Terms Of Loss Research Articles

A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions

In this paper, a new two-stage global maximum power point tracking (GMPPT) algorithm based on artificial neural network (ANN) is proposed. The novel ANN architecture is presented first, which requires fewer sampling points than other ANN-based GMPPT approaches, thereby reducing both the tracking time and power loss involved in the tracking. In addition, it does not require costly illuminance or temperature sensors and can be realized using a low-cost digital signal controller. According to the simulation results, the proposed method has the best performance among all methods in terms of tracking speed, tracking accuracy, and tracking loss for all the three tested shading patterns (SPs). The simulated results of 252 SPs show that the performance indexes (PIs) of the proposed method are the best among all the compared methods, which are: the average tracking time 0.18 seconds, average power loss 0.01 W. In addition, the proposed method can correctly predict the GMPP positions of all 252 SPs. Furthermore, the PIs of the proposed method are also the best among all the compared methods according to the experimental results, which are: the tracking speed 0.21 seconds, tracking accuracy 99.66%, and tracking loss 12.58 W, all the above are average values.

Enhanced Routing Protocol for Health Monitoring in Urban Traffic Networks via VANET: Analyzing IW-GeoR Against WGeoR

The primary aim of this initiative is to establish an effective routing system for managing both human-induced and natural calamities using a Wireless Body Sensor Network (WBSN) integrated with VANET within urban traffic networks. This is accomplished through a comparison between the Weighed Geographic Routing (WGeoR) protocol and the Improved Weighed Geographical Routing (IWGeoR) protocol, targeting minimized packet loss and reduced delay. The IWGeoR protocol stands out by efficiently selecting cluster heads to mitigate delay and packet loss, considering various factors such as differences in vehicle speeds, inter-vehicle distances, and traffic mobility. The protocol’s effectiveness relies on innovative node degrees, channel quality, proximity factors, and communication connection expiration times. For this study, twenty samples (n=20) were gathered from each of the two groups using diverse vehicles. Clinical.com established a pre-test power of 80% (G-power) for each group, with alpha and beta coefficients set at 0.05 and 0.2 respectively, in order to assess the protocol’s performance in terms of packet loss and average latency. The simulation outcomes indicate that the IWGeoR protocol surpasses the WGeoR protocol in crucial metrics such as packet loss and mean delay. Specifically, the IWGeoR protocol reduces packet loss by 10.77% and delay by 31.03%. The Independent Sample T-test calculates a significant p-value of 0.001 for the IWGeoR and WGeoR protocols, demonstrating statistical significance (p<0.005). Based on experimental findings and the Independent Sample T-test, the proposed IWGeoR protocol has demonstrated superior performance compared to the WGeoR protocol.

Actual Reasons Involving Turn-Off Losses Improvement With Increasing Load and Gate Resistance in MOSFETs Enhanced With Kelvin Source

This paper explains the actual reasons behind the improvement in terms of turn-off energy losses (Eoff) reduction, obtained using Kelvin pin (4-lead, 4L) MOSFETs instead of 3L ones. Eoff increases by increasing gate resistance and load, but the experimental results reveal that the increment is less in 4L than in 3L MOSFETs. Analysing the turn-off waveforms and adopting circuit analysis, the paper first proves that the common argument about the undesired effect of the parasitic source inductance in 3L MOSFETs is misleading. Then, the reasons behind the aforesaid improvement in 4L MOSFETs with increasing gate resistance and load are described by means of the derived analytical expressions. Finally, the analysis of the equations has provided helpful information at the design stage, also accounting for the different device technologies. In particular, the improvement obtained using 4L MOSFETs reduces as the driving loop parasitic inductance increases and evanesces if its value becomes comparable with the power loop parasitic inductance.

Intestinal toxicity alleviation and efficacy potentiation through therapeutic administration of Lactobacillus paracasei GY-1 in the treatment of gout flares with colchicine.

Gout flares have emerged as a significant public health concern. Colchicine (COL) is a first-line and standard drug for treating gout flares. However, its clinical use is limited due to various adverse effects. Besides, COL fails to adequately meet the needs of patients, particularly young patients. In this study, we investigate the therapeutic administration of Lactobacillus paracasei GY-1 (GY-1) to overcome the limitations of COL. Our results demonstrate that GY-1 attenuates COL toxicity in terms of body weight loss, decreased feed intake, mortality, reduced locomotor activity, colon shortening, increased oxidative stress, histological damage, and impaired gut permeability. Meanwhile, we demonstrate that GY-1 enhances the therapeutic effect for gout flares when combined with COL, as evidenced by the reduction in paw swelling, decreased levels of proinflammatory cytokines including IL-1β and TNF-α, and an increase in the anti-inflammatory cytokine IL-10. Additionally, the absolute quantification of the gut microbiota shows that GY-1 restores the gut microbiota imbalance caused by COL. Furthermore, GY-1 reduces the abundance of 4 Alistipes species and 6 Porphyromonadaceae species, which may be responsible for toxicity alleviation. At the same time, GY-1 increases the abundance of Bacteroides sartorii and Enterococcus sp., which may contribute to its therapeutic efficacy. This study demonstrates the feasibility of developing probiotic-based adjuvant therapy or bacteriotherapy for treating gout flares. To our knowledge, GY-1 is the first probiotic that could be used as an alternative synergetic agent with COL for the therapeutic treatment of gout flares.

Dynamic Response of Bowstring-arch Highway Bridge Subjected to Above and Below Deck Close-range Large Explosion

Explosion incidents that are unforeseen can lead to the occurrence of extreme loads, resulting in the generation of remarkably high stress levels within the materials comprising various structures. This can cause significant damage to crucial elements and potentially trigger a disproportionate collapse or even initiate a progressive collapse. Bridge structures, which serve as vital lifelines for cosmopolitan areas and strategic bordering environments, hold immense economic and political significance. The failure of these structures can have severe consequences with far-reaching implications. The use of a steel bowstring-arch bridge is a practical choice for congested crossings and remote border areas where spans are short. However, the current design codes for bridges do not take into account high-strain loadings such as blasts or impacts, nor do they provide recommendations for preventing these occurrences during construction or throughout the lifespan of the bridge. Explosive incidents cause greater damage in terms of material damage and loss compared to earthquakes. There has been limited investigation into how steel-concrete bridges respond to explosions in the past. This study examines the numerical analysis of a bowstring-arch highway girder bridge made of steel and concrete. The bridge is supported at both ends and is subjected to close-range concentric explosions above and below the deck at the center and end of the bridge. To model the bridge and predict its behavior, the authors utilized the Abaqus software suite. For the analysis, a significant quantity of TNT weighing 1.63-tonne has been positioned at the midpoint of the bridge and is defined using the Eulerian-Lagrangian scheme. The transmission of the explosive shockwaves within the bridge material under the given loading circumstances is illustrated and elucidated. The behavior of the bridge is examined in relation to plastic deformations, primary stress, displacement, size of the crater, and overall energy of damage.

Comparison of Chemical Composition, Quality, and Muscle Fiber Characteristics between Cull Sows and Commercial Pigs: The Relationship between Pork Quality Based on Muscle Fiber Characteristics.

This study aims to compare the chemical composition, quality, and muscle fiber characteristics of cull sows and commercial pigs, investigating the effect of changes in muscle fiber characteristics on pork quality. The proximate composition, color, pH, water-holding capacity (drip loss and cooking loss), protein solubility, total collagen content, and muscle fiber characteristics of cull sows (n=20) and commercial pigs (n=20) pork were compared. No significant differences were found between cull sows and commercial pigs in terms of proximate composition, drip loss, protein solubility, or total collagen content of their meat (p<0.05). However, cull sow pork exhibited a red color and a higher pH (p<0.05). This appears to be the result of changes in muscle fiber number and area composition (p<0.05). Cull sow meat also displayed better water-holding capacity as evident in a smaller cooking loss (p<0.05), which may be related to an increase in muscle fiber cross-sectional area (p<0.05). In conclusion, muscle fiber composition influences the pork quality; cull sow pork retains more moisture when cooked, resulting in minimal physical loss during processing and can offer more processing suitability.

The dynamics of the personality’s world image in a situation of loss of a loved one

The article presents the results of changing the image of the personality’s world in a situation of experiencing the loss of a loved one. The longitudinal study, which included three measurements with a difference of three months, involved 80 people who had experienced the death of a loved one from 1 month to 2 years ago. To study the dynamics of the image of the world, we used: a modified version of the semantic differential of Ch. Osgood, the test of existential motivations by A. Langle, the technology “Depth of meaningfulness of life” by P. Ibersol, the questionnaire of post-traumatic growth by R. Tadeshi and L. Calhoun. Statistical methods: frequency analysis, descriptive statistics, content analysis, one-factor analysis of variance (ANOVA), nonparametric Friedman criterion. Results: 1. The dynamics of the image of the world in a situation of loss is characterized by heterochrony and unevenness: changes in the perceptual layer of the image of the world are quite local in nature and show that the most sensitive to changes is the characteristic “chaotic — ordered”; changes in the semantic layer of the image of the world turned out to be the most dynamic and associated with the transformation of attitudes towards other people towards openness, positivity, compassion; changes in the nuclear layer of the image of the world are more concerned with the emergence of a sense of new opportunities, new interests, a desire for change, increasing the value of life, gaining new life meanings. 2. With regard to the object, term and cause of loss, as well as age and gender, their situational influence in the process of experiencing loss on changes in the image of the world and its components can be noted.

Video-assisted thoracoscopic surgery for non-cystic fibrosis bronchiectasis in children.

Pediatric bronchiectasis is a common respiratory disease in children. The use of video-assisted thoracoscopic surgery (VATS) for its treatment remains controversial. The objective of our study was to compare and analyze the clinical efficacy of thoracoscopic surgery and thoracotomy in the treatment of pediatric bronchiectasis and summarize the surgical treatment experience of VATS in children with bronchiectasis. Retrospective single-center cohort study. A retrospective analysis was conducted on the clinical data of 46 pediatric patients who underwent surgery with bronchiectasis at the Children's Hospital of Chongqing Medical University from May 2015 to May 2023. The patients were divided into two groups: the VATS group (25 cases) and the thoracotomy group (21 cases). Comparative analysis was performed on various parameters including basic clinical data, surgical methods, operation time, intraoperative blood loss, transfusion status, postoperative pain, postoperative mechanical ventilation time, chest tube drainage time, length of hospital stay, incidence of complications, and follow-up information. There were no statistically significant differences between the two groups of patients in terms of age, weight, gender, etiology, duration of symptoms, site of onset, and comorbidities (p > 0.05). The operation time in the VATS group was longer than that in the thoracotomy group (p < 0.001). However, the VATS group had better outcomes in terms of intraoperative blood loss, transfusion status, postoperative pain, postoperative mechanical ventilation time, chest tube drainage time, and length of hospital stay (p < 0.05). The incidence of postoperative complications in the VATS group was lower than that in the thoracotomy group, although the difference was not statistically significant (p = 0.152). Follow-up data showed no statistically significant difference in the surgical treatment outcomes between the two groups (p = 0.493). The incidence of complications and mortality in surgical treatment of bronchiectasis is acceptable. Compared with thoracotomy surgery, VATS has advantages such as smaller trauma, less pain, faster recovery, and fewer complications. For suitable pediatric patients with bronchiectasis, VATS is a safe and effective surgical method.

Sensory and physicochemical characteristics of Japanese jidori chicken (Choshu-Kurokashiwa) and broiler thigh meat: Effect of cooking procedure.

This study aimed to investigate the effects of various cooking procedures on the sensory and physicochemical properties of Japanese jidori chicken (Choshu-Kurokashiwa) and broiler thigh meat. Thigh meats were cooked using three different procedures: grilling, boiling, and deep-frying. Subsequently, we performed sensory evaluation by a trained panel and instrumental analysis on both raw and cooked meat to determine changes in pH, shear force value, cooking loss, nucleotide metabolite contents, and proximate, free amino acid, and fatty acid composition. From the sensory evaluation, "springiness" and "chewiness" were found to be characteristics of Choshu-Kurokashiwa thigh meat among all cooking procedures. In terms of "juiciness" and "sulfur flavor," chicken breed and cooking procedure interacted, and these were considerably higher in Choshu-Kurokashiwa meat than in broiler meat only when boiling. In the instrumental analysis, Choshu-Kurokashiwa meat showed a considerably higher shear force value and lower free amino acid content than broiler meat, regardless of the cooking procedure used. Again, in terms of cooking loss, chicken breed and cooking procedure interacted, and this was considerably lower in Choshu-Kurokashiwa meat than in broiler meat only when boiling. These results showed that cooking procedure should be considered when describing the sensory characteristics of jidori thigh meat.

Probabilistic Intersection-Over-Union for Training and Evaluation of Oriented Object Detectors.

Oriented object detection is a challenging and relatively new problem. Most existing approaches are based on deep learning and explore Oriented Bounding Boxes (OBBs) to represent the objects. They are typically based on adaptations of traditional detectors that work with Horizontal Bounding Boxes (HBBs), which have been exploring IoU-like loss functions to regress the HBBs. However, extending this idea for OBBs is challenging due to complex formulations or requirement for customized backpropagation implementations. Furthermore, using OBBs presents limitations for irregular or roughly circular objects, since the definition of the ideal OBB is an ambiguous and ill-posed problem. In this work, we jointly tackle the problem of training, representing, and evaluating oriented detectors. We explore Gaussian distributions-called Gaussian Bounding Boxes (GBBs)-as fuzzy representations for oriented objects and propose using a similarity metric between two GBBs based on the Hellinger distance. We show that this metric leads to a differentiable closed-form expression that can be directly used as a localization loss term to train OBB object detectors. We also show that GBBs present a natural representation as elliptical regions (called EBBs), which inherently mitigate ambiguity representation for circular objects. Finally, we empirically show that the proposed similarity metric computed between two GBBs strongly correlates with the IoU between the corresponding EBBs, motivating the name Probabilistic Intersection-over-Union (ProbIoU). Our experiments show that results using ProbIoU as a regression loss are competitive with state-of-the-art alternatives without requiring additional hyperparameters or customized implementations, and that ProbIoU is a promising alternative to evaluate oriented object detectors. Our code is available at https://github.com/ProbIOU/.

Advancing fluid dynamics simulations: A comprehensive approach to optimizing physics-informed neural networks

Flow modeling based on physics-informed neural networks (PINNs) is emerging as a potential artificial intelligence (AI) technique for solving fluid dynamics problems. However, conventional PINNs encounter inherent limitations when simulating incompressible fluids, such as difficulties in selecting the sampling points, balancing the loss items, and optimizing the hyperparameters. These limitations often lead to non-convergence of PINNs. To overcome these issues, an improved and generic PINN for fluid dynamic analysis is proposed. This approach incorporates three key improvements: residual-based adaptive sampling, which automatically samples points in areas with larger residuals; adaptive loss weights, which balance the loss terms effectively; and utilization of the differential evolution optimization algorithm. Then, three case studies at low Reynolds number, Kovasznay flow, vortex shedding past a cylinder, and Beltrami flow are employed to validate the improved PINNs. The contribution of each improvement to the final simulation results is investigated and quantified. The simulation results demonstrate good agreement with both analytical solutions and benchmarked computational fluid dynamics (CFD) calculation results, showcasing the efficiency and validity of the improved PINNs. These PINNs have the potential to reduce the reliance on CFD simulations for solving fluid dynamics problems.

3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs.

Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views of an image while pushing away others in the representation space as negatives. However, the state-of-the-art contrastive methods require large batch sizes and augmentations designed for natural images that are impractical for 3D medical images. To address these limitations, we propose a new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is designed to learn perturbation-invariant features for 3D optical coherence tomography (OCT) volumes, using augmentations specifically designed for OCT. We introduce a new non-contrastive similarity loss term that learns temporal information implicitly from intra-patient scans acquired at different times. Our experiments show that this temporal information is crucial for predicting progression of retinal diseases, such as age-related macular degeneration (AMD). After pretraining with 3DTINC, we evaluated the learned representations and the prognostic models on two large-scale longitudinal datasets of retinal OCTs where we predict the conversion to wet-AMD within a six-month interval. Our results demonstrate that each component of our contributions is crucial for learning meaningful representations useful in predicting disease progression from longitudinal volumetric scans.

An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds.

3D single object tracking in LiDAR point clouds (LiDAR SOT) plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. However, LiDAR point clouds are usually textureless and incomplete, which hinders effective appearance matching. Besides, previous methods greatly overlook the critical motion clues among targets. In this work, beyond 3D Siamese tracking, we introduce a motion-centric paradigm to handle LiDAR SOT from a new perspective. Following this paradigm, we propose a matching-free two-stage tracker M 2-Track. At the 1st-stage, M2-Track localizes the target within successive frames via motion transformation. Then it refines the target box through motion-assisted shape completion at the 2nd-stage. Due to the motion-centric nature, our method shows its impressive generalizability with limited training labels and provides good differentiability for end-to-end cycle training. This inspires us to explore semi-supervised LiDAR SOT by incorporating a pseudo-label-based motion augmentation and a self-supervised loss term. Under the fully-supervised setting, extensive experiments confirm that M2-Track significantly outperforms previous state-of-the-arts on three large-scale datasets while running at 57FPS ( ∼ 3%, ∼ 11% and ∼ 22% precision gains on KITTI, NuScenes, and Waymo Open Dataset respectively). While under the semi-supervised setting, our method performs on par with or even surpasses its fully-supervised counterpart using fewer than half labels from KITTI. Further analysis verifies each component's effectiveness and shows the motion-centric paradigm's promising potential for auto-labeling and unsupervised domain adaptation.

DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models

Due to enormous computing and storage overhead for well-trained Deep Neural Network (DNN) models, protecting the intellectual property of model owners is a pressing need. As the commercialization of deep models is becoming increasingly popular, the pre-trained models delivered to users may suffer from being illegally copied, redistributed, or abused. In this paper, we propose DeepDIST, the first end-to-end secure DNNs distribution framework in a black-box scenario. Specifically, our framework adopts a dual-level fingerprint (FP) mechanism to provide reliable ownership verification, and proposes two equivalent transformations that can resist collusion attacks, plus a newly designed similarity loss term to improve the security of the transformations. Unlike the existing passive defense schemes that detect colluding participants, we introduce an active defense strategy, namely damaging the performance of the model after the malicious collusion. The extensive experimental results show that DeepDIST can maintain the accuracy of the host DNN after embedding fingerprint conducted for true traitor tracing, and is robust against several popular model modifications. Furthermore, the anti-collusion effect is evaluated on two typical classification tasks (10-class and 100-class), and the proposed DeepDIST can drop the prediction accuracy of the collusion model to 10% and 1% (random guess), respectively.

A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages

Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate post-harvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the ‘Roghani’ Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was modified by adding some well-known CNN layers to the last layer. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting in addition to the network stability, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products.

Modified SADI-S in non-obese diabetic rats: Description of the surgical technique

BackgroundSingle anastomosis duodenoileal bypass with sleeve gastrectomy (SADI-S) is a predominantly malabsorptive technique that has shown excellent results in morbid obese patients. The aim of this study is to establish a rodent model modifying the SADI-S technique by performing a proximal duodenojejunal anastomosis. This model can be useful for the study of glucose metabolism without malabsorption observed after the SADI-S technique. MethodsGoto-Kakizaki rats, a genetic model of non-obese and non-hypertensive type 2 diabetes mellitus, that develop hyperglycemia at an early age was used. Surgery consisted in a sleeve gastrectomy, duodenojejunal anastomosis and duodenal exclusion using three different techniques: duodenal transection (DT), duodenal ligation with hem-o-lock (DLH), and duodenal ligation with suture (DLS). Surgery time, weight loss, morbidity and mortality were recorded. ResultsA total of 16 animals were subjected to surgical intervention and overall mortality was 25 %, with the DT group showing the highest mortality rate (42.9 %). No differences were observed among groups in terms of weight loss. ConclusionThe surgical technique described in this work is feasible and reproducible. Weight loss is comparable regardless of the technique used for duodenal exclusion.

A Study on the Comparison of Engine Performance and Vehicle Losses of a Gasoline Vehicle in WLTC and Artemis Rural Driving Cycles

In this study, engine performance and vehicle energy losses of a passenger-type vehicle with a gasoline internal combustion engine were examined in 2 different driving cycles of the new generation with the help of the GT-SUITE vehicle simulation program. All parameters of the vehicle and the driving cycle were defined with the help of the simulation program. According to the simulation results, the number of gear changes and transmission losses were higher in the Artemis driving cycle, which had a higher average acceleration and deceleration acceleration, along with it. Torque converter losses were 4.73 times less in the Artemis cycle, where the torque converter locking clutch was used in greater quantities. The high average acceleration also increases tire and braking losses. When a comparison was made in terms of vehicle energy losses, it was found that Artemis driving cycle losses were 22% higher.

Comparison of Modified Stoppa Approach versus Ilioinguinal Approach for Anterior Acetabular Fractures

Achieving an anatomically precise reduction of acetabular fractures is vital for obtaining the best possible outcome. The Modified Stoppa approach has demonstrated advantages such as reduced blood loss and shorter surgical durations when dealing with anterior acetabular fractures in comparison to the ilioinguinal approach. Objective: To evaluate the outcomes of the modified Stoppa approach in comparison to the ilioinguinal approach. Methods: The quasi-experimental study was conducted within the Department of Orthopaedic Surgery at Jinnah Hospital during the period from November 12, 2018, to May 11, 2019.The study involved 60 patients, with 30 individuals in each group. Group A underwent treatment for anterior acetabular fractures using the modified Stoppa method, while Group B received treatment using the ilioinguinal approach. Results: Patients ranged between 16-60 years of age with mean age of 36.9±11.2 in group-A and 35.6±10.3 year in group-B. Majority of the patients were male in both groups. Left anatomical side was involved in most of the patients in both groups. The mean duration of injury in group-A was 7.07±2.6, while in group-B, it was 6.6±2.4. A statistical analysis revealed a significant difference between the two groups in terms of mean operative time and blood loss (p&lt;0.001). Furthermore, stratification was performed based on age, gender, and the duration of injury, and significant differences were observed in these stratified subgroups as well. Conclusions: Our findings showed that modified Stoppa technique outperforms the ilioinguinal method by exhibiting reduced blood loss and shorter operative durations. This positions the modified Stoppa technique as a superior option for addressing anterior acetabular fractures.

POTENTIAL OF DIFFERENT MYCOTOXIN ADSORBENTS UNDER IN VITRO CONDITIONS

Mycotoxins are a large and chemically diverse group of toxic secondary metabolites. Regarding their prevalence in animal feed and the effect on animal health, the biggest problems in terms of safety and economic losses are caused by aflatoxins, fumonisins, ochratoxins, trichothecenes and zearalenone. Adsorbents are substances that are added to food contaminated with mycotoxins, in order to bind them in the gastrointestinal tract and thereby prevent or reduce their effect. The aim of this study was to examine the possibility of using pyrophyllite as a mineral adsorbent, as well as preparations made of ground peach pits of different particle sizes as organic adsorbents, for adsorption of deoxynivalenol and ochratoxin A. Mycotoxin adsorption experiments were performed in vitro in electrolyte solutions at pH 3 and 7. The adsorption efficiency of the adsorbent was expressed as adsorption index. Pyrophyllite had adsorption index ​​of 13.47% for ochratoxin A at pH 3, while at pH 7, as well as for deoxynivalenol, the same mycotoxin produced a negligible degree of adsorption. Ground peach stones (of larger diameter, d = 0.1 mm) had considerable adsorption rates for ochratoxin A at pH 3 (34.41%) and deoxynivalenol at pH 7 (18.57%). The values were similar for smaller diameter (d &lt; 0.1 mm) for ochratoxin A at pH 3 (42.71%) and deoxynivalenol at pH 7 (20.11%). The obtained results suggest that the potential of the preparation of ground peach stones for the adsorption of tested mycotoxins is higher compared to the potential of pyrophyllite, but there are differences in their efficiency depending on the pH value of the adsorption environment.

Flood susceptibility mapping using Sentinel 1 and frequency ratio technique in Jinjiram River watershed, India.

Assam is one of the most flood-prone states in India, and the state frequently experiences catastrophic floods that cause significant damage in terms of loss of life and property. Flood susceptibility is considered the most essential and crucial input for managing floodplains and fostering local and regional development. This study focuses on the generation of flood susceptibility maps using the Frequency Ratio (FR) technique and microwave remote sensing inputs in the Jinjiram watershed which experienced disastrous flooding in 2020. The study has been carried out by taking into consideration of different morphological, lithological, and hydrological factors. In this study the flood inventory map was created by extracting the time series SAR (Synthetic Aperture Radar) Sentinel 1 GRD (Ground Range Detected) images of flooded areas for the past 5years, from 2016 to 2020. A total of 72 inventory samples were identified of which 70% of total flooded samples were chosen for training and 30% for model testing at random basis. Applying these FR methods, the study determines a range of flood susceptibility which was then divided into five classes, from very low to very high. The Receiver Operating Characteristics (ROC) analysis was used to evaluate the accuracy of flood susceptibility maps generated using FR models. The AUC of ROC in flood susceptibility mapping is 0.81167 achieved, corresponding to a prediction accuracy of 81.17%. The findings can be used to calculate risk, develop flood control measures and infrastructural policies, and formulate sustainable water management policies for the watershed.