Spatial-Temporal Evolution Patterns and Influencing Factors of Hotels in Yellow River Basin from 2012 to 2022

Governmental attention towards the high-quality development of the Yellow River basin has brought new development opportunities for the hotel industry. This study aims to reveal the spatial-temporal evolution patterns and influencing factors of hotels in the Yellow River Basin from 2012 to 2022, based on economic, social, and physical geographic data of 190,000 hotels in the Yellow River flowing. With the help of a GIS technology system, the spatial-temporal evolution patterns of all hotels, star hotels, and ordinary hotels were explored, respectively. Then, the significant influencing factors of these patterns were revealed by using geographic detector and Person correlation analysis. The following conclusions were drawn: (1) the overall scale of the hotel industry in the Yellow River Basin expanded year by year, achieving rapid growth from 2016, and fluctuating around 2020 due to the impact of the novel coronavirus epidemic;the overall spatial distribution had significant regional differences, showing the structural characteristics of "southeast more, northwest less”;(2) there was a great difference in the degree of spatial autocorrelation agglomeration among prefecture-level cities, and the degree of agglomeration of both the hotel industry as a whole and general hotels decreased year by year, showing a random distribution in 2022;star hotels were always distributed randomly. Additionally, a strong synergistic correlation was shown between the number of ordinary hotels and the number of star hotels in local space;(3) overall, the development of the hotel industry was significantly affected by seven factors: structural force, macro force, ecological force, internal power, consumption power, intermediary power, and external power. There were differences in the forces acting on different types of hotels, which gives a pattern recognition in-depth.

Thermal Comfort Analysis Using System Dynamics Modeling—A Sustainable Scenario Proposition for Low-Income Housing in Brazil

As a riveting example of social housing in Brazil, the Minha Casa Minha Vida program was set in 2009 to diminish the 6-million-home housing deficit by offering affordable dwellings for low-income families. However, recurrent thermal discomfort complaints occur among dwellers, especially in the Baltimore Residential sample in Uberlândia City. To avoid negative effects of energy poverty, such as family budget constraints from the purchase of electric appliances and extra costs from power consumption, a simulation based on system dynamics modeling shows a natural ventilation strategy with a mixed combination of sustainable and energy-efficient materials (tilting window with up to 100% opening, green tempered glass, and expanded polystyrene wall) to observe the internal room temperature variation over time. With a 50% window opening ratio combined with a 3 mm regular glass window and a 12.5 cm rectangular 8-hole brick wall, this scenario presents the highest internal room temperature value held during the entire period. From the worst to the best-case scenario, a substantial reduction in the peak temperature was observed from window size variation, demonstrating that natural ventilation and constructive elements of low complexity and wide availability in the market contribute to the thermal comfort of residential rooms.

A Deep Learning Powered System to Lie Detection While Online Study

Many education facilities have recently switched to online learning due to the COVID-19 pandemic. The nature of online learning makes it easier for dishonest behaviors, such as cheating or lying during lessons. We propose a new artificial intelligence - powered solution to help educators solve this rising problem for a fairer learning environment. We created a visual representation contrastive learning method with the MobileNetV2 network as the backbone to improve predictability from an unlabeled dataset which can be deployed on low power consumption devices. The experiment shows an accuracy of up to 59%, better than several previous research, proving the usability of this approach.

Conceptual Design of a UVC-LED Air Purifier to Reduce Airborne Pathogen Transmission—A Feasibility Study

Existing indoor closed ultraviolet-C (UVC) air purifiers (UVC in a box) have faced technological challenges during the COVID-19 breakout, owing to demands of low energy consumption, high flow rates, and high kill rates at the same time. A new conceptual design of a novel UVC-LED (light-emitting diode) air purifier for a low-cost solution to mitigate airborne diseases is proposed. The concept focuses on performance and robustness. It contains a dust-filter assembly, an innovative UVC chamber, and a fan. The low-cost dust filter aims to suppress dust accumulation in the UVC chamber to ensure durability and is conceptually shown to be easily replaced while mitigating any possible contamination. The chamber includes novel turbulence-generating grids and a novel LED arrangement. The turbulent generator promotes air mixing, while the LEDs inactivate the pathogens at a high flow rate and sufficient kill rate. The conceptual design is portable and can fit into ventilation ducts. Computational fluid dynamics and UVC ray methods were used for analysis. The design produces a kill rate above 97% for COVID and tuberculosis and above 92% for influenza A at a flow rate of 100 L/s and power consumption of less than 300 W. An analysis of the dust-filter performance yields the irradiation and flow fields.

A Silicon-Based Radio Platform for Integrated Edge Sensing and Communication Toward Sustainable Healthcare

Faced with COVID-19 and the trend of aging, it is demanding to develop an online health metrics sensing solution for sustainable healthcare. An edge radio platform owning the function of integrated sensing and communications is promising to address the challenge. Radar demonstrates the capability for noncontact healthcare with high sensitivity and excellent privacy protection. Beyond conventional radar, this article presents a unique silicon-based radio platform for health status monitoring supported by coherent frequency-modulated continuous-wave (FMCW) radar at Ku-band and communication chip. The radar chip is fabricated by a 65-nm complementary metal–oxide–semiconductor (CMOS) process and demonstrates a 1.5-GHz chirp bandwidth with a 15-GHz center frequency in 220-mW power consumption. A specific small-volume antenna with modified Vivaldi architecture is utilized for emitting and receiving radar beams. Biomedical experiments were implemented based on the radio platform cooperating with the antenna and system-on-chip (SoC) field-programmable gate array (FPGA) edge unit. An industrial, scientific, and medical (ISM)-band frequency-shift keying (FSK) communication chip in 915-MHz center frequency with microwatt-level power consumption is used to attain communications on radar-detected health information. Through unified integration of radar chip, management software, and communication unit, the integrated radio platform featuring −72-dBm sensitivity with a 500-kb/s FSK data rate is exploited to drastically empower sustainable healthcare applications.

EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats.

Coronavirus disease 2019 (COVID-19) has caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, impacting effective diagnosis and treatment for any chronic illnesses and long-term health implications. In this worldwide crisis, the pandemic shows its daily extension (i.e., active cases) and genome variants (i.e., Alpha) within the virus class and diversifies the association with treatment outcomes and drug resistance. As a consequence, healthcare-related data including instances of sore throat, fever, fatigue, cough, and shortness of breath are given due consideration to assess the conditional state of patients. To gain unique insights, wearable sensors can be implanted in a patient's body that periodically generates an analysis report of the vital organs to a medical center. However, it is still challenging to analyze risks and predict their related countermeasures. Therefore, this paper presents an intelligent Edge-IoT framework (IE-IoT) to detect potential threats (i.e., behavioral and environmental) in the early stage of the disease. The prime objective of this framework is to apply a new pre-trained deep learning model enabled by self-supervised transfer learning to build an ensemble-based hybrid learning model and to offer an effective analysis of prediction accuracy. To construct proper clinical symptoms, treatment, and diagnosis, an effective analysis such as STL observes the impact of the learning models such as ANN, CNN, and RNN. The experimental analysis proves that the ANN model considers the most effective features and attains a better accuracy (~98.3%) than other learning models. Also, the proposed IE-IoT can utilize the communication technologies of IoT such as BLE, Zigbee, and 6LoWPAN to examine the factor of power consumption. Above all, the real-time analysis reveals that the proposed IE-IoT with 6LoWPAN consumes less power and response time than the other state-of-the-art approaches to infer the suspected victims at an early stage of development of the disease.

An ultralow power wearable vital sign sensor using an electromagnetically reactive near field.

Despite coronavirus disease 2019, cardiovascular disease, the leading cause of global death, requires timely detection and treatment for a high survival rate, underscoring the 24 h monitoring of vital signs. Therefore, telehealth using wearable devices with vital sign sensors is not only a fundamental response against the pandemic but a solution to provide prompt healthcare for the patients in remote sites. Former technologies which measured a couple of vital signs had features that disturbed practical applications to wearable devices, such as heavy power consumption. Here, we suggest an ultralow power (100 µW) sensor that collects all cardiopulmonary vital signs, including blood pressure, heart rate, and the respiration signal. The small and lightweight (2 g) sensor designed to be easily embedded in the flexible wristband generates an electromagnetically reactive near field to monitor the contraction and relaxation of the radial artery. The proposed ultralow power sensor measuring noninvasively continuous and accurate cardiopulmonary vital signs at once will be one of the most promising sensors for wearable devices to bring telehealth to our lives.

First Diagnosis of Immunoglobulin A Nephropathy Following SARS-CoV-2 mRNA Vaccination in

Pakistan is in a terrifying and devastating energy crisis. Recently, the prediction for energy consumption has intensified compared to its production capacity, which is problematic for Pakistan's social and economic stability. Hence, it is vital to examine the link between power consumption, power prices, urban transition, other electricity use, and economic expansion from 1970 to 2018 in Pakistan. For analysis, the second-generation econometric technique of Lee and Strazicich (2013), novel Augmented Autoregressive Distributed Lag (AARDL), and Frequency Domain Causality (FDC) is useful to detect the long-medium and short-run association among the variables. The results show that power consumption stimulates economic expansion in the short and long-run, though the rise in power prices declines economic activity in the short and long-run. Also, urban transition and other electricity use are a substantial positive and negative impact on economic expansion in the short and long-run. The outcome suggests that efficient energy supply, low-cost power prices, proper urban transition management, and other energy use could be useful for policymakers to achieve SDGs 7 and 11 in Pakistan.

E-safe: Smart Ecg-based Authentication On-wrist Healthcare Wearable System

E-SAFE, an IoT-based multimodal health monitoring system, offers a low-cost wrist-based gadget designed especially for the elderly facing the high risk of falling. This paper introduced an affordable solution to securely monitor the elderly health vitals, particularly in the current Covid-19 pandemic. The suggested system integrates a biometric ECGbased authentication process, allowing users to safely access a mobile health monitoring application in real-time. This on-wrist system is a proof-of-concept realized on the Arduino platform to monitor vital health parameters, namely temperature, ECG (electrocardiogram), oxygen saturation (SPO2), heart rate and falls. The obtained experimental results conducted on human subjects carrying out different daily activities show good ECGbased user identification accuracy of 94% using the SVM model and efficient fall-detection using only an accelerometer. [ FROM AUTHOR]

IMPACT OF LIFESTYLE CHANGES DURING COVID-19 PANDEMIC ON ELECTRICITY DEMAND IN DETACHED HOUSES (PART 2) : ANALYSIS BY HOUSEHOLD IN THE WINTER OF 2021 AND CONSIDERATION OF RENEWABLE ENERGY SELF-CONSUMPTION

This paper focused on the impact of lifestyle changes in response to the novel coronavirus infection (COVID-19) on the electricity demand of 1339 detached houses from October 2020 to March 2021. Analyzing with the lifestyle questionnaire survey, twelve months after the first state of emergency for COVID-19 at April 2020, “working from home” was the only factor that increased household power consumption for 11% and the other factors were gone. Space heating power consumption in this period did not increase significantly. Lifestyle changes have affected household timely electricity demand and increased self-consumption of renewable energy of photovoltaic power generation systems.

Visualizing electrical energy consumption with virtual augmented technology and the internet of things

Power consumption was considered one of the major expenses in households and small offices in the COVID pandemic situation. The proposed method employs virtual augmented technology and the Internet of Things (IoT) to provide a visual solution for monitoring the power consumption of electrical equipment. The IoT sensors are linked via mobile phones or portable communication devices. For the proposed work, the energy sensor devices are installed on the 4 single-phase electrical devices in the office room equipment. That equipment’s electrical consumption was saved, processed through the ESP8266 board, and then transmitted through WiFi to collect data on the cloud server. The saved electrical energy data in the dashboard can be displayed as a visual comparison with AR technology by using the mobile phone camera to scan the marker of each electrical device. The experimental results show that the value of electrical energy is accurate, and the data values can be used to manage the power consumption of electrical equipment.

A Deep Learning Powered System to Lie Detection While Online Study

Many education facilities have recently switched to online learning due to the COVID-19 pandemic. The nature of online learning makes it easier for dishonest behaviors, such as cheating or lying during lessons. We propose a new artificial intelligence - powered solution to help educators solve this rising problem for a fairer learning environment. We created a visual representation contrastive learning method with the MobileNetV2 network as the backbone to improve predictability from an unlabeled dataset which can be deployed on low power consumption devices. The experiment shows an accuracy of up to 59%, better than several previous research, proving the usability of this approach. © 2022 Lavoisier. All rights reserved.

Research on Wire-to-Two Fin Configuration for Positive Atmospheric Plasma by Electrokinetic Blower

Interest in the development of electro-fluid-dynamic devices (EFDs) based on corona discharge is growing due to their advantages and applicability across different industrial sectors. On the one hand, their performance as forced convection motors, in terms of weight, volume, and absence of noise and moving parts, make them competitive against traditional systems such as fans. On the other hand, the actions of the corona discharge, in terms of elimination of viruses and bacteria, are already known. This paper studies the characteristics of corona discharge in terms of air flow for a new proposed configuration and geometry of electrodes. A systematic study is performed through a parametric study of the distances, power consumption, and size of the corona electrode. The characteristic voltage–current (CVCCs) and flow–pressure curves obtained provide design rules to use the generated corona discharge and the device itself, as a silent air propeller, which may also sterilize the surrounding environment and surfaces.

A Carbon Nanotube-Functional Polymer Composite Film for Low-Power Indoor CO₂ Monitoring

Indoor air quality (IAQ) has been a growing concern in recent years, only to be expedited by the COVID-19 pandemic. A common provisional measure for IAQ is carbon dioxide (CO2), which is commonly used to inform the ventilation control of buildings. However, few commercially available sensors exist that can reliably measure CO2 while being low cost, exhibiting low power consumption, and being easily deployable for use in applications such as occupancy monitoring. This work presents a polymer composite-based chemiresistive CO2 sensor that leverages branched poly(ethylenimine) (PEI) and poly(ethylene glycol) (PEG) as the CO2 absorbing layer. This polymer blend was incorporated with single wall carbon nanotubes (CNT), which serve as the charge carriers. Prototype sensors were assessed in a bench-top environmental test chamber which varied temperature (22–26 °C), relative humidity level (20–80%), CO2 concentration (400–20,000 ppm), as well as other gas constituents to simulate typical and extreme indoor conditions. The results indicate that the proposed system could ultimately serve as a low-power alternative to current commercially available technologies for indoor CO2 monitoring.

Sustainable High-Rise Buildings: Toward Resilient Built Environment

This article examines outstanding “sustainable” skyscrapers that received international recognition, including LEED certification. It identifies vital green features in each building and summarizes the prominent elements for informing future projects. Overall, this research is significant because, given the mega-scale of skyscrapers, any improvement in their design, engineering, and construction will have mega impacts and major savings (e.g., structural materials, potable water, energy, etc.). Therefore, the extracted design elements, principles, and recommendations from the reviewed case studies are substantial. Further, the article debates controversial design elements such as wind turbines, photovoltaic panels, glass skin, green roofs, aerodynamic forms, and mixed-use schemes. Finally, it discusses greenwashing and the impact of COVID-19 on sustainable design. Copyright © 2022 Al-Kodmany.

The COVID-19, Power Generation and Economy- Case Study of a Developing Country

The global COVID-19 pandemic created profound impact on every nation’s economy, education, healthcare, social and cultural life, domestic and international mobility at an unprecedented level. Since the start of the COVID-19 pandemic in early 2020, most nations are undergoing through frequent full or partial lockdowns, resulting in significant economic losses, and unprecedented suffering of hundreds of millions of people worldwide. Given the crucial role of electric power in economic activities, the purpose of this study is to investigate the impact of COVID-19 pandemic on power sector and economy in a developing/ emerging country as a case study. The study examined electric power generation and consumption, GDP growth, export, import, remittances, and various government measures undertaken during the COVID-19 pandemic in Bangladesh. Autoregressive distributed lag (ARDL) model was used to investigate correlation between COVID-19 cases and power consumption during full and partial lockdowns. The research revealed a long-run negative relationship between COVID-19 cases and power consumption during partial lockdowns. The study also revealed that the targeted and partial lockdowns accompanied by nation-wide mass vaccination program can steer the economy along the power sector with minimal or no impact during the COVID-19 pandemic.

A Fuzzy Grouping Genetic Algorithm for Solving a Real-World Virtual Machine Placement Problem in a Healthcare-Cloud

Due to the large-scale development of cloud computing, data center electricity energy costs have increased rapidly. Energy saving has become a major research direction of virtual machine placement problems. At the same time, the multi-dimensional resources on the cloud should be used in a balanced manner in order to avoid resources waste. In this context, this paper addresses a real-world virtual machine placement problem arising in a Healthcare-Cloud (H-Cloud) of hospitals chain in Saudi Arabia, considering server power consumption and resource utilization. As a part of optimizing both objectives, user service quality has to be taken into account. In fact, user quality of service (QoS) is also considered by measuring the Service-Level Agreement (SLA) violation rate. This problem is modeled as a multi-objective virtual machine placement problem with the objective of minimizing power consumption, resource utilization, and SLA violation rate. To solve this challenging problem, a fuzzy grouping genetic algorithm (FGGA) is proposed. Considering that multiple optimization objectives may have different degrees of influence on the problem, the fitness function of the proposed algorithm is calculated with fuzzy logic-based function. The experimental results show the effectiveness of the proposed algorithm.

THE AHP QUANTIFICATION OF STUDENT POPULATION ATTITUDES IN WINE PURCHASING

According to the FAO (2021), slightly less than 6 litres of wine is consumed per capita. (2016) confirmed, based on the North Macedonian National Strategy for Viticulture and Wine production, that the wine market in the country has two consumer groups: middle-aged who have lower purchasing power and consume larger quantities of cheaper wine and younger to middle-aged with higher purchasing power who prefer smaller quantities of high-quality wine. [...]the research was conducted among students of Banja Luka University. (2017) stated that of all the factors analyzed in relation to consumers' behavior and preference, the dominant factors identified are demographic factors-age, region, family size and place of living, social factorseducation and income, and behavioral factors-price importance, place of purchase and product characteristics.

A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring

Self-powered RF passive sensors have potential application in temperature measurements of patients with health problems. Herein, this work presents the design and implementation of a self-powered UHF passive tag prototype for biomedical temperature monitoring. The proposed battery-free sensor is composed of three basic building blocks: a high-frequency section, a micro-power management stage, and a temperature sensor. This passive temperature sensor uses an 860 MHz to 960 MHz RF carrier and a 1 W Effective Isotropic Radiated Power (EIRP) to harvest energy for its operation, showing a read range of 9.5 m with a 13.75 µW power consumption, and an overall power consumption efficiency of 10.92% was achieved. The proposed device can measure temperature variations between 0 °C and 60 °C with a sensitivity of 823.29 Hz/°C and a standard error of 13.67 Hz/°C over linear regression. Circuit functionality was validated by means of post-layout simulations, characterization, and measurements of the manufactured prototype. The chip prototype was fabricated using a 0.18 µm CMOS standard technology with a silicon area consumption of 1065 µm × 560 µm. The overall size of the self-powered passive tag is 8 cm × 2 cm, including both chip and antenna. The self-powered tag prototype could be employed for human body temperature monitoring.

Modeling and Simulation of Household Appliances Power Consumption

Featured ApplicationThe method presented in this paper serves to predict the consumption of household appliances by modeling their behavior and by simulating accordingly.The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict household electricity consumption. As a novelty, this paper proposed a method of predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov–Smirnov Test and Pearson’s X2 test). To test the veracity of the evaluations, first, a set of random values was simulated for each hour, and their respective averages were calculated. These were compared with the averages of the real values for each hour. With the exception of HVAC during working days, great results were obtained. For the refrigerator, the maximum error was 3.91%, while for the lighting, it was 4.27%. At the point of consumption, the accuracy was even higher, with an error of 1.17% for the dryer while for the washing machine and dishwasher, their minimum errors were less than 1%. The error results confirm that the applied methodology is perfectly acceptable for modeling household appliance consumption and consequently predicting it. However, these consumptions can be only extrapolated to dwellings with similar surface areas and habitats.