Pattern of panic-buying and its psychosocial correlates among Pakistani adults during COVID-19 pandemic

Background: As the COVID-19 pandemic rages on, one bizarre yet ubiquitous human behavior has resurfaced. Globally, people have engaged in panic buying and hoarding (PBH). This irrational practice of panic buying is driven by deficient, manipulated news amid COVID-19 crisis.Methods: A cross-sectional study was conducted with Pakistani adults (≤18 years). Data was collected through an anonymous self-reported online survey from 17th April 2020 (0800 hours) through 20th April 2020 (2200 hours) via social media platforms. The survey consisted of sociodemographic characteristics, questions regarding food/essential supplies PBH and its psychosocial correlates- attitudes about COVID-19 severity (CA), values related to social responsibility (SR), social trust (ST), and self-interest (SI). Data was entered and analyzed using IBM Statistical package for social sciences (SPSS) version 20.Results: There were 786 participants in the survey; 59% were females. Mean age was 26.6±7.6 years. Mean PBH score was 2.31±0.85 (range: 1-5). Overall, 28.4% hoarded supplies a few times or more and 47% agreed to have bought more food/essential supplies due to COVID-19. Correlation analysis showed a 12.3% positive correlation of PBH with ST and 8.5% positive correlation with SI (p<0.05). In the multivariate regression model, PBH showed a statistically significant (p<0.05) positive impact on ST and SI.Conclusions: More than one-fourth of the individuals indulged in panic buying and hoarding during the COVID-19 in Pakistan. Fear of contracting the virus and uncertainty about the duration of lockdown was the common reasons behind PBH. Social trust and self-interest were significant psychosocial contributors to hoarding behavior.

Development of a compact and low-cost weather station for renewable energy applications

This paper describes the development of a weather station integrating several sensors which allows the measurement and data storage of the following environmental parameters: solar irradiance, temperature, humidity, wind speed, and wind direction. The collected data is later transferred to a mobile device, where it is stored in a database and processed in order to be visualized and analyzed by the user. For such purpose, a dedicated mobile app was developed and presented along the paper. The weather station also integrates small solar photovoltaic modules of three different technologies: polycrystalline, monocrystalline and amorphous silicon. Based on that, the weather station also collects information that may be employed to help the user in determining the most suitable solar photovoltaic technology for installation in a particular location. The developed system uses a Bluetooth Low Energy (BLE) wireless network to transfer the data to the mobile device when the user approaches the weather station. The system operation was validated through experimental tests that encompass all the main developed features, from the data acquisition in the weather station, to the visualization in the mobile device.- (undefined

Empiric transcatheter arterial embolization for massive or recurrent gastrointestinal bleeding: Ten-year experience from a single tertiary care center

Purpose In patients with massive or recurrent gastrointestinal bleeding (GIB) which is not amenable to endoscopic therapy, angiographic interventions are often employed. We report our ten-year experience of empiric transcatheter arterial embolization (TAE) for patients with massive or recurrent GIB. Methods All patients who had undergone empiric TAE at our hospital between March 2004 and June 2015 were identified using the institutional radiology information system. A retrospective chart review was performed using a structured pro forma. Technical success rate, 30-day clinical success rate, 30-day mortality rate, and rate of procedural complications were computed. Statistical analysis was performed using Statistical Package for Social Sciences (SPSS) version 20. Results A total of 32 patients had undergone empiric TAE for GIB during the study period. The median age of subjects was 56 years and two-thirds of them were male (68.7%). Gastroduodenal (n=24), ileocolic (n=3), left gastric (n=2), right gastroepiploic (n=1), and branches of superior and middle rectal arteries (n=1) were embolized using microcoils (n=25), polyvinyl alcohol particles (n=25), and gelatin sponge (n=3)--either alone or in combination. Technical and 30-day clinical success rates were 96.9% (31/32) and 71.9% (23/32), respectively. The 30-day mortality rate for our cohort was 21.9% (7/32). One patient developed re-bleeding at two days after the initial procedure and required repeat embolization. Coil migration (n=3) and access site hematoma (n=1) were the observed procedural complications. Conclusion Empiric TAE can be a useful treatment option for selected patients with massive or recurrent GIB that is not amenable to endoscopic therapy

Review—Smart Wearable Sensors for Health and Lifestyle Monitoring: Commercial and Emerging Solutions

The rapid growth of urbanisation has brought about various health concerns for citizens living in urban environments. Sedentary lifestyles, increased pollution levels, and high levels of stress have become prevalent issues affecting the overall well-being of urban populations. In recent years, the emergence of smart wearable devices has offered a promising avenue to address these health concerns and promote healthier lifestyles. This review evaluatse the effectiveness of smart wearables in mitigating health concerns and improving the lifestyles of urban citizens. The review involves 50 relevant peer-reviewed smart wearable studies and supporting literature from electronic databases PubMed, Ovid, Web of Science, and Scopus. Results indicate that smart wearables have the potential to positively impact the health of urban citizens by promoting physical activity, tracking vital signs, monitoring sleep patterns, and providing personalised feedback and recommendations to promote physical activity levels. Furthermore, these devices can help individuals manage stress levels, enhance self-awareness, and foster healthier behaviours. However, the review also identifies several challenges, including the accuracy and reliability of wearable data, user engagement and adherence, and ethical considerations regarding data privacy and security. </jats:p

AI-driven optimization of ethanol-powered internal combustion engines in alignment with multiple SDGs: A sustainable energy transition

With the escalating requirement for global sustainable energy solutions and the complexities linked with the complete transition to new technologies, internal combustion engines (ICEs) powered with biofuels like ethanol are gaining significance over time. However, problems linked to the performance and emissions of such ICEs necessitate accurate prediction and optimization. The study employed the integration of artificial neural networks (ANN) and multi-level historical design of response surface methodology (RSM) to address these challenges in alignment with the Sustainable Development Goals (SDGs). A single-cylinder spark ignition (SI) engine powered with ethanol-gasoline blends at different loads and speeds was used to gather data. Among six initially trained ANN models, the most efficient model with a regression coefficient (R2) of 0.9952 (training), 0.98579 (validation), 0.98847 (testing), and 0.99307 (overall) was employed to predict outputs such as brake power, brake specific fuel consumption (BSFC), brake thermal energy (BTE), concentration of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen NOx. Predicted outputs were optimized by incorporating RSM. On implementing optimized conditions, it was observed that BP and BTE increased by 19.9%, and 29.8%, respectively. Additionally, CO, and HC emissions experienced substantial reductions of 28.1%, and 40.6%, respectively. This research can help engine producers and researchers make refined decisions and achieve improved performance and emissions. The study directly supports SDG 7, SDG 9, SDG 12, SDG 13, and SGD 17, which call for achieving affordable, clean energy, sustainable industrialization, responsible consumption, and production, taking action on climate change, and partnership to advance the SDGs as a whole respectively

Refractive-index sensing with ultra-thin plasmonic nanotubes

We study the refractive-index sensing properties of plasmonic nanotubes with a dielectric core and ultra-thin metal shell. The few-nm thin metal shell is described by both the usual Drude model and the nonlocal hydrodynamic model to investigate the effects of nonlocality. We derive an analytical expression for the extinction cross section and show how sensing of the refractive index of the surrounding medium and the figure-of-merit are affected by the shape and size of the nanotubes. Comparison with other localized surface plasmon resonance sensors reveals that the nanotube exhibits superior sensitivity and comparable figure-of-merit

Using Ensembles of Machine Learning Techniques to Predict Reference Evapotranspiration (ET0) Using Limited Meteorological Data

To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET0 estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET0 using fewer climatic data are of critical importance. To estimate ET0 with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET0 with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R-2 = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET0 accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET0 using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R-2 Multan = 0.97, R2 Skardu = 0.99, R-2 ISB = 0.98, R2 Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET0 estimation and suggest applying it in other parts of the world to validate model performance

Path loss modelling at 60 GHz mmWave based on cognitive 3D ray tracing algorithm in 5G

The objective of the study is to consider the foremost high-tech issue of mobile radio propagation i.e. path loss for an outdoor and indoor environment for mmWave in a densely populated area.60 [GHz] mmWave is a win-win for the 5th Generation radio network. Several measurements and simulations are performed using the simulator “Smart Cognitive 3D Ray Tracer” build in MATLAB. Two of the main parameters (pathloss and received signal strength (RSS)) of the radio propagation are obtained in this study. To compute the pathloss and RSS, 5G 3GPP mobile propagation model is selected due to its flexibility of scenario and conditions beyond 6 GHz frequency. For indoor simulations, we again chose 5G 3GPP mobile propagation model. It is evident from the recent previous studies that there is still not enough findings in the ray tracing specially cognitive 3D ray tracing. The suggested alternative cognitive algorithm here deals with less iterations and effective use of resources. The conclusions of this work also comprise that the path loss is reliant on separation distance of base station and receiver. The above mentioned frequency and interconnected distance reported here provide better knowledge of mobile radio channel attributes and can be also used to design and estimate the performance of the future generation (5G) mobile networks