Search

Although Rand Short Form 36-item (SF-36) questionnaire is a frequently used tool for evaluating a patient's quality of life in both clinical and research settings, but its translation into Urdu language and its psychometric properties has not been tested before, in accordance to the pre-established guidelines. This study aimed to translate and assess the psychometric properties of Urdu version of SF-36 (SF-36-U) in lumbar radiculopathy patients.In this cross-sectional study, one hundred and eight patients of lumbar radiculopathy filled SF-36-U questionnaire, Urdu version of Oswestry Disability Index (ODI-U) and visual analogue scales for pain (VAS pain) at baseline and after 7 days. Two types of reliability i.e. internal consistency and test-retest reliability while three types of validity i.e. face, content, and construct validity were evaluated. Psychometric properties were assessed contrary to the a priori hypothesis and alpha level was kept at0.05.SF-36-U presented good to excellent test-retest reliability for all eight domains (ICCSF-36-U revealed acceptable psychometric properties and is considered as a reliable and valid questionnaire for assessing the health-related quality of life in Urdu-speaking patients having lumbar radiculopathy.

To compare the effects of balance training with and without Gaze Stabilization exercises on clinical outcomes in elderly patient with chronic dizziness.Total 64 participants after referral from neurologist at Lahore General Hospital were recruited in this study. These participants were randomly assigned into two groups i.e. Group A (Gaze group) and Group B (control group) with 32 participants in each group by sealed envelope method. Gaze group performed balance exercises with gaze stability exercise whereas, group B performed balance exercises with saccade eye exercises. Berg Balance Scale, Disability Handicapped Inventory, and Activity Specific Balance Confidence Scale were used to measure outcome variables.Demographic variables were represented by mean, standard deviation and frequency distribution. Results showed that there was no baseline difference between gaze and control group (p 0.05). Both groups showed improved results with the gaze group showing significantly more improvement as compare to the control group on balance (p 0.05, Mean Group A = 44.1, Mean group B = 40.91), perceived disability (p 0.05, Mean Group A = 52.84, Group B = 56.09), and risk of fall (p 0.05, Mean Group A = 53.84, Mean Group B = 50.72) respectively.This study concluded that gaze stability exercises with balance training are effective in improving balance, level of perceived disability, and risk of fall prevention among elderly patients with chronic dizziness.

Antioxidant proteins have been discovered closely associated with disease control due to its capability to eradicate excess free radicals. The accurate identification of antioxidant proteins is on the upsurge owing to their therapeutic significance. However, observing the rapid increases of this toxic disease in the human body, several machine learning algorithms have been applied and performed inadequately to identify antioxidant proteins. Therefore, measuring the effectiveness of antioxidant proteins on the human body, a reliable intelligent model is indispensable for the researchers. In this study, primary protein sequences are formulated using evolutionary and sequence-based numerical descriptors. Whereas, evolutionary features are collected using a bigram Position-specific scoring matrix, besides, K-space amino acid pair (KSAAP) and dipeptide composition are utilized to extract sequential information. Furthermore, in order to reduce the computational time and to eradicate irreverent and noisy features, the Sequential forward selection and Support vector machine (SFS-SVM) based ensemble approach is applied to select optimal features. At last, several distinct nature classification learning methods are applied to choose a suitable operational engine for our model. After evaluating the empirical results, SVM using optimal features achieved an accuracy of 97.54%, 93.71% using the training and independent dataset, respectively. It was found that our proposed model outperformed and reported the highest performance than the existing computational models. It is expected that the developed model may be played a useful role in research academia as well as proteomics and drug development. The source code and all datasets are publicly available at https://github.com/salman-khan-mrd/Antioxident_proteins .

Background: Low Intensity Pulsed Ultrasound (LIPUS) is beneficial in accelerating fracture recovery, enhancing their capacity to execute tasks of daily life and, as a result, their autonomy. Methods: Thirty-four (29 males and 5 females) pre-diagnosed patients referred by an orthopaedic surgeon exhibiting symptomatic low back pain for at least four months were recruited and randomly divided into LIPUS group and Routine Physical Therapy (RPT) group. The lottery method was used to randomly assign patients into two groups. Numeric Pain Rating Scale (NPRS) was utilized for the measurement of pain and Oswestry Disability Index (ODI) for functional disability. Patients were assessed at baseline, at the end of 12th and 20 th week. Interventions were applied by the physical therapist having more than eight years of clinical experience for 10 weeks on alternate days. Results: LIPUS group reported greater reduction in pain with mean change of 3.18 points (95% CI: 2.2, 4.2; p < 0.001) at 12 th week, 6.18 points (95% CI: 5.5, 6.8; p < 0.001) at 20th week follow-up and functional disability with mean change of 28.24 points (95% CI: 23.7, 36 32.8; p < 0.001) at 12th week and 39.47 points (95% CI: 31.8, 47.1; p < 0.001) at 20th week follow-up compared with the RPT group.Conclusion: Low-intensity pulsed ultrasound has significantly reduced pain and functional disability in patients with early-stage lumbar spondylolysis and could be preferred as a safe non-invasive treatment method for early bone healing. Trial registration: WHO-Iranian registry of clinical trials (IRCT20200206046396N1, Dated: 02/05/2020).

Although sequestering nitrate by anion exchange resins (AERs) is a proven and well-established technology, many process problems and challenges still exist, such as interference, nitrate dumping, total dissolved solids (TDS) limitations, brine waste disposal, etc. An engineered-integrated approach was devised and assessed to mitigate such limitations. The overall process optimization was based on screening, analyzing, and optimizing each respective step. A front-end sulfate removal step was suggested, evaluated, which yielded removal efficiencies >95%. Back-end options were also introduced, including reduced brine and brine wastes, recycling possibility, and usage as mixed-fertilizer with a NO3/Cl ratio of 0.65 as confirmed by mass balance calculations. Taguchi robust designs (TRD), Box-Behnken Designs (BBD), and central composite design (CCD) experimental design methods were used, enabling information mining from a minimum number of experiments. A one-factor-at-a-time strategy was applied and the regeneration process was optimized using TRD. Response analysis and regression revealed that acid concentration and volume are the most influential factors. Selectivity order was determined as SO42-->Cl−>HCO3− according to their relative coefficients of the BBD and CCD regression models. Field trials confirmed the robustness and viability of the proposed integrated approach. A process flow diagram was devised. Merits of the proposed system include: a) possible resolution of sulfate competition and nitrate dumping, b) elimination of brine waste problems, c) tolerate higher TDS levels than ever possible for AERs, d) improved economics, e) improved process robustness, f) mitigate the corrosiveness of produced water, and g) fulfilling zero liquid discharge criteria.

The environmental effects of China's nuclear energy consumption in a dynamic framework of the pollution haven hypothesis are examined. This study uses a dynamic autoregressive distributed lag simulation approach. Empirical evidence confirms that the pollution haven hypothesis does not exist for China; i.e., foreign direct investment plays a promising role in influencing environmental outcomes. Furthermore, empirical results concluded positive contribution of nuclear energy in pollution mitigation. From the results it is expected that encouraging foreign investment to increase generation of nuclear energy would benefit environmental quality by reducing CO2 emissions.

This work analyzed the dynamic interactive causal links among urban agglomeration, economic performance, carbon emissions, and health expenditures across developmental disparities in China. To this end, this work developed a health expenditures-augmented growth model to introduce health expenditures and carbon emissions as a shift factor of total factor productivity. A block of simultaneous equations is developed based on the health expenditures-augmented growth modeling framework. The System-variant/Difference-variant Generalized Method of Moments is employed to estimate the block of simultaneous equations in order to deal with the potential endogeneity of the variables used. Based on empirical results, the core findings are as follows: first, a Pendroni cointegration-based long-run stable association is verified. Second, the urban agglomeration exhibited a bidirectional positive causal linkage with the gross domestic product (GDP) growth and health expenditures growth. Third, the health expenditures growth demonstrated a bidirectional positive causal linkage with carbon emissions growth and GDP growth. Fourth, a unidirectional mixed causal linkage—positive (negative) for linear form (squared form) of urban agglomeration—existed from urban agglomeration to carbon emissions growth. Fifth, there is a negative causal linkage operating from carbon emissions growth to GDP growth. However, there is mixed causal linkage—positive for a linear form of GDP growth, and negative for the squared form of GDP—operating from GDP growth to carbon emissions growth. These findings exhibited consistency in the type of causal linkages across the developmental disparities and hence showed robustness. Nevertheless, the findings showed disparities in the degree of impact across the developmental disparities. In this regard, the strength of causal linkage is the largest (moderate/lowest) for highly (moderately/least) developed regions of China. Based on empirical results, inferences are extracted and discussed.

Biowaste valorization into lactic acid (LA) by treatment with indigenous microbiota has recently gained considerable attention. LA production from date pulp waste provides an opportunity for resource recovery, reduces environmental issues, and possibly turns biomass into wealth. This study aimed to compare the performance of batch and cyclic fermentation processes in LA production with and without enzymatic pretreatment. The fermentation studies were conducted in the absence of an external inoculum source (relying on indigenous microbiota) and without the addition of nutrients. The highest LA volumetric productivity (3.56 g/liter/day), yield (0.07 g/g-TS), and concentration (21.66 g/L) were attained with enzymatic pretreated date pulp in the cyclic-mode fermentation at the optimized conditions. The productivity rate of LA was enhanced in the cyclic-mode as compared to the batch process. Enzymatic pretreatment increased the digestibility of cellulose that led to higher LA yield. An Artificial Neural Network model was developed to optimize the process parameters and to predict the LA concentration from date pulp waste in both fermentation processes. The main advantage of the ANN approach is the ability to perform quick predictions without resource-consuming experiments. The model predicted optimal conditions well and demonstrated good agreement between experimental and predicted data.

Fossil fuels are unable to meet the current energy demands and polluting the environment with the emission of harmful gases. Therefore, clean energy technology is need of the modern era. One of the energy conversion devices is fuel cell which utilized fuel from renewable sources and convert into electricity in an efficient and clean way. However, for commercialization of this technology high operating temperature, degradation of electrodes and manufacture cost is the key challenges in conventional three layer fuel cell. Significant improvements have been made to reduce the cost and operating temperature by selecting suitable materials. Therefore, single layer fuel cell (SLFC) has been got much attention due to simple geometry. The mechanism inside the SLFC is still mystery which has been explained in this paper using quantum mechanical parameters like band gap and effect of particle size on charge transportation. In this research work, nanocomposite materials for single layer fuel cell have been synthesized by chemical routes. The x-ray diffraction shows the cubic perovskite structure with average crystallite size in the range of 23–37 nm. The particle size and surface area is found to be 23 nm and 86.42 m2 g−1, respectively. Raman spectrum of LBSCF-SDC shows a red shift compared to LBSCF and band gap of the composition 3LBSCF-7SDC is found to be 2.51 eV. Moreover, the conductivity of the sample 3LBSCF-7SDC has been found to be 0.02 Scm−1 at 750 °C. The quantum mechanical effects governing the working of single layer fuel cells are observed by different analyses. Photon confinement and Fano-Interactions phenomena resulted in a red shift using Raman analysis technique. The red shift in Raman spectrum is referred to a photon confined in a single layer fuel cell system. These effects are studied in single layer fuel cell for the first time with no previous analyses done in this newly field.

This paper deals with the development of a highly dense and stable electrolyte on the base of nanoionics oxide interface theory. This gives a comparative study of two-phase nanocomposite electrolytes that are developed for low temperature solid oxide fuel cells (LT-SOFCs). These nanocomposites are synthesised with different oxides, which are coated on the doped ceria that showed high oxide ion mobility for LT-SOFCs. These novel two-phase nanocomposite oxide ionic conductors (MCe0.8Sm0.2O2-MO2, where M = Zn2+/Ba2+/La3+/Zr2+/Al3+ ) were synthesised by a co-precipitation method. The interface study between these two phases was analysed by electrochemical impedance spectroscopy (EIS), while ionic conductivities were measured with DC conductivity (four probe method). The nanocomposite electrolytes exhibited higher conductivities with the increase of concentration of coated oxides but decreased at a certain level. The structural or morphological properties of the nanocomposite electrolytes were examined by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The thermal stability was investigated using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The maximum performance of 590 mW/cm2 at 550 °C was obtained for the Zn@SDC based cell, and the rest of the coated samples Ba@SDC, La@SDC, Zr@SDC and Al@SDC based cells showed values of 550 mW/cm2, 540 mW/cm2, 450 mW/cm2, 340 mW/cm2, respectively, with hydrogen as a fuel. Therefore, the coated-SDC based nanocomposite materials are a good approach for lowering the operating temperature to achieve the challenges of the solid oxide fuel cells (SOFC). These two-phase nanocomposite electrolytes satisfy the all requirements which one electrolyte should have, like high ionic conduction, thermodynamic stability and negligible electronic conduction.

With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods becomes important, as manual/visual inspection has limited applications. This research work deals with automatic detection of photovoltaic module defects in Infrared images with isolated deep learning and develop-model transfer deep learning techniques. An Infrared images dataset containing infrared images of normal operating and defective modules is collected and used to train the networks. The dataset is obtained from Infrared imaging performed on normal operating and defective photovoltaic modules with lab induced defects. An isolated learned model is trained from scratch using a light convolutional neural network design that achieved an average accuracy of 98.67%. For transfer learning, a base model is first developed (pre-trained) from electroluminescence images dataset of photovoltaic cells and then fine-tuned on infrared images dataset, that achieved an average accuracy of 99.23%. Both frameworks require low computation power and less time; and can be implemented with ordinary hardware. They also maintained real time prediction speed. The comparison shows that the develop-model transfer learning technique can help to improve the performance. In addition, we reviewed different kind of defects detectable from infrared imaging of photovoltaic modules, that can help in manual labelling for identifying different defect categories upon access to new huge data in future studies. Last of all, the presented frameworks are applied for experimental testing and qualitative evaluation.