Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of BNT162b2 booster dose on anti-SARS-CoV-2 spike trimeric IgG antibodies in seronegative individuals

Abstract Objectives We provide here an updated analysis of an ongoing serosurveillance study, presenting data on the effect of a third dose of Pfizer/BioNTech BNT162b2 vaccine on serum anti-SARS-CoV-2 IgG antibodies. Methods We tested baseline SARS-CoV-2 seronegative healthcare workers undergoing primary vaccination with the mRNA-based COVID-19 Comirnaty vaccine, followed by administration of homologous vaccine booster (third dose). Venous blood was collected before either dose of primary vaccination, at 1, 3 and 6 months afterwards, as well as before and 1 month after receiving the vaccine booster. The serum concentration of anti-SARS-CoV-2 IgG was assayed with DiaSorin Trimeric spike IgG immunoassay. Results The final study population included 53 SARS-CoV-2 seronegative healthcare workers (median age 46 years; 60% females). A first peak of anti-SARS-CoV-2 spike trimeric IgG values was reached 1 month after completing primary vaccination, after which the levels gradually declined until before receiving the vaccine booster. A second peak of anti-SARS-CoV-2 spike trimeric IgG concentration was observed 1 month after receiving the vaccine booster dose (8,700 kBAU/L), which was 39-fold higher than before receiving the vaccine booster (221 kBAU/L; p<0.001), but was also nearly threefold higher compared to values seen at the first peak (2,990 kBAU/L; p<0.001). The rate of subjects with protective anti-SARS-CoV-2 spike trimeric IgG values (i.e., >264 kBAU/L) increased from 47.2% to 100% after 1 month from vaccine booster. Conclusions These results support current policies fostering COVID-19 vaccine boosters to reinforce humoral immunity against SARS-CoV-2.
Behavioral intention and continued adoption of Facebook: An exploratory study of graduate students in Bangladesh during the Covid-19 pandemic

Summary Behavioral intention and continued adoption of Facebook: An exploratory study of graduate students in Bangladesh during the Covid-19 pandemic This study is designed to find out the fundamental reasons for students’ social media adoption during the Covid 19 epidemic in Bangladesh. The research object is to build a perceptual picture of the factors that will encourage and impact Facebook’s continued acceptance during this crisis. The sample was taken from 400 students from different universities in Bangladesh. Nine indicators (Trust, Perceived Usefulness, Privacy, Perceived Ease of Use, Subjective Norms, Educational Compatibility, Behavioral Intention, Personal Needs, and Continued Facebook Adoption) were used to experiment. The study results show that the fit indices of measurement model (fit /df = 1.436; GFI = 0.958; AGFI = 0.960; NFI = 0.947; CFI = 0.977; TLI = 0.978; SRMR = 0.031; RMSEA = 0.027; P-close = 1.00) are suitable and appropriate within their prescribed ranges. The mean, standard deviation, internal consistency (Cronbach Alpha > 0.7), composite reliability (CR > 0.8), and average variance extracted (AVE > 0.5) of each constructs are well and appropriate their recommended level which determine the designs of validity. The coefficient of all factors like Trust (0.29), Perceived Usefulness (0.19), Privacy (0.17), Perceived Ease of Use (0.26), Educational Compatibility (0.13), Behavioral Intention (0.45), Personal Needs (0.16), except subjective norms (0.01) have a meaningful and positive effect on the behavioral intention of Facebook that satisfactorily affects continued adoption behavior during covid-19. These observations illustrate the scientific rationale and views relevant to emerging economies like Bangladesh in the context of social media. Several implications have been presented based on the results.
circ_0020123 promotes cell proliferation and migration in lung adenocarcinoma via PDZD8

Abstract High circ_0020123 expression is associated with poor prognosis in patients with non-small cell lung cancer (NSCLC) as previously reported. Whether circ_0020123 also plays an oncogenic role in lung adenocarcinoma (LUAD) is still unknown. Additionally, circ_0020123 is derived from part of exon (1312–3851) from its host gene PDZ domain-containing protein 8 (PDZD8). We hypothesized that circ_0020123 might affect malignant behaviors of LUAD cells by regulating PDZD8. Reverse transcription quantitative polymerase chain reaction revealed that PDZD8 was highly expressed in LUAD tissues and cells. PDZD8 knockdown suppressed LUAD cell proliferation and migration as shown by colony formation assays, Ethynyl deoxyuridine incorporation assays, Transwell assays, and wound healing assays. circ_0020123 was also found to be upregulated in LUAD tissues and cells. Moreover, circ_0020123 positively regulated PDZD8 expression in LUAD cells but exerted no significant effect on the transcriptional level of PDZD8. Mechanistically, circ_0020123 act as a competing endogenous RNA (ceRNA) to interact with miR-1283, thereby releasing the repression on PDZD8. Moreover, PDZD8 overexpression rescued the suppressive effect of circ_0020123 knockdown on LUAD cell proliferation and migration. In conclusion, circ_0020123 interacts with miR-1283 as a ceRNA to regulate PDZD8 expression, thus promoting the proliferation and migration of LUAD cells. The study might provide new biomarkers for future LUAD investigation.
A 40nm 64kb 26.56TOPS/W 2.37Mb/mm2RRAM Binary/Compute-in-Memory Macro with 4.23x Improvement in Density and >75% Use of Sensing Dynamic Range

Compute-in-Memory (CIM) using emerging nonvolatile (eNVM) memory technologies, such as resistive random-access memory (RRAM), has been shown by several implemented macros to be an energy-efficient alternative to traditional von Neumann architectures [1]–[6]. Since moving data on- and off-chip has a high energy cost, area efficiency is important to the practical utility of CIM with RRAM. Many systems demonstrated so far have not reported area efficiency or addressed the challenges CIM with RRAM presents with respect to practical area-constrained integrated circuits.
Dynamic Hysteresis Modeling Taking Skin Effect Into Account for Magnetic Circuit Analysis and Validation for Various Core Materials

This article presents a novel magnetic circuit model which can represent the dynamic hysteresis characteristics considering the skin effect. The proposed model is mathematically equivalent to the previously proposed magnetic circuit model incorporating the play model and Cauer circuit. However, the previous one has a problem in practical use that the magnetic circuit analysis must be performed by coupling with the external electric equivalent circuit to take the skin effect into account. Thus, it is difficult to apply the previous method to the analyses of devices with complicated shapes such as electric motors due to model complexity and convergence deterioration. On the contrary, the proposed model is more practical since it is composed of only the magnetic circuit elements. The calculation accuracy of the proposed model and its versatility for various kinds of core materials are experimentally proved by using ring cores made of grain-oriented (GO) and non-oriented (NO) silicon steels, an amorphous alloy, and a soft magnetic composite (SMC).
A Battery-Input Sub-1V Output 92.9% Peak Efficiency 0.3A/mm2 Current Density Hybrid SC-Parallel-Inductor Buck Converter with Reduced Inductor Current in 65nm CMOS

The profile of portable and wearable devices keeps shrinking, demanding high current density power management integrated circuits. Switched-capacitor (SC) converters and buck converters are two common solutions. Unregulated SC converters can achieve high power density and high efficiency for specific voltage conversion ratios (VCRs). However, to cover wide input and output voltage ranges, they require a reconfigurable topology with multiple VCRs, thus increasing the system complexity. On the other hand, buck converters can reach a good efficiency over a wide continuous VCR range. Yet, they need a bulky inductor, which significantly degrades the power density. To address the power density and efficiency tradeoff, hybrid converters composed of both inductor and capacitor are among popular solutions [1 – 4]. When compared with the traditional buck converter, these hybrid converters feature lower voltage swing (smaller current ripple) of the inductor, higher switching frequency, and a larger duty cycle (D) for the same VCR, alleviating the requirements on the inductor, and resulting in higher power density. Nevertheless, as all the output current IOUT goes through the inductor, this implies a large volume for small DCR and conduction loss ${P_{\text{COND}}}$_L. Recently, hybrid converters with reduced inductor current IL have emerged [5 – 7]. In these hybrid converters, the SC not only lowers down the voltage of the switch node, but also offers another current path to the output, reducing the inductor current. In this work, we propose an SC-parallel-inductor buck topology (which we refer to as CPL-Buck since in the proposd structure there is a capacitor that is always in parallel with the inductor) that can further reduce /L to less than 0.5IOUT, meaning a PCOND_L reduction of over 75% for the same DCR. Measurement results show that, for 1.2A maximum IOUT, 3-to-4.2V input to 0.6-to-1V output, the proposed CPL-Buck obtains a peak efficiency of 92.9% and a peak current density of 0.3A/mm2 with a power inductor as small as 1.6×0.8×0.8mm3.
Effect of Chemical Constituents on The Mechanical Strength of Microbially Induced Bio-cemented Soil

The use of bio-based soil improvement techniques to enhance sandy soil's mechanical behavior has shown significant potential in the past two decades to replace conventional cementitious techniques. In this study, the effect of both cementing solution (CS) concentration and the number of treatment cycles on the compressive strength of bio-cemented sand using microbially induced carbonate precipitation (MICP). The study was conducted at only one bacterial concentration of 16 U/mL with four levels of CS concentration. It was found that the lower CS conditions (0.25 M); the higher the strength gain with the increase of the number of treatment cycles. This may be attributed to the carbonate crystals distribution that effectively bridges soil particles at lower CS concentrations with the increase of cycles.
A 1-Tb, 4b/Cell, 176-Stacked-WL 3D-NAND Flash Memory with Improved Read Latency and a 14.8Gb/mm2 Density

Triple-level-cell (TLC) NAND has prevailed the non-volatile memory market, yet the quad-level-cell (QLC) NAND is emerging as a suitable replacement for low-cost and high-density storage. However, despite its cost effectiveness QLC's market share is not increasing quickly, not only due to its worse reliability but also its slow sequential and random read performance. To increase random read performance an independent plane read operation has been introduced [1], [2], but the read and pass voltage noise, caused by high I/O and data-path current consumption, causes a shift in the threshold voltage $(\mathrm{V}_{\text{th}})$ distribution during plane-interleaved and cache read operations. Furthermore, for QLC with reduced program and erase (P/E) cycles the number raw bit errors exceeds the amount correctable by ECC. More read-retry steps need to be added to compensate, but the read latency increases.
A hybrid photovoltaic/solar chimney seawater desalination plant

A novel configuration of solar chimney-PV-water desalination system is proposed to investigate the feasibility of integrating solar power with freshwater production. The solar collector is composed of transparent PV cells to increase the utilization factor, and the installation of a water basin contributes to the generation of freshwater while enabling the cooling of the PV panel. A case study was developed for a rural community in Sharjah to meet the demand for water and electricity. ANSYS Fluent was used to simulate the steady flow and natural convection problem within the solar chimney. A full parametric study for a conventional solar chimney power plant with integrated transparent PV was simulated to investigate the effects of various parameters on air velocity, mass flow rate, air temperature, and turbine output at 850 W/m2 solar radiation. The results showed that the chimney output and efficiency were positively related to chimney height and collector radius, but negatively related to collector height. An analysis was developed to estimate the amount of evaporated and distilled water from the water pool. The results showed that the addition of the transparent PV system and the water basin increased the utilization factor by 20 times compared to the conventional solar chimney power plant. The results showed that the proposed system could simultaneously generate 761,120 kWh of electricity and 2733 tons of distilled water per year.
Optimization of Tungsten Inert Gas Welding Parameters using Grey Relational Analysis for joining AA 6082 Pipes

This work focusses on optimizing the tungsten inert gas welding parameters for welding AA6082 using multi-criteria decision-making grey relation analysis. Main tungsten inert gas welding parameters such as welding current, travel speed and arc length are investigated for the effect on the geometry of weld such as bead width, penetration, depth of heat affected zone and width of heat affected zone. The results of grey relational analysis proved that the optimum process parameters of the tungsten inert gas welding of AA6082 pipelines were 105 Ampere, 10 mm/min welding speed and 4 mm arc length. The least optimal parameters were 60 Ampere, 50 mm/min welding speed and 2 mm arc length. Factorial analysis proved that main effects of current, arc length and welding speed and interaction effects of current and welding speed have a significant effect on the bead characteristics such as bead width, penetration, depth and width of heat affected zone.
An 8-Mb DC-Current-Free Binary-to-8b Precision ReRAM Nonvolatile Computing-in-Memory Macro using Time-Space-Readout with 1286.4-21.6TOPS/W for Edge-AI Devices

Battery-powered edge-AI devices require nonvolatile computing-in-memory (nvCIM) macros for nonvolatile data storage and multiply-and-accumulate (MAC) operations. High inference accuracy requires MAC operations with high input (IN), weight (W), and output (OUT) precisions. A high energy efficiency $(\text{EF}_{\text{MAC}})$ and a short computing latency $(\mathrm{t}_{\text{AC}})$ are also required. Most existing silicon-verified nvCIM macros use current-mode signal generation; using current [1]–[3] or hybrid current-voltage readout schemes [4]–[5] for multibit MAC operations to compensate for the small BL -voltage swing and signal margin resulting from the low read-disturb-free voltage $(\mathrm{V}_{\text{RD}})$.
Compressive Strength and Durability of Geopolymer Concrete

The impacts of using Ground Granulated Blast Furnace Slag (GGBFS) and fly ash as a replacement to Ordinary Portland Cement (OPC) are evaluated. A total of 120 concrete cubic and cylindrical samples were casted and tested for four different binder compositions. Compressive strength testing conducted on cubes and cylinders at various ages revealed that incorporation of fly ash as binder reduces the compressive strength at various ages. In addition, it is also observed that geopolymer concrete provides late strength gain which is a major drawback of the usage of such concrete. Furthermore, it was noted that GGBFS- and fly ash- based concrete gain compressive strength at later ages compared to conventional concrete. This is due to the late activation process. On the other hand, durability testing of geopolymer concrete, represented by rapid chloride penetration test (RCPT), suggests that replacing OPC with GGBFS and fly ash in concrete provides massive improvements in durability at the various testing ages. In contrast, adding further fly ash reduces overall durability by increasing the water absorbed and water penetration depth. Finally, it is concluded that environment friendly materials such as GGBFS and fly ash can serve as appropriate alternatives to OPC in the binding material of concrete mixes.
Deep DR: Detection of Diabetic Retinopathy using a Convolutional Neural Network

Diabetic retinopathy (DR) is a consequence of diabetes that affects the back of the eye due to excessive blood sugar levels. If left misdiagnosed and untreated, it might result in blindness. Retinal screening aids in the early detection and treatment of DR. We created a deep learning model that can detect DR in its early to late stages to make the screening procedure easier. The proposed Convolutional Neural Network (CNN) model classifies between two classes i.e. the presence of DR or otherwise via an image-based dataset. Accuracy of up to 96% has been achieved.
DIMC: 2219TOPS/W 2569F2/b Digital In-Memory Computing Macro in 28nm Based on Approximate Arithmetic Hardware

In-memory-computing (IMC) SRAM architecture has gained significant attention as it achieves high energy efficiency for computing a convolutional neural network (CNN) model [1]. Recent works investigated the use of analog-mixed-signal (AMS) hardware for high area and energy efficiency [2], [3]. However, AMS hardware output is well known to be susceptible to process, voltage, and temperature (PVT) variations, limiting the computing precision and ultimately the inference accuracy of a CNN. We reconfirmed, through the simulation of a capacitor-based IMC SRAM macro that computes a 256D binary dot product, that the AMS computing hardware has a significant root-mean-square error (RMSE) of 22.5% across the worst-case voltage, temperature (Fig. 16.1.1 top left) and 3-sigma process variations (Fig. 16.1.1 top right). On the other hand, we can implement an IMC SRAM macro using robust digital logic [4], which can virtually eliminate the variability issue (Fig. 16.1.1 top). However, digital circuits require more devices than AMS counterparts (e.g., 28 transistors for a mirror full adder [FA]). As a result, a recent digital IMC SRAM shows a lower area efficiency of 6368F2/b (22nm, 4b/4b weight/activation) [5] than the AMS counterpart (1170F2/b, 65nm, 1b/1b) [3]. In light of this, we aim to adopt approximate arithmetic hardware to improve area and power efficiency and present two digital IMC macros (DIMC) with different levels of approximation (Fig. 16.1.1 bottom left). Also, we propose an approximation-aware training algorithm and a number format to minimize inference accuracy degradation induced by approximate hardware (Fig. 16.1.1 bottom right). We prototyped a 28nm test chip: for a 1b/1b CNN model for CIFAR-10 and across 0.5-to-1.1V supply, the DIMC with double-approximate hardware (DIMC-D) achieves 2569F2/b, 932-2219TOPS/W, 475-20032GOPS, and 86.96% accuracy, while for a 4b/1b CNN model, the DIMC with the single-approximate hardware (DIMC-S) achieves 3814F2/b, 458-990TOPS/W (normalized to 1b/1b), 405-19215GOPS (normalized to 1b/1b), and 90.41% accuracy.
Market Basket Analysis of Chicago Road Accidents

Over the years, data mining techniques have grown increasingly popular. Machine learning has been implemented as means of discovering new information in a wide variety of applications, one of which is the analysis of road accidents and crash data. While many techniques have been applied to this problem; Market Basket Analysis can provide practical insight for road safety and accident prevention efforts through its discovery of interesting accident patterns. In this paper, association rules are investigated with regards to the nature of accidents, their causes, number of cars involved, and crash characteristics. By the results obtained, recommendations are thus offered to help reduce certain accident types such as hit and runs or rear accidents, with emphasis on the timeframe (weekday vs weekend) and traffic type (intersection-related). On another note, a significant decrease in accident rates following the COVID-19 lockdown is also illustrated, and noticeable changes in accident patterns due to the situation are briefly explored.
A 0.004mm2 200MS/S Pipelined SAR ADC with kT/C Noise Cancellation and Robust Ring-Amp

Pipelined ADCs are widely used for high-speed high-resolution applications, but there are two challenges. First, limited by the kT/C noise requirement, its 1st-stage sampling capacitor has to be sufficiently large (e.g., several pF). This poses significant burdens for the ADC driver and the reference buffer, leading to high design complexity and huge power/area costs on the system level, especially when high linearity, high sampling rate, and low supply voltage are required. Second, it is challenging to design a low-power, high-speed, and PVT-robust residue amplifier in an advanced process. To address these two challenges, this work proposes a PVT-robust ring-amp with kT/C noise cancellation capability. It enables a 1.3mW 200MS/s 67dB-SNDR pipelined ADC with only 128fF input capacitance.
Reversible Comparator Design Using Quantum Dot-Cellular Automata

ABSTRACT Quantum dot-cellular automata (QCA) emerge as a research area to design nanometre scale logic circuit. In digital logic design, a comparator is the fundamental building block that performs the comparison of two numbers. This paper deals with the design of reversible building block for 1-bit comparator and its implementation in QCA. An improved QCA layout of Feynman gate is also achieved. The QCA Feynman gate is denser and has low delay than the existing circuit. The proposed reversible comparator has quantum cost 9, whereas the QCA reversible comparator has the quantum cost 0.927. The quantum cost based comparison of the proposed QCA reversible comparator with conventional reversible comparator shows the cost effective circuit design in QCA. The simulation result matched the truth table of comparator which approves the functional capability of the proposed QCA layout of comparator. All the proposed layouts dissipate very low power.
Performance Analysis, Parametric Study, and Optimization of CubeSat Solar Power System for Ship Tracking of Marine Traffic

One of the promising applications of CubeSats is in the transportation sector where a CubeSat is used to track the movement of vehicles. For a CubeSat mission to be successful, an appropriate design of the CubeSat’s electric power system (EPS) is needed. In this work, performance analysis of a solar-powered CubeSat EPS was carried out for the tracking of ships and vessels moving to and from Sharjah’s harbor. Simulations of the power production from the solar photovoltaic (PV) cells were carried out using STK software. The effect of the orbit altitude and orbit inclination on the power production of the CubeSat and the access time with Sharjah’s ground station was investigated. Furthermore, an optimization approach of the CubeSat power production as a function of the altitude, inclination, and solar cells efficiency was carried out using Response Surface Methodology (RSM). A new quadratic equation that gives the CubeSat average power production as a function of the altitude, inclination, and solar cells efficiency was reported. The obtained quadratic equation achieved superior accuracy in predicting the average power production as the obtained regression coefficient (R2) and adjusted regression coefficient (adjusted R2) values were 0.9995 and 0.9992, respectively. The optimal orbital parameters were combined with the best pointing model to yield the maximum power production of the CubeSat EPS.