An empirical model for predicting the length of a capillary tube

Any refrigerant device consists of several parts, and one of the most significant parts is the expansion device. This expansion device can be classified into several types according to the size of the refrigeration system. The capillary tube is used usually with a small refrigerant system size to reduce the higher pressure in the condenser into the low pressure in the evaporator. In this study, the effect of the capillary tube's diameter and that of the temperature of the condenser and evaporator on the length of such a device has been theoretically studied. Furthermore, a validation between the theoretical analysis and experimental findings from the literature review has been carried out. To achieve the theoretical aspect, MATLAB code has been developed. The results showed that the maximum difference between the theoretical and experimental results regarding temperature and pressure refrigeration is around 5% and 3.4%, respectively. Also, the results depict that the inner diameter and the condenser temperature have an effect on the length of the capillary tube. However, the effect of the inner diameter is higher compared with the condenser temperature. In addition, an equation to predict the length of the capillary tube has been developed with an accuracy of 98%. This equation is created as a function of the capillary tube's diameter and the temperature of the condenser and the evaporator. Moreover, this equation can be used to predict the length of the capillary tube for small refrigeration devices, especially those operating under 10 KW. The findings of this study can help make a mathematical approach used for the design of the capillary tube simpler and easier to apply.     

Histological technique to detect the physiological age of the malaria vector mosquito Anopheles pharoensis (Diptera: Culicidae)

The risk of malaria recurrence increases due to the main vector, Anopheles pharoensis. The physiological age of the mosquito population is needed to expect malaria vector dynamics. The number of completed gonotrophic cycles is of great importance in determining the physiological age of females. A technique has been described that focuses on the number of dilatations remaining in the ovarioles after each oviposition to determine how many blood meals have been taken. At each gonotrophic cycle, the chances of infection of the vectors are repeated. The histological changes that occur immediately in the ovarioles and ovulation itself were studied. Under the influence of the contractions of the ovarian muscles, the eggs begin to move over the distal end of the ovariole into the inner oviduct. The terminal pedicle is markedly dilated near the diameter of the eggs. After the expulsion of the mature eggs, ovariole dilations were found at the point of their development in the terminal pedicle due to the accumulation of nurse cell remnants and follicular epithelium. The results were used to develop epidemiological localization and to evaluate the effectiveness of antimalaria interventions. The ovarian inspection often provides a technique to distinguish nulliparous from parous female anophelines. In addition, this study can provide basic entomological knowledge on the physiological age of mosquitoes by considering the histological changes in the ovaries, which allow the evaluation of vector management strategies in the field.     

Influence of storage,brine concentration and in‐container heat treatment on the stability of white brined Nabulsi cheese

Microbial levels were significantly (P < 0.05) affected by the temperature‐time treatments used during in‐container heat treatment of the curd, brine concentration and storage duration. A heat treatment of 115 °C × 2 min, in the absence of NaCl, was sufficient to eliminate Mesophilic micro‐organisms, Coliforms, Staphylococcus aureus and Yeast and Moulds for the duration of 1 year of storage. Sensory scores for colour, appearance and texture increased significantly (P < 0.05) with the increase in the NaCl in brine concentration regardless of the temperature‐time treatment.     

Improved crossover firefly algorithm based deep Beleif network for low-resolution face recognition

Multimedia Tools and Applications - Face detection by low-resolution image (LR) is one of the key aspects of Human-Computer Interaction(HCI). Due to the LR image, which has changes in pose,...     

Surface-assembled Fe-Oxide colloidal nanoparticles for high performance electrocatalytic water oxidation

Nanoscale electrocatalytic materials having enhanced electroactive sites has been considered trendier and can drive kinetically uphill OER at much lower energy cost with high efficiency. However, very complex synthetic strategies, extensive functionalization processes, and less stability have stimulated quest for economically viable, straightforward and facile preparative methods for designing stable, robust and active nanoscale electrocatalysts engaging geologically abundant materials to ensure their industrial implications. Here we present surface-assembled Fe(OH)_x/FeO_x type colloidal catalytic thin-films, with or without post annealing, derived from Fe-colloidal NPs in simple carbonate system for efficient water oxidation. Comprehensive electrochemical studies including cyclic voltammetry, chronoamperometry, chronopotentiometry, impedance spectroscopy, Tafel slope analysis, mass activity, electrochemically active surface area measurements are conducted to comparatively evaluate the performance of simple (FeO_x/HCO₃^?@FTO and annealed (FeO_x/HCO₃^?@FTO₂₅₀, FeO_x/HCO₃^?@FTO₅₀₀) catalysts for oxygen evolution reaction (OER) under employed conditions. The FeO_x/HCO₃^?@FTO₂₅₀ annealed at 250 °C initiates water oxidation at much lower overpotential of 1.52 V vs. RHE with remarkable stability during long-term electrochemical experimentations. In addition to enhanced OER activity as evidence by better onset potential (<1.55 V vs. RHE), lower Tafel slope value (36 mV dec^1?) and negligible charge transfer resistance, the Fe(OH)_x/HCO₃^?@FTO type catalyst presented excellent electroactive nature during long term controlled potential electrolysis experiments where more and more electroactive sites were getting exposed during continuous hours of electrolysis. The catalysts behave as a potential enduring, inexpensive and competent candidate for catalyzing water oxidation reaction when tested under begin conditions.     

价格已经提高到了过去几十年从未见过的水平

从海湾合作委员会(GCC)生活用纸制造业的前景来看,回顾2021第四季度,或许中东、北非和远东地区的每位参与者都对供应链困境感到惊讶,其中大部分原材料供应比以往任何时候都要困难得多。这对供需前景造成了严重的负面影响,由于缺乏供应,需求大幅增加。然而,这个消极因素很可能会产生市场的连锁反应。价格也提高到了过去几十年从未见过的水平。纸张供应商之间的价格战以前从未发生过,值得注意的是,尽管销售量减少,但利润率令人惊讶地满意。从海湾合作委员会(GCC)一些首次公开募股(IPO)公司公布的业绩,就能理解与一年前的同期相比业绩明显向好。     

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed. Hence, this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis. This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment. This can be particularly helpful when dealing with smaller datasets. Furthermore, our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms. This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts. The experimental results validate that our model offers excellent results with a validation accuracy of 92.5% and an F1 measure of 0.92.     

Infrared transmitting glasses based on hafnium fluoride

The synthesis and properties of glasses in the systems HfF₄/1bBaF₂/1bThF₄, HfF₄/1bZrF₄/1bBaF₂/1bThF₄ and HfF₄/1bBaF₂/1bLaF₃ are reported. The glasses have higher densities and better glass formation tendencies than those in which ZrF₄ is the major network forming constituent. Optical measurements indicate that the HfF₄ based glasses transmit longer wavelengths in the IR than ZrF₄ based glasses. It is suggested that any increase in scattering loss in going from ZrF₄ to HfF₄ will be offset by the lower multiphonon losses in the latter.