In this study, powders of Phragmites australis (Phm) and Sawdust (SD) were utilized as biosorbents to remove Rhodamine 6G (R6G) as a cationic dye from wastewater. The surface characteristics of these biosorbents were examined using atomic force microscopy (AFM) and Fourier transform infrared spectroscopy (FT-IR) before the adsorption experiments. The batch adsorption method was employed to investigate the influence of various factors, including the amount of Phm and SD powder, adsorption period, initial concentration of adsorbate, temperature, and pH that affected on the adsorption process. The results showed that the highest removal efficiency (R%)for R6G dye was found at an adsorbent weight of 0.16g, a concentration of 10 mg/L, an adsorption time of 30 min, and a pH of 7 for both SD and Phm. Adsorption isotherms at different temperatures were observed following L-type, and through the adsorption isotherm models used, it was found that the adsorption of R6G dye is better described by the Freundlich isotherm (FrI) model. Thermodynamic parameters analysis suggests that the adsorption process is endothermic in nature, spontaneous, and occurs with an increase in disorder for SD as adsorbent, while for Phm it is an exothermic process that occurs with a decrease in disorder. In addition, the adsorption of R6G dye onto both Phm and SD is of a physical adsorption type. The kinetic data were analyzed by pseudo 1st and pseudo 2nd models, and were found to follow a pseudo 2nd model with correlation coefficients (R2 >0.9987and 0.9996) for Phm and SD, respectively. The study revealed that both SD and Phm represent potential low-cost biosorbents that effectively remove R6G dye from aqueous solutions.
Mapping soil characteristics is crucial to understanding the spatial distribution of those qualities and their sustainable uses; knowledge of soil fertility and agricultural yield requires an understanding of the physical and chemical characteristics, including soil acidity, electrical conductivity, and texture. This study employed interpolation techniques, Inverse Distance Weighting, to assess the spatial distribution of soil characteristics on the University of Baghdad campus in Iraq. Twenty-six soil samples were collected at 0–30 cm depth during autumn. The results revealed that sandy loam and loam were the most common textures in the study area. Soil's electrical conductivity ranged between 0.4 and 4.3 dS/m, indicating moderate to low salinity levels. Soil's pH readings did not show significant differences between sites, ranging between 7.00 and 7.11, with an average of 7.05. These results demonstrate that IDW has the potential to be a valuable tool for soil mapping, supporting precision agriculture and land management initiatives, particularly in areas with limited data. Geographic Information System approaches have proven highly effective in modeling and measuring soil characteristics, thereby providing advantages for preserving and enhancing soil health.
This paper proposes a blockchain-based system for managing Electronic Health Records (EHR) to address patient demands for immediate access to health information while ensuring privacy and security. It introduces a framework that includes five key entities: Registration Centre (RC), Patients, Doctors, Mobile Devices, and Blockchain (BC), and emphasizes the use of Elliptic Curve Cryptography (ECC) for enhanced security.The system utilizes a decentralized blockchain platform to securely store patient data and carry out operations, employing smart contracts for efficient and secure transactions. Access through the MetaMask wallet allows authorized users, such as doctors and patients, to easily manage and retrieve records while maintaining privacy. The approach aims to improve efficiency, trust, and security in health data storage and transfers.Overall, the proposed solution enhances interoperability, auditability, and automation while ensuring data privacy and security, demonstrating its viability across various blockchain networks.
Prostate cancer (PCa) is the second most common cancer among men worldwide. Immunological biomarkers play a crucial role in disease diagnosis, progression, and treatment. This study aimed to investigate the relationship between the rs5742621 (A>G) genetic variant and serum levels of insulin-like growth factor 1 (IGF-1) and other cytokines in patients with PCa compared to healthy controls. A total of 204 patients with PCa (aged 48–80) and 196 healthy controls were enrolled. Blood samples were collected to genotype the rs5742621 variant using the tetra-primer amplification refractory mutation system-polymerase chain reaction (ARMS-PCR). Serum levels of IGF1, macrophage migration inhibitory factor (MIF), CXC motif chemokine ligand 12 (CXCL12), and interleukin-27 (IL-27) were measured using a sandwich immunoassay. The heterozygous GA genotype of rs5742621 was more prevalent among patients with PCa, while the AA genotype was more prevalent in healthy controls. Additionally, serum IGF-1 levels were significantly elevated in PCa patients (median: 110 pg/mL) compared to healthy controls (median: 65 pg/mL; p < 0.001). Similarly, MIF levels were higher in PCa patients (85 pg/mL vs. 45 pg/mL; p < 0.001), as were CXCL12 concentrations (140 pg/mL vs. 40 pg/mL; p < 0.001). In contrast, IL-27 levels were lower in PCa patients (40 pg/mL) than in controls (65 pg/mL; p < 0.001). ROC curve analysis demonstrated that CXCL12 and MIF both achieved perfect diagnostic accuracy (AUC = 1.0), while IGF1 showed excellent diagnostic performance (AUC = 0.979) and IL-27 showed good performance (AUC = 0.903). The GA genotype of rs5742621 was significantly associated with elevated IGF-1 levels and increased PCa risk, promoting cell proliferation, inhibiting apoptosis, and accelerating tumor growth. IGF-1 and related immunological markers may serve as promising diagnostic biomarkers and potential therapeutic targets for prostate cancer.
This study employs a photometric technique to investigate the intensity of color gradients within image pixels. The MATLAB software was developed to evaluate laser spots by computing the average wavelength from pixel values in pictures, resulting in an overall error rate ranging from 1% to 10%, depending on the exact laser wavelength examined. The program executes pixel-wise wavelength estimation by transforming color intensity gradients into wavelength values and then conducts statistical analysis of several laser spots to ensure precision. These techniques facilitated the identification of spectral lines corresponding to the mean wavelength of each laser source. The methodology was initially validated by measuring the monochromatic wavelengths of the laser beams. The MATLAB program for image processing effectively identified laser wavelengths at 325, 473, 477, 535, 603, and 785 nm, producing outstanding results with negligible error rates. This study employs advanced methods to identify specific wavelengths associated with naturally occurring elements in soil or land. The methodology emphasizes the distinct wavelength properties of each element, incorporating Google Earth Maps for spatial analysis and MATLAB for data processing and visualization. The land area was progressively enlarged to identify the calcium deposit fields until it reached an elevation of approximately 700 m above sea level, with an image width of only 200 m, resulting in each pixel representing only 24 cm. A calcium deposit field in the Anbar region was selected as the specimen for the unexamined calcium field. The analysis identified calcium deposit locations on the map, demonstrating that Iraq possesses abundant calcium resources, particularly in the western and southwestern regions. The findings of this study enable companies to precisely identify calcium-rich areas, thereby minimizing extensive fieldwork, conserving financial resources, and decreasing the time required to locate the sources of this element and its compounds.
Prodigiosin is a secondary metabolite with red pigmentation produced by Serratia marcescens. Prodigiosin has been described for several biological activities, including antitumor. The study aimed to isolate S. marcescens that produces the prodigiosin pigment, purify it, and evaluate its activity against three human cancer cell lines, MM.1S, TP-53, and HL-60, with the HdFn normal cell line as a control. 145 urinary tract infection (UTI) samples and 35 soil samples were collected, and only 16 isolates were identified as S. marcescens using the VITEK system. The isolates were tested under primary and secondary screening to select the best prodigiosin-producing isolate. Isolate S2 had the highest productivity of the pigment (0.1704g/l). The pigment was purified by column chromatography, identified, and characterized using FTIR and UV-VIS spectroscopy. The pigment corresponded to prodigiosin, with a maximum absorption at 535 nm and a structural formula C20H25N3O. High cytotoxic effects of prodigiosin were observed against TP-53 and HL-60 cell lines. The MM.1s were more pigment resistant. The cytotoxic activity showed IC50 of 94.59 and 57.96 against HL-60 and TP-53 cell lines, respectively, and did not affect the normal cell line at all. Prodigiosin pigment exhibits cytotoxic effects on tumor cell lines and is safe for normal cell lines.
This paper primarily focuses on the study of an ecological model in which fractional differential equations (FDEs) are utilized to construct the model rather than using traditional first order differential equations. A model of tri-trophic food chain is explored using the Beddington–DeAngelis (BD) functional response with harvesting in each species. The impact of harvesting on the system’s behaviour is observed, that brings about the occurrence of both bifurcations, Transcritical and Hopf bifurcation, which depends not only on fractional order derivative parameters, but also on the harvesting parameters. The existence, uniqueness, non-negativity, and boundedness of the solutions are explored. Next, the stability of all feasible equilibrium points is determined locally. Additionally, sufficient conditions are established to make certain the global asymptotic stability of each point of equilibrium, except the trivial equilibrium point, by selecting an appropriate Lyapunov function. Finally, numerical simulations are given to illustrate the outcomes of our dynamical analysis of the model.
One of the most widespread global health problems is cancer, specifically skin cancer. These problems are attributed to environmental and immunological causes and can be associated with other disease conditions. According to the statistical increase of the disease, the need has become urgent to reach an alternative formula for chemotherapy due to its side effects on the body's organs. One of the most important anticancer agents is essential oils, especially tea tree oil, which has been extracted from the plant's dry leaves using the steam distillation method by Clevenger apparatus, then analysed using gas chromatography mass technique which revealed the presence of many important compounds such as (4-terpineol 22.45%, cymene 14.10%, γ-terpinene 6.69%, α-pinene 5.79%, α-terpineol 3.48%, sabinene 2.54% and α-terpinolene 2.33%); the oil was polymerized with casein to compare its effectiveness as an antioxidant and anticancer agent. The study proved that casein polymer compared to ascorbic acid scored highest inhibitory activity at concentration 100 (mg/ml) (34.34±11.92 and 54.08±15.89) and 80 (mg/ml) (27.15±10.92 and 38.77±12.83) and lowest inhibitory activity at concentration 40 (mg/ml) (14.26±4.98 and 20.91±8.92) and 20 (mg/ml) (14.89±6.29 and 18.36±6.87); meanwhile tea tree oil scored highest anticancer activity at doses 0, 31.25, and 62.5 µg/ml (100 ± 1.92, 92.20 ± 1.98, and 91.96 ± 1.95) and lowest anticancer activity at doses 250 and 500 µg/ml (53.6 ± 1.80 and 10.37 ± 2.86), while casein scored highest anticancer activity at doses 0, 7.81 and 31.25 µg/ml (100 ± 4.09, 89.94 ± 0.66 and 89.35 ± 3.30) and lowest anticancer activity at doses 250 µg/ml (74.09 ± 1.78) which considered the highest skin anticancer value for the polymer.
The integration of robotic systems with machine learning and computer vision has become increasingly critical in automating systems. This is specifically essential to accomplish complex and different tasks, hence enabling intelligent systems to operate with minimal human intervention. These tasks include object recognition, classification, and manipulation. However, accurate object localization and manipulation pose challenges for robotic systems relying on non-calibrated cameras due to positional inaccuracies. This paper introduces a new non-calibrated camera mapping technique to enhance precision in robotic pick-and-place operations. To this end, a robotic arm controller is implemented to pick and place objects using efficient machine learning and computer vision techniques. The proposed approach is used to pick and place different objects that are identified and labeled by certain classes and sorted accordingly. The location of the objects is found using a proposed non-calibrated camera approach. This approach eliminates the need for expensive calibration procedures while maintaining high accuracy. The proposed method is evaluated by comparing actual object locations with those found using the camera mapping, resulting in a 2.458% error rate. In addition, a comparison with other non-calibrated camera approaches demonstrated that the proposed method achieved higher accuracy and lower errors. These results show promise for applications in industrial automation and robotics systems.
Soil salinization is a pervasive global issue, particularly in semi-arid and dry areas. It constitutes the primary cause of soil degradation, significantly impairing soil functionality and agricultural productivity. Consequently, there is a pressing need for precise procedures to measure soil salinity in various areas, which are necessary for managing, correcting, monitoring, and utilizing saline soils effectively. This study employed remote sensing (RS) techniques as a robust approach for constructing predictive models of soil salinity by examining changes in soil salinity using different salinity indices and comparing them with electrical conductivity (EC) measurements for soil samples. Among the five salinity indices evaluated, the study identifies soil salinity (SI2) as the most accurate index that reflects Iraqi soil conditions. Consequently, the regression equations model is leveraged to predict the EC compatible with various soil types. Thus, the accuracy was increased by combining ground-truthing and sophisticated Remote sensing (RS). Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were calculated to study the relationship between them and soil salinity. It was found that LST changed during the study period at a rate of 61.3±2.8 and 99.8±1.9 for Baghdad and Basrah, respectively. The relationship between LST and salinity exhibits a weak correlation, indicating that temperature is not the primary factor contributing to the increase in soil salinity. This study highlights the potential of remote sensing in refining soil salinity prediction models. These realizations were used to build practical plans to mitigate the effects of soil salinity on agriculture and the surrounding ecosystem.
This research was carried out to identify the layers of the skin of Bufo viridis. Ten adult frogs were used in the study, which was conducted in the histology and embryology laboratory at the College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad. The skin was dissected and fixed in 10% formalin, then dehydrated, cleared, infiltrated, and embedded. The sections were stained with hematoxylin and eosin. The histological structure of the skin revealed that it is composed of two layers. The epidermis layer is composed of stratified squamous epithelial tissue and consists of three strata: the Corneum, Spinosum, and Germinativum. There are no papillae in the epidermis. The dermis layer is composed of loose connective tissue with blood vessels and consists of two layers: the spongy and compact layers. The spongy tissue consists of alveolar glands, both mucous and granular. The dorsal skin and the dermis consisted of pigment cells. The skin tissue consists of a two-layered structure: the epidermis, composed of stratified squamous epithelium, and the dermis, composed of loose connective tissue. Pigment cells are present in both layers, suggesting that they serve a role as coloration or UV radiation protection.
Cryptococcus neoformans a yeast pathogen, is attributed as the causative agent of cryptococcosis, a significant infectious disease in humans. Recently, microbiologists have tried to make new media to detect the C. neoformans from simple natural materials. In this study, a novel medium was prepared from caper leaves extract as an identification and differential medium for Cryptococcus neoformans. The isolates of Cryptococcus neoformans and Candida albicans (as negative control) were cultured on caper leaves agar and incubated at 37ºC for 48 hours. we revealed after 48 hours of incubation the fifty-isolates of C.neoformans were the caper leaves agar and colonies pigmented by dark brown, while C. albicans grew without pigment production. The results of analysis for fresh caper leaves by high-performance liquid chromatography (HPLC) revealed that the caper leaves content of Myrecelin, Ferrulic acid, Caffeic acid, Quinic acid, Ascorbic acid. Apigenin and Kaempferol are all those phenol compounds considered substrates to produce melanin as differential pigment. The present phenolic compound assured caper leaves agar useful as an innovative substrate for swift identification of C. neoformans in environmental and clinical samples.
Cytokines are immune components that serve as markers for either promoting or inhibiting inflammation, contingent upon their specific concentrations and the presence of additional modifying factors. This study evaluates the relationship between ulcerative colitis (UC) tissue and the expression levels of Interleukin-6 (IL-6) and Interleukin-10 (IL-10). This research involves the endoscopic biopsy of 35 specimens from ulcerative colitis and 15 specimens from normal colon tissue to evaluate the expression levels of IL-6 and IL-10. The immunohistochemical technique revealed that IL-6 levels were significantly elevated in patients with UC, whereas IL-10 expression was comparatively lower in these patients. The data suggest that IL-6 and IL-10 may significantly influence the progression of UC and could serve as useful biomarkers for monitoring this progression.
Fingerprint recognition has long been considered one of the most reliable biometric methods due to the uniqueness and consistency of fingerprint patterns. However, real-world scenarios often present challenges such as noise, smudges, partial prints, or deliberate adjustments that can hinder accurate identification. This study presents a hybrid approach to fingerprint recognition that combines fine detail features and large-scale static feature conversion descriptors to improve flexibility and accuracy. To enhance the clarity of fingerprint patterns, preprocessing techniques such as Canny edge detection and Sobel filtering are applied, enabling better feature extraction. Detailed points are taken out using the transit number way and authorized by filtering the area, reducing the mass, and analyzing the structure of the ridges. Sort descriptors are used to capture local texture information and are normalized to ensure uniform vector lengths. An audit dataset, comprised of original and synthetic fingerprints with different degrees of complexity, is used to validate the system. The combination of fine details, proofing features, with careful pre-processing obtained the best classification rate of 96% across tested methods. The proposed approach achieves good results on the identification of variable fingerprints with the performance efficiency required for real world, real time, and resource constrained forensics.
The classification of brain tumors has remained a strong area of research in the medical field, and this has been caused by the high number of cases of brain tumors reported all over the world. The incidence rate of brain tumors in India in 2023 was found to be more than 38,000 new cases. This is one of the widespread cancers, with fewer than 34,000 deaths occurring. The opposing issues in the classification of brain tumors using machine learning (ML) include the small scale of the data, feature selection, and significant classification errors. Although deep learning (DL) methods are continually evolving, this research will provide solutions using a hybrid scheme that combines mixed ML and DL methods. The models examined in this paper are: Convolutional Neural Networks (CNN), CNN with Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and EfficientNetB0. Four types of measures were taken to establish the accuracy of the results and to compare the five types of models applied in the study. The outcome indicated that EfficientNetB0 was the best model, with an accuracy of 99.80%, a precision of 99.90%, a recall of 99.80%, and an F1-score of 99.80%. The CNN, CNN-SVM, CNN-RF, and CNN-LR models, on the other hand, provided accuracies in the range of 89% to 91%. This outcome demonstrates that EfficientNetB0 outperforms other models in detecting brain tumors, as well as its ability to enhance diagnostic capabilities in clinical settings. The paper presented the performance of each tumour class (Glioma, Meningioma, Pituitary Tumor, No Tumor) in each model. It also employed statistical validation methods, including cross-validation and significance testing, to support the results.
Toxoplasmosis, caused by the intracellular parasite Toxoplasma gondii, is linked to neurological and mental diseases. Interferon-gamma (IFN-γ) is a vital immune system cytokine that regulates immunological response. This study examines the relationship between the serum level and gene expression of IFN-γ in children with neurodevelopmental disorders infected with T. gondii. A total of 200 blood samples were collected from neurology and pediatric physicians at the Central Teaching Hospital of Paediatrics in Baghdad from October 2022 to May 2023. The serum levels of IFN-γ were measured using a competitive ELISA technique. A Quantitative Real-Time Polymerase Chain Reaction was performed for gene expression. The control group included 68children without toxoplasmosis and 32 who were infected with Toxoplasma gondii. In comparison, the patient group had 31 children with neurodevelopmental disorders and 69 without them, according to the ELISA (Toxoplasma-IgM and IgG) detection test. The results demonstrates that serum levels of IFN-γ are slightly higher in patients compared to the control group, and this difference is statistically significant and shows no significant variations in serum IFN-γ levels across the studied groups. The age interval (0-12) in children with neurodevelopmental disorders infected with T. gondii showed high serum levels compared to other groups. In gene expression studies, children with neurodevelopmental issues and toxoplasmosis had greater IFN- expression in gene expression. The study found that children with toxoplasmosis, especially those with neurodevelopmental problems, have increased gene expression of IFN-γ despite similar serum levels. IFN-γ may play a role in the development of neurological problems linked to congenital toxoplasmosis. Person correlation was used to evaluate the strong relationship between the serum level of IFN-y and its gene expression. The results of this study show a positive relation between IFN-y and its gene expression in the toxoplasmosis area.
This study describes the synthesis of two novel cyclic imide derivatives incorporating drug components or heterocyclic moieties. The first series (compounds 6-13) features a cyclic imide connected to a hetero ring through methylene group, synthesized via the Mannich reaction using N-unsubstituted cyclic imide with heterocyclic amine and formaldehyde. The second series (compounds 15-18) involve cyclic imide fused with the drug molecule (sulfamethoxazole) through acetamido group, achieved via the Gabriel reaction by reacting the potassium salt of N-unsubstituted imide with chloro acetamido sulfamethoxazole. The antimicrobial activity of the newly synthesized imides was evaluated, yielding promising results. The synthesized compounds have been characterized by FTIR, 1HNMR and 13CNMR.
The Internal Transcribed Spacer (ITS) region is widely used for fungal identification in complex biological specimens. Fusarium is an opportunistic fungus responsible for a range of diseases in both plants and humans. This study aimed to identify Fusarium species at the species level and investigate their genetic variation. The ITS region of eighteen isolates was amplified by universal ITS primers (ITS1 and ITS4) using PCR. The PCR products of the ITS regions with a molecular weight of 588 bp were sent for sequencing, and then analyzed the sequence results by blast in the National Centre Biotechnology Information (NCBI) online to detect genetic variation in ITS regions. Phylogenetic analysis was compared with NCBI's reference sequences using MEGA X software. According to the NCBI GenBank database, four Fusarium species were first isolated in Iraq: F. pseudoanthophilum, F. fujikuroi, F. luffae, and F. boothii. Eleven isolates were identified as F. pseudoanthophilum (NR_163682.1) from the USA, with identities ranging from 98% to 100%. They were assigned the accession numbers MW577709-MW577719. Four isolates were identified as F. fujikuroi (NR_111889.1) from the USA, with identities ranging from 99% to 100%. They were assigned the accession numbers MW577720-MW577723. Two isolates were identified as F. luffae (NR_164594.1) from China, with an identity of 99%. They were assigned the accession numbers MW577724-MW577725. One isolate was identified as F. boothii (NR_121203.1) from the USA, with an identity of 99%. It was assigned the accession number MT658128.1. Thirty-five substitutions were detected by Bioedite software; most of them are deletions, accounting for 51.43%, followed by transitions, 31.43%, and transversions, 11.43%. These substitutions are found in most of the Fusarium spp—isolates, except for two isolates, which had no substitutions. The novelty of the present study lies in the first isolation of four Fusarium species in Iraq: F. pseudoanthophilum, F. fujikuroi, F. luffae, and F. boothii.
Accurate and contextually appropriate answer generation is increasingly important for applications in virtual assistants, educational tools, search engines, and so on, for answering any question about information found across electronic libraries. This research, “Answers Generation based on English Textual Analyzer (AGETA),” hopes to generate the correct answer as a complete comprehension sentence. It receives a passage with a related list of questions in English as unstructured typed texts. It performs the English textual analysis process using a series of natural language processing (NLP) techniques, followed by a hybrid method that combines extractive techniques with linguistic analysis to build an effective answer generator, as a set of its main applied techniques: tokenization, part-of-speech tagging, cosine similarity, T5 model, and then applying grammar-checking mechanism and English syntactic rules. AGETA was tested on questions related to different passages, with performance measured using two types of accuracy measurements. The first type is human performance, which achieved 92% for short answers and 96% for expanded answers. The second type is Sentence Transformers (BERT-based models), which achieved 90%. This indicates that the generated answers exhibit considerable unity with the corresponding ground truth answers. The proposed approach has potential applications in education, research, and customer support by enhancing the accessibility and relevance of textual information.
Inelastic Coulomb form factors with inclusion of the effect of short-range correlation functions for certain assignment excitations (such as 2+ (1.33), 2*(2.16), 4*(2.50), 4*(3.13), 4*(3.67), 3-(4.04), 3-(6.20), and 3-(7.05) MeV) as well as for uncertain assignment excitations (such as 4.85 (2+, 4+), 5.05 (4+, 6+) and 6.85 (2+, 5-) MeV) in 60Ni atomic nucleus have been scrutinized. The form factors of uncertain assignment excitations have been computed by decomposing the excitations 4.85, 5.05 and 6.85 MeV to (C2 and C4), (C4 and C6), and (C2 and C5) components, respectively. The charge density of the 60Ni has been also scrutinized using the one- and two-particle parts of cluster extension in collaboration with single-particle harmonic wave functions. The short-range correlation of Jastrow formula, which relies on the correlation parameter (β), has been incorporated into the two-particle part of cluster extension The nucleus of 60Ni consists of a 56Ni-core plus four energetic neutrons dispersed in f5p-model space, where this nucleus has no energetic protons outer the 56Ni-core. Thus, inelastic Coulomb form factors of 60Ni have been exclusively computed from the core polarization transition charge density using Tassie formulation, relying on the computed charge density. The oscillator parameter (b) and correlation parameter (β) have been applied to existing calculations, where b and β have been independently created for every specific nucleus through matching between the anticipated and measured elastic form factors. In this study, a single value for each of b and β has been used to compute the inelastic form factors for diverse excitations in 60Ni, where the computed results show a well accordance with available data. It is concluded that the influence of short-range correlation functions rules substantially the existing computations, where this effect seems to be crucial to creating an important modification to the predicated findings which ultimately leads to elucidate the data astoundingly across all assumed momentum transfers.
