Techno-economic and environmental analysis of hybrid energy system for industrial sector of Pakistan.

The industrial sector of Pakistan is currently facing severe load-shedding, which ultimately affects its unit production. The greater dependency on conventional energy resources (Thermal, Nuclear, etc.) results in higher production costs and environmental pollution. A sustainable, cost-effective, and environment-friendly solution can help the industrial growth of Pakistan. This article proposes an optimal hybrid energy system (HES) for the industrial sector of Pakistan to overcome the mentioned challenges. The proposed HES is developed in HOMER Pro. Three different energy cases (Case I: Existing energy system including a utility grid and diesel generator, Case II: On-grid Biogas system, and Case III: On-grid PV system with batteries) are considered for the Gourmet food Industry in the Sundar Industrial estate, Pakistan. The Load profile of the selected site was calculated through on-site visits and data provided by the designated utility grid feeder. The analysis shows that Case III is more effective than other cases, indicating reduced Net Present Cost (NPC), Cost of Energy (COE), and Operating Cost (OC) to $ 19.2 million, $0.034/kWh, and $ 573,371/year respectively. Moreover, the On-grid PV system with batteries (Case III) provides an environmentally friendly solution by reducing 63.82% [Formula: see text] by and 62.22% [Formula: see text]. Comparing the sensitivity analysis for various grid sell-back prices ($0/kWh, $0.043/kWh, $0.061/kWh, and $0.09/kWh), Case III is more cost-effective than Case II. The revenue generation in Case III is $128,499.41/yr, considering the supply of excess electricity into nearby small industrial loads at $0.065/kWh, this indicates that installing optimal HES in industries will not only help in overall cost reduction but also support in mitigating environmental pollution and load shedding.

Heart failure survival prediction using novel transfer learning based probabilistic features.

Heart failure is a complex cardiovascular condition characterized by the heart's inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims to develop a robust survival prediction model for heart failure patients using advanced machine learning techniques. We analyzed data from 299 hospitalized heart failure patients, addressing the issue of imbalanced data with the Synthetic Minority Oversampling (SMOTE) method. Additionally, we proposed a novel transfer learning-based feature engineering approach that generates a new probabilistic feature set from patient data using ensemble trees. Nine fine-tuned machine learning models are built and compared to evaluate performance in patient survival prediction. Our novel transfer learning mechanism applied to the random forest model outperformed other models and state-of-the-art studies, achieving a remarkable accuracy of 0.975. All models underwent evaluation using 10-fold cross-validation and tuning through hyperparameter optimization. The findings of this study have the potential to advance the field of cardiovascular medicine by providing more accurate and personalized prognostic assessments for individuals with heart failure.

Advancing crop classification in smallholder agriculture: A multifaceted approach combining frequency-domain image co-registration, transformer-based parcel segmentation, and Bi-LSTM for crop classification.

Agricultural Remote Sensing has the potential to enhance agricultural monitoring in smallholder economies to mitigate losses. However, its widespread adoption faces challenges, such as diminishing farm sizes, lack of reliable data-sets and high cost related to commercial satellite imagery. This research focuses on opportunities, practices and novel approaches for effective utilization of remote sensing in agriculture applications for smallholder economies. The work entails insights from experiments using datasets representative of major crops during different growing seasons. We propose an optimized solution for addressing challenges associated with remote sensing-based crop mapping in smallholder agriculture farms. Open source tools and data are used for inter and intra-sensor image registration, with a root mean square error of 0.3 or less. We also propose and emphasize on the use of delineated vegetation parcels through Segment Anything Model for Geospatial (SAM-GEOs). Furthermore a Bidirectional-Long Short-Term Memory-based (Bi-LSTM) deep learning model is developed and trained for crop classification, achieving results with accuracy of more than 94% and 96% for validation sets of two data sets collected in the field, during 2 growing seasons.

Genetic algorithm optimization of heliostat field layout for the design of a central receiver solar thermal power plant.

The heliostat field layout in a central receiver solar thermal power plant has significant optical losses that can ultimately affect the overall output power of the plant. In this paper, an optimized heliostat field layout based on annual efficiency and power of 50 MW for the local coordinates of Quetta, Pakistan, is proposed. The performance of two different heliostat field layouts such as radial staggered and Fermat's spiral distribution are evaluated and different design points in a year are considered for the analysis. The field layouts are then optimized using a rejection sampling based Genetic Algorithm (GA). It considers the output power and mean overall efficiency for vernal equinox, summer solstice, autumnal equinox, and winter solstice as objective functions. The GA optimizes the heliostat field parameters, namely, security distance (DS), tower height (TH), heliostat width to length ratio (WR), and the length of heliostats (LH). The study system was developed in MATLAB for validation. It was observed that for the radial staggered layout, the number of heliostats decreased by 364 and the efficiency was improved by 8.52 % using GA optimization relative to unoptimized results field layout. The annual efficiency for Fermat's spiral configuration was improved by 14.62 % and correspondingly, the number of heliostats decreased by 434.

Chemical profile and in vivo anti-hyperlipidaemic activity of chloroform fraction of Zygophyllum indicum in Triton X-100 induced hyperlipidaemic rats.

Anti-hyperlipidaemic effect of chloroform fraction of aerial parts of Zygophyllum indicum (Fagonia indica Burm.f.) was studied in rats. Adult Wistar albino rats were distributed into five groups. Rats of all groups except group I were given an intraperitoneal injection (Triton X-100) to induce hyperlipidaemia. Groups (I and II) served as normal and hyperlipidaemic control groups respectively. Group III and group IV were administered with 250 and 500 mg/kg chloroform fraction of the plant respectively after 18 h of inducing hyperlipidaemia. Group V was given 10 mg/kg of the standard atorvastatin. Chloroform fraction had significant (p < 0.05) hypolipidaemic effects on lipid profile and biochemical parameters with a protective effect on the liver in comparison to group II. F. indica with hypolipidaemic effect is useful in the management of hyperlipidaemia. Chloroform fraction with its constituents can be used as an antihyperlipidaemic supplement in developing countries for the development of novel therapeutic agents.

Human tracking robotic camera based on image processing for live streaming of conferences and seminars.

There are numerous scenarios where the photographer is in difficulty and unable to capture or shoot video as required. This could be due to several factors such as limited space, decreased visibility, and an obstacle in the way. Therefore, this project implements the idea to capture and shoot video of the desired subject through an automatically controlled robotic camera with no need for a photographic bloke. The system comprises functions such as detection, tracking, live streaming, and video/audio recording along with the features of Radio-Frequency-Identification (RFID). Therefore, this robotic camera will detect the desired subject, track and focus it with the help of its position driven through movable motors sensing the RFID tag in case the object is non-stationary. The video/audio will be recorded on a computer along with the live streaming available on an Android-based device. The Viola-Jones algorithm of the image processing technique is used to detect the particular subject features and C for accessing the movable camera protocols. The RFID transmitter and receiver are used to sense the RFID card and serve the purpose to track the subject using the algorithms of image processing, with the advantage of ignoring other obstacles between the camera and the detected subject. Thus, adding a novel functionality to the existing systems, that lacks the feature of focusing the camera on the subject, when an obstacle is detected in between. The live streaming is achieved wirelessly through an open-source platform X-operating system, Apache, MySQL, Php, Perl (XAMPP). The idea is verified through concluded arrangements in self-made scenarios in response to the speed, distance, light, and background noise of the detected subject, which delivered encouraging results. Therefore, the designed system can be used for live conferences, seminars, and other multimedia-required arrangements.

Three-Dimensional Modeling of the Optical Switch Based on Guided-Mode Resonances in Photonic Crystals.

Optical switching is an essential part of photonic integrated circuits and the focus of research at the moment. In this research, an optical switch design working on the phenomenon of guided-mode resonances in a 3D photonic-crystal-based structure is reported. The optical-switching mechanism is studied in a dielectric slab-waveguide-based structure operating in the near-infrared range in a telecom window of 1.55 µm. The mechanism is investigated via the interference of two signals, i.e., the data signal and the control signal. The data signal is coupled into the optical structure and filtered utilizing guided-mode resonance, whereas the control signal is index-guided in the optical structure. The amplification or de-amplification of the data signal is controlled by tuning the spectral properties of the optical sources and structural parameters of the device. The parameters are optimized first using a single-cell model with periodic boundary conditions and later in a finite 3D-FDTD model of the device. The numerical design is computed in an open-source Finite Difference Time Domain simulation platform. Optical amplification in the range of 13.75% is achieved in the data signal with a decrease in the linewidth up to 0.0079 µm, achieving a quality factor of 114.58. The proposed device presents great potential in the field of photonic integrated circuits, biomedical technology, and programmable photonics.

ECG classification using 1-D convolutional deep residual neural network.

An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier's performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.

Plasmonic Perfect Absorber Utilizing Polyhexamethylene Biguanide Polymer for Carbon Dioxide Gas Sensing Application.

In this paper a perfect absorber with a photonic crystal cavity (PhC-cavity) is numerically investigated for carbon dioxide (CO2) gas sensing application. Metallic structures in the form of silver are introduced for harnessing plasmonic effects to achieve perfect absorption. The sensor comprises a PhC-cavity, silver (Ag) stripes, and a host functional material-Polyhexamethylene biguanide polymer-deposited on the surface of the sensor. The PhC-cavity is implemented within the middle of the cell, helping to penetrate the EM waves into the sublayers of the structure. Therefore, corresponding to the concentration of the CO2 gas, as it increases, the refractive index of the host material decreases, causing a blue shift in the resonant wavelength and vice versa of the device. The sensor is used for the detection of 0-524 parts per million (ppm) concentration of the CO2 gas, with a maximum sensitivity of 17.32 pm (pico meter)/ppm achieved for a concentration of 366 ppm with a figure of merit (FOM) of 2.9 RIU-1. The four-layer device presents a straightforward and compact design that can be adopted in various sensing applications by using suitable host functional materials.

Establishment of Transcriptional Gene Silencing Targeting the Promoter Regions of GFP, PDS, and PSY Genes in Cotton using Virus-Induced Gene Silencing.

Virus-induced gene silencing (VIGS) by deploying viral-based vectors such as tobacco rattle virus (TRV) is a homology-based gene silencing technique in post-transcriptional gene silencing (PTGS) and transcriptional gene silencing (TGS) to validate the function of particular genes. The study presented here showed the induction of DNA methylation in the promoter regions of three phenotypic marker genes in different cotton accessions, including two endogenous genes such as phytoene desaturase (PDS) and phytoene synthase (PSY), and an exogenous gene, such as green fluorescent protein (GFP). First, DNA methylation was established in transgenic GFP cotton where methylation persisted up to S3 generation. Afterward, the promoter of PSY was targeted following the same conditions. Significant silencing of PSY was observed and methylation of the promoter was found up to S2 generation in red leaf cotton as detected in GFP cotton. Silencing of PDS resulted in a photobleaching phenotype; interestingly, the strength of this phenotype was diverse within the plants and was not observed in the next generation. Bisulfite sequencing results showed methylation percentage of the cytosine residues was high at CG and CHG sites of the targeted promoter sequences in the silenced plants. The findings of this paper suggest that TRV-based vector system can be used to monitor DNA methylation for both exogenous and endogenous gene levels in cotton and offer a very useful tool for plant epigenetic modification.

Transcriptome diversity assessment of Gossypium arboreum (FDH228) leaves under control, drought and whitefly infestation using PacBio long reads.

Alternative splicing (AS) and alternative polyadenylation (APA) are common mechanisms in eukaryotes to increase the complexity of transcriptomes and subsequently proteomes. Analysis of long reads transcriptomics data can result in the discovery of novel transcripts, splice sites, AS or APA events. Gossypium arboreum is an important cultivated cotton species and a putative contributor of the A sub-genome to the modern tetraploid cotton; and inherently tolerant to several biotic and abiotic stresses. Specifically, its variety 'FDH228' is considered to be an important resistance source. In this study, we sequenced the G. arboreum (var. FDH228) transcriptome using PacBio IsoSeq and illumina short read sequencing under three different conditions i.e. untreated/healthy, treated with biotic stress through whitefly infestation, and treated with abiotic stress via water deprivation, for the discovery and surveying of canonical and non-canonical AS, APA and transcript fusion events. We were able to obtain 15,419 unique transcripts from all samples representing 11,343 genes, out of which 10,832 were annotated and 520 were novel with respect to the published reference genome. These transcripts were grouped into different structural categories including 60 Antisense, 11,959 having a full-splice match, 999 with incomplete-splice match, 30 fusion transcripts, 177 genic, 479 intergenic, 771 novels in the catalog, and 944 Novel but not found in the catalog. Subsequently, randomly selected candidate transcripts were experimentally validated using qRT-PCR. Our comprehensive identification of canonical and non-canonical splicing events, and novel and fusion transcripts aids in the understanding of the resistance mechanisms for this specific germplasm.

Plasmonic Refractive Index and Temperature Sensor Based on Graphene and LiNbO3.

A high-efficiency dual-purpose plasmonic perfect absorber sensor based on LiNbO3 and graphene layers was investigated in this paper for the refractive index and thermal sensing. The sensor design was kept simple for easy fabrication, comprising a LiNbO3 substrate with a quartz layer, thin layer of graphene, four gold nanorods, and a nanocavity in each unit cell. The nanocavity is located in the middle of the cell to facilitate the penetration of EM energy to the subsurface layers. The proposed sensor design achieved an output response of 99.9% reflection, which was easy to detect without having any specialized conditions for operability. The performance of the device was numerically investigated for the biomedical refractive index range of 1.33 to 1.40, yielding a sensitivity value of 981 nm/RIU with a figure-of-merit of 61.31 RIU-1. By including an additional polydimethylsiloxane polymer functional layer on the top, the device was also tested as a thermal sensor, which yielded a sensitivity level of -0.23 nm/°C.

Prediction of Surface Roughness as a Function of Temperature for SiO2 Thin-Film in PECVD Process.

An analytical model to predict the surface roughness for the plasma-enhanced chemical vapor deposition (PECVD) process over a large range of temperature values is still nonexistent. By using an existing prediction model, the surface roughness can directly be calculated instead of repeating the experimental processes, which can largely save time and resources. This research work focuses on the investigation and analytical modeling of surface roughness of SiO2 deposition using the PECVD process for almost the whole range of operating temperatures, i.e., 80 to 450 °C. The proposed model is based on experimental data of surface roughness against different temperature conditions in the PECVD process measured using atomic force microscopy (AFM). The quality of these SiO2 layers was studied against an isolation layer in a microelectromechanical system (MEMS) for light steering applications. The analytical model employs different mathematical approaches such as linear and cubic regressions over the measured values to develop a prediction model for the whole operating temperature range of the PECVD process. The proposed prediction model is validated by calculating the percent match of the analytical model with experimental data for different temperature ranges, counting the correlations and error bars.

Dry ripe fruit of Aegle marmelos as anti-ulcer agent against ethanol induced gastric mucosal injury.

Aegle marmelos is cost-effective valuable South Asian tree. The folklore data reported its wide range pharmacological effects. In spite of vast reported work on various parts, the dry ripe fruit extract has not yet been studied for gastric ulcers. Present study is planned to investigate its potential protective effects against ethanol-induced gastric injury in rats. In current study the gastro protective effect of ethanolic crude extract of A. marmelos dried ripe fruit at 200, 400 and 800mg/kg body weight were studied in albino rats. Ranitidine used as standard drug (50mg/kg body weight). Absolute ethanol increase the degree of ulceration (UI) in rats while a significant improvement in the level of inhibition against ulceration was observed in test and standard groups as compare to control. Pre-fed test drug exhibited a significant reduction in the sore area (UI), accelerate % age protection and increased of gastric content in dose dependence manner. Test drug at 800mg/kg dose showed marked deduction in mean UI 3.0, significant increase in protection 83% with pH 7.3 (p<0.01). Standard drug exhibited 3.25 UI, 81% protection with pH 7.1. In conclusion, it was found that dry ripe fruit of A. marmelos possesses a significant anti-ulcer effect in rats.

Assessing the impact of climate conditions on the distribution of mosquito species in Qatar.

Qatar is a peninsular country with predominantly hot and humid weather, with 88% of the total population being immigrants. As such, it leaves the country liable to the introduction and dissemination of vector-borne diseases, in part due to the presence of native arthropod vectors. Qatar's weather is expected to become warmer with the changing climatic conditions across the globe. Environmental factors such as humidity and temperature contribute to the breeding and distribution of different types of mosquito species in a given region. If proper and timely precautions are not taken, a high rate of particular mosquito species can result in the transmission of various vector-borne diseases. In this study, we analyzed the environmental impact on the probability of occurrence of different mosquito species collected from several different sites in Qatar. The Naive Bayes model was used to calculate the posterior probability for various mosquito species. Further, the resulting Naive Bayes predictions were used to define the favorable environmental circumstances for identified mosquito species. The findings of this study will help in the planning and implementation of an active surveillance system and preventive measures to curb the spread of mosquitoes in Qatar.

Multistage Forward Path Regenerative Genetic Algorithm for Brain Magnetic Resonant Imaging Registration.

In image registration, the search space used to compute the optimal transformation between the images depends on the group of pixels in the vicinity. Favorable results can be achieved by significantly increasing the number of neighboring pixels in the search space; however, this strategy increases the computational load, thus making it challenging to realize the most desirable solution in a reasonable amount of time. To address the mentioned problem, the genetic algorithm is used to find the optimum solution and the solution lies in finding the best chromosomes. In rigid image registration problem, chromosomes contain a set of three parameters, x-translation, y-translation, and rotation. The genetic algorithm iteratively improves chromosomes from generation to generation and selects the best one having the best fittest value. Chromosomes with high fitness value are the ones with an optimal solution where the template image best aligns reference image. Fitness function in the genetic algorithm for image registration problem uses similarity measure index measure to find the amount of similarity between two images. The best fittest value is the one with a high similarity measure that shows the best-aligned template and reference image. Here we used the structural similarity index measure in fitness function that helps in evaluating the best chromosome, even for the compressed images with low quality, intensity nonuniformity (INU), and noise degradation. Building on the genetic algorithm, we propose a novel approach called multistage forward path regenerative genetic algorithm (MFRGA), abbreviated as MFRGA, with reducing search space at each stage. Compared with the single stage of genetic algorithm, our approach proved to be more reliable and accurate in terms of finding true rigid image transformation for alignment. At each increasing stage of MFRGA, results are computed with decreasing search space and increasing precision levels. Moreover, to prove the robustness of our algorithm, we utilized compressed images of brain magnetic resonant imaging that vary in compression qualities ranging from 10 to 100. Furthermore, we added noise levels of 1%, 3%, 5%, 7%, and 9% with an INU of 20% and 40%, respectively, provided by the online BrainWeb simulator. We achieved the monomodal rigid image registration that proves to be successful using MFRGA, even when the noise is critical, the compression quality is the least, and the intensity is nonuniform.

Rapid assessment of prevalence of blindness and cataract surgery in Kabul province, Afghanistan.

OBJECTIVE: To estimate prevalence and causes of blindness and vision impairment and assess cataract surgical coverage and quality of cataract surgery in Kabul. METHODS AND ANALYSIS: A total of 3751 adults aged 50 years and above were recruited from 77 randomly selected clusters. Each participant underwent presenting and pinhole visual acuity assessment and lens examination. Those with pinhole visual acuity <6/12 in either eye had a dilated fundus examination to determine the cause of reduced vision. Those with apparent lens opacity were interviewed on barriers to cataract surgery. RESULTS: The age-adjusted and sex-adjusted prevalence of blindness was 2.4% (95% CI: 1.8% to 3.0%). Prevalence of severe, moderate and mild vision impairment was 2.2% (95% CI: 1.7% to 2.7%), 6.9% (95% CI: 6.0% to 7.9%) and 8.7% (95% CI: 7.5% to 9.8%), respectively. Cataract was the main cause of blindness (36.8%), severe (54.4%) and moderate (46.1%) vision impairment. Uncorrected refractive error was the leading cause of mild vision impairment (20.3%). Age-related macular degeneration was the second leading cause of blindness (23.0%). In people with a presenting visual acuity of <3/60, cataract surgical coverage was 89.7%, and effective cataract surgical coverage was 67.8%. The major barriers to uptake of the available cataract surgical services were the need for surgery was not felt (23.7%) and cost (22.0%). CONCLUSION: Kabul province has a high prevalence of blindness, largely due to cataract and age-related macular generation. The quality of cataract surgery is also lagging in terms of good visual outcomes. This calls for immediate efforts to improving the reach and quality of existing eye services and readiness to respond to the increasing burden of posterior eye disease.

On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network.

This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country's Tobacco production. In order to analyze the performance of the developed classification framework, a pilot sub-region named Yar Hussain is selected for experimentation work. Yar Hussain is a tehsil of district Swabi, within KP province of Pakistan, having highest contribution to the gross production of the KP Tobacco crop. KP generally consists of a diverse crop land with different varieties of vegetation, having similar phenology which makes crop classification a challenging task. In this study, a temporal convolutional neural network (TempCNNs) model is implemented for crop classification, while considering remotely sensed imagery of the selected pilot region with specific focus on the Tobacco crop. In order to improve the performance of the proposed classification framework, instead of using the prevailing concept of utilizing a single satellite imagery, both Sentinel-2 and Planet-Scope imageries are stacked together to assist in providing more diverse features to the proposed classification framework. Furthermore, instead of using a single date satellite imagery, multiple satellite imageries with respect to the phenological cycle of Tobacco crop are temporally stacked together which resulted in a higher temporal resolution of the employed satellite imagery. The developed framework is trained using the ground truth data. The final output is obtained as an outcome of the SoftMax function of the developed model in the form of probabilistic values, for the classification of the selected classes. The proposed deep learning-based crop classification framework, while utilizing multi-satellite temporally stacked imagery resulted in an overall classification accuracy of 98.15%. Furthermore, as the developed classification framework evolved with specific focus on Tobacco crop, it resulted in best Tobacco crop classification accuracy of 99%.

Amlodipine/Valsartan (Avsar®): Efficacy in Hypertensive Patients - A Real World Observational Study (ALERT).

OBJECTIVES: Hypertension is a significant public health problem and one of the major noncommunicable diseases at the endemic level in Pakistan. This study was done to determine the efficacy of amlodipine/valsartan (Aml/Val) once-daily dose in reducing blood pressure (BP) after eight weeks of therapy. METHODS: This study is an open-labeled observational study carried out for a period of 12 months. Some 769 participants of either gender between the ages of 18 and 70 years selected after taking written informed consent had a BP of >139/89 mmHg (not controlled) on monotherapy with a minimum 30 days of treatment. Therapy to control their high BP was initiated with Aml/Val (Avsar®, PharmEvo Pvt Ltd, Karachi, Pakistan) at the time of their enrolment in the study. Pregnant females and patients with secondary hypertension were excluded. Data were analyzed using SPSS version 20.0 and chi-square test was used for inferential analysis. p-values less than 0.05 were considered significant. RESULTS: At the end of week one, less than half of the patients achieved the desired level of BP while the majority achieved this level by the end of the study. Some 75.6% patients achieved targeted BP with Aml/Val 80/5 mg tablet, 18.5% achieved targeted BP with Aml/Val 160/5 mg tablet, and 5.9% achieved the targeted BP with Aml/Val 160/10 mg tablet at the end of the eighth week. The compliance rate was 99.2% at the first week, 98.9% at the fourth week, and 99.9% at the eighth week of treatment. CONCLUSION: Our study concluded that Aml/Val (Avsar) combination therapy was very effective in controlling BP among patients who were uncontrolled with other monotherapies for at least one month.