Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperspectral Imaging Based on Compressive Sensing: Determining Cancer Margins in Human Pancreatic Tissue ex Vivo, a Pilot Study

Cancer is the second-leading cause of death in the United State and surgery remains the primary treatment for most solid mass tumors. However, accurately identifying tumor margins in real-time remains a challenge. In this study, the design and testing of hyperspectral imaging (HSI) system based on a single-pixel camera engine is discussed. The primary advantage of a single pixel architecture over traditional scanning HSI techniques is its high sensitivity and potential to function at low light levels. The objective for the imaging system described here is to detect changes in the reflectance spectra of tissue and to use these differences to delineate tumor margins. This paper presents the results of a 19-patient pilot study that assesses the ability of the HSI system to use reflectance imaging to delineate adenocarcinoma tumor margins in human pancreatic tissue imaged ex vivo. Pancreatic tissue excised during pancreatectomy was imaged immediately after being sent to the pathology lab. A pathologist sectioned the tissue and placed samples into standard tissue embedding cassettes. These tissue samples were then imaged us-ing the HSI system. After imaging, the samples were returned to the pathologist for processing and analysis. The HSI was later compared to the histological analysis. The spectral angle mapping (SAM) and support vector machine (SVM) algorithms were used to classify pixels in the HSI images as healthy or unhealthy in order to delineate margins. Good agreement between margins determined via HSI (using both SAM and SVM) and histology/white light imaging was found.
State‐of‐the‐art and progress in the optimization‐based simultaneous design and control for chemical processes

Significant progress in the area of simultaneous design and control for chemical processes has been achieved and various methodologies have been put forward to address this issue over the last several decades. These methods can be classified in two categories (1) controllability indicator-based frameworks that are capable of screening alternative designs, and (2) optimization-based frameworks that integrate the process design and control system design. The major objective is to give an up-to-date review of the state-of-the-art and progress in the challenging area of optimization-based simultaneous design and control. First, motivations and significances of simultaneous design and control are illustrated. Second, a general classification of existing methodologies of optimization-based simultaneous design and control is outlined. Subsequently, the mathematical formulations and relevant theoretical solution algorithms, their merits, strengths and shortcomings are highlighted. Last, based on the recent advances in this field, challenges and future research directions are discussed briefly. An attempt is made with the help of this review article to stimulate further research and disseminate the simultaneous design methods to challenging problem areas. In particular, the application of optimization-based simultaneous design and control methods to large-scale systems with highly inherent nonlinear dynamics often the case in industrial chemical processes remains a challenging task and yet to be solved. © 2012 American Institute of Chemical Engineers AIChE J, 58: 1640–1659, 2012
A POTENTIAL ACTIVITIES OF GYMNOSPORIA MONTANA AND JAGGERY AGAINST JAUNDICE: AN UPDATED REVIEW

Jaundice, also known as hyperbilirubinemia, is defined as a yellow discoloration of the body tissue resulting from the accumulation of excess bilirubin. This activity reviews the treatment of jaundice by use of combined Gymnosporia Montana and Jaggery. Gymnosporia montana (known as Vikro), occurring throughout the arid, dry areas of India, is traditionally claimed to be useful in various ailments. In the present communication the details of the plant like taxonomic position, distribution, traditional uses, pharmacognosy, chemistry and pharmacology has been also reviewed. It has great potential as hepatoprotective and anticancer drug. Jaggery is sugarcane based natural sweetener made by the concentration of sugarcane juice without any use of chemicals. It is available in the form of solid blocks and in semi-liquid form. It contains the natural sources of minerals and vitamins inherently Present in sugarcane juice and it is one of the most wholesome and healthy sugars in the world. INTRODUCTION Jaundice cause by excess amount of Bilirubin accumulated in tissue. Bilirubin has two components: unconjugated (indirect) and conjugated (direct), and hence elevation of any of these can result in jaundice. Icterus acts as an essential clinical indicator for liver disease, apart from various other insults. [1] Yellowing of skin sparing the sclerae is indicative of WORLD JOURNAL OF PHARMACY AND PHARMACEUTICAL SCIENCES SJIF Impact Factor 7.632 Volume 10, Issue 11, 731-739 Review Article ISSN 2278 – 4357 *Corresponding Author Tala Uday J. Student, Shree H.N Shukla Institute of Pharmaceutical Education & Research, Rajkot Gujarat. Article Received on 11 Sept. 2021, Revised on 01 October 2021, Accepted on 21 October 2021 DOI: 10.20959/wjpps202111-20509
Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study

Background There is still room for improvement in the modified LEMON (look, evaluate, Mallampati, obstruction, neck mobility) criteria for difficult airway prediction and no prediction tool for first-pass success in the emergency department (ED). Objective We applied modern machine learning approaches to predict difficult airways and first-pass success. Methods In a multicenter prospective study that enrolled consecutive patients who underwent tracheal intubation in 13 EDs, we developed 7 machine learning models (eg, random forest model) using routinely collected data (eg, demographics, initial airway assessment). The outcomes were difficult airway and first-pass success. Model performance was evaluated using c-statistics, calibration slopes, and association measures (eg, sensitivity) in the test set (randomly selected 20% of the data). Their performance was compared with the modified LEMON criteria for difficult airway success and a logistic regression model for first-pass success. Results Of 10,741 patients who underwent intubation, 543 patients (5.1%) had a difficult airway, and 7690 patients (71.6%) had first-pass success. In predicting a difficult airway, machine learning models—except for k-point nearest neighbor and multilayer perceptron—had higher discrimination ability than the modified LEMON criteria (all, P≤.001). For example, the ensemble method had the highest c-statistic (0.74 vs 0.62 with the modified LEMON criteria; P<.001). Machine learning models—except k-point nearest neighbor and random forest models—had higher discrimination ability for first-pass success. In particular, the ensemble model had the highest c-statistic (0.81 vs 0.76 with the reference regression; P<.001). Conclusions Machine learning models demonstrated greater ability for predicting difficult airway and first-pass success in the ED.
What You Extract Is What You Get: Different Methods of Protein Extraction from Hemp Seeds

Cannabis sativa L. seeds are rich in essential polyunsaturated fatty acids and highly digestible proteins, with a good nutritional value. Proteomics studies on hempseed reported so far have mainly been conducted on processed seeds and, to our knowledge, no optimization of protein extraction from hemp seeds has been performed. This study investigates the SDS-PAGE profile of hempseed proteins comparing different methods of extraction, (Osborne sequential extraction, TCA/acetone, MTBE/methanol, direct protein solubilization of defatted hempseed flour), two conditions to keep low temperature during seed grinding (liquid nitrogen or ice) and two solubilization buffers (urea-based or Laemmli buffer). Among the tested conditions, the combination of liquid nitrogen + TCA/acetone + Laemmli buffer was not compatible with SDS-PAGE of proteins. On the other hand, urea-based buffer achieved more reproducible results if combined with all the other conditions. TCA/acetone, MTBE/methanol, and direct protein solubilization of defatted hempseed flour demonstrated a good overview of protein content, but less abundant proteins were poorly represented. The Osborne sequential separation was helpful in diluting abundant proteins thus enhancing the method sensitivity.
Experimental Determination of Ornithopter Membrane Wing Shapes Used For Simple Aerodynamic Modeling

This paper addresses a method to analyze flapping membrane wing aerodynamics using kinema tic data. An alternative to computational fluid dynamics, this experimental method places small reflective markers on a wing and tracks them using a Vicon motion measurement system. The dynamic membrane shapes of the ornithopter wing are then quantified as moving points in a three dimensional coordinate system. This motion tracking process was completed for two ornithopters with approximately one meter span rip -stop nylon wings over a series of flapping frequencies. Aerodynamic loads were recorded in additi on to wing shape data to confirm the aerodynamic modeling results. The wing shape data was used to form an analytical aerodynamic model that uses blade element theory and quasi -steady aerodynamics to account for the local twist, stroke angle, membrane shap e, wing velocity and acceleration. Results from the aerodynamic model show good correlation between the magnitude of lift and thrust produced but some phase errors exist between the measured and calculated force curves .
A new look at impact ionization-Part I: A theory of gain, noise, breakdown probability, and frequency response

Impact ionization in thick multiplication regions is adequately described by models in which the ionization coefficients are functions only of the local electric field. In devices with thin multiplication lengths, nonlocal effects become significant, necessitating new models that account for the path that a carrier travels before gaining sufficient energy to impact ionize. This paper presents a new theory that incorporates history-dependent ionization coefficients, and it is shown that this model can be utilized to calculate the low-frequency properties of avalanche photodiodes (APD's) (gain, noise, and breakdown probability in the Geiger mode) and the frequency response. A conclusion of this work is that an ionization coefficient is not a fundamental material characteristic at a specific electric field and that any experimental determination of ionization coefficients is valid only for the particular structure on which the measurement was performed.
Investigation of the Impact of Illumination on Deep Learning-based Equipment Load Detection for Energy Demand Estimation

The main aim of this paper is to investigate the impact of lighting conditions on the detection accuracy of the vision-based equipment load detection approach. The work will be using artificial intelligence cameras to detect equipment information in different lighting levels, employing deep learning method to analyze and generate real-time equipment usage profiles for offices which can be inputted to the demand-based building controls to increase the efficiency of heating, ventilation, and air-conditioning systems. The performance of the developed approach in various illumination conditions was compared by using a building energy simulation tool. The results showed that as compared with the conventionally-scheduled heating, ventilation, and air-conditioning systems, the system with the use of equipment usage profiles conducted by the proposed approach can achieve up to 15% reduction in energy consumption depending on the setup of the camera in terms of indoor lighting levels. The finding indicates that adequate illumination level contributes to the decrease of building energy demand by achieving an effective deep learning approach.
Hyperspectral Uncertainty Quantification by Optimization

We leverage convex optimization techniques to perform Bayesian uncertainty quantification (UQ) for hyperspectral (HS) inverse imaging problems. The proposed approach generalizes our recent work for single-channel UQ [1]. Similarly, the Bayesian hypothesis test is formulated as a convex minimization problem and solved using a primaldual algorithm to quantify the uncertainty associated with particular 3D structures appearing in the maximum a posteriori (MAP) estimate of the HS cube. We investigate the interest of the proposed method for wideband radio-interferometric (RI) imaging that consists in inferring the wideband sky image from incomplete and noisy Fourier measurements. We showcase the performance of our approach on realistic simulations.
Green Dynamic Capability Analysis: Study on SME Sustainable Fashion in Semarang

The purpose of this study is to describe the green dynamic capability of SMEs in sustainable fashion in Semarang. Green dynamic capability is part of the dynamic capability that applied to the environment. The Resource Based View (RBV) approach shows that dynamic capabilities need to be owned by companies and can create a competitive advantage to be able to compete with other companies. Currently the fashion industry has a tremendous impact on environmental pollution so that Sustainable fashion is a new perspective. Green dynamic capability in this study is viewed from 3 dimensions, namely resource integration capability, resource reconfiguration capability and environmental insight capability. The research sample is 10 SMEs Sustainable Fashion. Methods of data collection with structured interviews and analyzed descriptively. The results of the study indicate that the dimension of resource integration capability, respondents have internal and external resources that are able to support their business. Dimensions of resource reconfiguration capability, respondents educate employees and families and try to find the uniqueness of the products produced. In the environmental insight capability dimension, respondents gain insight independently and also learn from communities and associations
Selection of Sustainability Indicators for Wastewater Treatment Technologies

Wastewater treatment systems must be measured and assessed in terms of its sustainability performance and to enable continuous improvement over the long term. This work involves the selection of indicators and methodologies used for incorporating sustainability consideration into the design of wastewater treatment module. The GoldSET is a decision support tool for development and implementation of sustainability in engineering projects. Following the findings of the indicators for a new wastewater treatment module, this thesis aims to contribute to these activities for the development of sustainable indicators as the tool for assessment improvements. These indicators are developed especially for the industrial wastewater treatment module but may be also suitable for municipal wastewater treatment. It proposes a general framework with a relatively simple, yet comprehensive set of indicators for identification of more sustainable practises for WWTP. The framework consists of a number of qualitative and quantitative indicators comprising the four dimensions of sustainability: environment, society, economy and technology. It then describes the weighing scheme which provides a mechanism to assess the performance of each option with respect to indicator. The largest and most balanced square with respect to the four apexes of environment, social, economic and technical performances gives the most sustainable option. Thus, it serves as a tool which can assist companies in accessing their performance regarding their goals and objectives in the field of sustainable development.
An Irregular Tiled Array Technique for Massive MIMO Systems

An irregular tiled array technique is proposed for the application of massive MIMO systems, where the uniformly spaced antenna array elements are irregularly divided into subarrays fed by only one radio frequency (RF) chain separately. As compared to conventional beamforming architecture, the number of RF chains is reduced to one-half or one-quarter of the original number by using domino-shaped subarrays or tetromino-shaped subarrays, respectively. Two beamforming architectures for such a framework are proposed and a minimum mean square error (MMSE) method is applied to design the layouts of irregular arrays efficiently. In the framework of single-user massive MIMO systems, numerical simulation results show that the proposed architectures outperform conventional beamforming architectures and antenna selection architecture under the same number of RF chains conditions. With saving half of the transceivers, the loss of gain of the state-of-the-art sparsely sampled array is 1.9dB in the broadside and 2.2 dB for scanning to 30° in comparison with reference array (i.e., one transceiver per element). By contrast, there is no loss of gain of domino-shaped irregular array (i.e., one transceiver per two elements) in the broadside as compared to reference array and the loss of gain will be about 1.5dB for scanning to 30°.
Pancytopenia and Kaposi sarcoma indicators of HIV infection diagnosis: A case report

During HIV infection, hematological manifestations are frequently associated with various pathogenic mechanisms, which require a multidisciplinary approach. We present the case of a 57-year-old patient with pancytopenia associated with Kaposi's sarcoma, as indicative manifestations of the acquired immunodeficiency syndrome. The late diagnosis of HIV infection, in the stage of severe immunosuppression, the association of opportunistic neoplasia, delayed oncological therapy and the development of immune reconstruction syndrome after starting antiretroviral therapy have contributed to the severe evolution and death of the patient.
Film bulk acoustic wave resonator technology

It is demonstrated that completely monolithic two-pole filters can be fabricated on silicon substrates using FBARs (film bulk acoustic resonators) and that these filters have low loss and reasonable bandwidth. A two-pole monolithic ladder filter operating in the 1-1.5 GHz range has been demonstrated. These filters (0.3*0.3*0.02 in) have a bandwidth of 30 MHz at 1 GHz and an insertion loss of about 1 dB. The small size serves to provide good shock and acceleration tolerance. Preliminary results on a stacked FBAR filter operated in high overtone mode show response up to 5.5 GHz. The authors review the development of FBAR technology, including piezoelectric film and membrane materials, fabrication techniques, temperature compensation schemes, and the progress made in processing monolithic filters. Issues relating to integration of FBARs with active circuits and IC processing compatibility are addressed. <<ETX>>
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

Medical experts employ electroencephalography (EEG) for analyzing the electrical activity in the brain to infer disorders. However, the time costs of human experts are very high, and the examination of EEGs by such experts, therefore, accounts for a plethora of medical resources. In this study, an improved one-dimensional CNN-only system of 25 layers has been proposed to identify abnormal and normal adult EEG signals using a single EEG montage without using any explicit feature extraction technique. Most of the previous systems based on deep learning, that have been proposed to solve this problem, use extremely deep architectures containing very large numbers of layers. This study also presents an independent preprocessing module that has been exhaustively evaluated for optimal parameters with the target of adult EEG signal classification. The achieved accuracy of the proposed classifier as a part of the decision support system is 82.24%, which is a substantial improvement of ~3% over the previous best reported classifier of comparable depth. The system also exhibits significantly higher F1-score and sensitivity as well as lower loss. The proposed system is intended to be a part of an expert system for overall brain health evaluation. INDEX TERMS Electroencephalography, Machine Learning, Decision Support Systems, Convolutional Neural Networks, Data Preprocessing, Savitzky-Golay Filtering, Optimization
Strain Engineering of Low‐Dimensional Materials for Emerging Quantum Phenomena and Functionalities

Recent discoveries of exotic physical phenomena, such as unconventional superconductivity in magic‐angle twisted bilayer graphene, dissipationless Dirac fermions in topological insulators, and quantum spin liquids, have triggered tremendous interest in quantum materials. The macroscopic revelation of quantum mechanical effects in quantum materials is associated with strong electron–electron correlations in the lattice, particularly where materials have reduced dimensionality. Owing to the strong correlations and confined geometry, altering atomic spacing and crystal symmetry via strain has emerged as an effective and versatile pathway for perturbing the subtle equilibrium of quantum states. This review highlights recent advances in strain‐tunable quantum phenomena and functionalities, with particular focus on low‐dimensional quantum materials. Experimental strategies for strain engineering are first discussed in terms of heterogeneity and elastic reconfigurability of strain distribution. The nontrivial quantum properties of several strain‐quantum coupled platforms, including 2D van der Waals materials and heterostructures, topological insulators, superconducting oxides, and metal halide perovskites, are next outlined, with current challenges and future opportunities in quantum straintronics followed. Overall, strain engineering of quantum phenomena and functionalities is a rich field for fundamental research of many‐body interactions and holds substantial promise for next‐generation electronics capable of ultrafast, dissipationless, and secure information processing and communications.
Field Detection of Rhizoctonia Root Rot in Sugar Beet by Near Infrared Spectrometry

Rhizoctonia root and crown rot (RRCR) is an important disease in sugar beet production areas, whose assessment and control are still challenging. Therefore, breeding for resistance is the most practical way to manage it. Although the use of spectroscopy methods has proven to be a useful tool to detect soil-borne pathogens through leaves reflectance, no study has been carried out so far applying near-infrared spectroscopy (NIRS) directly in the beets. We aimed to use NIRS on sugar beet root pulp to detect and quantify RRCR in the field, in parallel to the harvest process. For the construction of the calibration model, mainly beets from the field with natural RRCR infestation were used. To enrich the model, artificially inoculated beets were added. The model was developed based on Partial Least Squares Regression. The optimized model reached a Pearson correlation coefficient (R) of 0.972 and a Ratio of Prediction to Deviation (RPD) of 4.131. The prediction of the independent validation set showed a high correlation coefficient (R = 0.963) and a root mean square error of prediction (RMSEP) of 0.494. These results indicate that NIRS could be a helpful tool in the assessment of Rhizoctonia disease in the field.