Micro-gyroscopes based on the Coriolis principle are widely employed in inertial navigation, motion control, and vibration analysis applications. This paper presents our main contributions which include a novel dual-frame optomechanical gyroscope, a unique photonic crystal cavity design, and advanced numerical simulation and optimization methods. The proposed design utilizes an optical cavity formed between dual oscillating frames, whereby input rotation induces a measurable phase shift via optomechanical coupling. Actuation of the frames is achieved electrostatically via an interdigitated comb-drive design. Through theoretical modeling based on cavity optomechanics and finite element simulation, the operating principle and performance parameters are evaluated in detail. Results indicate an expected angular rate sensitivity of 22.8 mV/°/s and angle random walk of 7.1×10-5 °/h1/2, representing superior precision than to existing micro-electromechanical systems gyroscopes of comparable scale. Detailed analysis of the optomechanical transduction mechanism suggests this dual-frame approach could enable angular vibration detection with resolution exceeding state-of-the-art solutions.

There is a rising global concern for the ongoing outbreak of SARS-CoV-2 due to its high transmission rate and unavailability of treatment. Through the binding of its spike glycoprotein with angiotensin type 2 (ACE2), SARS-CoV-2 can efficiently get in the cells of patients and start its pandemic cycle. Herein, the biological diversity of SARS-CoV-2 infection was assessed in Babylon province of Iraq by investigating the possible genetic variations of the spike glycoprotein. A specific coding region of 795 bp within the viral spike (S) gene was amplified from 19 patients who suffered from obvious symptoms of SARS-CoV-2 infection. Sequencing results identified fifteen novel nucleic acid variations with a variety of distributions within the investigated samples. The electropherograms of all the identified variations showed obvious co-infections with at least two different viral strains per sample. Within these co-infections, the majority of samples exhibited three nonsense single nucleotide polymorphism (SNP)s, p.301Cdel, p.380Ydel, and p.436del, which yielded three truncated SARS-CoV-2 spike glycoproteins of 301, 380, and 436 amino acids length, respectively. The network and phylogenetic analyses indicated that for all viral infections were derived from multi-ancestral origins. Results inferred from the specific clade-based tree entailed that some viral strains were derived from European G-clade sequences. In conclusion, our data demonstrated the absence of any single strain infection among all investigated viral samples in the studied area, which may entail a higher risk of SARS-CoV-2 in this country. Through the identified high frequency of truncated spike proteins, we suggest that defective SARS-CoV-2 may depend on helper strains having intact spikes in its infection. Alternatively, another putative ACE2-independent route of viral infection way also suggested. To the best of our knowledge, this is the first report to describe the co-infection of multiple strains of SARS-CoV-2 in patients with COVID-19.

Change Detection (CD) of aerial images refers to identifying and analyzing changes between two or more aerial images of the same location taken at different times. The CD is a highly challenging task due to the need to distinguish relevant changes, such as urban expansion, deforestation, or post-disaster damage assessment, from irrelevant ones, such as light conditions, shadows, and seasonal variations. Many CD papers have recently been published, where most of the papers that proposed a new model contained a comparison between their proposed and state-of-the-art (SOTA) models. While many recent studies propose new deep learning (DL) models for improving CD performance, their comparative analyses are often restricted, lacking comprehensive insights into the proposed models' real-world generalizability, robustness, and performance trade-offs across diverse change characteristics. This paper presents a novel generic framework to systematically benchmark and assess DL-based CD models through three parallel pipelines: 1) cross-testing models on diverse benchmark datasets to evaluate generalization, 2) robustness analysis against different image corruptions, and 3) multi-faceted contour-level analytics evaluating detection sensitivity to change size/complexity. The framework is applied to comparatively evaluate five state-of-the-art DL-based CD models - Changeformer, BIT, Tiny, SNUNet, and CSA-CDGAN. Extensive experiments unveil each model's strengths, limitations and biases, highlighting their relative proficiencies in generalizing across data distributions, resilience to noise corruption, and discriminative capabilities for changes of varying characteristics. The proposed benchmarking framework demonstrates significant potential for guiding the selection of suitable CD models tailored to specific application requirements by comprehensively evaluating their generalizability, robustness, and detection capabilities across diverse real-world scenarios. This systematic evaluation approach can drive future research into developing more robust and versatile CD solutions aligned with practical needs.

Increasing HIV drug resistance (DR) among children with HIV (CHIV) on antiretroviral treatment (ART) is concerning. CHIV ages 1-14 years enrolled March 2019 to December 2020 from five facilities in Kisumu County, Kenya were included. Children were randomized 1:1 to control (standard-of-care) or intervention (point-of-care viral load (POC VL) testing every three months with targeted genotypic drug resistance testing (DRT) for VF (> 1000 copies/ml)). A multidisciplinary committee reviewed CHIV with DRT results and offered treatment recommendations. We describe DR mutations and present logistic regression models to identify factors associated with clinically significant DR. We enrolled 704 children in the study; median age was 9 years (interquartile range (IQR) 7,12), 344 (49%) were female and median time on ART was 5 years (IQR 3, 8). During the study period, 106 (15%) children had DRT results (84 intervention and 22 control). DRT detected mutations associate with DR in all participants tested, with 93 (88%) having major mutations, including 51 (54%) with dual class resistance. A history of VF in prior 2 years (adjusted odds ratio (aOR) 11.1; 95% confidence interval (CI) 6.3, 20.0) and less than 2 years on ART at enrollment (aOR 2.2; 95% CI 1.1, 4.4) were associated with increased odds of major DR. DR is highly prevalent among CHIV on ART with VF in Kenya. Factors associated with drug resistance may be used to determine which children should be prioritized for DRT.