Miracle-dz/UniFAR-FTUs huggingface dataset:
980-nm 14-pin butterfly module dual-channel CW QW semiconductor laser for pumping

Nowadays, with its mature progress, the 790 nm - 1000 nm wavelength semiconductor laser is widely used in the fields of laser machining, laser ranging, laser radar, laser imaging, laser anti-counterfeit, biomedical and etc. Best of all, the 980 nm wavelength laser has its widespread application in the pumping source of Er3+ -doped fiber amplifier, optic fiber gyroscopes and other devices. The output wavelength of the fiber amplifier which takes the 980 nm wavelength laser as its pumping source is between 1060 nm and 1550 nm. This type of laser has its extremely wide range of applications in optical communication and other fields. Moreover, some new application domains keep constantly being developed. The semiconductor laser with the dual-channel ridge wave guide and the 980 nm emission wavelength is presented in this paper. In our work, we fabricated Lasers with the using of multi-quantum well (MQW) wafer grew by MBE, and the PL-wavelength of the MQW was 970 nm. The standard photofabrication method and the inductively coupled plasma (ICP) etching technology are adopted in the process of making dual-channel ridge wave guide with the width of 4 μm and height of 830 nm. In the state of continuous work at room temperature, the laser could output the single mode beam of 70 mW stably under the current of 100 mA. The threshold current of the laser diode is 17 mA and the slope efficiency is 0.89 W/A. The 3 dB spectrum bandwidth of the laser beam is 0.2 nm. This laser outputs its beam by a pigtail fiber on which Bragg grating for frequency stabilization is carved. The laser diode, the tail fiber, and the built-in refrigeration and monitoring modules are sealed in a 14-pin butterfly packaging. It can be used directly as the pumping source of Er3+ - doped fiber amplifier or optic fiber gyroscopes.
Kinetic and Transesterification Properties of Lipase from Sprouted Melon ( Cucumeropsis manni ) Seeds

Crude lipase (acetone powder) was extracted from freshly sprouted melon seeds (Cucumeropsis manni). The activity, kinetic properties (effect of time, pH, and enzyme and substrate concentration, respectively) as well as the ability of the crude lipase to catalyze the production of methyl esters (biodiesel) were examined. The enzyme activity was determined using n-hexane as the solvent (1:2 v/w solvent: substrate ratio) and the transesterification product was analyzed by HPLC. A linear relationship was observed between reaction time and rate of lipolysis with the optimal activity at 2hr of incubation. Furthermore, the lipase was optimally active at acid pH 5 and lipolysis was achieved optimally when the amount of enzyme was 2.0g. Rate of lipolysis was observed to increase linearly at concentrations up to 5.0g of substrate above which a drop in the rate, with no apparent decrease in activity, was observed. The Km (6.25g) and Vmax (13.33%FFA/hr) were also determined. Analysis of the transesterification product yielded 0.61% alkyl ester, 0.81 %FFA, 93.17% TAG, 4.15% 1, 3-DAG and 1.26% 1, 2-DAG while transesterification efficiency was determined to be at 0.588%. Biodiesel (alkyl esters) prepared with the crude lipase was had a density of 0.872 g/mL while its cloud and pour points were 22C and 12C, respectively. The results from this research showed that an active lipase was isolated from sprouted melon seeds. However, the fuel properties of the biodiesel produced did not meet international transportation fuel standards. In order to be used industrially, better reaction conditions need to be established for the lipase. DOI:https://dx.doi. org/10.4314/jasem.v25i7. 34 Copyright: Copyright © 2021 Osemwenkhae and Uadia. This is an open access article distributed under the Creative Commons Attribution License (CCL), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Dates: Received: 09 May 2021; Revised: 12 August 2021; Accepted: 12 September 2021
4 × 4 MIMO Antenna System for Smart Eyewear in Wi-Fi 5G and Wi-Fi 6e Wireless Communication Applications

This paper proposes a small-slot antenna system (50 mm × 9 mm × 2.7 mm) for 4 × 4 multiple-input multiple-output (MIMO) on smart glasses devices. The antenna is set on the plastic temple, and the inverted F antenna radiates through the slot in the ground plane of the sputtered copper layer outside the temple. Two symmetrical antennas and slots on the same temple and series capacitive elements enhance the isolation between the two antenna ports. When both temples are equipped with the proposed antennas, 4 × 4 MIMO transmission can be achieved. The antenna substrate is made of polycarbonate (PC), and its thickness is 2.7 mm εr=2.85, tanδ=0.0092. According to the actual measurement results, this antenna has two working frequency bands when the reflection coefficient is lower than −10dB, its working frequency bandwidth at 4.58–5.72 GHz and 6.38–7.0 GHz. The proposed antenna has a peak gain of 4.3 dBi and antenna efficiency of 85.69% at 5.14 GHz. In addition, it also can obtain a peak gain of 3.3 dBi and antenna efficiency of 82.78% at 6.8 GHz. The measurement results show that this antenna has good performance, allowing future smart eyewear devices to be applied to Wi-Fi 5G (5.18–5.85 GHz) and Wi-Fi 6e (5.925–7.125 GHz).
Supplementary Materials of “Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks”

For image classification experiments on both CIFAR10 and ImageNet datasets, networks are trained from scratch. We set the batch size to 256, and use an SGD optimizer with a momentum of 0.9. The weight decay rate is assigned to 10−4. We initialize the learning rate to 0.1 and adopt a cosine learning rate scheduler. Following IR-Net [9], we balance and standardize weights in the forward propagation and adopt the error decay estimator to approximate the sign function in the backward propagation. We select Hardtanh as the activation function instead of ReLU when we binarize activations [9]. To networks considered in the experiments, we use standard data augmentation strategies following the original papers [8, 10, 13]. On CIFAR10, we train each model on a single V100 GPU with 1000 epochs. On ImageNet, we train each model on four V100 GPUs with 120 epochs for ResNet-18 and ResNet-34. For object detection, on both PASCAL VOC and MSCOCO datasets, networks are pre-trained on ImageNet [2] classification dataset. Following BiDet [12], the batch size is set to 32, and we adopt the Adam optimizer [5]. The learning rate is initialized to 0.001 which decays twice by multiplying 0.1 at the 6-th and 10-th epoch out of 12 epochs [5]. We adopt four V100 GPUs for training.
Laser Interferometry of Turbulent Atmosphere

The paper introduces a new laser interferometry–based method for diagnosis of random media by means of high-accuracy angle measurements and describes the results of its development and testing. Theoretical calculations of the dependence of the range of the laser interferometer on laser beam parameters, device geometry, and atmospheric turbulence characteristics are reported. It is demonstrated that at moderate turbulence intensities corresponding to those observed most frequently in turbulent atmosphere at moderate latitudes and with low interference contrast values, the performance range of the laser interferometer– based device exceeds 5 km.
The Potential Side Effect of the Face Mask

Face mask is a simple, loose fitting and disposable device that helps to block large particle droplets that may contain germs. It is widely used by the medical professions to maintain a sterile field during a surgery. On early January 2020, the general population was ordered to use face mask after the rapid spread of coronavirus worldwide. Therefore, face mask is playing an important role in controlling the spread of this virus. Its quality on daily wear is really an important issue to be studied so that it would be more protective and comfortable on wearing. This research was designed to study the important potential side effects of face mask and to determine the factors associated with skin allergy, headache, breathing difficulty, sight discomfort and skin breakdown when wearing a face mask. An analytical cross-sectional study was conducted from December 2020 to January 2021. A study population of 1500 undergraduate students in Melaka Manipal Medical College was selected. Purposive sampling was used to enroll students for this study and they were asked to answer the questionnaire. Frequency table, percentages, odd ratio and Chi-square test were used for data analysis. All the statistical significances were tested by two-tailed test (p≤0.05). A total of 183 medical students participated; 21.86% of the students experienced breathing difficulty problems while wearing the mask while 78.14% were not experiencing it. Besides that, 21.31% of the students participated reported having itchiness while wearing it and 78.69% of them did not have itchiness. Furthermore, 14.21% of the students experienced redness while wearing it. 132 students which is 72.13% showed presence of acne problem while wearing face mask. There were 62.30% of the students felt pain while wearing the mask and 37.7% of them did not have it. Dizziness or fatigue was experienced by 23.50% of the students which is 43 of them while wearing face mask. Last but not least, 67.76% of the students participated showed that sight discomfort was one of the problems faced by them while wearing face mask. In conclusion, the COVID-19 pandemic has forced the global population to adopt new ways of living, including the wearing of masks as a new norm. It has even accelerated R&D efforts in mask materials and design to offer better protection for users against airborne pollutants and pathogens. Mask wearing can be effective in the containment of communicable diseases.
Performance of Management Schools in Secondary School Examinations of Andhra Pradesh State by Data Envelopment Analysis

In this Paper we analyse the Board of Secondary data by Data Envelopment Analysis for the Andhra Pradesh state for the academic years 2009-2010 and 2010-2011 to see the Pattern of Efficiencies and Performance of the Management wise Results in Prior to the division of state in to two separate states . The Performance of the Management wise schools are presented along with the Peer Management Schools performance of the state as whole.
pLUTo: Enabling Massively Parallel Computation in DRAM via Lookup Tables

Data movement between the main memory and the processor is a key contributor to execution time and energy consumption in memory-intensive applications. This data movement bottleneck can be alleviated using Processing-in-Memory (PiM). One category of PiM is Processing-using-Memory (PuM), in which computation takes place inside the memory array by exploiting intrinsic analog properties of the memory device. PuM yields high performance and energy efficiency, but existing PuM techniques support a limited range of operations. As a result, current PuM architectures cannot efficiently perform some complex operations (e.g., multiplication, division, exponentiation) without large increases in chip area and design complexity. To overcome these limitations of existing PuM architectures, we introduce pLUTo (processing-using-memory with lookup table (LUT) operations), a DRAM-based PuM architecture that leverages the high storage density of DRAM to enable the massively parallel storing and querying of lookup tables (LUTs). The key idea of pLUTo is to replace complex operations with low-cost, bulk memory reads (i.e., LUT queries) instead of relying on complex extra logic. We evaluate pLUTo across 11 real-world workloads that showcase the limitations of prior PuM approaches and show that our solution outperforms optimized CPU and GPU base-lines by an average of $713 \times$ and $1.2 \times$, respectively, while simultaneously reducing energy consumption by an average of $1855 \times$ and $39.5 \times$. Across these workloads, pLUTo outperforms state-of-the-art PiM architectures by an average of $18.3 \times$. We also show that different versions of pLUTo provide different levels of flexibility and performance at different additional DRAM area overheads (between 10.2% and 23.1%). pLUTo’s source code and all scripts required to reproduce the results of this paper are openly and fully available at https://github.com/CMU-SAFARI/pLUTo.
A combined finite difference and analytic expression approach to crossover capacitance in a multilayer dielectric environment

The capacitances in the crossover regions of orthogonal transmission lines of finite thickness, fabricated on structures of the approximate dimensions of an integrated circuit, are evaluated in this paper by means of a combined finite difference and analytic expression method. The three-dimensional (3-D) finite difference method (FDM), using very fine mesh grids, is applied to an artificially defined region only a few microns in thickness, where orthogonal transmission lines on different metal layers of an integrated circuit cross one another. In the 600-/spl mu/m thick dielectric region above the ground plane on the lower surface of an integrated circuit, analytic expressions of the solution to the Laplace equation are formulated. An artificial boundary is assumed to separate the substrate of the 600-/spl mu/m thick integrated circuit from the thin active region on its uppermost surface where the active circuits and interconnects are actually fabricated, creating two separate regions which are treated using different approaches. An iterative procedure is employed to create a continuous interface between solutions across the boundary of the two regions generated by the two methods. The algorithm converges rapidly to very accurate solutions. The errors between this simulation method and laboratory measurements are within 8% for very small absolute values in the femtoFarad (fF) range. The field solutions are then converted into the equivalent circuit parameters. Finally, the waveshapes of propagating signal pulses are simulated by a networking program in a general electromagnetic modeling tool suite referred to as the Mayo Graphical Integrated Computer Aided Design Suite, or MagiCAD, in which the equivalent circuit model and capacitance values of the crossover problem are integrated.
Connection intervals in multi-scale dynamic networks

We consider a hybrid spatial communication system in which mobile nodes can connect to static sinks in a bounded number of intermediate relaying hops. We describe the distribution of the connection intervals of a typical mobile node, i.e., the intervals of uninterrupted connection to the family of sinks. This is achieved in the limit of many hops, sparse sinks and growing time horizons. We identify three regimes illustrating that the limiting distribution depends sensitively on the scaling of the time horizon.
BEEFMAN: COMPUTER SYSTEMS FOR EVALUATING MANAGEMENT OPTIONS FOR BEEF CATTLE IN NORTHERN AUSTRALIA

BEEFMAN is based on three computer systems: STATIC, HERD and PROBE. (1) STATIC is a steady-state model of a beef cattle herd. The total herd size, expressed as adult equivalents,is held constant. The model calculates the equilibrium herd structure for given weaning and mortality rates, age of turnoff, and replacement policy. The model is being used by extension officers in Queensland, and has provided important insights into the sensitivity of herd production to mortality and reproductive rates (Taylor and Rudder 1986). (2) HERD is a dynamic herd model which allows the changes in herd growth and decline to be simulated. HERD allows a user to describe a commercial herd or flock, etc. ,with 46 animal states. Animal states are used to describe the various groupings within the herd independently of age. The animal state name defines the animal groups by sex, and current and previous physiological status. The changes in animal states and ages are simulated on a monthly time-step. HERD provides a powerful and exact tool to examine the implications of herd management decisions and research findings. (3) PROBE simulates pasture production and steer growth for different forage options: native pasture, sown grass pastures, legume/grass mixtures and forage crops (oats, sorghum and lablab). Different feeding systems for steer growth can be designed and tested encompassing 30 of years variation in weather and prices (McKeon et al. 1986). At Emerald in Central Queensland, PROBE is being tested on commercial propertiesto examine its validity and its capacity to provide advice for producers. PROBE is supported by an on-line climate data bank for Queensland. Data from the Bureau of Meteorology and CSIRO (Division of Land Use Research) are available for use by extension officers. Demonstrations at field days have shown the demand for rainfall probability analysis within the grazing community.
Killing spinors and hypersurfaces

We consider spin manifolds with an Einstein metric, either Riemannian or indeﬁnite, for which there exists a Killing spinor. We describe the intrinsic geometry of nondegenerate hypersurfaces in terms of a PDE sat-isﬁed by a pair of induced spinors, akin to the generalized Killing spinor equation. Conversely, we prove an embedding result for real analytic pseudo-Riemannian manifolds carrying a pair of spinors satisfying this condition.
UWB Localization - Active and Passive Approach

The urticle presents the urchitecture of an e.rperimenta1 mulri-chnnnel UWB localizer. The localizer operates in the band /;-om near DC to 5 GHz. It wus built fyom the custom designed SiCr chips. Its perfirmoncc is shown by four dr//erent measurement examples. In thefirst one, ,he theoretical limitations qf the UWB localizer to derwmine the aacf pu1.w poxition (distance measurement) ore demonstrated. The furrher exuniples demonstrate UWB localizer's ability to measure 2D and 30 posifion of" moving object by two drflerent upprorrcha - active and pacsive. ~ UiVB localizer, dixtunce measurement, positioning
Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks

Understanding how activity in neural circuits reshapes following task learning could reveal fundamental mechanisms of learning. Thanks to the recent advances in neural imaging technologies, high-quality recordings can be obtained from hundreds of neurons over multiple days or even weeks. However, the complexity and dimensionality of population responses pose significant challenges for analysis. Existing methods of studying neuronal adaptation and learning often impose strong assumptions on the data or model, resulting in biased descriptions that do not generalize. In this work, we use a variant of deep generative models called - CycleGAN, to learn the unknown mapping between pre- and post-learning neural activities recorded $\textit{in vivo}$. We develop an end-to-end pipeline to preprocess, train and evaluate calcium fluorescence signals, and a procedure to interpret the resulting deep learning models. To assess the validity of our method, we first test our framework on a synthetic dataset with known ground-truth transformation. Subsequently, we applied our method to neural activities recorded from the primary visual cortex of behaving mice, where the mice transition from novice to expert-level performance in a visual-based virtual reality experiment. We evaluate model performance on generated calcium signals and their inferred spike trains. To maximize performance, we derive a novel approach to pre-sort neurons such that convolutional-based networks can take advantage of the spatial information that exists in neural activities. In addition, we incorporate visual explanation methods to improve the interpretability of our work and gain insights into the learning process as manifested in the cellular activities. Together, our results demonstrate that analyzing neuronal learning processes with data-driven deep unsupervised methods holds the potential to unravel changes in an unbiased way.
Evaluation of two MCDM methods for UAV emergency landing

One of the major impediments for the use of UAVs in civilian environment is the capability to replicate some of the functionality of safe manned aircraft operations. One critical aspect is emergency landing. Once the possible landing sites have been rated, a decision on the most suitable choice to land is required. This is a multi-criteria decision making (MCDM) problem which needs to take into account various factors in its selection of landing site. This report summarises relevant literature in MCDM in the context of emergency forced landing and proposes and compares two algorithms and methods for this task.
Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models

Sociodemographic biases are a common problem for natural language processing, affecting the fairness and integrity of its applications. Within sentiment analysis, these biases may undermine sentiment predictions for texts that mention personal attributes that unbiased human readers would consider neutral. Such discrimination can have great consequences in the applications of sentiment analysis both in the public and private sectors. For example, incorrect inferences in applications like online abuse and opinion analysis in social media platforms can lead to unwanted ramifications, such as wrongful censoring, towards certain populations. In this paper, we address the discrimination against people with disabilities, PWD, done by sentiment analysis and toxicity classification models. We provide an examination of sentiment and toxicity analysis models to understand in detail how they discriminate PWD. We present the Bias Identification Test in Sentiments (BITS), a corpus of 1,126 sentences designed to probe sentiment analysis models for biases in disability. We use this corpus to demonstrate statistically significant biases in four widely used sentiment analysis tools (TextBlob, VADER, Google Cloud Natural Language API and DistilBERT) and two toxicity analysis models trained to predict toxic comments on Jigsaw challenges (Toxic comment classification and Unintended Bias in Toxic comments). The results show that all exhibit strong negative biases on sentences that mention disability. We publicly release BITS Corpus for others to identify potential biases against disability in any sentiment analysis tools and also to update the corpus to be used as a test for other sociodemographic variables as well.
Using Machine Learning to Predict Poverty Status in Costa Rican Households

This study presents two supervised multiclassification machine learning models to predict the poverty status of Costa Rican households as a way to support government and business sectors make decisions in a rapidly changing social and economic environment. Using the Costa Rican household dataset collected via the proxy means test conducted by the Inter-American Development Bank, Random Forest and Gradient Boosted Trees achieved F1 scores of 64.9% and 68.4%, respectively. This study also reveals that education has the greatest impact on predicting poverty status.
Scheduling of cool storage using non-linear programming techniques

Thermal energy storage for space cooling is a load management technology that achieves load shifting by creating and storing cooling capacity during off-peak hours in anticipation of peak period loading. The charge and discharge process in a cool energy storage system is typically a 24-hour cycle. In this paper, an optimal controller for this process is designed that minimizes the operating costs of the storage facility. A 24-hour load-forecast model is developed to achieve dynamic system response to the variations in the ambient weather conditions. Hourly building cooling loads are predicted over the 24-hour horizon to schedule adequate charging of storage to meet peak-period loads. The degradation of performance of the optimal control algorithm attributable to the lack of perfect foresight of weather conditions and associated building loads is analyzed via a case study. A safety margin is also calculated and presented to compensate for same. >