Dual-band Air-Ground Radio Performance: Example Flight Test Results

We recently concluded a four-year University Leadership Initiative (ULI) project sponsored by NASA, which investigated multiple aviation communications technology areas aimed at enhancing future aviation safety. These areas were dual-band air-ground communications for air traffic management, detection and interdiction of small drones, and high-capacity terrestrial airport communications networking. In this paper we report on flight test results of our dual-band radios. These radios were designed to use a spectrally efficient multi-carrier modulation, filterbank multicarrier (FBMC), which we had previously shown to improve resilience to high-power distance measurement equipment (DME) adjacent-channel interference, in comparison to existing orthogonal frequency division multiplexing (OFDM) schemes. In our NASA project, we designed the FBMC radios to extend performance even further, using the following techniques: (i) simultaneous dual-band transmission and reception;(ii) ground station (GS) spatial diversity;(iii) higher-order modulation for a factor of 5 capacity increase over QPSK;(iv) a Doppler-resilient option using a smaller number of subcarriers;and, (v) 5-MHz bandwidth C-band transmissions for an order of magnitude capacity increase over existing 500-kHz channel schemes. To our knowledge, these are novel achievements for civil aviation, and our flight test results attained a technology readiness level (TRL) of 5. In this paper we briefly describe the project history, in which we spent approximately one year working with Boeing to participate in one of their Eco-Demonstrator flight trials, and obtained special temporary authorizations to transmit in both the L-band and C-band, from the FAA, the FCC, and the DoD. When COVID-19 dispersed worldwide, Boeing was no longer able to support us, so we revised our plans and teamed with the South Carolina Civil Air Patrol (SC CAP) to conduct smaller-scale flight tests. This paper summarizes the radio designs and the novel features we employed, as well as analyses, computer simulations, and laboratory tests prior to terrestrial mobile testing, all of which culminated in our successful flight tests. We show example flight test results that serve as proof of concept for all the five aforementioned radio performance enhancements. Example results include signal-to-noise ratio and bit error ratio, diversity gains, and throughput gains through both higher-order modulation and wider bandwidth channels. We also report on some lessons learned, and some ideas for future advancement of our work. © 2023 IEEE.

Joint Transmission Scheme and Coded Content Placement in Cluster-Centric UAV-Aided Cellular Networks

Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low-latency communication are of paramount importance. In cellular networks, the incorporation of unmanned aerial vehicles (UAVs) can result in enhanced connectivity for outdoor users due to the high probability of establishing Line of Sight (LoS) links. The UAV's limited battery life and its signal attenuation in indoor areas, however, make it inefficient to manage users' requests in indoor environments. Referred to as the cluster-centric and coded UAV-aided femtocaching (CCUF) framework, the network's coverage in both indoor and outdoor environments increases by considering a two-phase clustering framework for Femto access points (FAPs)' formation and UAVs' deployment. Our first objective is to increase the content diversity. In this context, we propose a coded content placement in a cluster-centric cellular network, which is integrated with the coordinated multipoint (CoMP) approach to mitigate the intercell interference in edge areas. Then, we compute, experimentally, the number of coded contents to be stored in each caching node to increase the cache-hit-ratio, signal-to-interference-plus-noise ratio (SINR), and cache diversity and decrease the users' access delay and cache redundancy for different content popularity profiles. Capitalizing on clustering, our second objective is to assign the best caching node to indoor/outdoor users for managing their requests. In this regard, we define the movement speed of ground users as the decision metric of the transmission scheme for serving outdoor users' requests to avoid frequent handovers between FAPs and increase the battery life of UAVs. Simulation results illustrate that the proposed CCUF implementation increases the cache-hit-ratio, SINR, and cache diversity and decrease the users' access delay, cache redundancy, and UAVs' energy consumption.

Performance and Early Results from the Earth Surface Mineral Dust Source Investigation (EMIT) Imaging Spectroscopy Mission

The Earth Surface Mineral Dust Source Investigation (EMIT) acquires new observations of the Earth from a state-of-the-art, optically fast F/1.8 visible to short wavelength infrared imaging spectrometer with high signal-to-noise ratio and excellent spectroscopic uniformity. EMIT was launched to the International Space Station from Cape Canaveral, Florida, on July 14, 2022 local time. The EMIT instrument is the latest in a series of more than 30 imaging spectrometers and testbeds developed at the Jet Propulsion Laboratory, beginning with the Airborne Imaging Spectrometer that first flew in 1982. EMIT's science objectives use the spectral signatures of minerals observed across the Earth's arid and semi-arid lands containing dust sources to update the soil composition of advanced Earth System Models (ESMs) to better understand and reduce uncertainties in mineral dust aerosol radiative forcing at the local, regional, and global scale, now and in the future. EMIT has begun to collect and deliver high-quality mineral composition determinations for the arid land regions of our planet. Over 1 billion high-quality mineral determinations are expected over the course of the one-year nominal science mission. Currently, detailed knowledge of the composition of the Earth's mineral dust source regions is uncertain and traced to less than 5,000 surface sample mineralogical analyses. The development of the EMIT imaging spectrometer instrumentation was completed successfully, despite the severe impacts of the COVID-19 pandemic. The EMIT Science Data System is complete and running with the full set of algorithms required. These tested algorithms are open source and will be made available to the broader community. These include calibration to measured radiance, atmospheric correction to surface reflectance, mineral composition determination, aggregation to ESM resolution, and ESM runs to address the science objectives. In this paper, the instrument characteristics, ground calibration, in-orbit performance, and early science results are reported. © 2023 IEEE.

Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.32.2.021404] © 2023 SPIE. All rights reserved.

Modeling and Analyzing Online Food Delivery Services Using Design Thinking: An Optimization Approach

The COVID-19 pandemic has significantly strained online food delivery services (OFDS) globally. This has challenged OFDS businesses to redesign and deploy technologies to meet customer demand. The purpose of this article is to identify the optimal factors contributing to customer experience with OFDS services during a black swan event such as the COVID-19 pandemic. We followed a four-step research design to identify the optimal factors for OFDS. First, we identified the major episodes in the OFDS process. Second, these episodes were evaluated by customers using the sequential incidence technique. Third, we used an orthogonal design to analyze the episodes at different levels based on customer preferences. Finally, we used the Taguchi approach to calculate the signal-to-noise ratios and identify the optimal factors and their preferred levels. We classify the optimal factors into customer-oriented and service-provider-oriented propositions. The option to select the delivery person and delivery conditions was found to be the most optimal customer-oriented attribute. We discuss the theoretical and managerial implications of the study and suggest major avenues for digital transformations in OFDS for better customer experience. IEEE

Raspberry-based Low-resolution Thermal image system using a Smoothing Filter-based Kalman

Since the emergence of global epidemics such as SARS-CoV-2, H1N1, SARS and MERS, a wide range of systems for measuring temperature have been developed based on computer vision to reduce and prevent the virus contagious. By implementing a Raspberry-based Low-resolution embedded system based and a FLIR Lepton® sensor human body temperature is measured and improved by four different algorithms implemented. Firstly, three traditional time-series processes solving such as, Simple Mean (SM), Simple Moving Average (SMA), and Multi Lineal Regression (MLR), and secondly, and online filter-based Kalman predictor were implemented to increase the signal to noise ratio of the acquired temperature magnitude. Results of average prediction for different benchmarks demonstrate the best performance of Kalman Filter upon traditional processes. In addition, this algorithm achieves to smooth output temperature with fewer samples (∼10% of total samples) in comparison MLR and SMA. Finally, Raspberry-based Low-resolution Thermal image system is a feasible tool as a high-speed temperature estimator, by implementation of algorithms codified in Python language. © 2022 IEEE.

Multiplex and Ultrasensitive Detection of Severe Acute Respiratory Syndrome Coronavirus 2 and Influenza A/B Nucleocapsid Proteins Based on Nanometer-Sized Core-Shell Superparamagnetic Antifouling Probes

Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A (Flu A), and influenza B (Flu B) show similar clinical symptoms, such as cough, fever, and dyspnea, but patients infected by these viruses should be treated differently. The rapid and accurate diagnosis of infections caused by SARS-CoV-2, Flu A or Flu B is critical during the influenza season. Herein, we synthesized core-shell magnetic particles (MNPs) with excellent antifouling properties and applied them in the MNP-based immunochromatographic test (MICT) for simultaneous detection of SARS-CoV-2, Flu A, and Flu B nucleocapsid(N) proteins in 20 min. Two kinds of carboxyl-modified MNPs, MNP@pMBAA and MNP@Si-SA, were prepared and evaluated as probes in the MICT. Among them, the MNP@pMBAA showed lower nonspecific adsorption of proteins and low background noise in the application in MICTs. Particularly, the MNP@pMBAA50 bead-based MICT strip exhibited the highest signal-to-noise ratio for SARS-CoV-2 N protein detection with a limit of detection (LOD) of 0.072 ng/mL. Moreover, the proposed MICT strip demonstrated a minimal cross-reactivity and a broad linear dynamic detection range under a magnetic assay reader in the simultaneous detection of SARS-CoV-2, Flu A, and Flu B N proteins with relative LOD values of 0.0086, 0.012, and 0.018 ng/mL, respectively. The results demonstrated that the synthesized MNPs showed great potential for use as MICT probes for sensitive and multiplex detection of biomarkers in the development of point-of-care testing systems. © 2023 American Chemical Society.

A novel medical image enhancement technique based on hybrid method

Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm's efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics. © Published under licence by IOP Publishing Ltd.

A novel medical image enhancement technique based on hybrid method

Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm's efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics.

Effects of visual information on the face masked speech communication in Covid-secure classrooms

Acoustical measurements and speech intelligibility tests were carried out to investigate the effects of masks on speech communication experienced in real Covid-secure university classrooms during the pandemic. Face-masked speech levels and noise levels were measured to understand the acoustical effects of masks on speech sounds during 15 multiple lectures in 3 university classrooms. The speech intelligibility scores were also evaluated for lower and higher SNR (signal-to-noise ratio) conditions, and for with and without the presence of visual information conditions to investigate the effects of both the acoustic and visual signals in understanding speech communication in actual classroom situations. In the 3 active university classrooms the students experienced on average: speech levels of 55.1 dBA (σ = 5.5 dBA), noise levels of 42.3 dBA (σ = 3.9 dBA), and a speech-to-noise ratio of 12.8 dBA σ = 5.2 dBA). The mean SNR values at the listener's position for the 15 lectures varied from 3.6 dBA to 20.0 dBA. The use of a portable sound amplification system increases the face-masked speech levels mostly at mid and high frequencies (500–4 kHz), thus it can be more useful for achieving higher SNR values in classrooms. The presence of visual cues have little effect on achieving more higher speech intelligibility scores in higher SNR conditions. The present results show that visual obstruction of the talker's mouth decreases speech intelligibility scores by a maximum of 10% in lower SNR conditions, particularly at a SNR of 6 dBA or lower. © 2023 Elsevier Ltd

ALPINE: Improving Remote Heart Rate Estimation using Contrastive Learning

Heart rate (HR) is a crucial physiological indicator of human health and can be used to detect cardiovascular disorders. The traditional HR estimation methods, such as electrocardiograms (ECG) and photoplethysmographs, require skin contact. Due to the increased risk of viral in- fection from skin contact, these approaches are avoided in the ongoing COVID-19 pandemic. Alternatively, one can use the non-contact HR estimation technique, remote photo- plethysmography (rPPG), wherein HR is estimated from the facial videos of a person. Unfortunately, the existing rPPG methods perform poorly in the presence of facial deformations. Recently, there has been a proliferation of deep learning networks for rPPG. However, these networks require large-scale labelled data for better generalization. To alleviate these shortcomings, we propose a method ALPINE, that is, A noveL rPPG technique for Improving the remote heart rate estimatioN using contrastive lEarning. ALPINE utilizes the contrastive learning framework during training to address the issue of limited labelled data and introduces diversity in the data samples for better network generalization. Additionally, we introduce a novel hybrid loss comprising contrastive loss, signal-to-noise ratio (SNR) loss and data fidelity loss. Our novel contrastive loss maximizes the similarity between the rPPG information from different facial regions, thereby minimizing the effect of local noise. The SNR loss improves the quality of temporal signals, and the data fidelity loss ensures that the correct rPPG signal is extracted. Our extensive experiments on publicly available datasets demonstrate that the proposed method, ALPINE outperforms the previous well-known rPPG methods. © 2023 IEEE.

Ultra-low-dose chest CT protocol during the second wave of COVID-19 pandemic: a double-observer prospective study on 250 patients to evaluate its detection accuracy

BackgroundWhile the second wave of COVID-19 pandemic almost reached its climax, unfortunately, new viral strains are rapidly spreading, and numbers of infected young adults are rising. Consequently, chest high-resolution computed tomography (HRCT) demands are increasing, regarding patients' screening, initial evaluation and follow up. This study aims to evaluate the detection accuracy of ultra-low-dose chest CT in comparison with the routine low-dose chest CT to reduce the irradiation exposure hazards.ResultsThis study was prospectively conducted on 250 patients during the period from 15th December 2020 to 10th February 2021. All of the included patients were clinically suspected of COVID-19 infection. All patients were subjected to routine low-dose (45 mAs) and ultra-low-dose (22 mAs) chest CT examinations. Finally, all patients had confirmatory PCR swab tests and other dedicated laboratory tests. They included 149 males and 101 females (59.6%:40.4%). Their age ranged from 16 to 84 years (mean age 50 ± 34 SD). Patients were divided according to body weight;104 patients were less than 80 kg, and 146 patients were more than 80 kg. HRCT findings were examined by two expert consultant radiologists independently, and data analysis was performed by other two expert specialist and consultant radiologists. The inter-observer agreement (IOA) was excellent (96–100%). The ultra-low-dose chest CT reached 93.53–96.84% sensitivity and 90.38–93.84% accuracy. The signal-to-noise ratio (SNR) is 12.8:16.1;CTDIvol (mGy) = 1.1 ± 0.3, DLP (mGy cm) = 42.2 ± 7.9, mean effective dose (mSv/mGy cm) = 0.59 and absolute cancer risk = 0.02 × 10-4.ConclusionUltra-low-dose HRCT can be reliably used during the second wave of COVID-19 pandemic to reduce the irradiation exposure hazards.

Trace determination of the hydrogen sulfide biomarker thiosulfate in human urine by HPLC coupled with element selective ICPMS/MS detection

Hydrogen sulfide is a toxic gas but also established as a naturally occurring gaseous signaling molecule in humans, playing key physiological roles with particular involvement in lung disease including COVID-19. Thiosulfate is the conventional biomarker of hydrogen sulfide and is excreted in human urine at low micromolar levels. Thiosulfate is amenable to detection by the element-selective inductively coupled plasma tandem mass spectrometry (ICPMS/MS), but sulfur speciation in human samples at trace levels is challenging due to the high complexity of human sulfur metabolome and the utility of this detector under such settings has not been demonstrated. We report a method for thiosulfate determination in human urine at trace physiological levels by HPLC-ICPMS/MS. The method involved one-step derivatization to improve chromatographic behavior followed by direct injection. The instrumental limit of detection was 1.4 μg S L−1 (0.02 μM or 0.1 pmol). In a group of samples from volunteers (n = 24), measured thiosulfate concentrations in the diluted urine matrix were down to 8.0 μg S L−1 with a signal-to-noise ratio >10. The method was validated for recovery (80–110%), repeatability (RSD% <5%), and linearity (r2 = 0.9999, at a tested working concentration range of 0.01–1.0 mg S L−1), and the accuracy was assessed by comparing with HPLC-ESIMS/MS which showed agreement within ±20%. This work demonstrates the applicability of HPLC-ICPMS/MS for sulfur speciation at trace levels in a matrix with complex sulfur metabolome as human urine and provides a sensitive method for the determination of the hydrogen sulfide biomarker. © 2022 The Authors

An Optimal Image Segmentation Method based on HGSO for CT Images

Finding the infected regions in medical image modalities is a crucial and challenging task. In this paper, a new image segmentation method is introduced to detect the COVID-19 infection in CT images. In this method, a bi-level-thresholding based image segmentation is proposed using Henry gas solubility optimization. This method used Kapur entropy as a fitness function. Efficiency of the developed segmentation method has been validated on publicly available CT images of COVID-19 patients in terms of PSNR (Pick Signal-to-Noise Ratio), MSE (Mean Square Error), SSIM (Structural Similarity Index Measure) and FSIM (Feature Similarity Index Measure). Moreover, the proposed HGSO-based segmentation method has been compared with SCA, SSA, GWO, CPSOGSA, and MFO-based image segmentation methods to show its efficacy. © 2022 IEEE.

Azolla Quantity Prediction using WSN and Machine Learning

Due to COVID-19 pandemic, the expenditures on pellets and feeds in broiler and fish industries increase every year, leading to price overshoots in various agricultural products. Azolla is an emerging protein source alternative for tilapia and other livestock breeders that is known for its fast reproduction. This study aims to enhance the yield production of Azolla ponds in Nevalga Farm, Brgy. Sala, City of Cabuyao, Laguna by employing wireless sensor network (WSN) technology and predictive machine-learning (ML) methods. LoRa-based WSN was designed to measure the parameters that affect the growth and reproduction of Azolla. Throughout the 24-day monitoring period, the average received signal strength indication (RSSI) and signal-to-noise ratio (SNR) of the packets from the three sensing nodes ranged from -50.86 dBm to -71.39 dBm and 8.92 dB to 9.81 dB, respectively. A total of 3582 data sets were obtained during the observation. Among the three regression ML models used, K-Nearest Neighbor algorithm outperformed Linear Regression and Support Vector Machine in predicting Azolla quantity parameters on both training and validation datasets by yielding the smallest values of root mean square error (RMSE) and absolute error on the seven quantity indicators and achieving squared correlation that varied from 0.935 to 0.997. © 2022 IEEE.

Prospective evaluation of thoracic diseases using a compact flat-panel detector spiral computed tomographic scanner.

Objective: To prospectively evaluate the image quality and diagnostic performance of a compact flat-panel detector (FD) scanner for thoracic diseases compared to a clinical CT scanner. Materials and methods: The institutional review board approved this single-center prospective study, and all participants provided informed consent. From December 2020 to May 2021, 30 patients (mean age, 67.1 ± 8.3 years) underwent two same-day low-dose chest CT scans using clinical state-of-art and compact FDCT scanners. Image quality was assessed visually and quantitatively. Two readers evaluated the diagnostic performance for nodules, parenchymal opacifications, bronchiectasis, linear opacities, and pleural abnormalities in 40 paired CT scans. The other 20 paired CT scans were used to examine the agreement of semi-quantitative CT scoring regarding bronchiectasis, bronchiolitis, nodules, airspace consolidations, and cavities. Results: FDCT images had significantly lower visual image quality than clinical CT images (all p < 0.001). The two CT image sets showed no significant differences in signal-to-noise and contrast-to-noise ratios (56.8 ± 12.5 vs. 57.3 ± 15.2; p = 0.985 and 62.9 ± 11.7 vs. 60.7 ± 16.9; p = 0.615). The pooled sensitivity was comparable for nodules, parenchymal opacifications, linear opacities, and pleural abnormalities (p = 0.065-0.625), whereas the sensitivity was significantly lower in FDCT images than in clinical CT images for micronodules (p = 0.007) and bronchiectasis (p = 0.004). The specificity was mostly 1.0. Semi-quantitative CT scores were similar between the CT image sets (p > 0.05), and intraclass correlation coefficients were around 0.950 or higher, except for bronchiectasis (0.869). Conclusion: Compact FDCT images provided lower image quality but comparable diagnostic performance to clinical CT images for nodules, parenchymal opacifications, linear opacities, and pleural abnormalities.

Federated Remote Physiological Measurement with Imperfect Data

The growing need for technology that supports remote healthcare is being acutely highlighted by an aging population and the COVID-19 pandemic. In health-related machine learning applications the ability to learn predictive models without data leaving a private device is attractive, especially when these data might contain features (e.g., photographs or videos of the body) that make identifying a subject trivial and/or the training data volume is large (e.g., uncompressed video). Camera-based remote physiological sensing facilitates scalable and low-cost measurement, but is a prime example of a task that involves analysing high bit-rate videos containing identifiable images and sensitive health information. Federated learning enables privacy-preserving decentralized training which has several properties beneficial for camera-based sensing. We develop the first mobile federated learning camera-based sensing system and show that it can perform competitively with traditional state-of-the-art supervised approaches. However, in the presence of corrupted data (e.g., video or label noise) from a few devices the performance of weight averaging quickly degrades. To address this, we leverage knowledge about the expected noise profile within the video to intelligently adjust how the model weights are averaged on the server. Our results show that this significantly improves upon the robustness of models even when the signal-to-noise ratio is low. © 2022 IEEE.

Performance Analysis of Wiener filter in Restoration of Covid-19 Chest X-Ray Images, Ultrasound Images and Mammograms

Medical images such as X-Ray images, Mammograms and Ultrasound images are very useful diagnostic techniques used for understanding the functions of different internal organs, bones, tissues, etc. Most of the times these medical images are degraded by some noises and different kinds of blur. Image blurring and degradation leads to loss of quality of images which in hand causes difficulty in proper diagnosis. This paper emphases on the efficacy of Wiener filter in image de blurring and denoising Chest X-Ray of Covid-19 patients, ultrasound images of fetal abdominal cyst, umbilical cord cyst and Common Carotid Artery, Mammogram of both pathological and non-pathological breasts. Performance of Wiener filter is analyzed using image restoration parameters like Structural Similarity (SSIM), Histogram, Peak Signal to Noise Ratio and Mean Square Error. © 2022 IEEE.

Optimising Casting Film Parameters for LPDE Material Assessment

The growing demand for disposable gloves, especially from the healthcare industry amidst the ongoing Covid-19 pandemic and rising awareness about Healthcare-Associated Infections (HAIs). One of the ways to produce disposable gloves is using cast LDPE film machine. The quality of the products depends on material resin used, machine casting film design, part design and the selection of process parameters. However, the part design and casting film design are done at the initial stage of product development, it cannot be change easily. To manufacture a better quality of cast LDPE gloves, the best LDPE casting film parameters have to be identified. This research aims to identify the best LDPE casting film parameters in producing disposable gloves in terms of strong sealed but edges failed defect rate in production line. The three LDPE casting film parameters such as tensile strength, melt flow index (MFI) and load weight of resin were chosen to study their effect on the defect rate. In this research, the Taguchi method is used to optimize the best process parameters. On the other hand, an orthogonal array (OA), signal-to-noise (S/N) ratio, and ANOVA were employed to investigate the strong sealed but edges failed defect rate. According to the results obtained, the tensile strength of 34 MPa, melt flow index of 3 g/10 min and load weight of 2 kg were found to be the best combination of LDPE casting film parameters to fabricate the better performance of LDPE disposable gloves which give the lowest strong sealed but edges failed defect rate with 2%. Based on the statistical ANOVA analysis results, the most significant parameter affecting the strong sealed but edges failed defect rate of LDPE disposable gloves is tensile strength, which is indicated by the percentage contribution of P = 55.56%, followed by melt flow index with 38.89%. The load weight of LDPE resin is the least significant parameter with 5.55%. To conclude, Taguchi and ANOVA method show that tensile strength is the most significant parameter to get the least strong sealed but edges failed defect rate. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Towards a New MAX-DOAS Measurement Site in the Po Valley: NO2 Total VCDs

Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments are used worldwide to retrieve pollutant information from visible (VIS) and ultra-violet (UV) diffuse solar spectra. A similar instrument, able to meet the Fiducial Reference Measurements for DOAS (FRM4DOAS) standard requirements, is not yet present in the Po Valley (Italy), one of the most polluted regions in Europe. Our purpose is to close this gap exploiting the SkySpec-2D, a FRM4DOAS-compliant MAX-DOAS instrument bought by the Italian research institute CNR-ISAC in May 2021. As a first step, SkySpec-2D was involved in two measurement campaigns to assess its performance: the first one in August 2021 in Bologna where TROPOGAS, a research-grade custom-built MAX-DOAS instrument is located;the second one in September 2021 at the BAQUNIN facility at La Sapienza University (Rome) near the Pandora#117 instrument. Both campaigns revealed a good quality of SkySpec-2D measurements. Indeed, good agreement was found with TROPOGAS (correlation 0.77), Pandora#117 (correlation 0.9) and satellite (TROPOMI and OMI) measurements. Having assessed its performance, the SkySpec-2D was permanently moved to the “Giorgio Fea” observatory in San Petro Capofiume, located in the middle of the Po Valley, where it has been continuously acquiring zenith and off-axis diffuse solar spectra from the 1 October 2021. Nowadays, its MAX-DOAS measurements are routinely provided to the FRM4DOAS team with the purpose to be soon included in the FRM4DOAS validation network.