Synthesis and characterization of CoFe2O4/MWCNTs nanocomposites and high-frequency analysis of their dielectric properties.

Nanoparticles of CoFe2O4 were synthesized by chemical co-precipitation method. The CoFe2O4/MWCNT nanocomposites were synthesized with increasing contents of MWCNTs, i.e., 0.0, 2.0, 3.0, and 5.0% by weight via ultrasonication method in a dispersive medium using ortho-xylene. The synthesized cobalt ferrite nanoparticles and their nanocomposites were characterized by impedance analyzer, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), and x-ray diffraction (XRD) techniques. The XRD indexed patterns confirmed the face-centered cubic structure of CoFe2O4/MWCNT nanocomposites. The average crystallite size in all the samples was in the range of 15 to 35 nm. The decorations of CoFe2O4 on MWCNTs were confirmed by SEM images. The FTIR results showed two vibrational bands. With the increasing contents of multi-walled carbon nanotubes in the cobalt ferrite/MWCNT nanocomposites, the dielectric properties were also enhanced. At 1 MHz, dielectric constant, dielectric loss, and tangent loss factor were increased from 26, 15.1, and 0.580 for pure cobalt ferrite to 47, 28.9, and 0.614 for loading of 5% MWCNTs, respectively. At 1 GHz, dielectric constant, dielectric loss, and tangent loss factor were increased from 11.6, 0.33, and 0.028 for pure cobalt ferrite to 19.4, 0.61, and 0.031 for loading of 5% MWCNTs, respectively. Such a huge increase in the dielectric properties of cobalt ferrite and multi-walled carbon nanocomposites exploited their applications at high frequency

Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning

Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds

CLIMATE CHANGE ACTION AND STATE SOVEREIGNTY

Climate change is a reality recognized globally. Although global efforts are accelerating, there are fears in the underdeveloped world regarding the erosion of their sovereignty through climate change action and response mechanisms. Remedial actions taken at various levels are not a compensating reflection of this reality. There is a need to establish a well-thought-out mechanism and support fast-track climate change action and responses. This study, therefore, highlights the impact of climate change action on state sovereignty through in-depth analysis by interviewing climate experts and officials. It reckons that the issue revolves around interference in internal policies through the prism of climate change action incorporating world organisations. It concludes that developing states may have fears regarding the overreach of developed states in their remedial actions, as seen in the Global South and Global North divide.   Bibliography Entry Shafi, Khalid Mahmood, Arif Ullah Khan, and Rafaqat Islam. 2021. "Climate Change Action and State Sovereignty." Margalla Papers 25 (2): 98-108

Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time–Frequency EEG Images

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to human error. Recognizing this limitation, the rise in deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time–frequency domain along with pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time–frequency (CWT) images processed using autoencoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: a remarkable 100% accuracy in most of the binary classifications, exceeding 95% accuracy in three-class and four-class challenges, and a commendable rate, exceeding 93.5% for the five-class classification

Impact of Community Orgnizations (CO’S) on Rice Productivity in District Malakand Khyber Pukhtunkhwa, Pakistan

The research study was conducted in selected villages of district Malakand to study the impact of Community Organizations (CO’s) on rice productivity. A total of 70 respondents having different characteristics were interviewed. The main dependent variable was rice productivity which was studied in relation to other variables, inputs and in their application. Contact with various government departments and private sector by (CO’s) to the respondents. The results of the research study show that 7% of the farmers stated the problem of low productivity due to insect/pest attack, reasons were increase cost of the of the insecticides and pesticides. Majority of the respondents as 42.9 % purchased their seed for growing rice crop while the second dominant group was those with their own seed numbered about 32.9 %. Some of the respondents were those who used to grow seed of their own as well they also purchased some of the seed from market, were about 22.9 %. Majority of the sample respondents stated that their source of information regarding various inputs was (CO’s) following by Agri. Extension and fellow farmers. The satisfaction level of the sampled respondents was 81.4 % while 82.9 % sampled respondents stated that their production was increased due to the assistance and credit provided to them by (CO’s). The statistics of the paired T-test shows that the result was highly significant at the rate of 5% sample size and the production was increased by 10.39 % which show the effectiveness of (CO’s) activities in the area. It was concluded from the study that majority of respondents were literate, got proper technical assistance from (CO’s), agricultural extension departments and private companies. On the basis of findings it was recommended that all type of farmers should be involved in such activities and state should encourage the organizations to increase the productivity of various crops, the same techniques of assistance and credit should be adopted for other regions of the country. Keywords: Community Organizations, Rice productivity, variables, statics, technical assistance

Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight

Particle swarm optimization is a stochastic optimal search algorithm inspired by observing schools of fishes and flocks of birds. It is prevalent due to its easy implementation and fast convergence. However, PSO has been known to succumb to local optima when dealing with complex and higher dimensional optimization problems. To handle the problem of premutature convergence in PSO, this paper presents a novel adaptive inertia weight strategy and modifies the velocity update equation with the new Sbest term. To maintain the diversity of the population a particular radius r is introduced to impulse cluster particles. To validate the effectiveness of the proposed algorithm, various test functions and typical engineering applications are employed, and the experimental results show that with the changing of the proposed parameter the performance of PSO improves when dealing with these complex and high dimensional problems

Hybrid Wi-Fi and PLC network for efficient e-health communication in hospitals: a prototype

E-health is being adapted in modern hospitals as a significant addition to the existing healthcare services. To this end, modern hospitals urgently require a mobile, high-capacity, secure, and cost-effective communication infrastructure. In this paper, we explore potential applications of a hybrid broadband power line communication (PLC) and Wi-Fi in an indoor hospital scenario. It utilizes the existing power line cables and Wi-Fi plug-and-play devices for indoor broadband communication. Broadband power line (BPL) adaptors with Wi-Fi outputs are used to build an access network in hospitals, particularly in areas where the wireless router signal is poor. The Tenda PH10 AV1,000 AC Wi-Fi power line adapter is a set of BPL adapters that offer operational bandwidth of up to 1,000 Mbps. These adapters are based on the HomePlug AV2 protocol and can provide a data rate up to 200 Mbps on the physical layer. An experiment using the PLC Wi-Fi kit is carried out to show that a Wi-Fi and PLC hybrid network is the best candidate to provide wide range of practical applications in a hospital including, but not limited to, telemedicine, electronic medical records, early-stage disease diagnosis, health management, real-time monitoring, and remote surgeries

IoT-Enabled Vehicle Speed Monitoring System

Millions of people lose their lives each year worldwide due to traffic law violations, specifically, over speeding. The existing systems fail to report most of such violations due to their respective flaws. For instance, speed guns work in isolation and cannot measure speed of all vehicles on roads at all spatial points. They can only detect the speed of the vehicle the line of sight of the camera. A solution is to deploy a huge number of speed guns at different locations on the road to detect and report vehicles that are over speeding. However, this solution is not feasible because it demands a large amount of equipment and computational resources to process such a big amount of data. In this paper, a speed detection framework is developed to detect vehicles’ speeds with only two speed guns, which can report speed even when the vehicle is not within the camera’s line of sight. The system is specifically designed for an irregular traffic scenario such as that of Pakistan, where it is inconvenient to install conventional systems. The idea is to calculate the average speed of vehicles traveling in a specific region, for instance, between two spatial points. A low-cost Raspberry Pi (RPi) module and an ordinary camera are deployed to detect the registration numbers on vehicle license plates. This hardware presents a more stable system since it is powered by a low consumption Raspberry Pi that can operate for hours without crashing or malfunctioning. More specifically, the entrance and exit locations and the time taken to get from one point to another are recorded. An automatic alert to traffic authorities is generated when a driver is over speeding. A detailed explanation of the hardware prototype and the algorithms is given, along with the setup configurations of the hardware prototype, the website, and the mobile device applications

Changes in Liver Fibrosis as Determined by FIB-4 Score Following Sofosbuvir-Based Treatment Regimes Without Interferon

Objective: To determine the mean change in liver fibrosis as evaluated using the FIB-4 score following Sofosbuvir based treatment regimens without interferon. Methodology: This prospective observational study was conducted at the Department of Medicine, Federal Government Services Hospital, Islamabad, from January 09, 2019 to January 03, 2020. A total of seventy (n=70) patients of either gender between age 18-75 years who were diagnosed with cases of HCV infection were enrolled in this study. All patients were treated with Sofosbuvir-based treatment regimens and were assessed for liver fibrosis using the FIB-4 score at baseline, at end of treatment (EOT) and 12 weeks after EOT. Results: The mean FIB-4 score at baseline was 2.45±0.42, at EOT was 1.0981±0.33 and at 12 weeks after EOT was 1.51±0.32.  As compared to the baseline, the mean FIB-4 score was significantly lesser at EOT (P=0.001) and at 12 weeks after EOT (P=0.001). A similar trend was observed across all stratified groups, i.e., age, gender, and type of patients (P<0.05 across all groups). Conclusion: The sofosbuvir-based treatment regimen significantly reduced liver fibrosis at EOT and 12 weeks after EOT, as evidenced by FIB-4 scores that were significantly lower than baseline at EOT and 12 weeks after EOT

Impacted Mandibular Third Molars, A Nuisance to Neighbouring Mandibular Molars; A Radiographic Study

Objective: To know the distal caries in mandibular second molars due to impacted mandibular third molars and their association with the type of impaction of patients who visited a tertiary care dental centre in Lahore, Pakistan. Study Design: Cross-sectional study. Place and Duration of Study: 28 Military Dental Centre, Combined Military Hospital, Lahore Pakistan from Dec 2020 to Nov 2021. Methodology: The dental records of 378 patients reported at 28 Military Dental Centres were retrieved from the data bank,then decoded and analysed. Results: Out of 378 patients, 242(64%) males and 136(36%) females were referred to Oral and Maxillofacial clinics of 28 MDC to undergo surgical extraction of mandibular third molar teeth. Mesio-angular 242(64%), depth Class-B 281(74.3%), and Ramal relationship I 224(59.3) were the most common impactions referred for surgical removal. 289(76.5%) impactions of all categories were unilateral. 178(73.6%) mesioangular impactions had neighbouring second molar distal caries on Orthopentomogram while none of the transverse impactions had neighbouring second molar caries. A significant association between angulation of impaction and neighbouring second molar caries has been noted (p<0.001). Conclusion: Increased mandibular second molar distal caries were noted adjacent to Mesioangular, depth B, Class 1 Ramal relationship mandibular wisdom tooth impaction. Keywords: Distal caries, Mesioangular impaction, Orthopantomograms, Radiographic study, Surgical extraction