Study on preoperative treatment of acute Type-A aortic dissection with endotracheal intubation combined with deep analgesia and sedation.

Objectives: To investigate the efficacy and safety of endotracheal intubation combined with deep analgesia and sedation in the prevention of preoperative dissection rupture in acute Standford type A aortic dissection. Methods: This study evaluated the impact of preoperative endotracheal intubation combined with deep analgesia and sedation on acute Stanford Type-A aortic dissection. Conducted at the First Affiliated Hospital of the University of South China's cardiac intensive care unit from June 2018 to December 2021, 134 diagnosed patients participated. They were divided into experimental (n=42) and control (n=92) groups. Data collected included clinical details, biochemical markers, VAS and SAS scores, and preoperative dissection rupture occurrences. Criteria involved acute Stanford Type-A aortic dissection diagnosis and complete data. Exclusions encompassed rupture, vital sign instability after vasoactive drugs, or prolonged coma. Standardized methods were used for sample collection and analysis. The study's design, duration, and location ensured comprehensive evaluation of the intervention's effects on patients. Results: The experimental group showed significantly fewer deaths due to dissection rupture compared to the control group (P < 0.05). Initial VAS and SAS scores (T0) were similar between groups (P > 0.05), indicating good comparability. However, at T1, T2, and T3, analgesia and sedation were significantly better in the experimental group (P < 0.05). By T4, patient numbers were too low in both groups for a significant difference (P > 0.05). Conclusion: Preoperative endotracheal intubation combined with deep analgesia and sedation in patients with acute Stanford Type-A aortic dissection can produce good analgesic and sedative effects, effectively reduce the incidence of preoperative dissection rupture, and create conditions for subsequent surgical treatment of patients.

Application of broken windows theory to identify flow disruptions in neurosurgery procedure.

Addressing flow disruptions (FDs) in neurosurgery requires a multifaceted approach. Strategies like improved communication protocols, minimizing interruptions, improving coordination among team, optimizing operating room layout, and promoting user-centered design can help mitigate the challenges and enhance the overall flow and safety of neurosurgical procedures. Thirty neurosurgery cases were observed at two tertiary care facilities. The data collected were from wheels into the operating room to wheels out from the operating room. Data points were categorized using a human factors taxonomy known as RIPCHORD-TWA (Realizing Improved Patient Care Through Human-Centered Operating Room Design for Threat Window Analysis). Of the 541 total disruptions observed, coordination issues were the most prevalent (26.25%), followed by layout issues (26.06%), issues related to interruption (22.55%), communication (22.37%), equipment issues (2.40%) and usability issues (0.37%) comprised the remainder of the observations. This translated into one disruption every 2.7 min. Instead of focusing exclusively on errors and adverse events, we propose conceptualizing the accumulation of disruptions as "threat windows" to analyze potential threats to the integrity of the care system. This perspective allows for the improved identification of system weaknesses or threats, affording us the ability to address these inefficiencies and intervene before errors and adverse events may occur.

Surgical treatment of spondylolisthesis by oblique lumbar interbody fusion and transpedicular screw fixation: Comparison between conventional double position versus navigation-assisted single lateral position.

BACKGROUND AND OBJECTIVES: Oblique lumbar interbody fusion (OLIF) procedures involve anterior insertion of interbody cage in lateral position. Following OLIF, insertion of pedicle screws and rod system is performed in a prone position (OLIF-con). The location of the cage is important for restoration of lumbar lordosis and indirect decompression. However, inserting the cage at the desired location is difficult without reduction of spondylolisthesis, and reduction after insertion of interbody cage may limit the amount of reduction. Recent introduction of spinal navigation enabled both surgical procedures in one lateral position (OLIF-one). Therefore, reduction of spondylolisthesis can be performed prior to insertion of interbody cage. The objective of this study was to compare the reduction of spondylolisthesis and the placement of cage between OLIF-one and OLIF-con. METHODS: We retrospectively reviewed 72 consecutive patients with spondylolisthesis for this study; 30 patients underwent OLIF-one and 42 underwent OLIF-con. Spinal navigation system was used for OLIF-one. In OLIF-one, the interbody cage was inserted after reducing spondylolisthesis, whereas in OLIF-con, the cage was inserted before reduction. The following parameters were measured on X-rays: pre- and postoperative spondylolisthesis slippage, reduction degree, and the location of the cage in the disc space. RESULTS: Both groups showed significant improvement in back and leg pains (p < .05). Transient motor or sensory changes occurred in three patients after OLIF-con and in two patients after OLIF-one. Pre- and postoperative slips were 26.3±7.7% and 6.6±6.2% in OLIF-one, and 23.1±7.0% and 7.4±5.8% in OLIF-con. The reduction of slippage was 74.4±6.3% after OLIF-one and 65.4±5.7% after OLIF-con, with a significant difference between the two groups (p = .04). The cage was located at 34.2±8.9% after OLIF-one and at 42.8±10.3% after OLIF-con, with a significant difference between the two groups (p = .004). CONCLUSION: Switching the sequence of surgical procedures with OLIF-one facilitated both the reduction of spondylolisthesis and the placement of the cage at the desired location.

Effect of Phacoemulsification on Sub Foveal Choroidal and Central Macular Thickness as measured by Swept Source Optical Coherence Tomography.

Objectives: To study the effect of Phacoemulsification on Sub Foveal Choroidal Thickness (SFCT) and Central Macular Thickness (CMT) as measured by Swept Source Optical Coherence Tomography (OCT). Methods: This experimental study was conducted at Armed Forces Institute of Ophthalmology (AFIO), Rawalpindi from April 2021 to February 2022. One hundred eyes of 100 patients with age related cataract underwent uneventful phacoemulsification surgery. Pre-operative SFCT and CMT was measured and compared with SFCT and CMT at one week, one month and three months after surgery using swept source OCT. Results: Mean age of study population was 56.76±8.31 years. Out of 100 patients, 46 (46%) were males and 54 (54%) were females. Mean pre-operative CMT, one week, one month and three months post-operative CMT was 233.95±9.46 µm, 232.88±8.59 µm, 230.38±10.62 µm and 230.67±7.55 µm respectively. Mean pre-operative SFCT, one week, one month and three months post-operative SFCT was 337.14±8.41 µm, 339.14±9.63 µm, 339.39±11.96 µm and 351.39±9.19 µm respectively. The difference of mean change in CMT from baseline at one week, one month and three months post-operatively was not statistically significant. The difference of mean change in SFCT from baseline at one week and one month post-operatively was not statistically significant. However, the difference of mean change in SFCT from baseline at three months post-operatively was statistically significant (p<0.05). Conclusion: Uneventful phacoemulsification surgery does not have any effect on central macular thickness, however there is a significant increase in subfoveal choroidal thickness at three months after surgery.

Bilateral Anteriorly Displaced Microspherophakia in a Female Child With Marfanoid Habitus.

Microspherophakia is a rare congenital anomaly characterized by an abnormally small and spherical crystalline lens, which can be associated with several systemic syndromes. We present an extremely rare case of bilateral anteriorly displaced microspherophakia in a female child with Marfanoid habitus. The patient displayed phenotypic features resembling Marfan syndrome, including tall stature, muscle hypotonia, dolichostenomelia, and increased arm span than body length. However, unlike Marfan syndrome, Marfanoid habitus is not associated with mutations in the fibrillin-1 gene. The association between microspherophakia and Marfanoid habitus is a unique presentation that has not been reported in the literature. This case report aims to increase awareness of microspherophakia as a possible ocular association of Marfanoid habitus.

Clinical Application of Proximal Arch Cannulation in the Surgical Treatment of Acute Type I Aortic Dissection.

Objective The goal is to determine the best location for inserting a catheter into the aortic arch of patients with a certain type of aortic dissection (DeBakey type I) by analyzing images of the patient's aortic arch before surgery. This analysis will take into account the shape and structure of the patient's aortic arch to find the most optimal location for cannulation. Methods A retrospective analysis was conducted on 100 patients with acute DeBakey type I aortic dissection diagnosed between January 2021 and February 2023, utilizing the Carestream medical imaging software Image Suite V4 (New York, USA). The study included 67 cases that underwent surgery and 33 cases that did not. The study aimed to evaluate the optimal intubation position on the patient's aortic arch by analyzing the true and false lumen classification, true and false lumen area, and hematoma thickness on the patient's aortic arch, as observed in the aortic computed tomography angiography (CTA) conducted upon admission. Results The vascular axis analysis showed a significant difference in the true lumen area among the three regions that were examined (P < 0.001). Zone 1 had a larger true lumen area of 6.40 ± 2.71 cm2 compared to zone 2 with 5.75 ± 2.13 cm2 and zone 3 with 4.85 ± 1.70 cm2, as determined by statistical analysis. In addition, the statistical analysis of hematoma thickness in the three regions where cannulation can be performed revealed a significant difference among the three groups (P = 0.027). Further analysis showed that there was no significant difference between zone 1 and zone 2 (P = 1.000), a significant difference between zone 1 and zone 3 (P < 0.046), and no significant difference between zone 2 and zone 3 (P = 0.080). The difference between zone 1 false lumen thickness of 1.55 ± 0.51 cm and zone 3 false lumen thickness of 1.33 ± 0.55 cm was found to be small. Conclusion Cannulation of the aortic arch is a common strategy used in cardiac surgery. Accurate cannulation is critical to the success of the procedure. The use of CTA provides valuable guidance for the cannulation procedure. A thorough examination of CTA and precise measurement of relevant parameters can help guide the surgeon to determine the optimal cannulation site. The study found that zone 1 of the aortic arch has the largest area and is the most suitable for cannulation, in accordance with the physiological characteristics and surgical practices of a surgeon. Furthermore, cannulation of the aortic arch has been found to be a safe and effective strategy for cannulation. Overall, careful examination of CTA and accurate measurement of relevant parameters can have a significant guiding effect on the cannulation of the aortic arch, which can lead to improved outcomes in cardiac surgery.

A computational classification method of breast cancer images using the VGGNet model.

Cancer is one of the most prevalent diseases worldwide. The most prevalent condition in women when aberrant cells develop out of control is breast cancer. Breast cancer detection and classification are exceedingly difficult tasks. As a result, several computational techniques, including k-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), and genetic algorithms, have been applied in the current computing world for the diagnosis and classification of breast cancer. However, each method has its own limitations to how accurately it can be utilized. A novel convolutional neural network (CNN) model based on the Visual Geometry Group network (VGGNet) was also suggested in this study. The 16 layers in the current VGGNet-16 model lead to overfitting on the training and test data. We, thus, propose the VGGNet-12 model for breast cancer classification. The VGGNet-16 model has the problem of overfitting the breast cancer classification dataset. Based on the overfitting issues in the existing model, this research reduced the number of different layers in the VGGNet-16 model to solve the overfitting problem in this model. Because various models of the VGGNet, such as VGGNet-13 and VGGNet-19, were developed, this study proposed a new version of the VGGNet model, that is, the VGGNet-12 model. The performance of this model is checked using the breast cancer dataset, as compared to the CNN and LeNet models. From the simulation result, it can be seen that the proposed VGGNet-12 model enhances the simulation result as compared to the model used in this study. Overall, the experimental findings indicate that the suggested VGGNet-12 model did well in classifying breast cancer in terms of several characteristics.

Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier.

Cardiovascular disease includes coronary artery diseases (CAD), which include angina and myocardial infarction (commonly known as a heart attack), and coronary heart diseases (CHD), which are marked by the buildup of a waxy material called plaque inside the coronary arteries. Heart attacks are still the main cause of death worldwide, and if not treated right they have the potential to cause major health problems, such as diabetes. If ignored, diabetes can result in a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Machine learning methods can be used to identify and diagnose diabetes and other illnesses. Diabetes and cardiovascular disease both can be diagnosed using several classifier types. Naive Bayes, K-Nearest neighbor (KNN), linear regression, decision trees (DT), and support vector machines (SVM) were among the classifiers employed, although all of these models had poor accuracy. Therefore, due to a lack of significant effort and poor accuracy, new research is required to diagnose diabetes and cardiovascular disease. This study developed an ensemble approach called "Stacking Classifier" in order to improve the performance of integrated flexible individual classifiers and decrease the likelihood of misclassifying a single instance. Naive Bayes, KNN, Linear Discriminant Analysis (LDA), and Decision Tree (DT) are just a few of the classifiers used in this study. As a meta-classifier, Random Forest and SVM are used. The suggested stacking classifier obtains a superior accuracy of 0.9735 percent when compared to current models for diagnosing diabetes, such as Naive Bayes, KNN, DT, and LDA, which are 0.7646 percent, 0.7460 percent, 0.7857 percent, and 0.7735 percent, respectively. Furthermore, for cardiovascular disease, when compared to current models such as KNN, NB, DT, LDA, and SVM, which are 0.8377 percent, 0.8256 percent, 0.8426 percent, 0.8523 percent, and 0.8472 percent, respectively, the suggested stacking classifier performed better and obtained a higher accuracy of 0.8871 percent.

RTF-RCNN: An Architecture for Real-Time Tomato Plant Leaf Diseases Detection in Video Streaming Using Faster-RCNN.

In today's era, vegetables are considered a very important part of many foods. Even though every individual can harvest their vegetables in the home kitchen garden, in vegetable crops, Tomatoes are the most popular and can be used normally in every kind of food item. Tomato plants get affected by various diseases during their growing season, like many other crops. Normally, in tomato plants, 40-60% may be damaged due to leaf diseases in the field if the cultivators do not focus on control measures. In tomato production, these diseases can bring a great loss. Therefore, a proper mechanism is needed for the detection of these problems. Different techniques were proposed by researchers for detecting these plant diseases and these mechanisms are vector machines, artificial neural networks, and Convolutional Neural Network (CNN) models. In earlier times, a technique was used for detecting diseases called the benchmark feature extraction technique. In this area of study for detecting tomato plant diseases, another model was proposed, which was known as the real-time faster region convolutional neural network (RTF-RCNN) model, using both images and real-time video streaming. For the RTF-RCNN, we used different parameters like precision, accuracy, and recall while comparing them with the Alex net and CNN models. Hence the final result shows that the accuracy of the proposed RTF-RCNN is 97.42%, which is higher than the rate of the Alex net and CNN models, which were respectively 96.32% and 92.21%.

QoS-Aware Cost Minimization Strategy for AMI Applications in Smart Grid Using Cloud Computing.

Cloud computing coupled with Internet of Things technology provides a wide range of cloud services such as memory, storage, computational processing, network bandwidth, and database application to the end users on demand over the Internet. More specifically, cloud computing provides efficient services such as "pay as per usage". However, Utility providers in Smart Grid are facing challenges in the design and implementation of such architecture in order to minimize the cost of underlying hardware, software, and network services. In Smart Grid, smart meters generate a large volume of different traffics, due to which efficient utilization of available resources such as buffer, storage, limited processing, and bandwidth is required in a cost-effective manner in the underlying network infrastructure. In such context, this article introduces a QoS-aware Hybrid Queue Scheduling (HQS) model that can be seen over the IoT-based network integrated with cloud environment for different advanced metering infrastructure (AMI) application traffic, which have different QoS levels in the Smart Grid network. The proposed optimization model supports, classifies, and prioritizes the AMI application traffic. The main objective is to reduce the cost of buffer, processing power, and network bandwidth utilized by AMI applications in the cloud environment. For this, we developed a simulation model in the CloudSim simulator that uses a simple mathematical model in order to achieve the objective function. During the simulations, the effects of various numbers of cloudlets on the cost of virtual machine resources such as RAM, CPU processing, and available bandwidth have been investigated in cloud computing. The obtained simulation results exhibited that our proposed model successfully competes with the previous schemes in terms of minimizing the processing, memory, and bandwidth cost by a significant margin. Moreover, the simulation results confirmed that the proposed optimization model behaves as expected and is realistic for AMI application traffic in the Smart Grid network using cloud computing.

A Precise Medical Imaging Approach for Brain MRI Image Classification.

Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.

An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences.

Task scheduling in parallel multiple sequence alignment (MSA) through improved dynamic programming optimization speeds up alignment processing. The increased importance of multiple matching sequences also needs the utilization of parallel processor systems. This dynamic algorithm proposes improved task scheduling in case of parallel MSA. Specifically, the alignment of several tertiary structured proteins is computationally complex than simple word-based MSA. Parallel task processing is computationally more efficient for protein-structured based superposition. The basic condition for the application of dynamic programming is also fulfilled, because the task scheduling problem has multiple possible solutions or options. Search space reduction for speedy processing of this algorithm is carried out through greedy strategy. Performance in terms of better results is ensured through computationally expensive recursive and iterative greedy approaches. Any optimal scheduling schemes show better performance in heterogeneous resources using CPU or GPU.

Multiconstraint-Aware Routing Mechanism for Wireless Body Sensor Networks.

The merger of wireless sensor technologies, pervasive computing, and biomedical engineering has resulted in the emergence of wireless body sensor network (WBSN). WBSNs assist human beings in various monitoring applications such as health-care, entertainment, rehabilitation systems, and sports. Life-critical health-care applications of WBSNs consider both reliability and delay as major Quality of Service (QoS) parameters. In addition to the common limitations and challenges of wireless sensor networks (WSNs), WBSNs pose distinct constraints due to the behavior and chemistry of the human body. The biomedical sensor nodes (BMSNs) adopt multihop communication while reporting the heterogeneous natured physiological parameters to the nearby base station also called local coordinator. Routing in WBSNs becomes a challenging job due to the necessary QoS considerations, overheated in-body BMSNs, and high and dynamic path loss. To the best of our knowledge, none of the existing routing protocols integrate the aforementioned issues in their designs. In this research work, a multiconstraint-aware routing mechanism (modular-based) is proposed which considers the QoS parameters, dynamic and high path loss, and the overheated nodes issue. Two types of network frameworks, with and without relay/forwarder nodes, are being used. The data packets containing physiological parameters of the human body are categorized into delay-constrained, reliability-constrained, critical (both delay- and reliability-constrained), and nonconstrained data packets. NS-2 is being used to carry out the simulations of the proposed mechanism. The simulation results reveal that the proposed mechanism has improved the QoS-aware routing for WBSNs by adopting the proposed multiconstraint-aware strategy.

Efficacy of YAG Laser Embolysis in Retinal Artery Occlusion.

OBJECTIVE: To compare the anatomical and functional success between conventional medical method and Neodymium-Doped Yttrium Aluminum Garnet (Nd:YAG) laser embolysis in retinal artery occlusion. METHODS: This randomized control trial was conducted at Armed Forces Institute of Ophthalmology (AFIO) Rawalpindi from July 2018 to May 2020. A total of 14 eyes of 14 patients were received with fovea involving branch or hemiretinal artery occlusion within 24 hours of onset of symptoms. They were divided randomly in two groups. Initial treatment was given to all cases, and seven eyes received Nd:YAG laser treatment for embolysis. Both groups were analysed for anatomical success (reperfusion) and functional success (defined as improvement in visual acuity to better than 6/60 on Snellen's visual acuity chart from baseline visual acuity). RESULTS: In conventional group, anatomical success was achieved in 2 (28.6%) eyes, while significant visual improvement was seen in 3 (42.8%) eyes. In Nd:YAG laser embolysis group, anatomical success was achieved in 5 (71.4%) eyes, while significant visual improvement was seen in 6 (85.7%) eyes. All eyes which showed functional improvement underwent Nd:YAG laser embolysis within 6 hours of onset of symptoms. CONCLUSIONS: Nd: YAG laser embolysis is more effective in management of fovea threatening retinal artery occlusion, as compared to conventional medical treatment, if performed within six hours of onset of symptoms.

Comparing the Degradation Potential of Copper(II), Iron(II), Iron(III) Oxides, and Their Composite Nanoparticles in a Heterogeneous Photo-Fenton System.

Heterogeneous photo-Fenton systems offer efficient solutions for the treatment of wastewaters in the textile industry. This study investigated the fabrication and structural characterization of novel peculiar-shaped CuIIO, FeIII 2O3, and FeIIO nanoparticles (NPs) compared to the properties of the iron(II)-doped copper ferrite CuII 0.4FeII 0.6FeIII 2O4. The photocatalytic efficiencies of these NPs and the composite of the simple oxides (CuIIO/FeIIO/FeIII 2O3) regarding the degradation of methylene blue (MB) and rhodamine B (RhB) as model dyes were also determined. The catalysts were synthesized via simple co-precipitation and calcination technique. X-ray diffractometry (XRD), scanning electron microscopy (SEM), and diffuse reflectance spectroscopy (DRS) were utilized for structural characterization. The structure of CuIIO was bead-like connected into threads, FeIII 2O3 was rod-like, while FeIIO pallet-like, with average crystallite sizes of 18.9, 36.9, and 37.1 nm, respectively. The highest degradation efficiency was achieved by CuIIO for RhB and by CuII 0.4FeII 0.6FeIII 2O4 for MB. The CuIIO/FeIIO/FeIII 2O3 composite proved to be the second-best catalyst in both cases, with excellent reusability. Hence, these NPs can be successfully applied as heterogeneous photo-Fenton catalysts for the removal of hazardous pollutants. Moreover, the simple metal oxides and the iron(II)-doped copper ferrite displayed a sufficient antibacterial activity against Gram-negative Vibrio fischeri.

Clinical profile and treatment outcomes of patients with rheumatoid arthritis at a tertiary care hospital of Pakistan.

OBJECTIVE: To examine the clinical and laboratory features and to measure treatment outcomes after using different disease-modifying antirheumatic drugs in patients of rheumatoid arthritis. METHODS: The observational study was conducted at the Rheumatology Unit of Federal Government Polyclinic Hospital, Islamabad, Pakistan,from March 15, 2014,to September 14, 2015, and comprised rheumatoid arthritis patients of either gender diagnosed according to the American College of Rheumatology criteria.Disease activity score-28 and a thorough examination of the joints were employed to assess disease activity. Data was analysed using SPSS 20. RESULTS: Of the 63 patients, 18(28.6%) were males and 45(71.4%) were females. The overall mean age was 43.09±13.03 years and mean duration of disease was 5.05±5.58 years. Seropositive disease was noted in 58(92.1%) patients and they had a higher level of erythrocyte sedimentation rate. Mean disease activity score-28 score at baseline was 5.52±0.99. At the end of 6 months, 44(69.8%) patients were in remission, 18(28.6%) had low disease activity and 1(1.6%) had moderate disease activity. The mean DAS score reduced to 3.11 0.77 at 6 months. Overall, 28(44.4%) patients had joint deformities. CONCLUSION: Females had a higher incidence of rheumatoid arthritis compared to males, and, overall, there was a high prevalence of joint deformities.

Synthesis, Characterization and Application of Iron(II) Doped Copper Ferrites (CuII(x)FeII(1-x)FeIII2O4) as Novel Heterogeneous Photo-Fenton Catalysts.

The heterogeneous photo-Fenton type system has huge fame in the field of wastewater treatment due to its reusability and appreciable photoactivity within a wide pH range. This research investigates the synthesis and characterization of iron(II) doped copper ferrite (CuII(x)FeII(1-x)FeIII2O4 nanoparticles (NPs) and their photocatalytic applications for the degradation of methylene blue (MB) as a model dye. The NPs were prepared via simple co-precipitation technique and calcination. The NPs were characterized by using Raman spectroscopy, X-ray diffractometry (XRD), scanning electron microscopy (SEM), and diffuse reflectance spectroscopy (DRS). SEM reveals the structural change from the spherical-like particles into needle-like fine particles as the consequence of the increasing ratio of copper(II) in the ferrites, accompanied by the decrease of the optical band-gap energies from 2.02 to 1.25 eV. The three major determinants of heterogeneous photo-Fenton system, namely NPs concentration, hydrogen peroxide concentration and pH, on the photocatalytic degradation of MB were studied. The reusability of NPs was found to be continuously increasing during 4 cycles. It was concluded that iron(II) doped copper ferrites, due to their favorable band-gap energies and peculiar structures, exhibit a strong potential for photocatalytic-degradation of dyes, for example, MB.

Optimal Operation of the Hybrid Electricity Generation System Using Multiverse Optimization Algorithm.

The ongoing load-shedding and energy crises due to mismanagement of energy produced by different sources in Pakistan and increasing dependency on those sources which produce energy using expensive fuels have contributed to rise in load shedding and price of energy per kilo watt hour. In this paper, we have presented the linear programming model of 95 energy production systems in Pakistan. An improved multiverse optimizer is implemented to generate a dataset of 100000 different solutions, which are suggesting to fulfill the overall demand of energy in the country ranging from 9587 MW to 27208 MW. We found that, if some of the power-generating systems are down due to some technical problems, still we can get our demand by following another solution from the dataset, which is partially utilizing the particular faulty power system. According to different case studies, taken in the present study, based on the reports about the electricity short falls been published in news from time to time, we have presented our solutions, respectively, for each case. It is interesting to note that it is easy to reduce the load shedding in the country, by following the solutions presented in our dataset. Graphical analysis is presented to further elaborate our findings. By comparing our results with state-of-the-art algorithms, it is interesting to note that an improved multiverse optimizer is better in getting solutions with lower power generation costs.

Efficacy of brow suspension with autogenous fascia lata in simple congenital ptosis.

OBJECTIVE: To assess the mean change in interpalpebral fissure height and marginal reflex distance after brow suspension with autogenous fascia lata sling in patients of ptosis. METHODS: This was a Quasi experimental study conducted at Department of Ophthalmology, Mayo Hospital, King Edwards Medical University Lahore, from Jan 2013 to June 2016. Included were the patients who had unilateral or bilateral ptosis with poor levator function (< 5 mm). Informed consent was obtained from all patients after explaining about the research project. Patients were admitted in ward and all of them underwent surgery by a single surgical team. The surgical procedure was performed in supine position under general anesthesia in children and uncooperative patients. Patients were followed at week 4, 8, 12 and 24 to observe vertical interpalpebral fissure height and marginal reflex distance. RESULTS: The mean age of the patients was 9.03 ± 5.26 years. The mean Inter palpebral fissure height (IPFH) was 4.40±0.91 mm and mean MRD was 0.50 ± 1.00 mm before surgery while after surgery it was 7.41±0.76 mm and 3.10 ± 1.50 mm respectively at 04 weeks. The mean IPFH and MRD at 24 weeks postoperatively were 8.43±0.98 mm and 3.60 + 1.50 mm respectively. The mean change in IPFH and MRD at 24th week, were 3.90 ± 0.34 mm and 3.50 ± 1.00 mm. CONCLUSION: Brow suspension with fascia lata sling is safe and effective technique for correction of ptosis with poor levator function.