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According to the World Health Organization, over 300 million people worldwide are affected by major depressive disorder (MDD) [1]. Individuals battling this mental condition may exhibit symptoms including anxiety, fatigue, and self-harm, all of which severely affect well-being and quality of life. Current trends in social media and population behavior bring up an urgent need for health professionals to strengthen mental health resources, improve access and accurately diagnose depression [2]. To mitigate the disparate impact of depression on people of different social and racial groups, this study identifies factors that strongly correlate with the prevalence of depression in U.S. adults using health data from the 2019 pre-pandemic National Health Institute Survey (NHIS) [3]. In this study we trained a random forest model capable of performing a classification task on American-adults survey data with an accuracy of 98.7%. Our results conclude that age, education, income, and household demographics are the primary factors impacting mental health. Awareness of these mental health stressors may motivate medical professionals, institutions, and governments to identify more effectively the at-risk people and alleviate their potential suffering from MDD. By receiving adequate mental health services, Americans can improve their quality of life and form a more fulfilling society.

Acute lymphoblastic leukemia (ALL) is one of the most common types of cancer among children. It can rapidly become fatal within weeks, hence early diagnosis is critical. Problematically, the ALL diagnosis mainly involves manual blood smear analysis relying on the expertise of medical professionals, which is error-prone and time-consuming. Thus, it is important to develop artificial intelligence tools that will identify leukemic cells from a microscopic image faster, more accurately, and cheaper. Here, we investigate the capabilities of a traditional convolutional neural network (CNN) and You Only Look Once (YOLO) models for real-time detection of leukemic cells. The YOLOv5s model shows 97.2% accuracy for the task of object detection of ALL cells, with the inference speed allowing 80 image frames to be processed per second. These new findings can provide valuable insight in applying real-time object detection algorithms for improving the efficiency of blood cancer diagnosis.

We report integration of chalcogenide phase change materials (GSST and Sb2Se3) with doped silicon-on-insulator heater arrays fabricated in a 90-nm silicon foundry. Reversible electrothermal switching of transmissive metasurfaces and integrated photonic devices are demonstrated.

The quest for efficient programmable photonic circuits requires active optical components with low insertion loss, small form factor, and low electrical power consumption [1] . The most common low-loss phase shifter – the key building block – employs electrically driven heaters to exploit thermo-optic effects. However, these devices require a constant power supply, suffer from thermal crosstalk, and their sizes are typically >100 µm. Here, we demonstrate a novel alternative that meets all the aforementioned requirements. We use silicon-on-insulator (SOI) waveguides with patterned n ++ - n - n ++ doped-silicon microheaters to electrothermally switch, with a single pulse, a cell of Sb 2 Se 3 between its two optically distinct, nonvolatile states: amorphous and crystalline. Sb 2 Se 3 is a phase change material (PCM) that offers a ∆n ≈ 0.77 upon switching between states with vanishingly small losses at 1550nm [2] . This unique set of properties allows for phase-only modulation [3] , which sets a precedent in a growing PCM photonics field familiar with PCMs for amplitude modulation at the telecom band [4] .

We report the observation of enhanced third-harmonic generation in a mid-infrared metasurface based on the phase change material Ge 2 Sb 2 Se 4 Te 1 .

Optical metasurface is a recently emerged paradigm for controlling light propagation, which enables implementation of ultra-compact optical devices with extended functionalities. Nowadays the main challenge in the field is to realize active metasurfaces with high quality, high efficiency, and large tuning range. Here we present a design approach for constructing a two-state reconfigurable metalens made of low-loss optical phase-change material (O-PCM). The metalens design is capable to produce diffraction limited focusing, large change in focal length (from 1.5 mm to 2mm), and decent focusing efficiency of about 20% in both states. The proposed design methodology is generic and can be easily extended towards constructing metasurfaces, which can switch between two or more arbitrary phase maps.

Hyperbolic metamaterials possess unique optical properties owing to their hyperbolic dispersion. As hyperbolic metamaterials can be constructed just from periodic multilayers of metals and dielectrics, they have attracted considerable attention in the nanophotonics community. Here, we review some of our recent works and demonstrate the benefits of using hyperbolic metamaterials in plasmonic waveguides and light scattering. We also discuss nonlocal topological transitions in the hyperbolic metamaterials that effectively induce a zero refractive index.

We have studied the effect of a high photonic density of states provided by a metamaterial environment on the dynamics of nanodiamond based nitrogen-vacancy (NV) centers. We have experimentally demonstrated the broadband enhancement of single-photon emission from nanodiamond NV centers coupled to a planar multilayer metamaterial with hyperbolic dispersion. In addition, we have studied the influence of Purcell effect on the fluorescent contrast between the NV center spin states.