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In this work, we investigated the finite-time passivity problem of neutral-type complex-valued neural networks with time-varying delays. On the basis of the Lyapunov functional, Wirtinger-type inequality technique, and linear matrix inequalities (LMIs) approach, new sufficient conditions were derived to ensure the finite-time boundedness (FTB) and finite-time passivity (FTP) of the concerned network model. At last, two numerical examples with simulations were presented to demonstrate the validity of our criteria.

Organic electronics have great potential due to their flexible structure, high performance, and their ability to build effective and low-cost photodetectors. We investigated the parameters of the P3HT and PCBM layers for device performance and optimization. SCAPS-1D simulations were employed to optimize the thicknesses of the P3HT and PCBM layers, investigate the effects of shallow doping in the P3HT layer, and assess the influence of the back contact electrode’s work function on device performance. Furthermore, this study explored the impact of interface defect layer density on the characteristics of the device. Through systematic analyses, the optimal parameters for enhancing device responsivity were identified. The findings indicate that a P3HT layer thickness of 1200 nm, a PCBM layer thickness of 20 nm, and a back contact electrode with a work function of 4.9 eV achieve the highest responsivity. Notably, at a bias of −0.5 V, the responsivity exceeds 0.4 A/W within the wavelength range of 450 nm to 630 nm. These optimized parameters underscore the significant potential of the developed device as an organic photodetector, particularly for visible light detection.

Despite massive investments in the education sector to empower youth in Qatar, a vital concern remains to retain students in science, technology, engineering, and mathematics (STEM) disciplines at the undergraduate level. Even though the country is committed to fostering a knowledge-based society, the low interest of undergraduates in STEM disciplines remains a vital challenge. To investigate this, the current study uses a survey methodology to investigate the perspectives of 172 undergraduate students to understand quantitatively the factors that influence their individual interests in STEM disciplines. Non-parametric significance tests and binary logistic regressions were employed to quantitatively measure the direct factors and predictors that affect students’ individual interests. Findings indicated that aspects like students’ reason/motivation to join STEM, their interaction with faculty, the habit of skipping classes, the difficulty faced in the curriculum, and their parents’ highest educational qualification have an association with individual interests. Also, it was found that demographics such as age group, ethnicity, undergraduate discipline, undergraduate year, parent’s employment status, and mother’s highest educational qualification do not contribute to significant differences in students’ individual interests. These conclusions provide important implications for educationists and policymakers to devise constructive reforms to enhance undergraduate students’ individual interests, thereby improving their persistence in STEM.

Indoor air quality and respiratory health have always been an area of prime interest across the globe. The significance of low-cost air quality sensing and indoor public health practices spiked during the pandemic when indoor air pollution became a threat to living beings, especially human beings. Problem Definition: Indoor respiration-associated diseases are hard to diagnose if they are due to indoor environmental conditions. A major challenge was observed in establishing a baseline between indoor air quality sensors and associated respiratory diseases. Methods: In this work, 10,000+ articles from top literature databases were reviewed using six bibliometric analysis methods (Lorenz Curve of Citations, Hirch’s H-Index, Kosmulski’s H2-Index, Harzing’s Hl-Norm-Index, Sidoropolous’s HC-Index, and Schrieber’s HM-index) to formulate indoor air quality sensor and disease correlation publication rubrics to critically review 482 articles. Results: A set of 152 articles was found based on systematic review parameters in six bibliometric indices for publications that used WHO, NIH, US EPA, CDC, and FDA-defined principles. Five major respiratory diseases were found to be causing major death toll (up to 32%) due to five key pollutants, measured by 30+ low-cost sensors and further optimized by seven calibration systems for seven practical parameters tailored to respiratory disease baselines evaluated through 10 cost parameters. Impact: This review was conducted to assist end-users, public health facilities, state agencies, researchers, scientists, and air quality protection agencies.

The current COVID-19 pandemic has raised huge concerns about outdoor air quality due to the expected lung deterioration. These concerns include the challenges associated with an increase of harmful gases like carbon dioxide, the iterative/repetitive inhalation due to mask usage, and harsh environmental temperatures. Even in the presence of air quality sensing devices, these challenges can hinder the prevention and treatment of respiratory diseases, epidemics, and pandemics in severe cases. In this research, a dual time series with a bi-cluster sensor data-stream-based novel optimized regression algorithm was proposed with optimization predictors and responses that use an automated iterative optimization of the model based on the similarity coefficient index. The algorithm was implemented over SeReNoV2 sensor nodes data, i.e., a multi-variate dual time-series sensor, of the environmental and US Environmental Protection Agency standard, which measures variables for the air quality index using air quality sensors with geospatial profiling. The SeReNoV2 systems were placed at four locations that were 3 km apart to monitor the air quality and their data was collected at Ubidots IoT platform over GSM. The results have shown that the proposed technique achieved a root mean square error (RMSE) of 1.0042 with a training time of 469.28 s for the control and an RMSE of 1.646 in a training time of 28.53 s when optimized. The estimated R-Squared error was 0.03, with the Mean-Square Error for temperature being 1.0084 °C, and 293.98 ppm for CO2. Furthermore, the Mean-Absolute Error (MAE) for temperature was 0.66226 °C and 10.252 ppm for the correlated-CO2 at a predicted speed of ~5100 observations/s. In the sample cluster for temperature, 45,000 observations/s for CO2 was achieved due to the iterative optimization of the training time (469.28 s). The correlated temperature and a time of 28.53 s for CO2 were very promising in forecasting COVID-19 countermeasures before time.

Abstract Perovskite solar cells (PSCs) expressed great potentials for offering a feasible alternative to conventional photovoltaic technologies. 2D/3D hybrid PSCs, where a 2D capping layer is used over the 3D film to avoid the instability issues associated with perovskite film, have been reported with improved stabilities and high power conversion efficiencies (PCE). However, the profound analysis of the PSCs with prolonged operational lifetime still needs to be described further. Heading towards efficient and long-life PSCs, in-depth insight into the complicated degradation processes and charge dynamics occurring at PSCs' interfaces is vital. In particular, the Au/HTM/perovskite interface got a substantial consideration due to the quest for better charge transfer; and this interface is debatably the trickiest to explain and analyze. In this study, multiple characterization techniques were put together to understand thoroughly the processes that occur at the Au/HTM/perovskite interface. Inquest analysis using current–voltage (I–V), electric field induced second harmonic generation (EFISHG), and impedance spectroscopy (IS) was performed. These techniques showed that the degradation at the Au/HTM/perovskite interface significantly contribute to the increase of charge accumulation and change in impedance value of the PSCs, hence resulting in efficiency fading. The 3D and 2D/3D hybrid cells, with PCEs of 18.87% and 20.21%, respectively, were used in this study, and the analysis was performed over the aging time of 5000 h. Our findings propose that the Au/HTM/perovskite interface engineering is exclusively essential for attaining a reliable performance of the PSCs and provides a new perspective towards the stability enhancement for the perovskite-based future emerging photovoltaic technology.

The heterogeneity and levels of chemicals released into the environment have dramatically grown in the last few years. Therefore, new low-cost tools are increasingly required to monitor pollution and follow its trends over time. Recent approaches in electronics and wireless communications permit the expansion of low-power, low-cost, and multiparametric sensor nodes that are limited in size and communicate untethered in small distances. For such a monitoring system to be ultimately feasible, a suitable power source for these nodes must be found. The present research falls within the frame of this global effort. The study sits within the context discussed above with the particular aim of developing groundbreaking technology-based solutions by means of efficient environmentally powered wireless smart sensors. This paper presents a multiparametric sensor node for indoor/outdoor air quality monitoring, able to work without battery and human intervention, harvesting energy from the surrounding environment for perpetual operation. The complete system design of the sensor and experimental results are reported. The evaluation of the energy-harvesting blocks with a budget allocation of the power consumption is also discussed.

Thermoelectricity is a promising technology that directly converts heat energy into electricity and finds its use in enormous applications. This technology can be used for waste heat recovery from automobile exhausts and industrial sectors and convert the heat from solar energy, especially in hot and humid areas such as Qatar. The large-scale, cost-effective commercialization of thermoelectric generators requires the processing and fabrication of nanostructured materials with quick, easy, and inexpensive techniques. Moreover, the methods should be replicable and reproducible, along with stability in terms of electrical, thermal, and mechanical properties of the TE material. This report summarizes and compares the up-to-date technologies available for batch production of the earth-abundant and ecofriendly materials along with some notorious works in this domain. We have also evaluated and assessed the pros and cons of each technique and its effect on the properties of the materials. The simplicity, time, and cost of each synthesis technique have also been discussed and compared with the conventional methods.

This article presents recent research directions in the study of Earth-abundant, cost-effective, and low-toxic advanced nanostructured materials for thermoelectric generator (TEG) applications. This study’s critical aspect is to systematically evaluate the development of high-performance nanostructured thermoelectric (TE) materials from sustainable sources, which are expected to have a meaningful and enduring impact in developing a cost-effective TE system. We review both the performance and limitation aspects of these materials at multiple temperatures from experimental and theoretical viewpoints. Recent developments in these materials towards enhancing the dimensionless figure of merit, Seebeck coefficient, reduction of the thermal conductivity, and improvement of electrical conductivity have also been discussed in detail. Finally, the future direction and the prospects of these nanostructured materials have been proposed.

Halloysite-polypyrrole-silver nanocomposite has been prepared via in situ photopolymerizations of pyrrole in the presence of silanized halloysite and silver nitrate as a photoinitiator. The halloysite nanoclay (HNT) was modified using the hydrogen donor silane coupling agent (DMA) in order to provide anchoring sites for the polypyrrole/silver composite (PPy@Ag). The mass loadings for both PPy and Ag have been estimated to be 21 and 26 wt%, respectively. The anchored Ag particles were found in the metallic state. The resulting PPy@Ag-modified silanized HNT has been evaluated for the potential application for impedance humidity sensors. HNT-DMA-PPy@Ag nanocomposite with different weight % of PPy@Ag (0.25 wt%, 0.5 wt%, and 1 wt%) was deposited on the pre-patterned interdigital Indium Tin Oxide (ITO) electrodes by spin coating technique. The addition of Ag nanoparticles within the nanocomposite enhances the hydrophilicity of the sensing film, which improves the sensitivity of the humidity sensors. The HNT-DMA-PPy@Ag (0.5 wt%) nanocomposite-based impedance sensors showed good sensitivity and lowered hysteresis as compared to the other ratios of the composite. The maximum calculated hysteresis loss of the HNT-DMA-PPy@Ag (0.5 wt%)-based humidity sensor is around 4.5% at 80% RH (relative humidity), and the minimum hysteresis loss estimated to be 0.05% at 20% RH levels. The response and recovery time of HNT-DMA-PPy@Ag (0.5 wt%) nanocomposite-based impedance sensors were found to be 30 and 35 s, respectively. The interesting humidity-dependent impedance properties of this novel composite make it promising in humidity sensing.

Electrochemical gas sensors require regular maintenance to check and secure proper functioning. Standard procedures usually involve testing and recalibration of the sensors, for which working environments are needed. Periodic calibration is therefore necessary to ensure reliable and accurate measurements. This paper proposes a dedicated smart calibration rig with a set of novel features enabling simultaneous calibration of multiple sensors. The proposed calibration rig system comprises a gas mixing system, temperature control system, a test chamber, and a process-control PC that controls all calibration phases. The calibration process is automated by a LabVIEW-based platform that controls the calibration environment for the sensor nodes, logs sensor data, and best fit equation based on interpolation for every sensor on the node and uploads it to the sensor node for next deployments. The communication between the PC and the sensor nodes is performed using the same IEEE 802.15.4 (ZigBee) protocol that the nodes also use in field deployment for air quality measurement. The results presented demonstrate the effectiveness of the sensors calibration rig.

Earthquakes are one of the major natural calamities as well as a prime subject of interest for seismologists, state agencies, and ground motion instrumentation scientists. The real-time data analysis of multi-sensor instrumentation is a valuable knowledge repository for real-time early warning and trustworthy seismic events detection. In this work, an early warning in the first 1 micro-second and seismic wave detection in the first 1.7 milliseconds after event initialization is proposed using a seismic wave event detection algorithm (SWEDA). The SWEDA with nine low-computation-cost operations is being proposed for smart geospatial bi-axial inclinometer nodes (SGBINs) also utilized in structural health monitoring systems. SWEDA detects four types of seismic waves, i.e., primary (P) or compression, secondary (S) or shear, Love (L), and Rayleigh (R) waves using time and frequency domain parameters mapped on a 2D mapping interpretation scheme. The SWEDA proved automated heterogeneous surface adaptability, multi-clustered sensing, ubiquitous monitoring with dynamic Savitzky−Golay filtering and detection using nine optimized sequential and structured event characterization techniques. Furthermore, situation-conscious (context-aware) and automated computation of short-time average over long-time average (STA/LTA) triggering parameters by peak-detection and run-time scaling arrays with manual computation support were achieved.

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner in form of a SHM geo-informatics system. The proposed UCM consists of networked SHM systems that send geometrical SHM variables to SHM-UCM gateways. Every gateway is routing the data to SHM-UCM servers running a geo-spatial patch health assessment and prediction algorithm. The inputs of the prediction algorithm are geometrical variables, environmental variables, and payloads. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This has been tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU routers and zone calculation models and were then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

Weather conditions and chemical composition of the atmosphere are the most uncontrollable challenges for free-space optical (FSO) communications. Environmental parameters contribute directly in the characterization of atmospheric turbulence. Thus, it is very interesting to prepare a test setup to study the effect of such parameters on FSO transmissions especially in a region like Qatar, which is arid and has harsh climate. Unlike that of Europe and USA, Qatar’s climate is characterized by high temperatures throughout the year, exceeding 14 °C even in winter. The experiment was carried at Qatar University (QU) using a system that comprises of two Field Programmable Gate Array (FPGA) boards and two FSO terminals installed at 600 m apart from each other. Each terminal operates at 1550 nm and had a capacity of 1 Gb/s. On the other hand, the environment parameters were recorded using a weather station installed near the transmitter. To have a wider set of data for analysis, parameters from four different seasons were recorded and analyzed. We present a comparison between the FSO performances for the different seasons in terms of Packet Delivery Ratio (PDR). We notice a significant difference in the behavior of FSO during summer and winter seasons. Furthermore, using statistics on the data collected from the weather station and based on Blaunstein model (BKB), we correlate the FSO behavior to the variation of refractive index (Cn2) in winter. Also a closed form expression, estimated from the statistical data, has been derived to relate the PDR with environmental parameters for the summer season.

This paper shows how Q-learning approach can be used in a successful way to deal with the problem of mobile robot navigation. In real situations where a large number of obstacles are involved, normal Q-learning approach would encounter two major problems due to excessively large state space. First, learning the Q-values in tabular form may be infeasible because of the excessive amount of memory needed to store the table. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. In this paper, we propose a navigation approach for mobile robot, in which the prior knowledge is used within Q-learning. We address the issue of individual behavior design using fuzzy logic. The strategy of behaviors based navigation reduces the complexity of the navigation problem by dividing them in small actions easier for design and implementation. The Q-Learning algorithm is applied to coordinate between these behaviors, which make a great reduction in learning convergence times. Simulation and experimental results confirm the convergence to the desired results in terms of saved time and computational resources.

The abundance of solar energy is a blessing in the Arabian Peninsula, where more than 2000 kWh/m2 density has been recorded annually. This has resulted in sincere consideration of PV harvesting in the energy matrix and smart grid. However, artefacts such as degradation of PV efficiency due to the high temperature effect have to be addressed. This paper presents a novel design of a PV cooling system using water to mitigate the effect of high temperature. Several experiments have been conducted, and the results have been analyzed. It has been found that the collected water from the panel after 40 min of cooling gained a temperature of 10 °C approximately, during December 2016. Eventually, the efficiency was improved by 10.35% (without using MPPT) using water at ambient temperature (24 °C) compared to the non-cooled panel. Moreover, the temperature of the panel during solar peak hours dropped from 64.3 °C to 32 °C and from 59 °C to 27 °C in 3 min for the back and front surface, respectively. These results, which are the first of their kind in Qatar, constitute good incentives and pave the way for further investigation to enhance PV efficiency in harsh environments. This would be of paramount significance, especially for scaling up PV deployment, as is planned in Qatar and GCC countries in their 2030 vision.

The impact of air quality on health and on life comfort is well established. In many societies, vulnerable elderly and young populations spend most of their time indoors. Therefore, indoor air quality monitoring (IAQM) is of great importance to human health. Engineers and researchers are increasingly focusing their efforts on the design of real-time IAQM systems using wireless sensor networks. This paper presents an end-to-end IAQM system enabling measurement of CO2, CO, SO2, NO2, O3, Cl2, ambient temperature, and relative humidity. In IAQM systems, remote users usually use a local gateway to connect wireless sensor nodes in a given monitoring site to the external world for ubiquitous access of data. In this work, the role of the gateway in processing collected air quality data and its reliable dissemination to end-users through a web-server is emphasized. A mechanism for the backup and the restoration of the collected data in the case of Internet outage is presented. The system is adapted to an open-source Internet-of-Things (IoT) web-server platform, called Emoncms, for live monitoring and long-term storage of the collected IAQM data. A modular IAQM architecture is adopted, which results in a smart scalable system that allows seamless integration of various sensing technologies, wireless sensor networks (WSNs) and smart mobile standards. The paper gives full hardware and software details of the proposed solution. Sample IAQM results collected in various locations are also presented to demonstrate the abilities of the system.

A long term healthcare monitoring system requires battery operated devices with low-power technologies. Researchers tried to adapt various short-range technologies for Wireless Body Area Networks (WBANs) in ubiquitous health monitoring. The classical Bluetooth is known for its greedy power consumption, IrDA and NFC require line-of-sight conditions, and ANT has weak coexistence features and interference issues. A typical choice remains ZigBee/6LoWPAN over IEEE 802.15.4 based solutions in WBANs because of their low-power consumption. However, the recently proposed Bluetooth Low Energy (BLE) announced more compelling features in various aspects. Only few studies have been published supporting these claims on BLE. In this paper, we present a BLE based remote healthcare monitoring platform and we study its compatibility for ECG monitoring. ECG data requires continuous and real-time transmissions, making it particularly challenging for resource constrained devices. In our system, a BLE112 module from Bluegiga and a BLE USB dongle are used for WBAN. The performance of the system is evaluated experimentally and the results showed good potential of this proposed BLE platform in meeting the main QoS requirements of medical applications in terms of throughput, end-to-end delay, and packet error rate, while staying energy efficient.