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BackgroundThe use of artificial intelligence has revolutionized every area of life such as business and trade, social and electronic media, education and learning, manufacturing industries, medicine and sciences, and every other sector. The new reforms and advanced technologies of artificial intelligence have enabled data analysts to transmute raw data generated by these sectors into meaningful insights for an effective decision-making process. Health care is one of the integral sectors where a large amount of data is generated daily, and making effective decisions based on these data is therefore a challenge. In this study, cases related to childbirth either by the traditional method of vaginal delivery or cesarean delivery were investigated. Cesarean delivery is performed to save both the mother and the fetus when complications related to vaginal birth arise. ObjectiveThe aim of this study was to develop reliable prediction models for a maternity care decision support system to predict the mode of delivery before childbirth. MethodsThis study was conducted in 2 parts for identifying the mode of childbirth: first, the existing data set was enriched and second, previous medical records about the mode of delivery were investigated using machine learning algorithms and by extracting meaningful insights from unseen cases. Several prediction models were trained to achieve this objective, such as decision tree, random forest, AdaBoostM1, bagging, and k-nearest neighbor, based on original and enriched data sets. ResultsThe prediction models based on enriched data performed well in terms of accuracy, sensitivity, specificity, F-measure, and receiver operating characteristic curves in the outcomes. Specifically, the accuracy of k-nearest neighbor was 84.38%, that of bagging was 83.75%, that of random forest was 83.13%, that of decision tree was 81.25%, and that of AdaBoostM1 was 80.63%. Enrichment of the data set had a good impact on improving the accuracy of the prediction process, which supports maternity care practitioners in making decisions in critical cases. ConclusionsOur study shows that enriching the data set improves the accuracy of the prediction process, thereby supporting maternity care practitioners in making informed decisions in critical cases. The enriched data set used in this study yields good results, but this data set can become even better if the records are increased with real clinical data.

Software-based technical indicators have been widely used for the stock market forecasting, aiming to predict market direction. Even though many algorithms for the software based technical indicators are presented, there are almost no hardware implementations reported in the literature. In this paper, the hardware implementation is presented for three commonly used technical indicators: Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), and Aroon. Latency evaluation is conducted for Bitcoin and Ethereum within a single-day timeframe, utilizing the Xilinx Zynq-7000 programmable SoC XC7Z020-CLG484-1 platform.Additionally, various hardware/software (HW/SW) partitioning strategies are explored to leverage the flexibility of software alongside the performance advantages of hardware via the Zynq SoC platform. The results show that the best performing technical indicator is MACD with a speedup of 30 times over its software only counterpart. Furthermore, a hybrid design integrating multiple technical indicators is proposed, pairing MACD with RSI due to their competitive throughput values, differing by only 0.38 microseconds. This hybrid approach capitalizes on the parallel processing capabilities of hardware, enabling multiple systems to operate simultaneously.

Stock market forecasting is one of the most challenging problems in today's financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model.

OBJECTIVES: This study was designed to assess the prevalence of malnutrition in terms of wasting and stunting in children between 3- and 5 years of age and to analyze the risk factors associated with malnutrition among them in the district of Peshawar. METHODOLOGY: This cross-sectional study was conducted in the district of Peshawar. Multi-staging simple random probability sampling selected a sample of 225 children between 3 and 5 years. The data was collected through a structured questionnaire containing demographic, risk factors and anthropometry. RESULTS: In a sample of 225 children, the prevalence of malnutrition was quite alarming. Stunting and wasting were observed in the age group 3-5 years. Low weight- for height was observed at 20%, and leanness through mid-arm circumference among children between 3-5 years of age in Peshawar district was 28%, although the mean values for all the indicators fell at the 50th percentile. About 76.9% were exclusively breastfed, and 29.8% of children were partially vaccinated. The association of low MUAC with breastfeeding (chi-square value of 28.9 & P=

The novel coronavirus disease 2019 (COVID-19) continues to plague the world. Hence, there is been an effort to mitigate this virus and its effects with several means including vaccination which is one of the most effective ways of controlling the virus. However, efforts at getting people to vaccinate have met several challenges. To help with understanding the reasons underlying an individual's willingness to take COVID-19 vaccine or not, a scale called Motors of COVID-19 Vaccination Acceptance Scale (MoVac-COVID19S) was developed. To expand its usability worldwide (as it has currently been limited to only China and Taiwan), data were collected in other countries (regions) too. Therefore, this MoVac-COVID19S data is from five countries (that is, India, Ghana, Afghanistan, Taiwan, and mainland China) which cut across five regions. A total of 6053 participants across the stated countries completed the survey between January and March 2021 using a cross-sectional survey design. The different sections of the survey solicited sociodemographic information (e.g., country, age, gender, educational level, and profession) and the MoVac-COVID19S data from the participants. The data collected from this survey were analyzed using descriptive statistics, which were carried out using the IBM SPSS version 22.0.

OBJECTIVES: To determine the association between diabetic retinopathy (DR) and severity of coronary artery disease (CAD) and evaluate the relation of stage of DR with severity of CAD. METHODS: This cross-sectional study was conducted in Cardiology Unit, Lady Reading Hospital, Peshawar from January-June, 2017. Patients with diabetes mellitus for >5years who underwent coronary angiography (CA) were included. All patients underwent fundoscopy and categorized into: No-DR, pre-proliferative-DR and proliferative-DR. CA was performed to assess the severity of CAD and patients were categorized into: none, mild, moderate and severe CAD on the basis of number of vessels involved or left main stem (LMS) disease. The correlation between DR and CAD was determined by chi-square test and prevalence odd ratios (POR) were calculated by using logistic regression model. RESULTS: A total of 166 patients with mean age of 55.5±8.8years were included in the study, of which 79 were males, 35 had no-DR, 110 had pre-proliferative-DR and 21 had proliferative-DR while 63 patients had mild CAD, 50 had moderate CAD and 18 had severe CAD. By using Chi square test, association between DR and severity of CAD was calculated to be 86.68 (p-0.000). After adjustments for various risk factors, PORs for severity of CAD with increasing stage of DR were found to be significantly increased from 0.27 times for no-DR to 4.27 times for pre-proliferative-DR and 6.33 times for proliferative-DR. CONCLUSION: DR is not only strongly associated with CAD but higher stage of retinopathy predicts a more severe CAD increasing the odds of CAD by 2.27 times.

OBJECTIVES: To determine the incidence of ventricular septal rupture (VSR) following acute myocardial infarction (AMI) and to find out factors associated with its mortality. METHODS: This retrospective study was done at Lady Reading Hospital, Peshawar, Pakistan in which 03years (January-2014 to December-2016) data of AMI patients was studied. Patients with VSR after AMI, diagnosed on echocardiography were included. Chi-square test was applied to correlate cardiovascular risk factors and clinical parameters with the mortality in post-MI VSR patients. RESULTS: A data of 6240 patients with AMI was analyzed in which 60 patients had VSR (incidence rate of 0.96%) with 64.7% having acute anterior MI. It is 8.5 times greater in first-MI and have a delayed onset. Twenty-six patients were excluded due to presence of concomitant complications and rest of 34 were studied in detail. Mean age of post-MI VSR patients was 63.21±8.9years, among them 52.9% were males. Mean time of development of VSR was 4.1±2.2days with the minimum of 01day and maximum of 10days after diagnosis of AMI. Nineteen patients (55.9%) didn’t receive streptokinase. The mortality rate was 38.2% which was significantly associated with diabetes, tachycardia, shock, high Killip class, renal impairment and multiple VSR’s (P-value of 0.012, 0.021, 0.032, 0.031, 0.036, and 0.016 respectively). CONCLUSIONS: VSR incidence after AMI in our study was 0.96% with 8.5 times greater in first-MI and have a delayed onset. Diabetes mellitus, multiple lesions, presence of shock, tachycardia, renal impairment and high Killip class after development of VSR are associated with high mortality.

The spread of infectious diseases such as COVID-19, flu influenza, malaria, dengue, mumps, and rubella in a population is a big threat to public health. The infectious diseases spread from one person to another person through close contact. Without proper planning, an infectious disease can become an epidemic and can result in large human and financial losses. To better respond to the spread of infectious disease and take measures for its control, the public health authorities need models and simulations to study the spread of such diseases. In this paper, an agent-based simulation engine is presented that models the spread of infectious diseases in the population. The simulation takes as an input the human-to-human interactions, population dynamics, disease transmissibility and disease states and shows the spread of disease over time. The simulation engine supports non-pharmaceutical interventions and shows its impact on the disease spread across locations. A unique feature of this tool is that it is generic; therefore, it can simulate a wide variety of infectious disease models (SIR), susceptible-infectious-susceptible (SIS) and susceptible-infectious (SI). The proposed simulation engine will help the policy-makers and public health authorities study the behavior of disease spreading; thus, allowing for better planning.

This paper proposes a new maximum power point tracking (MPPT) algorithm for thermoelectric generators (TEG). The new-presented method is based on implementing an indirect open circuit voltage detection and short circuit current estimation methods, which will be used to directly control the TEG interface power converter, resulting in reaching the maximum power point (MPP) in minimal number of steps. Two modes of operation are used in the proposed algorithm, namely the perturb and observe (P&O) method for fine-tuning and the transient mode for coarse tracking of the MPP during fast changes that occur to the temperature gradient across the structure. A novel voltage sensing technique as well is proposed in this work, to reduce the number of voltage sensors used to control and monitor the power converter. The proposed strategy employs a novel approach to sense two different voltages using the same voltage sensor. The input and output voltage information is collected from an intermediate point in the converter. The reconstructed voltages are used in the control loops as well as for monitoring the battery output or load voltages. Simulation and experimental results are provided to validate the effectiveness of the proposed algorithm and the sensing technique.

Abstract Background Mountain ecosystems all over the world support a high biological diversity and provide home and services to some 12% of the global human population, who use their traditional ecological knowledge to utilise local natural resources. The Himalayas are the world's youngest, highest and largest mountain range and support a high plant biodiversity. In this remote mountainous region of the Himalaya, people depend upon local plant resources to supply a range of goods and services, including grazing for livestock and medicinal supplies for themselves. Due to their remote location, harsh climate, rough terrain and topography, many areas within this region still remain poorly known for its floristic diversity, plant species distribution and vegetation ecosystem service. Methods The Naran valley in the north-western Pakistan is among such valleys and occupies a distinctive geographical location on the edge of the Western Himalaya range, close to the Hindu Kush range to the west and the Karakorum Mountains to the north. It is also located on climatic and geological divides, which further add to its botanical interest. In the present project 120 informants were interviewed at 12 main localities along the 60 km long valley. This paper focuses on assessment of medicinal plant species valued by local communities using their traditional knowledge. Results Results revealed that 101 species belonging to 52 families (51.5% of the total plants) were used for 97 prominent therapeutic purposes. The largest number of ailments cured with medicinal plants were associated with the digestive system (32.76% responses) followed by those associated with the respiratory and urinary systems (13.72% and 9.13% respectively). The ailments associated with the blood circulatory and reproductive systems and the skin were 7.37%, 7.04% and 7.03%, respectively. The results also indicate that whole plants were used in 54% of recipes followed by rhizomes (21%), fruits (9.5%) and roots (5.5%). Conclusion Our findings demonstrate the range of ecosystem services that are provided by the vegetation and assess how utilisation of plants will impact on future resource sustainability. The study not only contributes to an improved understanding of traditional ethno-ecological knowledge amongst the peoples of the Western Himalaya but also identifies priorities at species and habitat level for local and regional plant conservation strategies.

We present a case of 30 years old female with a parasagittal sinus meningioma, our surgical, technique and the successful outcome.

In this paper, a controller design methodology is proposed for a CLLLC-type bidirectional resonant converter. The soft switching of all devices in this topology and the very high operating frequency lead to increased overall system efficiency. However, the dynamic nature of this converter is highly dependent on loading conditions, which proves challenging when designing the voltage and current closed-loop controllers. System instability is mainly due to the high-Q resonant peaking, which is observed in the open-loop bode-plots. In this paper, the controller design methodology is proposed, which accounts for the dynamics behavior due to load variations. The controller stability will be evaluated against the entire range of operating switching frequency. Both battery charging and regeneration modes will be described and analyzed. The focal contribution of this paper will focus on defining the worst operating scenarios for the converter using system-level modeling and analysis. In addition, the controller will be defined based on these operating points. To validate and verify the controller design methodology proposed, a 3.5-kW converter is designed with the appropriate output voltage and current loop controllers. The step response verified a stable system designed and thus proving the proposed controller design methodology.

Integrating decentralised energy sources into the traditional distribution networks can result in technical issues impacting the power quality. Innovative ideas are, therefore, needed to promote the transformation of systems to a smart grid. Distribution System operator (DSO) could make use of the flexibility of emerging technologies as a method to address these power quality issues. This study aims to present an overview of a local flexibility market (LFM) which will allow DSO requirements to be fulfilled through the (VPP) as an energy flexibility provider. The required optimization loads, generators and as well as storage units, are undertaken in the general algebraic modeling simulation (GAMS) environment. The aim of the optimization problem is to provide DSOs the opportunity to increase or curtail the local generations and loads in order to satisfy their requirement. The VPP will then be responsible for handling the relevant requests in real time to ensure the correct operating schedule of a resource is applied. The preliminary results of simulation studies presented in this paper have shown that the local market framework for flexibility could have potential for deferring investments in distribution network capacity, minimizing energy costs and improving the hosting capacity of distribution networks.

Regional floras are primary resources for plant identification, an essential step in developing conservation strategies. They also provide students with a scientific window on the plants around them and help them learn botanical terminology, but they are expensive to maintain and publish. We are developing web-accessible updates for different floras, as part of which we are using online resources to help us work more effectively while rapidly providing richer resources. We use Key Base for sharing dichotomous keys, linking the terminal taxa to subsidiary keys or descriptive taxon pages. Taxon pages are generated in OpenHerbarium which enables integrating specimen and observation data with descriptions, line drawings, and images and displaying maps based on georeferenced specimen data. Its nomenclatural backbone is easily modified to reflect new treatment and can also handle multiple taxonomies. We are examining is the possibility of using a Wikipedia approach to provide a glossary.

Submerged floating tunnel (SFT) is a new solution for the transportation infrastructure through sea straits, fjords, and inland waters and can be a good alternative to long span suspension bridges and immersed tunnels. The mooring cables/anchors are main structural components to provide restoring capacity to the SFT. The time domain dynamic problem of SFT moored by vertical and inclined mooring cables/anchors is formulated. The dynamic analysis of SFT subjected to hydrodynamic and seismic excitations is performed. As the cable stiffness determines the deformation ability of SFT, therefore it becomes crucial to evaluate the effect of mooring cable stiffness on the response of SFT. The displacements and internal forces of SFT clearly specify that the vertical/tension leg mooring cables provide very small stiffness as compared to inclined mooring cables. In order to keep the SFT displacements within an acceptable limit, the effect of cable stiffness should be properly evaluated for practical design of SFT.

Mooring line is a major source of stability and plays a key role in the global response of offshore floating wind turbine. In the current state of the research, a formulation based on the analytical catenary equation is most commonly used for the analysis of mooring lines. However, due to the inability of catenary equations to consider the ocean current drag forces on mooring lines, the effect of drag forces on fairlead restoring forces has not been investigated yet. In this study, we have investigated the influence of drag forces on fairlead forces using discrete catenary formulation for modeling mooring line. The discrete catenary formulation has the ability to incorporate ocean current drag forces. Three types of elements; fully suspended, touchdown and seabed element are formulated to model the suspended, touchdown and seabed portion of a slack mooring line, respectively. The influence of viscous drag on the fairlead restoring forces is demonstrated through the analysis of OC3-Hywind mooring system subjected to ocean currents. It was found that the viscous drag significantly influences the fairlead forces.