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PurposeThis study aims to show the dyeing behaviour of polyester fabrics using four novel heterocyclic disperse dyes.Design/methodology/approachThe four dyes were synthesized based on 5, 5'-(1, 4-phenylene) bis (1, 3, 4-thiadiazol-2-amine) as a diazonium compound. The UV/Vis absorption spectroscopic data of these disperse dyes while dyeing polyester fabrics were investigated. Following this, the dyeing properties of these dyes on polyester fabrics were investigated under acid condition.FindingsThe results showed that increasing the dyeing temperature from 80°C to 100°C led to an increase in dye uptake for all dyes, but further increases of the temperature to 130°C led to higher dye uptake for dye 3 as the dye exhaustion increased by about 50% from 55.9% to 91.4%.Originality/valueThis study is important as it introduces new dyes for the dyeing of polyethylene terephthalate (PET) fibres with colours that range from yellowish orange to bluish yellow and scarlet red and all with excellent brightness, levelness and depth of shade.

Owing to their comfort, handle, and aesthetic characteristics, cotton fabrics will always be the first and primary choice for clothing and apparel. In recent years, regenerated cellulosic fabrics like bamboo, Tencel, and modal fabrics have had many natural advantages. Fabrics based on blending cotton fibres with regenerated cellulosic fibres are considered promising products in textile industry sectors. Use of urea poses ecological problems associated with the high nitrogen content of the printing effluent. Therefore, urea reduction or elimination in reactive dye print pastes is of ecological interest. We report the use of trisodium nitrilotriacetate as a complete substitution of urea and alkali in the conventional reactive printing of cotton/cellulosic regenerated blended fabrics. CI Reactive Black 5 was selected for the present study. Three different print pastes containing urea/alkali, trisodium nitrilotriacetate/alkali and trisodium nitrilotriacetate without alkali were thoroughly investigated. Different factors that may affect the printability of cotton/cellulosic regenerated blended fabrics, such as the concentrations of dye, trisodium nitrilotriacetate, urea, absence or presence of alkali and steaming time in the prints obtained, were evaluated concerning colour strength, dye fixation, dye penetration, levelling, colure, and fastness properties. All printed fabrics using three print pastes obtained excellent to good fastness. The results proved the viability of using TNA as an environmentally friendly approach for urea/alkali-free printing of cellulosics with reactive dyes.

We herein report a direct and facil sol–gel method for the preparation of porous silicon dioxide nanoparticles using dissolved silica gel and nitric acid. We prepared SiO 2 nanoparticles at three different pH values: 6, 7, and 8. The washed and dried products were calcined at 800 °C for 2 h. The average crystallite size of the prepared SiO 2 nanoparticles was ca. 37.7 nm. The products were characterized by using FT-IR, TEM, FE-SEM, and XRD analyses. The as-prepared SiO 2 nanoparticles showed high adsorption capacities; ca. 32.2 mg g −1 and 42.2 mg g −1 , for the removal of Pb(II) and Cd(II) ions, respectively, from aqueous media. The adsorption data followed well the pseudo-second-order and Langmuir isotherm models. The determined thermodynamic parameters: ΔG° (from − 5.026 to − 5.180 kJ/mol for Cd(II) ion adsorption and from − 5.528 to − 5.732 kJ/mol for Pb(II) ion adsorption) and ΔH° (− 7.00 and − 7.607 kJ/mol, respectively), indicate the spontaneous, exothermic, and physisorption nature of the adsorption process. Besides, the excellent reusability and adsorption capacity for SiO 2 nanoparticles revealed their good efficiency for the removal of Pb(II) and Cd(II) ions from aqueous media.

The effects of climate change and rapid population growth increase the demand for freshwater, particularly in arid and hyper-arid environments, considering that groundwater is an essential water resource in these regions. The main focus of this research was to generate a groundwater potential map in the Center Eastern Desert, Egypt, using a random forest classification machine learning model. Based on satellite data, geological maps and field survey, fifteen effective features influencing groundwater potentiality were created. These effective features include elevation, slope angle, slope aspect, terrain ruggedness index, curvature, lithology, lineament density, distance from major fractures, topographic wetness index, stream power index, drainage density, rainfall, as well as distance from rivers and channels, soil type and land use/land cover. Collinearity analysis was used for feature selection. A 100 dependent points (57 water points and 43 non-potential mountainous areas) were labeled and classified according to hydrogeological conditions in the three main aquifers (Basement, Nubian and Quaternary Aquifers) in the study area. The random forest algorithm was trained using (70%) of the dependent points. Then, it was validated using (30%) and the hyper-parameters were optimized. Groundwater potential map was predicted and classified as good (5.1%), moderate (0.1%), poor (4.2%) and non-potentiality (90.6%). Sensitivity (92%), F1-score (94%) and accuracy (97%) are validation methods used due to the imbalanced dataset problem. The most important effective features for groundwater potential map were determined based on the random forest and the receiver operating characteristics curve. Groundwater management sustainability was discussed based on the predicted groundwater potential map and aquifer conditions. Therefore, the random forest model is helpful for delineating groundwater potential zones and can be used in similar locations all over the world.

In the era of Large Language Models, there is still potential for improvement in current Natural Language Processing (NLP) methods in terms of verifiability and consistency. NLP classical approaches are computationally expensive due to their high-power consumption, computing power, and storage requirements. Another computationally efficient approach to NLP is categorical quantum mechanics, which combines grammatical structure and individual word meaning to deduce the sentence meaning. As both quantum theory and natural language use vector space to describe states which are more efficient on quantum hardware, QNLP models can achieve up to quadratic speedup over classical direct calculation methods. In recent years, there is significant progress in utilizing quantum features such as superposition and entanglement to represent linguistic meaning on quantum hardware. Earlier research work has already demonstrated QNLP’s potential quantum advantage in terms of speeding up search, enhancing classification tasks’ accuracy and providing an exponentially large quantum state space in which complex linguistic structures can be efficiently embedded. In this work, a QNLP model is used to determine if two sentences are related to the same topic or not. By comparing our QNLP model to a classical tensor network-based one, our model improved training accuracy by up to 45% and validation accuracy by 35%, respectively. The QNLP model convergence is also studied when varying: first, the problem size, second, parametrized quantum circuits used for model’s training, and last, the backend quantum simulator noise model. The experimental results show that strongly entangled ansatz designs result in fastest model convergence.

The increasing demand for freshwater supplies and the effects of climate change in arid and hyper-arid regions are pushing governments to explore new water resources for food security assurance. Groundwater is one of the most valuable water resources in these regions, which are facing water scarcity due to climatic conditions and limited rainfall. In this manuscript, we provide an integrated approach of remote sensing, geographic information systems, and analytical hierarchical process (AHP) to identify the groundwater potential zone in the central Eastern Desert, Egypt. A knowledge-driven GIS-technique-based method for distinguishing groundwater potential zones used multi-criteria decision analysis and AHP. Ten factors influencing groundwater were considered in this study, including elevation, slope steepness, rainfall, drainage density, lineament density, the distance from major fractures, land use/land cover, lithology, soil type, and the distance from the channel network. Three classes of groundwater prospective zones were identified, namely good potential (3.5%), moderate potential (7.8%), and poor potential (88.6%) zones. Well data from the study area were used to cross-validate the results with 82.5% accuracy. During the last 8 years, the static water level of the Quaternary alluvium aquifer greatly decreased (14 m) due to excessive over pumping in the El-Dir area, with no recorded recharges reaching this site. Since 1997, there has been a noticeable decline in major rainfall storms as a result of climate change. The current study introduces a cost-effective multidisciplinary approach to exploring groundwater resources, especially in arid environments. Moreover, a significant modern recharge for shallow groundwater aquifers is taking place, even in hyper-arid conditions.

Although seemingly benign, the presence of a patent foramen ovale (PFO) may play an important role in the pathophysiology of disease, specifically a paradoxical embolism leading to cryptogenic stroke. The European Society of Cardiology recently published guidelines detailing how PFOs are associated with paradoxical embolism and how they are diagnosed and managed. This review guides physicians in the diagnostic and referral process to a multidisciplinary team involved in PFO closure. It reviews the clinical trials comparing device closure with medical therapy and highlights the current NHS England commissioning process on PFO management. Finally, we give an overview of other conditions where PFO device closure may need to be considered.

To assess the impact of the COVID-19 pandemic on urgent retina practice and factors influencing adherence of physicians and patients to safety measures. In this clinical audit, urgent or emergent vitreoretinal surgical disorders that presented to our hospital during the period of 15th March-15th May 2020 were compared with the period just before the pandemic declaration (15th December 2019-15th February 2020). Additionally, two questionnaires assessing the adherence to safety measures were circulated to the medical personnel and a sample of patients. The collected data were analyzed, and accordingly, recommendations were proposed to the hospital administration and specific corrective measures were applied. The outcome of applying these corrective measures was assessed in the re-audit cycle during the period of 15th June-15th August 2020. There was a significant decrease in the number of urgent or emergent vitreoretinal surgical disorders that presented to our hospital during the pandemic (161 versus 302 cases in a similar period before the pandemic; = 0.022). Just with the pandemic recession, there was a significant increase in the number of urgent cases (391 versus 161 cases during the pandemic; = 0.006), also there was an increased number of complex cases. Residents and fellows were less compliant than attending physicians in adherence to safety measures. Delayed presentation of urgent retinal cases during the pandemic highlights the importance of public awareness of urgent conditions that need immediate medical or surgical care. Attention to young physicians during the pandemic is crucial as they are less adherent to safety measures due to work overload.

Due to the development of hacking programs, it has become easy to penetrate systems. Hence, there is a need for strong security mechanisms. The use of traditional passwords has become insufficient to secure systems. Biometric authentication is now widely used for security applications, and it has proven to be superior compared to traditional authentication methods. However, two issues need to be considered in biometric systems. The first is not to keep biometric data in its original form in the database. If biometric traits are hacked, they will no longer be of use. Biometric data should be kept in cancelable forms for reuse. The second issue is the reliance on a single biometric, which limits the verification accuracy. This can be solved by using a multimodal biometric system. Using steganography and cryptography, this paper introduces a cancelable multimodal biometric system. As voiceprints, facial images, and fingerprint images are used. In this paper, the verification is performed through the Mel frequency cepstral coefficients (MFCCs) of the voiceprints. Steganography is used as a tool to secure features extracted from voiceprints by embedding them into the facial image using block-based singular value decomposition (BSVD). Double random phase encoding (DRPE) is utilized as an encryption algorithm to generate the final cancelable templates. To increase the level of system security, fingerprint images are used as random phase masks (RPMs). Verification is performed by estimating the correlation between registered and test MFCCs. The correlation value is then compared with a threshold value, which is calculated using the distribution curves for the genuine and imposter correlations. Equal error rate (EER) values close to zero and an area under the receiver operator characteristic curve (AROC) that is close to one are obtained from the simulation results, demonstrating the outstanding performance of the suggested system. The proposed system achieves good performance in different domains.