Hydrogenolysis of Glycerol over γ-Al2O3-Supported Iridium Catalyst

In recent years, much attention has been focused on the hydrogenolysis of biodiesel derived glycerol to other high value products for the sustainable development and efficient valorization strategies. In the present work, alumina-supported Ir catalyst was prepared by the incipient wetness impregnation method and tested in the glycerol hydrogenolysis reaction. The synthesized catalyst was characterized by neutron activation analysis, N2 physisorption, and H2 chemisorption techniques. The experiments standard conditions were 150 mL feed volume, 0.3 g catalyst, 1500 rpm stirring speed, and 5 wt% glycerol aqueous solution for 4 h. The effects of catalyst amount, temperature, hydrogen pressure, stirring speed, and solution pH on glycerol conversion and selectivity of the principal products obtained were also investigated. The glycerol conversion and the 1,2-propanediol selectivity varied from 4.9% to 22% and from 23.8% to 70.3%, respectively. It was found that the selectivity of 1,2-propanediol increased significantly with the increased alkalinity of the reaction medium

Automatic Software Test Data Generation for Spanning Sets Coverage Using Genetic Algorithms

Software testing takes a considerable amount of time and resources spent on producing software. Therefore, it would be useful to have ways to reduce the cost of software testing. The new concepts of spanning sets of entities suggested by Marré and Bertolino are useful for reducing the cost of testing. In fact, to reduce the testing effort, the generation of test data can be targeted to cover the entities in the spanning set, rather than all the entities in the tested program. Marré and Bertolino presented an algorithm based on the subsumption relation between entities to find spanning sets for a family of control flow and data flow-based test coverage criteria. This paper presents a new general technique for the automatic test data generation for spanning sets coverage. The proposed technique applies to the algorithm proposed recently by Marré and Bertolino to automatically generate the spanning sets of program entities that satisfy a wide range of control flow and data flow-based test coverage criteria. Then, it uses a genetic algorithm to automatically generate sets of test data to cover these spanning sets. The proposed technique employed the concepts of spanning sets to limit the number of test cases, guide the test case selection, overcome the problem of the redundant test cases and automate the test path generation

Binary liquid film condensation from water-ammonia vapors mixture flowing downward along a parallel plate condenser

The ammonia-water film condensation is used as an efficient working fluid in industrial applications such as refrigeration, plate condenser and evaporator, absorber/generator heat exchange, air-conditioning, heat pumps and separation processes. The present work focuses on a numerical investigation of water-ammonia condensation on a falling binary liquid film inside a parallel plate condenser by mixed convection. The parallel plate condenser is composed by two parallel vertical plates. One of the plates is wetted by liquidfilm and cooled by the thermal flux cooling while the other plate is isothermal and dry. Parametric computations were performed to investigate the effects of the inlet parameters of gas, the properties of the binary liquid film as well as the thermal flux cooling on the combined mass and heat transfer and on the efficiency of the parallel plate condenser. The results show that an increase in the inlet vapor of ammonia as well as of vapor water enhances the efficiency of the parallel plate condenser. It is shown also that an enhancement of efficiency of the parallel plate condenser has been recorded when the thermal flux cooling and inlet liquid flow rate is elevated. Whereas the increase of the inlet liquid concentration of ammonia inhibits the efficiency of the parallel plate condenser

First Case Report of Primary Testicular Diffuse Large B-Cell Lymphoma from the Western Region of Saudi Arabia

Primary testicular lymphoma (PTL) represents 1-2% of all types of non-Hodgkin lymphomas (NHLs) and 1-10% of testicular neoplasms. Up to the best of my knowledge, this is the first case of PTL of the diffuse large B-cell lymphoma (DLBCL) in a 60-year-old man presented with a painless mass in the left testis as revealed by physical examination in a tertiary care hospital in Al-Madinah Al-Munwarah in the western region of the Kingdom of Saudi Arabia (KSA). Radiological examination revealed a large well-defined heterogeneous predominantly hypo-echoic mass with increased vascularity in the upper portion of the testis. On the other hand, histopathological examination revealed a tumor involving the whole left testis, which was large (measuring 6 3.5  3.3 cm), solid and dark red with focal areas of hemorrhage and epididymal infiltration. Immunohistochemistry showed positivity of leucocyte common antigen (LCA), pan B-cell marker (CD20) and negativity of pan T-cell marker (CD3). Other immunohistochemical markers such as CD10, placental alkaline phosphatase (PLAP), cytokeratin, vimentin, desmin and S100 protein were also negative. However, there was a marked expression of Ki67 and Bcl2 markers. Accordingly, the diagnosis of DLBCL was established. The tumor was classified as stage I according to the Ann Arbor system. The case was treated by orchiectomy followed by prophylactic anthracycline-based chemotherapy and irradiation of the contralateral testis and central nervous system

Modified arithmetic optimization algorithm with Deep Learning based data analytics for depression detection

Depression detection is the procedure of recognizing the individuals exhibiting depression symptoms, which is a mental illness that is characterized by hopelessness, feelings of sadness, persistence and loss of interest in day-to-day activities. Depression detection in Social Networking Sites (SNS) is a challenging task due to the huge volume of data and its complicated variations. However, it is feasible to detect the depression of the individuals by examining the user-generated content utilizing Deep Learning (DL), Machine Learning (ML) and Natural Language Processing (NLP) approaches. These techniques demonstrate optimum outcomes in early and accurate detection of depression, which in turn can support in enhancing the treatment outcomes and avoid more complications related to depression. In order to provide more insights, both ML and DL approaches possibly offer unique features. These features support the evaluation of unique patterns that are hidden in online interactions and address them to expose the mental state amongst the SNS users. In the current study, we develop the Modified Arithmetic Optimization Algorithm with Deep Learning for Depression Detection in Twitter Data (MAOADL-DDTD) technique. The presented MAOADL-DDTD technique focuses on identification and classification of the depression sentiments in Twitter data. In the presented MAOADL-DDTD technique, the noise in the tweets is pre-processed in different ways. In addition to this, the Glove word embedding technique is used to extract the features from the preprocessed data. For depression detection, the Sparse Autoencoder (SAE) model is applied. The MAOA is used for optimum hyperparameter tuning of the SAE approach so as to optimize the performance of the SAE model, which helps in accomplishing better detection performance. The MAOADL-DDTD algorithm is simulated using the benchmark database and experimentally validated. The experimental values of the MAOADL-DDTD methodology establish its promising performance over another recent state-of-the-art approaches

Review of the Recent Advances in Electrospun Nanofibers Applications in Water Purification

Recently, nanofibers have come to be considered one of the sustainable routes with enormous applicability in different fields, such as wastewater treatment. Electrospun nanofibers can be fabricated from various materials, such as synthetic and natural polymers, and contribute to the synthesis of novel nanomaterials and nanocomposites. Therefore, they have promising properties, such as an interconnected porous structure, light weight, high porosity, and large surface area, and are easily modified with other polymeric materials or nanomaterials to enhance their suitability for specific applications. As such, this review surveys recent progress made in the use of electrospun nanofibers to purify polluted water, wherein the distinctive characteristics of this type of nanofiber are essential when using them to remove organic and inorganic pollutants from wastewater, as well as for oil/water (O/W) separation

Recent Progress and Potential Biomedical Applications of Electrospun Nanofibers in Regeneration of Tissues and Organs

Electrospun techniques are promising and flexible technologies to fabricate ultrafine fiber/nanofiber materials from diverse materials with unique characteristics under optimum conditions. These fabricated fibers/nanofibers via electrospinning can be easily assembled into several shapes of three-dimensional (3D) structures and can be combined with other nanomaterials. Therefore, electrospun nanofibers, with their structural and functional advantages, have gained considerable attention from scientific communities as suitable candidates in biomedical fields, such as the regeneration of tissues and organs, where they can mimic the network structure of collagen fiber in its natural extracellular matrix(es). Due to these special features, electrospinning has been revolutionized as a successful technique to fabricate such nanomaterials from polymer media. Therefore, this review reports on recent progress in electrospun nanofibers and their applications in various biomedical fields, such as bone cell proliferation, nerve regeneration, and vascular tissue, and skin tissue, engineering. The functionalization of the fabricated electrospun nanofibers with different materials furnishes them with promising properties to enhance their employment in various fields of biomedical applications. Finally, we highlight the challenges and outlooks to improve and enhance the application of electrospun nanofibers in these applications

Effectiveness of Shock Wave Therapy versus Intra-Articular Corticosteroid Injection in Diabetic Frozen Shoulder Patients’ Management: Randomized Controlled Trial

Frozen shoulder is a major musculoskeletal illness in diabetic patients. This study aimed to compare the effectiveness of shock wave and corticosteroid injection in the management of diabetic frozen shoulder patients. Fifty subjects with diabetic frozen shoulder were divided randomly into group A (the intra-articular corticosteroid injection group) and group B that received 12 sessions of shock wave therapy, while each patient in both groups received the traditional physiotherapy program. The level of pain and disability, the range of motion, as well as the glucose triad were evaluated before patient assignment to each group, during the study and at the end of the study. Compared to the pretreatment evaluations there were significant improvements of shoulder pain and disability and in shoulder flexion and abduction range of motion in both groups (p < 0.05). The shock wave group revealed a more significant improvement the intra-articular corticosteroid injection group, where p was 0.001 for shoulder pain and disability and shoulder flexion and abduction. Regarding the effect of both interventions on the glucose triad, there were significant improvements in glucose control with group B, where p was 0.001. Shock waves provide a more effective and safer treatment modality for diabetic frozen shoulder treatment than corticosteroid intra-articular injection

New analysis of VSC-based modular multilevel DC-DC converter with low interfacing inductor for hybrid LCC/VSC HVDC network interconnections

The integration of multiterminal hybrid HVDC grids connecting LCC- and VSC-based networks faces several technical challenges such as DC fault isolation, ensuring multi-vendor interoperability, managing high DC voltage levels, and facilitating high-speed power reversal without interruptions. The two-stage DC-DC converter emerges as a key solution to address these challenges. By implementing the modular multilevel converter (MMC) structure, the converter's basic topology includes half-bridge sub-modules on the VSC side and full-bridge sub-modules on the LCC side. However, while this topology has been discussed in the literature, its connection to an LCC-based network with controlled current magnitude lacks detailed analysis regarding operational challenges, control strategies under various scenarios, and design considerations. This paper fills this gap by providing comprehensive mathematical analysis, design insights, and control strategies for the modular DC-DC converter to regulate DC voltage on the LCC-HVDC side. Additionally, the proposed control scheme minimizes the interfacing inductor between the two bridges, ensuring uninterrupted power flow during reversal and effective handling of DC faults. Validation through Control-Hardware-in-the-Loop testing across diverse operational and fault scenarios, along with a comparative analysis of different converters, further strengthens the findings

Fabrication and Characterization of Effective Biochar Biosorbent Derived from Agricultural Waste to Remove Cationic Dyes from Wastewater

The main aim of this work is to treat sugarcane bagasse agricultural waste and prepare an efficient, promising, and eco-friendly adsorbent material. Biochar is an example of such a material, and it is an extremely versatile and eco-friendly biosorbent to treat wastewater. Crystal violet (CV)-dye and methylene blue (MB)-dye species are examples of serious organic pollutants. Herein, biochar was prepared firstly from sugarcane bagasse (SCB), and then a biochar biosorbent was synthesized through pyrolysis and surface activation with NaOH. SEM, TEM, FTIR, Raman, surface area, XRD, and EDX were used to characterize the investigated materials. The reuse of such waste materials is considered eco-friendly in nature. After that, the adsorption of MB and CV-species from synthetically prepared wastewater using treated biochar was investigated under various conditions. To demonstrate the study’s effectiveness, it was attempted to achieve optimum effectiveness at an optimum level by working with time, adsorbent dose, dye concentration, NaCl, pH, and temperature. The number of adsorbed dyes reduced as the dye concentrations increased and marginally decreased with NaCl but increased with the adsorbent dosage, pH, and temperature of the solution increased. Furthermore, it climbed for around 15 min before reaching equilibrium, indicating that all pores were almost full. Under the optimum condition, the removal perecentages of both MB and CV-dyes were ≥98%. The obtained equilibrium data was represented by Langmuir and Freundlich isotherm models. Additionally, the thermodynamic parameters were examined at various temperatures. The results illustrated that the Langmuir isotherm was utilized to explain the experimental adsorption processes with maximum adsorption capacities of MB and CV-dyes were 114.42 and 99.50 mgg$_{−1}$, respectively. The kinetic data were estimated by pseudo-first and pseudo-second-order equations. The best correlation coefficients of the investigated adsorption processes were described by the pseudo-second-order kinetic model. Finally, the data obtained were compared with some works published during the last four years