Miracle-dz/UniFAR-FTUs huggingface dataset:
Significance of PSA Screening in Niigata, Japan: Survey of Actual Status of New Cases of Prostate Cancer

Purpose This study aims to investigate the utility of prostate-specific antigen (PSA) screening by conducting an all-case survey of newly diagnosed prostate cancer patients at Niigata Prefecture, Japan. Patients and Methods Depending on whether patients were subjected to screening, information was prospectively collected on all prostate cancer patients newly diagnosed between October 1, 2019, and September 30, 2020, at all institutions in Niigata Prefecture where urologists performing prostate biopsy routinely work and differences in clinical parameters were investigated. Results PSA was measured in 478 out of 1332 patients (35.8%) as part of a community health screening. The rate of metastatic carcinoma (M1) in all patients was 14.9%. When patients were divided into three categories of population-based screening (community health screening and workplace health screening), opportunistic screening (PSA measurements at complete medical check-ups or on patient request), and testing triggered by clinical symptoms or findings, the proportion of metastatic cancer was 4.5%, 3.7%, and 30.6%, respectively, demonstrating that the number of distant metastases was significantly lesser in all patients who underwent screening. Conclusion The one-year all-case survey of newly diagnosed prostate cancer patients demonstrated that PSA screening significantly contributed to the early diagnosis of current prostate cancer in Japan.
Photonic angular super-resolution using twisted N00N-states

Photonic N00N-states, i.e. states of light where N photons are in an extremal superposition between two orthogonal states $\frac{1}{{\sqrt 2 }}\left({\left| {N,0} \right\rangle + \left| {0,N} \right\rangle }\right)$ , have an increased phase-sensitivity in comparison to their classical counterparts. This phase sensitivity has been harnessed in multiple proof-of-principle experiments with one implementation even showing this advantage unconditionally [1] . Using the increased phase sensitivity, that quantum states of light offer, in conjunction with the increased angular sensitivity of states with large orbital angular momentum (OAM) [2] , has been the target of theoretical studies [3] as well experiments utilizing squeezed states [4] and entangled photon pairs [5] . Until recently however [6] , a reliable way of generating two-photon OAM N00N-states, in a single beam-line and for arbitrary OAM-values, had not been demonstrated.
Simulation Study on the Performance of Vertical Axis Wind Turbine

The present study considered the design improvement of Savonius rotor, in order to increase the efficiency of output power. An investigation was conducted to study the effect of geometrical configuration on the performance of the rotor in terms of coefficient of torque, coefficient of power and power output. Modification of conventional geometry has been designed by combining the effect of number of blades and shielding method. CFD simulation was conducted to analyze the flow characteristic and calculate the torque coefficient of all the rotor configurations. The continuity and Reynolds Averaged Navier-Stokes (RANS) equations and realizable k-ε epsilon turbulence model are numerically solved by commercial software Ansys-Fluent 14.0. The results obtained by transient and steady method for the conventional two bladed Savonius rotor are in agreement with those obtained experimentally by other authors and this indicates that the methods can be successfully applied for such analysis. The modified 3 and 4 bladed rotors with hybrid shielding method gave the highest maximum power coefficient which 0.37 at TSR 0.5 and output power exceed 4 watts with rotor dimensions of 0.2m width and 0.2m height. This blade configuration also is the best configuration by several percentages compared to the other model from the previous study
Statistical Surrogate Formulations for Simulation-Based Design Optimization

Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima, and the failure of the analysis to return a value to the optimizer. One possible remedy to alleviate these issues is to use surrogate models in lieu of the computational models or simulations and derivative-free optimization algorithms. In this work, we use the R dynaTree package to build statistical surrogates of the blackboxes and the direct search method for derivative-free optimization. We present different formulations for the surrogate problem (SP) considered at each search step of the mesh adaptive direct search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on 20 analytical benchmark problems and two simulation-based multidisciplinary design optimization (MDO) problems. Numerical results confirm that the use of statistical surrogates in MADS improves the efficiency of the optimization algorithm. [DOI: 10.1115/1.4028756]
Effect of Galangal (Alpinia galanga) Essential Oil Supplementation on Milk Production, Composition, and Characteristics of Fatty Acids in Dairy Cows

| Galangal essential oil (EO) has a promising impact as a rumen modifier because it contains 24% of cineole and known to have antimicrobial activity on Gram positive and Gram negative bacteria. Improving the quality of milk composition can be achieved by modification of microbial abundance in the rumen. The study was conducted to measure milk production and composition of Holstein Friesian cow ration, which was supplemented with galangal (EO). The experiment was designed a completely randomized design, 60% of mott elephant grass and 40% of concentrate (dry matter basis) were supplemented by galangal EO as much as 0, 1.25 and 2.50 g/head/day with four replications. The data on milk production were collected every day of milking. Milk composition (lactose, protein, and fat) were analyzed each week for 30 days, and milk fatty acids were analyzed at the end of the research. The results showed no significant difference in milk production, milk composition among rations. Furthermore, milk urea nitrogen (MUN) and blood urea nitrogen (BUN) concentration was also absorbed and galangal EO did not effect on both treatments. Galangal EO at dose of 1.25 g/head/day decreased significantly on blood of volatile fatty acids. The concentration of unsaturated fatty acids (ɣ-linolenic acids) ratio 1.25 was higher than 0 and 250 g/head/day. In conclusion, Galangal EO supplementation increased the polyunsaturated fatty acids ratio at level 1.25 g/head/day of galangal EO, but did not affect milk production, lactose, protein, and fat content.
Restoration of Authentic Position of Unidentified Vessels in SAR Imagery: A Deep Learning Based Approach

Enhancement of vessel detection performance in synthetic aperture radar (SAR) images generated academic advancements related to amelioration of the algorithmic accuracy and training data procurement. For practical implementation of vessel detection algorithm to maritime surveillance, however, presentation of authentic position of vessels was essential. Accordingly, this article aimed to propose an algorithm, which demonstrated realistic and azimuth shift-corrected position of vessel, especially out of conventional vessel monitoring apparatus: automated identification system (AIS) and vessel-pass (VPASS) information. Two different analyses regarding the vessel detection output utilization were, therefore, presented. Primary analysis demonstrated a vessel identification algorithm, comparing the vessel detection output with elaborately preprocessed AIS and VPASS information, which indicated the discrete position and velocity of vessel. The other presented a position restoration algorithm via i) velocity estimator complementing the conventional fractional Fourier transform velocity estimation analysis, while investigating the effect of range acceleration in deriving the azimuth velocity and ii) measuring the vessel orientation angle from Radon transform. Both algorithms were implemented to the vessel detection output in Cosmo-SkyMed SAR images, depicting an enhanced accuracy compared to the conventional algorithm both in velocity estimation and azimuth shift estimation; velocity offset reduced from 1.64 m/s to 1.29 m/s and average azimuth shift offset reduced from 70.75 m to 62.39 m. The presented algorithms would be decisive in terms of practicality if robustly attached to convolutional neural network-based vessel detection algorithms demonstrating ideal detection performances.
3-D construction of monolithic passive components for RF and microwave ICs using thick-metal surface micromachining technology

As a viable technological option to address today's strong demands for high-performance monolithic low-cost passive components in RF and microwave integrated circuits (ICs), a new CMOS-compatible versatile thick-metal surface micromachining technology has been developed. This technology enables to build arbitrary three-dimensional (3-D) metal microstructures on standard silicon substrate as post-IC processes at low temperature below 120/spl deg/C. Using this technology, various highly suspended 3-D microstructures have been successfully demonstrated for RF and microwave IC applications. We have demonstrated spiral inductors suspended 100 /spl mu/m over the substrate, coplanar waveguides suspended 50 /spl mu/m over the substrate, and complicated microcoaxial lines, which have 50-/spl mu/m-suspended center signal lines surrounded by inclined ground shields of 100 /spl mu/m in height. The microwave performance of the microcoaxial transmission line fabricated on a glass substrate has been evaluated to achieve very low attenuation of 0.03 dB/mm at 10 GHz with an effective dielectric constant of 1.6. The process variation/manufacturability, mechanical stability, and package issues also have been discussed in detail.
Pharmaceutical Effects on Placental Morphology in Women with Gestational Diabetes Mellitus

Objective: The purpose of this study was to see how food organization and Metformin affected placental morphology in women with GDM. Methods: 66 GDMs were registered after providing informed consent permission. 35 patients of GDM through blood sugar levels 140 mg/dl remained allocated Set B (2500-3000Kcal/day and 30-minute walk three times a week). They remained reserved on diet control, while 34 patients of GDM through blood sugar levels >140 mg/dl have been delegated Set C and remained reserved on diet with tablet Metformin (550mg TDS). Lastly, 28 healthy pregnant women remained retained in Set A as controls. Placentas were stored and analyzed for morphology after delivery. Results: Heavy placentae thru extensive villous immature, charangoists, and syncytial knots were observed in set B, while fibrinoid necrosis and calcification were observed in set C. Placental and cord width were significant in Set B against A, but only cord width was relevant in Set C against A in gross morphology. In light microscopy, charangoists, infarction, and syncytial loops showed detected in sample 2 against with a villous maturity; moreover, charangoists and syncytial knots have been found in appendix B versus C placental width, but C versus A results were negligible. Conclusion: In comparison to the diet group, metformin exhibited beneficial benefits on placental morphology that were equivalent to normal controls.
Border Security Control via Distributed WSN Technology

Wireless Sensors Network (WSN) combine sensing, signal processing, decision capability, and wireless networking capability in a compact, low power system. Among countries, border protection is a sensitive issue and measures are being taken to improve security at the borders. In addition to physical fencing, smart methods using technology are being employed to increase the alertness of security officials at the borders. Border control using wireless sensor network is one way to do.
Use of neural networks in modeling semiconductor manufacturing processes: an example for plasma etch modeling

System modeling using the plasma etch process is used as a vehicle to demonstrate the application of adaptive nonlinear neural networks in complex process modeling. The system is an active manufacturing process for tantalum silicide plasma gate etching. The model is for a single process recipe, a single technology (1.25 mu m), and a single machine. The model uses a few process signatures to successfully predict the oxide remaining (within 7 AA). The model is pattern density invariant. It is demonstrated that the backpropagation algorithm is adequate for building neural network models of complex nonlinear processes using production databases. The neural network model has a correlation of 0.68 while the statistical model has a correlation of 0.45. >
Aiding the development of the conceptual model for hybrid simulation: Representing the modelling frame

Abstract Hybrid simulation has emerged as a popular method of modelling complex systems and addressing real-world problems. However, in the existing literature, there is no clear method for documenting the approach of a hybrid simulation study. We aim to address this problem by presenting a method for general representation of the modelling frame, i.e. the choice and combination of modelling approaches, in hybrid modelling studies. To demonstrate the use of the representation method, we retrospectively compare and contrast two real-world examples of hybrid simulation undertaken to support the rail operations of Eurostar International Limited. We discuss the advantages of our proposed representation method in distilling the key features and aims of a hybrid simulation study and show how our representation method can be used as a tool for communicating to other modellers and stakeholders the value added from hybridisation. We further discuss, how our representation method can be used at any stage throughout the model development process including to support the conceptual modelling activity when undertaking a hybrid simulation study.
Current-mode analogue signal processing

The authors describe their work on current-mode analog signal processing. Extending the conventional voltage-mode op-amp to provide current-mode performance has resulted in many useful application circuits being developed, ranging from universal follower amplifiers and current-conveyor-based impedance converters and filters, through to precision rectification and instrumentation amplifiers. Particular attention is given to work centered on developments of novel bipolar IC structures, including an operational floating current-conveyor, and developments toward a fully differential current-mode op-amp. <<ETX>>
Use of automakers’ technological knowledge in component suppliers’ innovations: Different effects in different situations

Abstract Automakers have been emphasized as an important source of technological knowledge for vehicle parts suppliers. However, the situations in which such technological knowledge can be applied more effectively are unclear. Thus, theoretical instructions for suppliers’ strategic decisions on the use of this important knowledge in different situations are lacking. To reveal the different effects of suppliers’ use of automakers’ technological knowledge in different situations, this paper differentiates the situations based on two criteria: (1) the nature of the inventions (i.e., exploitation and exploration) and (2) the level of the suppliers’ knowledge-creating capability. Using patents from 47 suppliers of the US automobile manufacturing industry as the sample, we explored the characteristics of using automakers’ technological knowledge in vehicle parts suppliers’ inventions in different situations. Automakers’ technological knowledge is most useful in exploitative inventions or for suppliers with a lower level of creation capability. This finding implies that automakers’ knowledge is a preliminary element in suppliers’ inventions.
2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022.

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes. org/SOC.
C‐POOL: A scheme for modelling convective cold pools in the Met Office Unified Model

A new parametrization scheme representing the lifecycle and effects of convective cold pools (C‐POOL) is presented. The scheme comprises a column‐crossing prognostic model of the initiation by downdraughts, interaction and decay of cold pools, based upon approximations taken from similarity modelling of gravity currents. The prognostic variables are drawn upon to feed back into the convection parametrization scheme. These feedbacks affect the convective diagnosis, initiation from the boundary layer, parcel ascent and entrainment. Tests of the model show that it improves representation of the diurnal cycle of tropical rainfall, previously well‐known as a weakness of parametrized convection.
STATISTICAL ANALYSIS OF CURRENT VOLTAGE CHARACTERISTICS OF PV MODULES

Abstract Variations on the current-voltage characteristics of pv cells and pv modules cause a power loss due to their mismatch, when they are connected in an array. This topic was studied in the past from the hypothesis that the Tsc, Voe, FF and Rs are random variables having normal distributions This work intends to show the experimental validation of such an assumption from the analysis of a wide collection of data corresponding to several pv modules series. It is concluded that the above mentioned hypothesis can be accepted for FF, Voc and Rs but not for Isc.
Nuclear Medicine Instrumentation

Doody's Book Review - Score: 95, 4 Stars! Nuclear Medicine Instrumentation is the first text to covers instruments vital to nuclear medicine at a technologist's level. It provides students with concise and straightforward information pertaining to the operation and use of each instrument. It is specifically designed to prepare students for typical scenarios and potential pitfalls they may encounter throughout their careers. Nuclear Medicine Instrumentation is broken into four main parts: * Small Instruments * Gamma Camera * Single Photon Emission Computed Tomography (SPECT) * Positron Emission Tomography (PET) Topics discussed include factors relating to Radiation Measurements, Quality Control of Gamma Cameras, Basic Principles and Image Display Techniques for Single -Photon Emission Computed Technology and much more! Each new print copy of this review guide ncludes an interactive CD-ROM with review questions, answers, and explanations. Please note: Electronic formats of this review guide do not include the CD ROM.
Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network

Simple Summary Breast cancer is leading cancer increases the death rate in women. Early diagnosis of breast cancer in women can save their lives. The current study proposed a novel scheme to detect architectural distortion from mammogram images to predict breast cancer using a deep learning approach. Results are evaluated on a public and a private dataset which may help to improve the diagnostic ability of breast cancer of radiologists and doctors in daily clinical routines. Furthermore, the proposed method achieved maximum accuracy as compared with previous approaches. This study can be interesting and valuable in the healthcare predictive modeling domain and will add a real contribution to society. Abstract Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically removes the noise and detects the location of objects from varying patterns. The current study investigated the gap to detect architectural distortion ROIs (region of interest) from mammograms using computer vision techniques. Proposed an automated computer-aided diagnostic system based on architectural distortion using computer vision and deep learning to predict breast cancer from digital mammograms. The proposed mammogram classification framework pertains to four steps such as image preprocessing, augmentation and image pixel-wise segmentation. Architectural distortion ROI’s detection, training deep learning, and machine learning networks to classify AD’s ROIs into malignant and benign classes. The proposed method has been evaluated on three databases, the PINUM, the CBIS-DDSM, and the DDSM mammogram images, using computer vision and depth-wise 2D V-net 64 convolutional neural networks and achieved 0.95, 0.97, and 0.98 accuracies, respectively. Experimental results reveal that our proposed method outperforms as compared with the ShuffelNet, MobileNet, SVM, K-NN, RF, and previous studies.
An integrated scheduling and material-handling approach for complex job shops: a computational study

We suggest an extension of the shifting bottleneck heuristic for complex job shops that takes the operations of automated material-handling systems (AMHS) into account. The heuristic is used within a rolling horizon approach. The job-shop environment contains parallel batching machines, machines with sequence-dependent setup times, and re-entrant process flows. Jobs are transported by an AMHS. Semiconductor wafer fabrication facilities (wafer fabs) are typical examples for manufacturing systems with these characteristics. Our primary performance measure is total weighted tardiness (TWT). The shifting bottleneck heuristic (SBH) uses a disjunctive graph to decompose the overall scheduling problem into scheduling problems for single machine groups and for transport operations. The scheduling algorithms for these scheduling problems are called subproblem solution procedures (SSPs). We consider SSPs based on dispatching rules. In this paper, we are also interested in how much we can gain in terms of TWT if we apply more sophisticated SSPs for scheduling the transport operations. We suggest a Variable Neighbourhood Search (VNS) based SSP for this situation. We conduct simulation experiments in a dynamic job-shop environment in order to assess the performance of the suggested algorithms. The integrated SBH outperforms common dispatching rules in many situations. Using near to optimal SSPs leads to improved results compared with dispatching based SSPs for the transport operations.