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Purpose While there is a large body of research looking at consumers’ perception of debt, much of this previous research focuses on older generations. Less is known about the perceptions and attitudes toward debt of younger consumers, specifically those from Generation Z who are from rural areas in the USA. The purpose of this paper is to identify young rural consumers’ perceptions and attitudes toward debt. Design/methodology/approach This quantitative research used surveys. A well-established scale for measuring consumer attitudes toward debt has been adopted and applied. Because of the sensitive and personal nature of debt, anonymous, self-reporting questionnaires were used to allow respondents to respond freely and minimize potential bias that could be caused by socially desirable responses. The young respondents who participated in the research were invited verbally or via email by the investigators to complete the survey online via QuestionPro. Findings This study found the majority of consumers from Generation Z reported that using credit is basically wrong. Also, these young consumers claimed that being in debt is never a good thing. Additionally, the authors found gender differences. Young male consumers were more likely to claim that they had their debt under control, and young female consumers were more likely to claim that financial debt had influenced their life. Originality/value These Generation Z perceptions provide constructive data for use in evaluating and amending marketers’ strategies to better connect with the young customers. Companies may want to stress how their products are risk adverse, provide a sort of financial security and will not leave the customer in debt. This is especially important following the COVID-19 pandemic as local businesses in a college community are trying to attract students back to their establishments.

The registration system reform with information disclosure as the core is a major institutional innovation in China's capital market in recent years. Compared with the "government-led" approval system, the pilot registration-based review system for the Science and Technology Innovation Board is characterized by "market-led, government-supervised”. In this context, how to better play the role of intermediaries and realize the benign interaction of "gatekeeper" supervision to protect the interests of investors is an important issue. An empirical analysis is made on IPO approval and IPO time used using the measurement of accountancy firm expertise model. The results of both models show that accountancy firm expertise have no significant impact on IPO approval and IPO time used, by contrast, ROA and have significant impact on IPO approval and IPO time used, that is to say, it depends more on the financial situation of the enterprise to influence the IPO approval and IPO time used of the enterprise.

We present a fast and precise deep-learning architecture, which we term O-Net, for obtaining super-resolved images from conventional phase-modulated optical microscopical techniques, such as phase-contrast microscopy and differential interference contrast microscopy. O-Net represents a novel deep convolutional neural network that can be trained on both simulated and experimental data, the latter of which is being demonstrated in the present context. The present study demonstrates the ability of the proposed method to achieve super-resolved images even under poor signal-to-noise ratios and does not require prior information on the point spread function or optical character of the system. Moreover, unlike previous state-of-the-art deep neural networks (such as U-Nets), the O-Net architecture seemingly demonstrates an immunity to network hallucination, a commonly cited issue caused by network overfitting when U-Nets are employed. Models derived from the proposed O-Net architecture are validated through empirical comparison with a similar sample imaged via scanning electron microscopy (SEM) and are found to generate ultra-resolved images which came close to that of the actual SEM micrograph.

As a successor to the present 4G technology, 5G is a modern technology with a new interface that is being developed. The main purpose of 5G is to deliver a diversified collection of services to clients worldwide, including fast data speeds, wider coverage, low latency, cheap cost, high system capacity, and a variety of connectivity alternatives. Every major carrier intends to build both millimetre wave and sub-6 5G networks, but they choose to start with the lowest frequency bands and work their way up the frequency spectrum. The sub-6 spectrums are a better option for 5G. The recent deployments of the 5G networks are focused on the sub-6GHz spectrums. However, there are limited works reported on the 5G in sub-6GHz and this has motivated us to evaluate the performance of the 5G network in sub-6GHz spectrums. This project evaluates the performance of a 5G sub-6GHz network and the performance of the 5G is compared with the 4G Long-Term Evolution (LTE) network. The Vienna 5G System Level Simulator is a numerical model of wireless communication networks that is used to develop and improve mobile communication standards. It allows the community to do repeatable simulations of crucial scenarios in preparation for 5G and beyond. The performance of the 5G sub-6GHz throughputs is evaluated using the Vienna 5G Simulator. Extensive simulation works that considered a variety of factors, i.e., bandwidths, the number of users, users speed, and carrier frequencies were carried out to evaluate the performance of the 5G sub-6GHz network. The numerical findings indicate that 5G sub-6GHz performance is always better than LTE performance under identical simulation circumstances, demonstrating that 5G always outperforms LTE. The average cell throughput of the 5G sub-6GHz is 8 times more than the 4G network. The average peak throughput dropped when the mobility speed of the users increased. The throughput of the 5G network is directly proportional to the frequency bandwidth allocated.

Waste management is an important aspect to be addressed in Smart Cities and buildings. An effective waste management system can reduce the reliance on workforce and also reduce the labor cost required for systematic waste collection. The advancement in IoT technology enables waste management to be carried out effectively and promptly when the garbage is fully occupied. This work aims to develop a smart waste management system based on the Internet of Things (IoT), implemented with sensors that can track the level of waste in the garbage bin. The developed idea here is novel, deploying a simple IoT enabled ultrasonic sensor setup to realize waste level monitoring and remotely making the data available to users so that waste collection can be carried out effectively and timely upon full occupancy.

The increase in the population of senior citizens created a new challenge of the shortage of healthcare workers to take care of the elderly. The elderly with multiple chronic conditions face problems in managing their daily medication intake. This has inspired us to design a low-cost Smart Internet of Things (IoT) Mobile Medication Dispenser (SMMD) to take care of the daily medication intake of the elderly. SMMD consists of hardware (medication dispenser) and software (an app for the user to control the SMMD and program the time to dispense the medication). The NodeMCU is used to control the stepper motor, organic light-emitting diode (OLED), and motor driver. The OLED displays the current time and the time set by the caregiver/elderly to take the medicine. The SMMD with three wheels enables it to move and dispense medication to the elderly. The NodeMCU is connected to the Firebase database to access the time required to dispense the medicine. The total cost of SMMD is USD50 and is affordable for the elderly from the lower-income group and making the process of taking medicine not a hassle for the elderly. The price of SMMD can be much lower when it is mass-produced.

The noise in the automobile affects the driving experience. The noise in the car is dominated by low-frequency noise which can be effectively reduced by active noise cancellation (ANC). The commercial ANC system employed in the car is often adopting digital signal processing (DSP) to achieve noise cancellation, in which the cost is relatively high, and it is pre-built in the automobile itself. In this work, a low-cost portable analog ANC system is designed to cancel the noise in the automobile. Extensive experimental tests are carried out to suppress the single-tone noise in the automobile, ranging from 100Hz to 500Hz in a frequency step of 100Hz. The proposed ANC system managed to reduce a maximum of 16.7dB of noise at 200Hz and a minimum of 1.7dB of noise at 400Hz. The results show that the analog ANC system designed can significantly reduce the low-frequency noise in the automobile.

The 5G wireless network standard was created through the 3rd Generation Partnership Project as the next big leap in wireless network technology. Offering several advantages over previous generations of wireless network technology 5G, promises to bring with it higher speeds, lower latency rates, better reliability, and much more. All these improvements offered by the Fifth-Generation network allowed for a variety of new applications that increased the complexity of maintaining the quality of service across the network due to their stringent and heterogeneous quality of service requirements, which must be met simultaneously. This increased complexity has motivated many researchers to propose innovative approaches in the literature, such as implementing network schedulers that can leverage different kinds of machine learning, network slicing, or combinations of several different techniques. This has motivated us to investigate the existing research regarding machine learning in network orchestration, to determine the most capable methods and algorithms to satisfy the quality-of-service requirements of the heterogeneous traffic flows across the 5G wireless network.

Solar energy is considered as a prominent source of renewable energy, mainly due to the vast abundance of sunlight and rapid advancements of photovoltaic (PV) technology. The performance, reliability and lifespan of PV systems are severely affected by numerous environmental factors and fault occurrences, which include: (1) extreme swing in the operating temperature; (2) low solar irradiation levels which appear undetected in PV systems, resulting in energy losses and degradation of PV panels; and (3) non-homogenous shading and accumulation of dirt on PV panels, causing thermal imbalance and hotspots on the panels. Therefore, it is important to monitor the operating temperature and homogeneous detection of sunlight on the PV modules to guarantee efficient energy production. In this paper, we present the development and demonstration of a sensor-assisted Internet of Things (IoT)-based photovoltaic-battery renewable energy system. The adoption of the IoT solution for monitoring the real-time variations in environmental factors and system performance is discussed here. For the PV-battery hardware module, solar panels along with rechargeable batteries are constructed to supply the system. Inverters and controllers are used to synchronize the voltage level and transformation of AC power from DC power. In the design of the IoT system, the Arduino Mega microcontroller and ESP32 TTGO board are used along with sensors for recording the temperature, presence of dust/dirt, and voltage and current levels. The working prototype enables real-time data to be captured and sent to the cloud database for data collection, performance analysis, and diagnosis/detection of faults in the proposed system.