long-term period. Presence of LBBB was not associated with super-response. LVPEP, NT-proBNP level, and age at baseline can be used as independent predictors of CRT super-response.

Keywords: cardiac resynchronisation therapy, superresponse, congestive heart failure, left bundle branch block

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Authors: Anderson Braga Mendes

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Itaipu Hydroelectric Power Plant is settled on the Paraná River, which is a natural boundary between Brazil and Paraguay; thus, the facility is shared by both countries. Itaipu Power Plant is the biggest hydroelectric generator in the world, and provides clean and renewable electrical energy supply for 17% and 76% of Brazil and Paraguay, respectively. The plant started its generation in 1984. It counts on 20 Francis turbines and has installed capacity of 14,000 MWh. Its historic generation record occurred in 2016 (103,098,366 MWh), and since the beginning of its operation until the last day of 2016 the plant has achieved the sum of 2,415,789,823 MWh. The distinct sedimentologic aspects of the drainage area of Itaipu Power Plant, from its stretch upstream (Porto Primavera and Rosana dams) to downstream (Itaipu dam itself), were taken into account in order to best estimate the increase/decrease in the sediment yield by using data from 2001 to 2016. Such data are collected through a network of 14 automatic sedimentometric stations managed by the company itself and operating in an hourly basis, covering an area of around 136,000 km² (92% of the incremental drainage area of the undertaking). Since 1972, a series of lifespan studies for the Itaipu Power Plant have been made, being first assessed by Sir Hans Albert Einstein, at the time of the feasibility studies for the enterprise. From that date onwards, eight further studies were made through the last 44 years aiming to confer more precision upon the estimates based on more updated data sets. From the analysis of each monitoring station, it was clearly noticed strong increase tendencies in the sediment yield through the last 14 years, mainly in the Iguatemi, Ivaí, São Francisco Falso and Carapá Rivers, the latter situated in Paraguay, whereas the others are utterly in Brazilian territory. Five lifespan scenarios considering different sediment yield tendencies were simulated with the aid of the softwares SEDIMENT and DPOSIT, both developed by the author of the present work. Such softwares thoroughly follow the Borland & Miller methodology (empirical method of area-reduction). The soundest scenario out of the five ones under analysis indicated a lifespan foresight of 168 years, being the reservoir only 1.8% silted by the end of 2016, after 32 years of operation. Besides, the mass balance in the reservoir (water inflows minus outflows) between 1986 and 2016 shows that 2% of the whole Itaipu lake is silted nowadays. Owing to the convergence of both results, which were acquired by using different methodologies and independent input data, it is worth concluding that the mathematical modeling is satisfactory and calibrated, thus assigning credibility to this most recent lifespan estimate.

Keywords: Borland and Miller method, hydroelectricity, Itaipu Power Plant, lifespan, mass balance

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Authors: Rajeev Ravindran, Amit K. Jaiswal

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Waste from the food-processing industry is produced in large amount and contains high levels of lignocellulose. Due to continuous accumulation throughout the year in large quantities, it creates a major environmental problem worldwide. The chemical composition of these wastes (up to 75% of its composition is contributed by polysaccharide) makes it inexpensive raw material for the production of value-added products such as biofuel, bio-solvents, nanocrystalline cellulose and enzymes. In order to use lignocellulose as the raw material for the microbial fermentation, the substrate is subjected to enzymatic treatment, which leads to the release of reducing sugars such as glucose and xylose. However, the inherent properties of lignocellulose such as presence of lignin, pectin, acetyl groups and the presence of crystalline cellulose contribute to recalcitrance. This leads to poor sugar yields upon enzymatic hydrolysis of lignocellulose. A pre-treatment method is generally applied before enzymatic treatment of lignocellulose that essentially removes recalcitrant components in biomass through structural breakdown. Present study is carried out to find out the best pre-treatment method for the maximum liberation of reducing sugars from spent coffee waste (SPW). SPW was subjected to a range of physical, chemical and physico-chemical pre-treatment followed by a sequential, combinatorial pre-treatment strategy is also applied on to attain maximum sugar yield by combining two or more pre-treatments. All the pre-treated samples were analysed for total reducing sugar followed by identification and quantification of individual sugar by HPLC coupled with RI detector. Besides, generation of any inhibitory compounds such furfural, hydroxymethyl furfural (HMF) which can hinder microbial growth and enzyme activity is also monitored. Results showed that ultrasound treatment (31.06 mg/L) proved to be the best pre-treatment method based on total reducing content followed by dilute acid hydrolysis (10.03 mg/L) while galactose was found to be the major monosaccharide present in the pre-treated SPW. Finally, the results obtained from the study were used to design a sequential lignocellulose pre-treatment protocol to decrease the formation of enzyme inhibitors and increase sugar yield on enzymatic hydrolysis by employing cellulase-hemicellulase consortium. Sequential, combinatorial treatment was found better in terms of total reducing yield and low content of the inhibitory compounds formation, which could be due to the fact that this mode of pre-treatment combines several mild treatment methods rather than formulating a single one. It eliminates the need for a detoxification step and potential application in the valorisation of lignocellulosic food waste.

Keywords: lignocellulose, enzymatic hydrolysis, pre-treatment, ultrasound

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Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

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Authors: Shotiros Protong

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In recent years, Thailand has been affected by climate change phenomenon, which is clearly seen from the season change for different times. The occurrence of violent storms, heavy rains, floods, and drought were found in several areas. In a long dry period, the water supply is not adequate in drought areas. Nowadays, it is renowned that there is a significant decrease of rainwater use for household consumption in rural area of Thailand. Rainwater harvesting is the practice of collection and storage of rainwater in storage tanks before it is lost as surface run-off. Rooftop rainwater harvesting is used to provide drinking water, domestic water, and water for livestock. Rainwater harvesting in households is an alternative for people to readily prepare water resources for their own consumptions during the drought season, can help mitigate flooding of flooded plains, and also may reduce demand on the basin and well. It also helps in the availability of potable water, as rainwater is substantially free of salts. Application of rainwater harvesting in rural water system provide a substantial benefit for both water supply and wastewater subsystems by reducing the need for clean water in water distribution systems, less generated storm water in sewer systems, and a reduction in storm water runoff polluting freshwater bodies. The combination of rainwater quality and rainfall quantity is used to determine proper rainwater harvesting for household consumption to be safe and adequate for survivals. Rainwater quality analysis is compared with the drinking water standard. In terms of rainfall quantity, the observed rainfall data are interpolated by GIS 10.5 and showed by map during 1980 to 2020, used to assess the annual yield for household consumptions.

Keywords: rainwater harvesting, drinking water standard, annual yield, rainfall quantity

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Authors: Anshu Tyagi

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Zeolitic imidazolate frameworks (ZIFs), a subclass of metal-organic frameworks (MOFs) with structures resembling aluminosilicate zeolites, are gaining significant attention due to their unique properties. ZIF-8, in particular, has shown high surface area and enhanced hydrophobicity, making it a promising candidate for catalytic applications. In this study, ZIF-8 was synthesized in an aqueous medium by mixing 2-methylimidazole (mIm) with zinc nitrate hexahydrate (Zn) in deionized water. To improve the basicity and catalytic performance of ZIF-8, a series of K-loaded ZIF-8 catalysts (K/ZIF-8) were prepared by varying the KOH content from 5 to 10 wt%. Characterization of the synthesized catalysts was conducted using powder X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), high-resolution transmission electron microscopy (HRTEM), and temperature-programmed desorption (TPD) techniques. The ZIF-8 and K/ZIF-8 catalysts were applied in the transesterification of glycerol (GL) and dimethyl carbonate (DMC) to form glycerol carbonate (GLC). Various reaction parameters, including DMC/GL molar ratio, KOH loading, catalyst amount, and reaction temperature, were systematically studied to optimize the GLC yield. Under optimized conditions, the 10 wt% KOH-loaded ZIF-8 catalyst (10-K/ZIF-8) demonstrated excellent catalytic activity, achieving up to 95% GLC yield at a DMC/GL molar ratio of 3:1 within 0.5 hours. Remarkably, despite the hygroscopic nature of potassium, the catalyst exhibited significant water resistance, maintaining performance with up to 5 wt% water in relation to GL. Furthermore, the catalyst retained its activity after three recycling cycles without any notable loss in catalytic efficiency. This study highlights the potential of K/ZIF-8 as an efficient, water-tolerant catalyst for the transesterification of GL with DMC, offering high GLC yields and recyclability.

Keywords: metal-organic frameworks (MOFs), zeolitic imidazolate frameworks (ZIFs), transesterification, sustainable catalytic

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Authors: Yuexin Liu, Gordon B. Mills, Russell R. Broaddus, John N. Weinstein

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The lethality of endometrioid endometrial cancer (EEC) is primarily attributable to the high-stage diseases. However, there are no available biomarkers that predict EEC patient staging at the time of diagnosis. We aim to develop a predictive scheme to help in this regards. Using reverse-phase protein array expression profiles for 210 EEC cases from The Cancer Genome Atlas (TCGA), we constructed a Protein Scoring of EEC Staging (PSES) scheme for surgical stage prediction. We validated and evaluated its diagnostic potential in an independent cohort of 184 EEC cases obtained at MD Anderson Cancer Center (MDACC) using receiver operating characteristic curve analyses. Kaplan-Meier survival analysis was used to examine the association of PSES score with patient outcome, and Ingenuity pathway analysis was used to identify relevant signaling pathways. Two-sided statistical tests were used. PSES robustly distinguished high- from low-stage tumors in the TCGA cohort (area under the ROC curve [AUC]=0.74; 95% confidence interval [CI], 0.68 to 0.82) and in the validation cohort (AUC=0.67; 95% CI, 0.58 to 0.76). Even among grade 1 or 2 tumors, PSES was significantly higher in high- than in low-stage tumors in both the TCGA (P = 0.005) and MDACC (P = 0.006) cohorts. Patients with positive PSES score had significantly shorter progression-free survival than those with negative PSES in the TCGA (hazard ratio [HR], 2.033; 95% CI, 1.031 to 3.809; P = 0.04) and validation (HR, 3.306; 95% CI, 1.836 to 9.436; P = 0.0007) cohorts. The ErbB signaling pathway was most significantly enriched in the PSES proteins and downregulated in high-stage tumors. PSES may provide clinically useful prediction of high-risk tumors and offer new insights into tumor biology in EEC.

Keywords: endometrial carcinoma, protein, protein scoring of EEC staging (PSES), stage

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Authors: Anuruddha Jayasuriya, Thanakorn Pheeraphan

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In this paper, a review of different mathematical models which can be used as prediction tools to assess the time to crack reinforced concrete (RC) due to corrosion is investigated. This investigation leads to an experimental study to validate a selected prediction model. Most of these mathematical models depend upon the mechanical behaviors, chemical behaviors, electrochemical behaviors or geometric aspects of the RC members during a corrosion process. The experimental program is designed to verify the accuracy of a well-selected mathematical model from a rigorous literature study. Fundamentally, the experimental program exemplifies both one-dimensional chloride diffusion using RC squared slab elements of 500 mm by 500 mm and two-dimensional chloride diffusion using RC squared column elements of 225 mm by 225 mm by 500 mm. Each set consists of three water-to-cement ratios (w/c); 0.4, 0.5, 0.6 and two cover depths; 25 mm and 50 mm. 12 mm bars are used for column elements and 16 mm bars are used for slab elements. All the samples are subjected to accelerated chloride corrosion in a chloride bath of 5% (w/w) sodium chloride (NaCl) solution. Based on a pre-screening of different models, it is clear that the well-selected mathematical model had included mechanical properties, chemical and electrochemical properties, nature of corrosion whether it is accelerated or natural, and the amount of porous area that rust products can accommodate before exerting expansive pressure on the surrounding concrete. The experimental results have shown that the selected model for both one-dimensional and two-dimensional chloride diffusion had ±20% and ±10% respective accuracies compared to the experimental output. The half-cell potential readings are also used to see the corrosion probability, and experimental results have shown that the mass loss is proportional to the negative half-cell potential readings that are obtained. Additionally, a statistical analysis is carried out in order to determine the most influential factor that affects the time to corrode the reinforcement in the concrete due to chloride diffusion. The factors considered for this analysis are w/c, bar diameter, and cover depth. The analysis is accomplished by using Minitab statistical software, and it showed that cover depth is the significant effect on the time to crack the concrete from chloride induced corrosion than other factors considered. Thus, the time predictions can be illustrated through the selected mathematical model as it covers a wide range of factors affecting the corrosion process, and it can be used to predetermine the durability concern of RC structures that are vulnerable to chloride exposure. And eventually, it is further concluded that cover thickness plays a vital role in durability in terms of chloride diffusion.

Keywords: accelerated corrosion, chloride diffusion, corrosion cracks, passivation layer, reinforcement corrosion

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: Mounir Baccar, Amel Gammoudi, Abdelhak Ayadi

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The efficient mixing of highly viscous fluids is required for many industries such as food, polymers or paints production. The homogeneity is a challenging operation for this fluids type since they operate at low Reynolds number to reduce the required power of the used impellers. Particularly, close-clearance impellers, mainly helical ribbons, are chosen for highly viscous fluids agitated in laminar regime which is currently heated through vessel wall. Indeed, they are characterized by high shear strains closer to the vessel wall, which causes a disturbing thermal boundary layer and ensures the homogenization of the bulk volume by axial and radial vortices. The hydrodynamic and thermal behaviors of Newtonian fluids in vessels agitated by helical ribbon impellers, has been mostly studied by many researchers. However, rarely researchers investigated numerically the agitation of yield stress fluid by means of helical ribbon impellers. This paper aims to study the effect of the Double Helical Ribbon (DHR) stirrers on both the hydrodynamic and the thermal behaviors of yield stress fluids treated in a cylindrical vessel by means of numerical simulation approach. For this purpose, continuity, momentum, and thermal equations were solved by means of 3D finite volume technique. The effect of Oldroyd (Od) and Reynolds (Re) numbers on the power (Po) and Nusselt (Nu) numbers for the mentioned stirrer type have been studied. Also, the velocity and thermal fields, the dissipation function and the apparent viscosity have been presented in different (r-z) and (r-θ) planes.

Keywords: Bingham fluid, Hydrodynamic and thermal behavior, helical ribbon, mixing, numerical modelling

Procedia PDF Downloads 306

Authors: M. T. Asadollahzadeh, A. Ghasemian, A. R. Saraeian, H. Resalati, P. R. Lennartsson, M. J. Taherzadeh

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Since filamentous fungi are capable of assimilating several types of sugars (hexoses and pentoses), they are potential candidates for bioconversion of spent sulfite liquor (SSL). Three filamentous fungi such as Aspergillus oryzae, Mucor indicus, and Rhizopus oryzae were investigated in this work. The SSL was diluted in order to obtain concentrations of 50, 60, 70, 80, and 90% and supplemented with two types of nutrients. The results from cultivations in shake flask showed that A. oryzae and M. indicus were not able to grow in pure SSL and SSL90% while R. oryzae could grow only in SSL50% and SSL60%. Cultivation with A. oryzae resulted in the highest yield of produced fungal biomass, while R. oryzae cultivation resulted in the lowest fungal biomass yield. Although, the mediums containing yeast extract, (NH₄)₂SO₄, KH₂PO₄, CaCl₂∙2H₂O, and MgSO₄∙7H₂O as nutrients supplementations produced higher fungal biomass compared to the mediums containing NH₄H₂PO₄ and ammonia, but there was no significant difference between two types of nutrients in terms of sugars and acetic acid consumption rate. The sugars consumption in M. indicus cultivation was faster than A. oryzae and R. oryzae cultivation. Acetic acid present in SSL was completely consumed during cultivation of all fungi. M. indicus was the best and fastest ethanol producer from SSL among the fungi examined, when yeast extract and salts were used as nutrients supplementations. Furthermore, no further improvement in ethanol concentration and rate of sugars consumption was obtained in medium supplemented with NH₄H₂PO₄ and ammonia compared to medium containing yeast extract, (NH₄)₂SO₄, KH₂PO₄, CaCl₂∙2H₂O, and MgSO₄∙7H₂O. On the other hand, the higher dilution of SSL resulted in a better fermentability, and better consumption of sugars and acetic acid.

Keywords: ethanol, filamentous fungi, fungal biomass, spent sulfite liquor

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Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000 cm-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R2 = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R2 = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, pasta, moisture determination, food engineering

Procedia PDF Downloads 258

Authors: Minbale Aschale, Bitew K. Dessie, Endaweke Assegide, Yosef Abebe, Tena Alamirew, Claire L. Walsh, Gete Zeleke

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The upper Awash Basin faces pollution challenges due to urbanization, population growth, and expanding industries. It receives various pollutants from its catchments. The study aimed to assess the impact of wastewater irrigation on vegetables and inform stakeholders about pollution challenges and consequences. Eighty-two composite samples of matured vegetables were randomly collected from twenty-one agricultural farm sites. These samples were analyzed for potentially toxic elements, including Cd, Pb, Cr, Hg, As, Ni, Sr, B, Co, Cu, Mn, Fe, Zn, and Se. The results indicated significant variations in concentrations across different sites, with localized contributions from various contaminants. Cr, Cd, and Pb concentrations in most vegetables exceeded recommended levels. Pollution levels varied with metals and vegetable types. Different vegetables contribute differently to health risks. The relative contributions of Ethiopian kale, cabbage, red beet, lettuce, Swiss chard, Gurage cabbage, tomato, zucchini, carrot, onion, watermelon, and potato to the aggregated risk were 12.69%, 12.25%, 11.83%, 11.20%, 10.21%, 9.91%, 8.49%, 5.66%, 3.96%, 3.35%, 3.10%, and 2.72%, respectively. Comparison with permissible standards revealed inadequate environmental management by relevant regulatory bodies and industries. Despite good laws and standards at the federal and regional levels, they are ineffectively implemented or enforced to prevent environmental pollution. Mitigation measures are urgently recommended to address the potential health implications of toxic substances.

Keywords: pollution, upper Awash Basin, health risk, Ethiopia

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Authors: Mariamawit T. Belete

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Cereal production and marketing are the means of livelihood for millions of households in Ethiopia. However, cereal production is constrained by technical and socio-economic factors. Among the technical factors, cereal crop diseases are the major contributing factors to the low yield. The aim of this research is to develop an integration of data mining and knowledge based system for sorghum anthracnose disease diagnosis that assists agriculture experts and development agents to make timely decisions. Anthracnose diagnosing systems gather information from Melkassa agricultural research center and attempt to score anthracnose severity scale. Empirical research is designed for data exploration, modeling, and confirmatory procedures for testing hypothesis and prediction to draw a sound conclusion. WEKA (Waikato Environment for Knowledge Analysis) was employed for the modeling. Knowledge based system has come across a variety of approaches based on the knowledge representation method; case-based reasoning (CBR) is one of the popular approaches used in knowledge-based system. CBR is a problem solving strategy that uses previous cases to solve new problems. The system utilizes hidden knowledge extracted by employing clustering algorithms, specifically K-means clustering from sampled anthracnose dataset. Clustered cases with centroid value are mapped to jCOLIBRI, and then the integrator application is created using NetBeans with JDK 8.0.2. The important part of a case based reasoning model includes case retrieval; the similarity measuring stage, reuse; which allows domain expert to transfer retrieval case solution to suit for the current case, revise; to test the solution, and retain to store the confirmed solution to the case base for future use. Evaluation of the system was done for both system performance and user acceptance. For testing the prototype, seven test cases were used. Experimental result shows that the system achieves an average precision and recall values of 70% and 83%, respectively. User acceptance testing also performed by involving five domain experts, and an average of 83% acceptance is achieved. Although the result of this study is promising, however, further study should be done an investigation on hybrid approach such as rule based reasoning, and pictorial retrieval process are recommended.

Keywords: sorghum anthracnose, data mining, case based reasoning, integration

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Authors: Seongwon Im, Jimin Kim, Kyeongcheol Kim, Dong-Hoon Kim

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Cattle manure (CM) produced from farmhas been utilized to soils for increasing crop production owing to high nutrients content and effective microorganisms. Some cities with the concentrated activity of livestock industry have suffered from environmental problems, such as odorous gas emissions and soil and water pollution, caused by excessive use of compost. As an alternative option, the anaerobic digestion (AD) process can be utilized, which can reduce the volume of organic waste but also produce energy. According to Korea-Ministry of Trade, Industry, and Energy (KMTIE), the energy potential of CM via biogas production was estimated to be 0.8 million TOE per year, which is higher than that of other organic wastes. However, limited energy is recovered since useful organic matter, capable of converting to biogas, may be degraded during the long storage period (1-6 months).In this study, the effect of storage period on biogas potential of CM was investigated. Compared to fresh CM (VS 14±1 g/L, COD 205±5 g/L, TKN 7.4±0.8 g/L, NH4+-N 1.5±0.1), old CM has higher organic (35-37%) and nitrogen content (50-100%) due to the drying process during storage. After stabilization period, biogas potential of 0.09 L CH4/g VS was obtained in R1 (old CM supplement) at HRT of 150-100 d, and it was decreased further to 0.06 L CH4/g VS at HRT of 80 d. The drop of pH and organic acids accumulation were not observed during the whole operation of R1. Ammonia stripping and pretreatment of CM were found to be not effective to increase CH4 yield. On the other hand, a sudden increase of biogas potential to 0.19-0.22 L CH4/g VS was achieved in R2 after changing feedstock to fresh CM. The expected reason for the low biogas potential of old CM might be related with the composition of organic matters in CM. Easily biodegradable organic matters in the fresh CM were contained in high concentration, butthey were removed by microorganisms during storing CM in a farm, resulting low biogas yield. This study implies that fresh storage is important to make AD process applicable for CM.

Keywords: storage period, cattle manure, biogas potential, microbial analysis

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Authors: Carlotta D’Alessandro, Giuseppe Ioppolo, Katarzyna Szopik-Depczyńska

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— The growing scarcity of resources and the mounting pressures of climate change, water pollution, and chemical contamination have prompted societies, governments, and businesses to seek ways to minimize their environmental impact. To combat climate change, and foster sustainability, Industrial Symbiosis (IS) offers a powerful approach, facilitating the shift toward a circular economic model. IS has gained prominence in the European Union's policy framework as crucial enabler of resource efficiency and circular economy practices. The essence of IS lies in the collaborative sharing of resources such as energy, material by-products, waste, and water, thanks to geographic proximity. It can be exemplified by eco-industrial parks (EIPs), which are natural environments for boosting cooperation and resource sharing between businesses. EIPs are characterized by group of businesses situated in proximity, connected by a network of both cooperative and competitive interactions. They represent a sustainable industrial model aimed at reducing resource use, waste, and environmental impact while fostering economic and social wellbeing. IS, combined with Artificial Intelligence (AI)-driven technologies, can further optimize resource sharing and efficiency within EIPs. This research, supported by the “CE_IPs” project, aims to analyze the potential for IS and AI, in advancing circularity and sustainability at Fosso Imperatore. The Fosso Imperatore Industrial Park in Nocera Inferiore, Italy, specializes in agriculture and the industrial transformation of agricultural products, particularly tomatoes, tobacco, and textile fibers. This unique industrial cluster, centered around tomato cultivation and processing, also includes mechanical engineering enterprises and agricultural packaging firms. To stimulate the shift from a traditional to a circular economic model, an AI-powered Local Development Plan (LDP) is developed for Fosso Imperatore. It can leverage data analytics, predictive modeling, and stakeholder engagement to optimize resource utilization, reduce waste, and promote sustainable industrial practices. A comprehensive SWOT analysis of the AI-powered LDP revealed several key factors influencing its potential success and challenges. Among the notable strengths and opportunities arising from AI implementation are reduced processing times, fewer human errors, and increased revenue generation. Furthermore, predictive analytics minimize downtime, bolster productivity, and elevate quality while mitigating workplace hazards. However, the integration of AI also presents potential weaknesses and threats, including significant financial investment, since implementing and maintaining AI systems can be costly. The widespread adoption of AI could lead to job losses in certain sectors. Lastly, AI systems are susceptible to cyberattacks, posing risks to data security and operational continuity. Moreover, an Analytic Hierarchy Process (AHP) analysis was employed to yield a prioritized ranking of the outlined AI-driven LDP practices based on the stakeholder input, ensuring a more comprehensive and representative understanding of their relative significance for achieving sustainability in Fosso Imperatore Industrial Park. While this study provides valuable insights into the potential of AIpowered LDP at the Fosso Imperatore, it is important to note that the findings may not be directly applicable to all industrial parks, particularly those with different sizes, geographic locations, or industry compositions. Additional study is necessary to scrutinize the generalizability of these results and to identify best practices for implementing AI-driven LDP in diverse contexts.

Keywords: artificial intelligence, climate change, Fosso Imperatore, industrial park, industrial symbiosis

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Authors: Mursleen Yasin, Sunil Panchal, Michelle Mak, Zhonghua Chen

Abstract:

“The use of agricultural and horticultural waste to obtain beneficial products. Thus reduce its environmental impact and help the general population.” Every year 20 billion dollars of food is wasted in the world. All the energy, resources, nutrients and metabolites are lost to the landfills as well. On farm production losses are a main issue in agriculture. Almost 25% vegetables never leave the farm because they are not considered perfect for supermarkets and treated as waste material along with the rest of the plant parts. For capsicums, this waste is 56% of the total crop. Capsicum genus is enriched with a group of compounds called capsaicinoids which are a source of spiciness of these fruits. Capsaicin and dihydrocapsaicin are the major members comprising almost 90% of this group. The major production and accumulation site is the non-edible part of fruit i.e., placenta. Other parts of the plant, like stem, leaves, pericarp and seeds, also contain these pungent compounds. Capsaicinoids are enriched with properties like analgesic, antioxidants, anti-inflammatory, antibacterial, anti-virulence anti-carcinogenic, chemo preventive, chemotherapeutic, antidiabetic etc. They are also effective in treating problems related to gastrointestinal tract, lowering cholesterol and triglycerides in obesity. The aim of the study is to develop a standardised technique for capsaicinoids extraction and to identify better nutrient treatment for fruit and capsaicinoids yield. For research 3 capsicum and 2 chilli varieties were grown in a high-tech glass house facility in Sydney, Australia. Plants were treated with three levels of nutrient treatments i.e., EC 1.8, EC 2.8 and EC 3.8 in order to check its effect on fruit yield and capsaicinoids concentration. Solvent extraction procedure is used with 75% ethanol to extract these secondary metabolites. Physiological, post-harvest and waste biomass measurement and metabolomic analysis are also performed. The results showed that EC 2.8 gave the better fruit yield of capsicums, and those fruits have the higher capsaicinoids concentration. For chillies, higher EC levels had better results than lower treatment. The UHPLC analysis is done to quantify the compounds, and a decrease in capsaicin concentration is observed with the crop maturation. The outcome of this project is a sustainable technique for extraction of capsaicinoids which can easily be adopted by farmers. In this way, farmers can help in value adding of waste by extracting and selling capsaicinoids to nutraceutical and pharmaceutical industries and also earn some secondary income from the 56% waste of capsicum crop.

Keywords: capsaicinoids, plant waste, capsicum, solvent extraction, waste biomass

Procedia PDF Downloads 79