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The semantic word similarity task aims to quantify the degree of similarity between a pair of words. In literature, efforts have been made to create standard evaluation resources to develop, evaluate, and compare various methods for semantic word similarity. The majority of these efforts focused on English and some other languages. However, the problem of semantic word similarity has not been thoroughly explored for South Asian languages, particularly Urdu. To fill this gap, this study presents a large benchmark corpus of 518 word pairs for the Urdu semantic word similarity task, which were manually annotated by 12 annotators. To demonstrate how our proposed corpus can be used for the development and evaluation of Urdu semantic word similarity systems, we applied two state-of-the-art methods: (1) a word embedding–based method and (2) a Sentence Transformer–based method. As another major contribution, we proposed a feature fusion method based on Sentence Transformers and word embedding methods. The best results were obtained using our proposed feature fusion method (the combination of best features of both methods) with a Pearson correlation score of 0.67. To foster research in Urdu (an under-resourced language), our proposed corpus will be free and publicly available for research purposes.

The facial looks have an impact on self-esteem and emotional well-being, playing an important role in social interactions. Changing in these functions will therefore affect the standard of living of children. Objective: The purpose of the study was to evaluate the effect of malocclusion on psychological well-being on school going children using the OASIS aged between 13-17 years attending government high schools in Mardan District. Methods: This was a Descriptive Cross-Sectional Survey conducted at the government schools of Mardan. It was carried out within a period of six month from December, 2021 till May 2022 after consent from Institutional Review Board (IRB) of Bacha Khan Medical College, Mardan vide No. 39/2021/ERB. The sample was chosen using a random cluster sampling technique with probability related to size. The design effect was taken into account as the cluster sampling approach was applied, and a sample size of 850 was achieved. (600 boys and 250 girl participants were involved respectively from government high schools). Results: A total of 850 subjects were included in the study of which there were 600 (70%) males and 250 (30%) were females. The age range was 13-17 years with a mean age 15 years +1.37 (S.D) years. Conclusions: Angle’s malocclusion was established in 73.1% of the subjects. The least affected psychologically was normal occlusion with (100%) good psychological well-being followed by Angle’s class I malocclusion having good psychological well-being (76.8%).

Computed total and partial density of states plot for Ba2ZnS3 material.

Magnetic resonance imaging (MRI) is commonly used in medical diagnosis and minimally invasive image-guided operations. During an MRI scan, the patient’s electrocardiogram (ECG) may be required for either gating or patient monitoring. However, the challenging environment of an MRI scanner, with its several types of magnetic fields, creates significant distortions of the collected ECG data due to the Magnetohydrodynamic (MHD) effect. These changes can be seen as irregular heartbeats. These distortions and abnormalities hamper the detection of QRS complexes, and a more in-depth diagnosis based on the ECG. This study aims to reliably detect R-peaks in the ECG waveforms in 3 Tesla (T) and 7T magnetic fields. A novel model, Self-Attention MHDNet, is proposed to detect R peaks from the MHD corrupted ECG signal through 1D-segmentation. The proposed model achieves a recall and precision of 99.83% and 99.68%, respectively, for the ECG data acquired in a 3T setting, while 99.87% and 99.78%, respectively, in a 7T setting. This model can thus be used in accurately gating the trigger pulse for the cardiovascular functional MRI.

Objective: The aim of the study was to determine the usage and attitude toward aspirating dental syringes among dental practitioners in Khyber Pakhtunkhwa province of Pakistan. Study design: Descriptive, cross sectional Place and Duration: Rural Health Centre Koghuzi, February 2021 to May 2022. Methodology: A cross sectional conducted on dental practitioners in Khyber Pakhtunkhwa. A purpose developed, self-administered questionnaire was used to collect data. It included both open and close ended questions. SPSS version 23 was used and data were analyzed using Chi-square test. Results: 40.7% of respondents were qualified as specialist and the 59.3% were general practitioners. Only 31.6% of the dental practitioners have dedicated aspirating syringe available to them. Overall only 6.9% of the dental practitioners practice aspiration in all injection types. Most respondents agree that use of aspirating syringes contributes to safety of local anesthesia but only few think they had complication in their practice resulting from use of non-aspirating syringes. Most respondents think the might consider using aspirating syringes in the future. Conclusion: Despite the generally positive attitude towards aspirating syringes, few dental practitioners have dedicated aspirating syringe available to them and even few actually practice aspiration during local anesthesia. Keywords: Attitude, Local anesthesia, Aspiration, Aspirating dental syringes

The computed density of states for NaLiS material.

Objective: The main purpose of this study is to compare the mortality between health workers and general population infected with coronavirus disease. Study Design: Comparative/Retrospective study Place and Duration: Study was conducted at Medicine and Gastroenterology department of Ayub Teaching Hospital, Abbottabad for duration of six months from 1st January 2021 to ¬¬¬¬¬¬30th June 2021. Methods: In this study 250 patients of both genders with coronavirus infection were presented. Age of the patients was between 18-70 years. Informed written consent was taken from all the cases for baseline details including age, sex, body mass index, socio-economic status and residency. Included patients were both symptomatic and asymptomatic to disease. Among 250 cases 125 patients were in the general population included in group I while other 125 cases were health workers included in group II. Patients were admitted to the hospital and examined for recovery. Outcomes among both groups were assessed and compared in terms of ICU admission, ventilation requirement and rate of mortality. We used the SPSS 25.0 version to analyze complete data. Results: In group I, 65 (52%) patients were males and 60 (48%) cases were females with mean age 47.66±8.87 years and in group II, 70 (56%) were males and 55 (45%) females with mean age 27.66±8.87. Mean BMI in group I was 25.11±8.33 kg/m2 and in group II, body mass index was 22.32±7.54 kg/m2. Majority of the patients i.e 73 (58.4%) in group I had poor socio economic status but in group II 50 (40%) cases had poor economic status. Majority of the cases among both groups were from urban areas 75 (60%) and 85 (64%). 48 (38.4%) were symptomatic in group I and 53 (42.4%) were in group II. Hypertension, diabetes mellitus and heart disease were the most common comorbidities. Frequency of ICU admission, ventilation requirement and mortality was significantly higher in general population 19 (15.2%), 24 (19.2%), 14 (11.2%) as compared to health workers 6 (4.8%), 7 (5.6%) and 5 (4%) with p value < 0.05. Conclusion: We concluded in this study that the severity of pandemic disease among general population was higher because of less use of preventive measures as compared to health workers and frequency of deaths, ICU admission and use of invasive ventilation in general population were also very high. Keywords: Mortality, ICU, Ventilation, General Population, Health workers, Coronavirus

9Be(p,xn) and 7Li(p,xn) are two reactions for the accelerator-based compact neutron source at low energy protons conditions, especially for BNCT facilities and it is necessary to simulate the beam characteristics accurately. The different reference physics lists recommended by GEANT4 have been used to calculate the neutron beam characteristics for the 9Be and 7Li targets bombarded by proton beams of 5 ∼ 10 MeV and the accuracy of GEANT4 simulations has been discussed. By comparing with the reference data such as experimental data, MCNP6 simulations and numerical calculations, the results calculated by QGSP_BIC_AllHP show good agreement with a relative error of less than 10%. The neutron yields level out and have a tendency to move forward, when the thickness is greater than the range of protons in targets. However, the neutron yields are significantly underestimated by other physics lists except for QGSP_BIC_AllHP. In addition, two fitted curve equations are acquired to quickly estimate the neutron yield for 9Be and 7Li targets below 20 MeV. Therefore, due to the limitation of the particle type and energy range in this study, GEANT4 with QGSP_BIC_AllHP is recommended to calculate hadronic interactions of protons with matter below 20 MeV.

Introduction This study was conducted to assess the diagnostic accuracy of three-dimensional computed tomography (3D-CT) in detection of cervical spine injuries in symptomatic post-trauma patients using multiplanar computed tomography (MP-CT) as reference standard. Approach This cross-sectional study was conducted at Aga Khan University from July 2016 to January 2017. Patients were included using a non-probability, consecutive sampling. MP-CT and 3D- CT images were obtained and evaluated by a senior radiologist to identify cervical spine injuries. Results 205 patients were included in the study. For fractures, 3D-CT images had sensitivity of 71%, specificity of 100%, positive predictive value (PPV) of 100%, negative predictive value (NPV) of 96.8% and diagnostic accuracy of 97%. For dislocations, 3D-CT reported sensitivity of 83.34%, specificity of 100%, positive predictive value of 100% and negative predictive value of 99.5% and diagnostic accuracy of 99.5%. Conclusion 3D-CT has good diagnostic accuracy for injuries of the cervical spine but must be reviewed simultaneously with multiplanar CT images.

The globally soaring energy prices and electricity shortfall are major hurdles in the economic development of Pakistan. To cope with periodic power outages, small and medium enterprise (SME) business owners have to fall back on alternate power sources such as backup generators and uninterruptible power supplies (UPS), which further increase the per kWh cost of electricity, power quality issues, and greenhouse gas (GHG) emissions. On the contrary, grid-tied solar photovoltaic (PV) systems are not only economical and sustainable but support the national power grid to mitigate environmental emissions. This study aims to investigate and compare the techno-economic viability of grid-connected solar photovoltaic power plants for the manufacturing SME sector in four different districts of Punjab, Pakistan. Based on the technical, financial, and environmental indicators, a detailed techno-economic, sensitivity, and GHG emission analysis is conducted using RETScreen Expert software. The research findings clearly show that the proposed solar PV projects for all four locations are technically, financially, and environmentally viable, however, Sargodha as compared to other sites is the most feasible location with the highest capacity factor of 17.8 %, highest internal rate of return 14.9 %, lowest payback period 7.7 years, and least levelized cost of electricity 8.5 ¢/kWh. For validation, the simulation results are compared with performance metrics from PV plants erected in various parts of the world. Applying the same research approach to the whole industrial sector of Punjab recommends adding 13,469 MW of PV capacity to satisfy the industry’s 20446.21 GWh annual energy consumption and to cut emissions by 90,17,581 t CO2 per year. This research work presents guidelines for researchers to evaluate the feasibility of suitable PV technologies for the SME sector thereby helping investors to have a holistic view of potential investment zones.

Sound event detection (SED) plays an important role in understanding the sounds in different environments. Recent studies on standardized datasets have shown the growing interest of the scientific community in the SED problem, however, these did not pay sufficient attention to the detection of artificial and natural sound. In order to tackle this issue, the present article uses different features in combination for detection of machine-generated and natural sounds. In this article, we trained and compared a Stacked Convolutional Recurrent Neural Network (S-CRNN), a Convolutional Recurrent Neural Network (CRNN), and an Artificial Neural Network Classifier (ANN) using the DCASE 2017 Task-3 dataset. Relative spectral–perceptual linear prediction (RASTA-PLP) and Mel-frequency cepstrum (MFCC) features are used as input to the proposed multi-model. The performance of monaural and binaural approaches provided to the classifier as an input is compared. In our proposed S-CRNN model, we classified the sound events in the dataset into two sub-classes. When compared with the baseline model, our obtained results show that the PLP-based ANN classifier improves the individual error rate (ER) for each sound event, e.g., the error rate (ER) is improved to 0.23 for heavy vehicle events and 0.32 for people walking, and minor gains are shown in other events as compared to the baseline. Our proposed CRNN performs well when compare to the baseline and to our proposed ANN model. Moreover, in cross-validation trials, the results in the evaluation stage demonstrate a significant improvement compared to the best performance of DCASE 2017 Task-3, reducing the ER to 0.11 and increasing the F1-score by 10% in the evaluation dataset. Erosion and dilation were used during post-processing.

Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard. This work was partially supported by QUST-2-CENG-2022-711 from Qatar University and NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, and a member of Qatar Foundation, Doha, Qatar. The statements made herein are solely the responsibility of the authors. Scopus

Objective: To find out the gram negative bacteria causing the diabetic foot ulcers and most effective antibiotic therapy. Study Design: Cross-sectional descriptive study Place and Duration of Study: Diabetic Center Hayatabad, Kabir Medical College Peshawar from 1st September 2021 to 28th February 2022. Methodology: Sixty nine admitted patients for the treatment of diabetes having type 1 diabetes mellitus and type 2 diabetes mellitus were enrolled. All the patients were on treatment of antibiotic such as gentacin, augmentin, amikacin and clindamycin. The specimens were analyze in microbiology laboratory and extracted by needle aspiration of material from the infected site and inoculate within 1hour after collection using gram staining smear for the detection or cytology of bacteria and its presence and absence in a specimens, for the isolation specimens were plated onto chocolate, phenyl ethyl alcohol (PEA) and MacConkey agar plate. To check the antibiotic susceptibility pattern Kirby Bauer test was performed. Results: There were eight anaerobic gram negative bacteria included in the study. In type 1 diabetes the Escherichia coli extended-spectrum-β-lactamase (ESBL) was 4.8% while in type 2 it was 95.2% in case of Klebsiella oxytoca. There is no bacteria in type1 while in type2, 100% were detected among 13 samples out of 69. E. coli (ESBL) was found in 66.7% in diabetic patients >10 years with foot ulcers while 33.3% in 10 years while in

The structural, electronic, optical and thermoelectric properties of copper-based ternary chalcogenides ACuSe2 (A = Sc, Y and La) were investigated within the framework of the density functional theory (DFT). The electronic band structures and density of states exhibit that ScCuSe2 and YCuSe2 have the indirect band gaps, while LaCuSe2 displays a direct band gap-type transition. The band structure calculations agree well with other results in the literature. The optical behavior of the studied materials was analyzed in terms of dielectric functions, refractive index, extinction coefficient, absorption coefficient, optical conductivity, reflectivity and energy loss factor. The refractive indices increase to the maximum values of 4.4, 4 and 4.1 at the short infrared and visible wavelengths for ScCuSe2, YCuSe2 and LaCuSe2, respectively. Then, they decrease to get a value below 1.0 at the UV wavelengths. Moreover, the material response with temperature was investigated by Seebeck coefficient, figure of merit, specific heat capacity, power factor, thermal conductivity and susceptibility. The high Seebeck effect and large power factor values confirm the efficiency of these materials in thermoelectric energy converter technology. Among the three studied ternary materials, YCuSe2 has the highest value of dimensionless figure of merit of 0.45 at room temperature. These results would probably provide a new route to the experimentalists for the potential usage and applications of ScCuSe2, YCuSe2 and LaCuSe2 in thermoelectric and optoelectronic devices.

Cogeneration system based on Small Modular Lead‐cooled Fast Reactor (SMLFR) becomes attractive due to its good characteristics of flexible location, safety, thermal efficiency, and economy. The conventional cogeneration systems using coal, gas, or renewable energy as the thermal resource have lower thermal performance as compared to LFR based cogeneration systems due to lack of sustainable energy resources. A modified concept design of cogeneration system based on a 35‐MWth SMLFR has been proposed to improve the thermal performance. A new concept of District Heating (DH) structure layout along with optimization based on the exhaust steam/water drainage position. The thermodynamic model based on energy and exergy methods has been used to design and calculate the energy losses of the cogeneration system components. The energy utilization rate can be increased significantly by optimizing the DH. The thermal efficiency of the proposed system reaches up to 73.64% with an increase of 3.51% and the exergy efficiency reaches up to 59.31% with an increase of 5.01%. The increase of thermal performance under the different heating demands will lead to better energy conservation and environmental safety. This study can be further used as the reference for the design and optimization of SMLFR cogeneration system based on thermodynamic and exergy analysis.

The concept of multilayers and filler content has been utilized as an effective technique to acquire excellent microwave absorption performance. MoS2@MXene nanocomposites have been successfully synthesized after exfoliation of the MXene and MoS2 particles were embedded on its surface. MoS2@MXene achieved the minimum Reflection Loss (RL) of −51 dB with 30% ratio in wax. The involved mechanisms are discussed in detail including the synergetic effect between MXene and MoS2 particles which improved the dielectric loss of the MoS2@MXene composites as a result of multiple reflections in the layered structure of MXene, abundant functional groups on the MXene surface and positive effect of the increase in surface area. Moreover, the radar cross-section reduction performance has also been confirmed by using simulation technology. The obtained MoS2@MXene composite proved as an excellent microwave absorbing material for future applications.

This paper introduces a novel Energy Efficient Mobility-Based Watchman Algorithm (E2-MBWA) to intensification packet delivery ratio of mitigating the Hotspot issue in Wireless Sensor and Actor Networks (WSAN). Hotspot issue mostly causes of network breakdown and decrease of data packet delivery. Therefore, it is required to design a new technique for data packet forwarding that can resolve these issues in the network. In this study, E2-MBWA has introduced, that cope with the layer-by-layer mechanism for data packet forwarding. The proposed algorithm works with the help of the Data Packet Forwarding Algorithm (DFPA) and Watchman Layer Update Mechanism (WLUM). Furthermore, it also rescues the data storage issues, for this, used secondary nods as substitutes. Moreover, proposed technique is compared with some latest baseline’s approaches, for example, Efficient Traffic Load Reduction Algorithm (ETLRA). The analytical energy model is also described for the best health of the network to measure the accuracy level of the Hotspot issue.

Aim: To find out the prevalence of needle stick injury, its reporting system and the reasons behind it. Study design: Descriptive cross-sectional Place and duration of study: Study was conducted at Jinnah post-graduate medical center (JPMC) Karachi during the period of March to September 2019 Methodology: A self-designed, self-explanatory questionnaire was used, consisting of two parts, the first part was about demographic information while second part is for information related to needle stick injury like probable cause, frequency, response after injury, post-exposure prophylaxis and about reporting of the incident. Questionnaire was validated by calculating the Cronbach’s alpha which was 0.78. data was analyzed by using the Statistical Package for the Social Sciences (SPSS) version 20. Results: Majority of the study participants were female (67.2%) and about 50% were postgraduate students. Out of total 134 doctors about 64.2% of the doctors had needle stick injury during their career. Finding out the most probable cause of needle stick injury during the survey it was found out that increased work load and prolonged working hours were the main reasons. Majority of the cases occurred in emergency department (41.9%). About 95.5% of the doctors didn’t get any post-exposure prophylaxis. Majority of the participants (96.3%) did not report to any authority because of the lack of knowledge about the reporting policy, it was noted that about 38.8% were confused either the reporting system exist or not. Most of the injuries occur during the procedure of suturing followed by recapping syringes. Conclusion: It has been concluded that majority of the doctors had faced needle stick injury during their career and a very negligible number of them got any post-exposure prophylaxis. Majority of them did not report to any authority. So there is a need of implication of safety measures and reporting policies for early detection and treatment of infections after needle stick injury.

The intensified quest for efficient materials drives us to study the alkali (Na)-based niobate (NaNbO3) and tantalate (NaTaO3) perovskites while exploiting the first-principles approach based on density functional theory, coded within WIEN2K. While using the Birch Murnaghan fit, we find these materials to be stable structurally. Similarly, the ab-initio molecular dynamics simulations (AIMD) at room temperature reveals that the compounds exhibit no structural distortion and are stable at room temperature. By using the recommended modified Becke–Johnson potential, we determine the electronic characteristics of the present materials providing insight into their nature: they are revealed to be indirect semiconductors with the calculated bandgaps of 2.5 and 3.8 eV for NaNbO3 and NaTaO3, respectively. We also determine the total and partial density of states for both materials and the results obtained for the bandgap energies of these materials are consistent with those determined by the band structure. We find that both compounds exhibit transparency to the striking photon at low energy and demonstrate absorption and optical conduction in the UV region. The elastic study shows that these compounds are mechanically stable, whereas NaNbO3 exhibits stronger ability to withstand compressive as well as shear stresses and resists change in shape while NaTaO3 demonstrates weaker ability to resist change in volume. We also find that none of the compound is perfectly isotropic and NaNbO3 and NaTaO3 are ductile and brittle in nature, respectively. By studying the optical properties of these materials, we infer that they are promising candidates for applications in optoelectronic devices. We believe that this report will invoke the experimental studies for further investigation.

This article examines entreprise activities developed by internally displaced religious minorities (IDRM) and the role that social capital plays in supporting such activities. In particular, the paper examines how social capital is linked to micro enterprise development and the economic survival/revival of internally displaced religious minorities in Pakistan and why the link between entrepreneurship and social capital is critical for contexts with absent or poorly designed enterprise development policies. A three-staged, sequential research design was adopted, which comprised the analysis of secondary data on IDRM, face-to-face survey of entrepreneurs and interviews in two selected study sites. \ud Our evidence shows how the role of social capital in supporting entrepreneurial activities is determined by socioeconomic inequalities as well as the characteristics of the formal enterprise support infrastructure, i.e. where formal institutions are weak, social capital is the main source of entrepreneurial support, with different types of social capital networks delivering different outcomes.

The coexistence of plant-herbivore populations in an ecological system is a fundamental topic of research in mathematical ecology. Plant-herbivore interactions are often described by using discrete-time models in the case of non-overlapping generations: such generations have some specific time interval of life and their old generations are replaced by new generations after some regular interval of time. Keeping in mind the dynamical reliability of continuous-time models we presented two discrete-time plant-herbivore models. Mainly, by applying Euler's forward method a discrete-time plant-herbivore model is obtained from a continuous-time plant-herbivore model. In addition, a dynamically consistent discrete-time plant-herbivore model is obtained by applying a nonstandard difference scheme. Moreover, local stability is discussed and the existence of bifurcation about positive equilibrium is shown under some mathematical conditions. To control bifurcation and chaos, a modified hybrid technique is developed. Finally, to support our theocratical results and to show the dynamical reliability of the nonstandard difference scheme some numerical examples are provided.

Fast and accurate detection of the disease can significantly help in reducing the strain on the healthcare facility of any country to reduce the mortality during any pandemic. The goal of this work is to create a multimodal system using a novel machine learning framework that uses both Chest X-ray (CXR) images and clinical data to predict severity in COVID-19 patients. In addition, the study presents a nomogram-based scoring technique for predicting the likelihood of death in high-risk patients. This study uses 25 biomarkers and CXR images in predicting the risk in 930 COVID-19 patients admitted during the first wave of COVID-19 (March-June 2020) in Italy. The proposed multimodal stacking technique produced the precision, sensitivity, and F1-score, of 89.03%, 90.44%, and 89.03%, respectively to identify low or high-risk patients. This multimodal approach improved the accuracy by 6% in comparison to the CXR image or clinical data alone. Finally, nomogram scoring system using multivariate logistic regression -- was used to stratify the mortality risk among the high-risk patients identified in the first stage. Lactate Dehydrogenase (LDH), O2 percentage, White Blood Cells (WBC) Count, Age, and C-reactive protein (CRP) were identified as useful predictor using random forest feature selection model. Five predictors parameters and a CXR image based nomogram score was developed for quantifying the probability of death and categorizing them into two risk groups: survived (=50%), respectively. The multi-modal technique was able to predict the death probability of high-risk patients with an F1 score of 92.88 %. The area under the curves for the development and validation cohorts are 0.981 and 0.939, respectively., 25 pages, 8 Tables, 10 Figures

Aim: To find out the frequency of sensorineural hearing loss among children with pyogenic meningitis. Study Design: Cross-sectional. Place and Duration of Study: Study was conducted at Ziauddin university hospital during the period of July 2019 to February 2020. Methodology: About 96 participants were enrolled in the study between the age group of one month up to 12 years. Patients were included on the basis of clinical presentation and laboratory findings of pyogenic meningitis. An expert audiologist performed the brainstem evoked response audiometry test before discharging the patient from the hospital. For data analysis SPSS version-20 was used. All the quantitative variables were calculated as mean with standard deviation while qualitative data were presented as frequency and percentages. To find out association between variables, the Mann Whitney U-test and chi-square test was applied while P-value ≤0.05 was considered as significant. Results: Mean age with standard deviation was 6.8 ±2.3. Majority of the study participants were boys (57%). The frequency of sensorineural hearing loss was 17%. It was more among females than their male counter parts that was 64.7% and 35.3% respectively but no significant association was reported. The younger age group was having higher frequency of sensorineural hearing loss (47.1%), followed by the age group of 6-8 years (29.4%) and the very small number of participants were affected from the age group of 9-12 years (23.5%) but all the age groups were having no significant association with frequency of hearing loss. Conclusion: It can be concluded that sensorineural hearing loss is the most common complication reported among the children with pyogenic meningitis in current setup so there is a need of early evaluation of hearing problems in all patients diagnosed with pyogenic meningitis.

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also overcome the shortcomings of continuous human monitoring. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet on 18,161 plain and segmented tomato leaf images to classify tomato diseases. The performance of two segmentation models i.e., U-net and Modified U-net, for the segmentation of leaves is reported. The comparative performance of the models for binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. The modified U-net segmentation model showed accuracy, IoU, and Dice score of 98.66%, 98.5%, and 98.73%, respectively, for the segmentation of leaf images. EfficientNet-B7 showed superior performance for the binary classification and six-class classification using segmented images with an accuracy of 99.95% and 99.12%, respectively. Finally, EfficientNet-B4 achieved an accuracy of 99.89% for ten-class classification using segmented images. It can be concluded that all the architectures performed better in classifying the diseases when trained with deeper networks on segmented images. The performance of each of the experimental studies reported in this work outperforms the existing literature. 2021 by the authors. Licensee MDPI, Basel, Switzerland. Funding: The open-access publication of this article was funded by the Qatar National Library and this work was made possible by HSREP02-1230-190019 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar. The statements made herein are solely the responsibility of the authors. Scopus

Aim: To find out the knowledge and attitude of medical undergraduate students towards breast cancer. Study Design: Descriptive cross-sectional study. Place and Duration of Study: Study was performed in Ziauddin medical university during the period of October to December 2020. Methodology: All the undergraduate Bachelor of Medicine and Bachelor of Surgery (MBBS) female students were included in the study irrespective of year of study. A self-designed, self-explanatory questionnaire was made and validated by doing a pilot study. Data was analyzed by using the Statistical Package for Social Sciences (SPSS version-20). Results: Mean age of the study participants was 22.2 ± 1.7 years. Data regarding knowledge about the risk factors reported that overall more than 62% of the students were aware of the risk factors leading to breast cancer. Looking over the individual risk factors, about 77.3% recognized increased age as a major contributing factor, followed by lack of breast feeding, nulliparity, obesity, use of contraceptive pills, late menopause, early menarche and smoking with 68.3%,54.9%,51.1%, 64.8%,61.6%,39,2% and 48.6%respectively. Focusing specifically the year of MBBS student, the level of medical education increases the awareness about the risk factor also increase. Looking over the attitude of undergraduate MBBS students, about 76.8% of the students consulted to the doctor for breast lump, out of them 41.6% immediately consulted, 27.3% delayed it for weeks and 7.9% waited for months for self-recovery. Conclusion: It can be concluded that majority of undergraduate medical students were having enough knowledge about the breast cancer, associated risk factors, symptoms and diagnostic modalities along with having positive attitude towards seeking medical help.

Big data analytics has shown tremendous success in several fields such as businesses, agriculture, health, and meteorology, and education is no exception. Concerning its role in education, it is used to boost students’ learning process by predicting their performance in advance and adapting the relevant instructional design strategies. This study primarily intends to develop a system that can predict students’ performance and help teachers to timely introduce corrective interventions to uplift the performance of low-performing students. As a secondary part of this research, it also explores the potential of collaborative learning as an intervention to act in combination with the prediction system to improve the performance of students. To support such changes, a visualization system is also developed to track and monitor the performance of students, groups, and overall class to help teachers in the regrouping of students concerning their performance. Several well-known machine learning models are applied to predict students performance. Results suggest that experimental groups performed better after treatment than before treatment. The students who took part in each class activity, prepared and submitted their tasks perform much better than other students. Overall, the study found that collaborative learning methods play a significant role to enhance the learning capability of the students.

This paper considers the problem of anomaly detection in an outdoor environment where surveillance cameras are usually installed to monitor activities of general public. A novel solution is proposed which combines audio and visual data to automatically detect abnormal activities. The proposed anomaly detection algorithm makes use of both visual and audio features to automatically detect anomalous activities in scenes. Visual features such as optical flow technique combined with particle swam optimization and social force model are used, whereas, acoustic features such as, energy, zero crossing rate, volume, spectral-centroid, spectral spread, spectral roll-off, spectral flux, cross correlation and the mel-frequency cepstral coefficients (MFCCs) are used. An anomaly inference is developed which is based on both visual and audio features. The performance of the proposed algorithm is evaluated by testing it on the publicly available UMN datasets combined with the audio recordings. The proposed algorithm is compared with state-of-the-art techniques and is shown to achieve improved performance in terms of accuracy.

In this manuscript, we discuss a four-dimensional cubic autocatalator chemical reaction model in continuous form. We investigate the existence of one and only positive fixed point and then we have obtained some parametric conditions for local stability of continuous system by using Routh-Hurwitz stability criteria. Moreover, we discretize the four-dimensional continuous cubic autocatalator chemical reaction model by using Euler’s forward method and then by using a nonstandard difference scheme we obtained a consistent discrete-time counterpart of four-dimensional cubic autocatalator chemical reaction model. Parametric conditions for local asymptotic stability of one and only positive fixed point of obtained system are also discussed. It is shown that the obtained system experiences the Neimark-Sacker bifurcation at one and only positive fixed point by using a general standard for Neimark-Sacker bifurcation. The discrete-time counterpart of genuine four-dimensional system displays chaotic dynamics at different standards of bifurcation parameter. Furthermore, the control of Neimark-Sacker bifurcation and chaos is also deliberated by using a generalized hybrid control scheme, which is based on parameter perturbation and feedback control. Finally, some numerical examples are given to strengthen our theoretical results.

BackgroundAs a novel disease, understanding the relationship between the clinical and demographic characteristics of coronavirus disease 2019 (COVID-19) patients and their outcome is critical. We investigated this relationship in hospitalized patients in a tertiary healthcare setting.Aims/objectivesTo study COVID-19 severity and outcomes in relation to clinical and demographic characteristics of in admitted patientsMethodologyIn this cross-sectional study, medical records for 1087 COVID-19 patients were reviewed to extract symptoms, comorbidities, demographic characteristics, and outcomes data. Statistical analyses included the post-stratification chi-square test, independent sample t-test, multivariate logistic regression, and time-to-event analysis.ResultsThe majority of the study participants were >50 years old (67%) and male (59%) and had the following symptoms: fever (96%), cough (95%), shortness of breath (73%), loss of taste (77%), and loss of smell (77%). Regarding worst outcome, multivariate regression analysis showed that these characteristics were statistically significant: shortness of breath (adjusted odds ratio [aOR] 31.3; 95% CI, 11.87–82.53; p < 0.001), intensive care unit (ICU) admission (aOR 28.3; 95% CI,9.0–89.6; p < 0.001), diabetes mellitus (aOR 5.1; 95% CI;3.2–8.2; p < 0.001), ischemic heart disease (aOR 3.4; 95% CI,1.6–7; p = 0.001), nausea and vomiting (aOR 3.3; 95% CI, 1.7–6.6; p = 0.001), and prolonged hospital stay (aOR 1.04; 95% CI, 1.02–1.08; p = 0.001), while patients with rhinorrhea were significantly protected (aOR 0.3; 95% CI, 0.2–0.5; p < 0.001). A Kaplan–Meier curve showed that the symptoms of shortness of breath, ICU admission, fever, nausea and vomiting, and diarrhea increased the risk of mortality.ConclusionIncreasing age, certain comorbidities and symptoms, and direct admission to the ICU increased the risk of worse outcomes. Further research is needed to determine risk factors that may increase disease severity and devise a proper risk-scoring system to initiate timely management.

Research on the social capital of women entrepreneurs in contexts characterized by gender segregation between men's and women's trading spaces is underdeveloped. The literature on women's entrepreneurship and the marketplace in Pakistan is one such example. This article contributes to the literature on the social capital of female entrepreneurs from a critical perspective by drawing on an exploratory case study of women's entrepreneurship and entrepreneurial networks in the Malakand District of the Khyber Pakhtunkhwa province of Pakistan. Women's social capital was found to play a crucial role in the survival and expansion of their business activities by facilitating access to the men's trading sphere of the marketplace. It was also found that women exercise active agency in developing cross-gender economic networks but do so in ways that do not overtly challenge social norms.

Theoretical debate on the relationship between the quality of government, social capital and corruption remains underspecified in relation to the analysis of local governance. This paper asks how quality of government (QoG) impacts on the role of social capital (SC), and how SC connects with corruption in the local governance context. The paper develops a local governance approach in order to better understand this relationship through an in-depth qualitative case-study of the governance of Batkhela Bazaar in Malakand District of Pakistan. Three findings emerge: Firstly, the results demonstrate how QoG and socioeconomic inequalities shape the context for SC development and its role in corruption, which feeds into the poor QoG. Secondly, unlike the existing mainstream literature, the results show the fundamental importance of petty corruption to levels of trust within a society. Thirdly, reciprocity plays a crucial role in maintaining trusting ties in the context of ineffective formal institutions.

This paper proposes a novel multidomain features-based genetic algorithm (GA) optimized artificial immune system (AIS) framework for fault detection in real systems. Different from native real-valued negative selection algorithm (RNSA) that operates in original data space, this algorithm utilizes feature space transformation and diversity factor-based GA for optimized detector distribution in nonself feature space. The proposed framework comprises three stages namely; feature extraction, unsupervised feature selection, and GA optimized AIS. In the first stage, signal processing methods are applied to extract multidomain features (time-domain statistical, frequency domain statistical, and special features) of the system. In the second stage, two unsupervised methods namely, ${k}$ -NN clustering and pretraining using deep learning neural network are proposed for dominant fault-characterizing feature selection. Finally, in the third stage, the fault-characterizing feature vectors are used for system status categorization (i.e., normal, fault) using selected (fault-characterizing) features-based AIS method. The efficacy of the proposed framework is verified through experiments on motor bearing fault detection using vibration signal. The major accomplishment of the proposed combination of space transformation, feature selection and AIS (anomaly classification) techniques is the alleviation of computational burden on RNSA implementation. Moreover, GA optimized AIS fault diagnosis based on well-established features gives improved detection performance.