We report on a ZnO-based LC-type passive humidity sensor (HS) using a scalable, large-area thin-film semiconductor fabrication process. The reported sensor is capable of monitoring relative humidity (RH) remotely. The fabricated sensor is 30 mm in diameter and comprises an LC resonator formed via an octagonal planar inductor and a moisture sensitive interdigitated ZnO capacitor in series. A printed circuit board reader coil, which can sense the sensor output from <25 mm distance, is also reported. The HS is demonstrated to read 30%-90% of RH by interrogating the change in resonance frequency (fR) of the reader-senor system. The reading resolution was ± 2.38% RH and the sensor sensitivity ranged from 53.33 kHz to 93.33 kHz for a 1% change in RH during measurements above 45% RH. Experimental results also showed that the fabricated sensor is operational for a range of 0 °C-75 °C as long as calibration is performed for temperature drifts of ≥±3 °C. The reported results are promising to expedite the deployment of novel inexpensive sensor, for example, in sealed locations to remotely monitor humidity without a need for on-sensor power.

The state of the art video standard H.264/AVC is very popular worldwide for its efficient coding techniques. But recently the ITU-T Video Coding Experts Group (VCEG) and ISO/IEC Moving Picture Expert Group (MPEG) have been jointly developing the next generation video standard, called High Efficiency Video Coding (HEVC) which is expected to be more efficient in coding video. In this paper, we present a shared architecture which can compute the 8×8 Inverse Discrete Cosine Transform (IDCT) of the HEVC from that of the H.264/AVC using a new mapping technique. The hardware share approach ensures the maximum circuits reuse during the computation. The architecture is designed with only adders and shifters to reduce the hardware cost significantly. The design is implemented on FPGA and later synthesized in CMOS 0.18um technology. The results show that the proposed design has the maximum decoding capability of 67fps for 1080p full HD video.

The Kingdom of Saudi Arabia (KSA) faces challenges in protecting its limited natural water resources. The centralized sewage treatment plants (STPs) contribute to conserving natural water resources in various regions of KSA. For sustainable wastewater management strategies in arid environments, the performance of STPs needs to be assessed for various beneficial uses in addition to the existing regulations. The present study used extensive effluent quality data of four STPs to develop a performance assessment approach using multi-criteria and principal component analyses. The performance levels were derived from the compliance rate of effluent standards with different reuses and the efficacy of biosolids management. The proposed approach evaluated the STPs based on the planned applications of treated effluents, considering the land use characteristics of each plant’s disposal point. The factors loadings obtained for the STPs, encompassing total suspended and dissolved solids, and total dissolved solids, oxidizing demanding pollutants (BOD and COD), and nutrients (NO₃ and PO₄), with 3 to 4 principal components demonstrating above 60% of the effluent monitoring data, established the importance of the effluent quality parameters. The effluent quality index (EQI) was developed for existing and potential reuses, e.g., irrigation, landscaping, fishery, and recreation, during dry (summer) and wet (winter) periods. All STPs showed high EQI for both periods for the present reuse standards of unrestricted irrigation and landscaping. The overall performance index, aggregating EQI and the efficacy of sludge management practices, illustrated declining performance, suggesting staged improvements (tertiary-level treatment, air floatation, sludge digestion, and waste-to-energy) through a periodic assessment process. STPs must enhance their sludge management efficacy to produce Class-A biosolids and waste-to-energy. The proposed approach will help make strategic decisions regarding improvements for STPs and the allocation of financial resources to protect natural resources in various regions across KSA.

Polylactic acid (PLA), the second most produced biopolymer, was selected for the fabrication of mixed-matrix membranes (MMMs) via the incorporation of HKUST-1 metal–organic framework (MOF) particles into a PLA matrix with the aim of improving mechanical characteristics. A deep learning neural network (DLNN) model was developed on the TensorFlow 2 backend to predict the mechanical properties, stress, strain, elastic modulus, and toughness of the PLA/HKUST-1 MMMs with different input parameters, such as PLA wt%, HKUST-1 wt%, casting thickness, and immersion time. The model was trained and validated with 1214 interpolated datasets in stratified fivefold cross validation. Dropout and early stopping regularizations were applied to prevent model overfitting in the training phase. The model performed consistently for the unknown interpolated datasets and 26 original experimental datasets, with coefficients of determination (R2) of 0.93–0.97 and 0.78–0.88, respectively. The results suggest that the proposed method can build effective DLNN models using a small dataset to predict material properties.