Miracle-dz/UniFAR-FTUs huggingface dataset:
Steady state thermal characterization of multiple output devices using linear superposition theory and a non-linear matrix multiplier

Traditionally, the steady state thermal performance of packaged semiconductor devices has been characterized by only a single junction-to-ambient thermal resistance constant commonly referred to as /spl theta//sub ja/. This is particularly inadequate for multiple output devices, where several power dissipating junctions reside within the same integrated circuit. This paper presents a study which discusses how linear superposition theory can be applied to better thermally characterize "small" multiple output devices (i.e. devices where the thermal contribution of the individual power outputs combine to form a singular cooling region at the package surface). Unlike previous superposition studies, the generation of a matrix multiplier is presented which corrects for the nonlinear effects of both radiation and natural convection heat transfer. The end product is a linear matrix equation (and nonlinear matrix multiplier) which can be utilized by the customer to calculate individual output junction temperatures over a wide variety of power configurations. The Motorola MC1413P High Voltage, High Current Darlington Transistor Array, which encompasses seven power junctions, is used as an example. The study steps through the generation of the linear matrix equation and evaluates the accuracy of the superposition model with and without the utilization of the nonlinear matrix multiplier. A brief overview discussing the elements of device thermal characterization is provided for those readers who are unfamiliar with this field.
SYNTHESIS, CHARACTERIZATION, DOCKING STUDIES AND ANTIMICROBIAL EVALUATION OF 3-(3-CHLORO)-2-(2-OXIDO)-(4-SUBSTITUTED PHENYL)-UREIDO- BENZO[D][1,3,2]DIOXAPHOSPHOL-5-YL-4-OXOAZETIDIN-1-YL)THIOPHENE-2- CARBOXAMIDES AND CARBAMOYL THIOPHENE-CHLORO-4-OXOAZETIDIN- DIOXAPHOSPHOL-(MORPHOLINE/PIPERIDINE/4-MET

3-(3-chloro)-2-(2-oxido)-(4-substituted phenyl)-ureido-benzo[d][1,3,2]dioxaphosphol-5-yl-4-oxoazetidin-1yl)thiophene-2-carboxamides (9a-f) were synthesized by condensing 3-(3-chloro-2-(3,4-dihydroxyphenyl)-4oxoazetidin-1-yl)thiophene-2-carboxamide(7) with 4-substituted phenyl-carbamido-phosphoric acid dichlorides (8a-f). Similarly carbamoyl thiophene-chloro-4-oxoazetidin-dioxaphosphol Morpholine/Piperidine/4-methyl piperizine-carboxamides (9g-i) were synthesized by condensing (7) with Morpholinyl/Piperidinyl/N-methyl piperizinyl-carbamido-phosphoric acid dichlorides (8g-i). The synthon(7) was synthesized by hydrolysis of 3-(3chloro-2-(3,4-dimethoxyphenyl)-4-oxoazetidin-1-yl)thiophene-2-carboxamide(6). The intermediate (6) was synthesized by condensing 3-((3,4-dimethoxybenzylidene)amino)thiophene-2-carboxamide(5) with monochloro acetyl chloride in the presence of triethyl amine in dioxane. The synthon(5) was synthesized by reaction between 3aminothiophene-2-caroxamide(3) and 3,4-dimethoxybenzaldehyde(4) in the presence of few drops of acetic acid. Starting intermediate (3) was synthesized by condensation reaction between 2-cyano acetamide(1) and 1,4dithiane-2,5-diol(2) in the presence of catalytic amount of tri ethyl amine in ethanol. The target molecules (9a-i) were characterized by IR, 1 H-NMR, 13 C-NMR, Mass and elemental analysis. The target molecules were subjected to biological evaluation and docking studies. The results observed in the present investigation were reported in this present research article.
On the multi-species Boltzmann equation with uncertainty and its stochastic Galerkin approximation

In this paper the nonlinear multi-species Boltzmann equation with random uncertainty coming from the initial data and collision kernel is studied. Well-posedness and long-time behavior – exponential decay to the global equilibrium – of the analytical solution, and spectral gap estimate for the corresponding linearized gPC-based stochastic Galerkin system are obtained, by using and extending the analytical tools provided in [M. Briant and E. S. Daus, Arch. Ration. Mech. Anal. , 3, 1367–1443, 2016] for the deterministic problem in the perturbative regime, and in [E. S. Daus, S. Jin and L. Liu, Kinet. Relat. Models, 12, 909–922, 2019] for the single-species problem with uncertainty. The well-posedness result of the sensitivity system presented here has not been obtained so far even for the single-species case.
General Plan Evaluation Criteria: An Approach to Making Better Plans

Abstract The planning profession has developed relatively few criteria for evaluating the quality of general plans. Evaluation criteria have become more important with the increasing number of states that mandate general plans. Several kinds of plan evaluation exist, and these are distinguished and described before a review of different concepts of plans as a source of the appropriate criteria to evaluate them. A list of suggested criteria for plan evaluation during plan preparation is then presented, to be used to make the plan better. Appropriate criteria for a plan are not easy to devise, and the postmodern critique of planning makes this task more difficult still. These issues are explored as well.
Structural Engineering Applications with Augmented Neural Networks

This paper presents an augmented neural network (ANN), a novel neural network architecture, and examines its efficiency and accuracy for structural engineering applications. The proposed architecture is that of a standard backpropagation neural network with augmented neurons, that is, logarithm neurons and exponent neurons are added to the network's input and output layers. The principles of augmented neural networks are (1) the augmented neurons are highly sensitive in the boundary domain, thereby facilitating construction of accurate mapping in the model's boundary domain, and (2) the network denotes each input variable with multiple input neurons, thus allowing a highly interactive function on hidden neurons to be easily formed. Therefore, the hidden neurons can more easily construct an accurate network output for a highly interactive mapping model. Experimental results demonstrate that the network's logarithm and exponent neurons provide a markedly enhanced network architecture capable of improving the network's performance for structural engineering applications.
RINC: Real-Time Inference-based Network Diagnosis in the Cloud

Cloud tenants experience performance problems due to issues within their virtual machines (VMs) or within the cloud infrastructure. To offer good and predictable performance, cloud providers must be able to detect and diagnose performance problems in real time. However, existing cloud diagnosis techniques are either unable to detect problems in the tenant’s VMs or are too costly. We argue that rather than collecting all statistics, cloud diagnosis should proceed in phases, with each phase selectively collecting heavier weight measurements. To this end, we introduce a set of novel techniques for inferring the internal state of a VM’s midstream network connections which allows us to accurately collect measurements at any point during a connection. Our framework, RINC, runs within the hypervisor, using these techniques to selectively monitor a tenant’s connections. RINCprovides a simple query interface to its cloud-wide platform that allows cloud operators to easily write diagnosis applications. We evaluate RINC on a testbed and with a simulator using a combination of real data center traces and synthetic workloads. Our evaluations validate RINC’s accuracy and show that, by being selective, RINC is able to scale to a cloud with 100K physical servers or 1Million VMs. Moreover we demonstrate RINC’s ﬂexibility and ex-pressibility by implementing ﬁve diagnosis applications.
The complete mitogenome of the European mantis, Mantis religiosa, from Italy: implications for the origin of North American mantis population

The European mantis, Mantis religiosa L. (Mantodea Mantidae), is distributed all over Southern Europe, Africa and Asia, and has been reported as alien species in North America. Here we present the mitogenome sequence of an Italian individual and compare it with previously sequenced Chinese and Canadian samples. The assembled mitogenome has a length of 15,530 nucleotides and includes 13 protein coding genes, two ribosomal RNA genes, 23 tRNA genes (including the additional Arginine tRNA already observed in other M. religiosa mitogenomes), and the control region. Based on the inferred phylogenetic relationships, the Canadian sample is more closely related to the Italian than to the Chinese one, in line with the putative European origin of the North American invasive population. Time-calibrated phylogeny dated the divergence among extant European Mantis lineages at 2.33 million years ago, consistent with the first appearance of M. religiosa fossils. Our results support a European origin of the North American M. religiosa population and suggest that selective processes acting on mitogenome may have contributed to its adaptation in the new area.
Optimising and simulating the assembly line balancing problem in a motorcycle manufacturing company: a case study

In this paper, we present a real life assembly line balancing problem for a motorcycle manufacturing company. Results from the initial situation in the company are compared to those provided by a heuristic method and by a novel neighbourhood search method. An ARENA simulation model is developed to test the different proposals. The model was validated by comparing the simulation results from the initial company scenario with the real operation results. Consequently, the extension to the developed proposals allows us to select an adequate assembly line for the company, resulting in a greater productivity level and a more balanced line with respect to the operation times in the stations and the activity of the workers. The company has subsequently implemented our proposed schedule, obtaining remarkable improvements in its productivity.
Behavior of a uniform fiber Bragg grating under impulsive strain

The article contains the analysis of influence of the length of a uniform fiber Bragg grating on the accuracy of transducing an impulsive strain. Using the matrix description of the grating, derived from the coupled mode theory, the waveforms were calculated of the power spectrum of the beam reflected by the grating, caused by the strain impulse propagating along it. On the basis of the introduced centroidal frequency of the grating, the mean square transducing error of the grating was calculated, as a function of the ratio of its length to the strain length, for rectangular, triangular, half-sinusoidal and trapezoidal strain impulses. This error was also calculated depending on full width at half maximum.
Understanding Reverberating Chambers as an Alternative Facility for EMC Testing

A relatively new facility called a reverberating chamber designed for EMC testing is described. The purpose is to create a statistically uniform electric field inside a metal enclosure for testing radiated susceptibility or immunity of equipment. Design criteria in terms of the number of cavity modes, mode density, and composite quality factor are presented in details in order to understand the physical insight and to enhance interpretations of measurement results. Recent experimental data are included to illustrate the underlying principle.
(Mis)allocation of Managerial Training Within the Firm*

We study the allocation and productivity consequences of managerial training within the firm via a randomized controlled trial among production line supervisors in an Indian ready-made garment firm. We designed a training program using best practices identified in previous work in a similar setting (Adhvaryu et al. 2021). We asked factory and floor managers (FFMs), who are directly above supervisors in the hierarchy, to identify which supervisors should be prioritized for training, and then randomized access to the program within these rankings. Productivity on lines managed by treated supervisors increased by 7.3% during training relative to control, and remained 5.8% higher relative to control six months after program completion. These gains exhibit substantial heterogeneity across FFM rankings: lines managed by highly recommended supervisors showed nearly zero impact on productivity, while lines managed by supervisors with low recommendation rankings showed impacts of more than 11%. We show that this was not due to a lack of information about baseline skills, or about who would likely gain the most. It also does not appear to be due to discrimination or favoritism along observable dimensions. Instead, consistent with the fact that attrition has large *Adhvaryu: University of Michigan, BREAD, NBER, J-PAL, William Davidson Institute & Good Business Lab; adhvaryu@umich.edu; Gunnsteinsson: Independent Researcher; Murathanoglu: University of Michigan; emirmur@umich.edu; Nyshadham: University of Michigan, BREAD, NBER, J-PAL & Good Business Lab; nyshadha@umich.edu. We are very grateful to Anant Ahuja and Chitra Ramdas for their coordination, enthusiasm, and guidance. We acknowledge funding from the International Growth Centre (IGC). Many thanks to Sofia Calderon, Bopanna Changappa, Sadish Dhakal, Priota Paul, and Varun Jagannath for excellent field coordination and research assistance. The views expressed herein do not represent IGC, or Shahi Exports. All errors are our own.
Optimal Out-of-Sample Forecast Evaluation under Stationarity

It is common practice to split time-series into in-sample and pseudo out-of-sample segments and to estimate the out-of-sample loss of a given statistical model by evaluating forecasting performance over the pseudo out-of-sample segment. We propose an alternative estimator of the out-of-sample loss which, contrary to conventional wisdom, utilizes both measured in-sample and out-of-sample performance via a carefully constructed system of affine weights. We prove that, provided that the time-series is stationary, the proposed estimator is the best linear unbiased estimator of the out-of-sample loss and outperforms the conventional estimator in terms of sampling variance. Applying the optimal estimator to Diebold-Mariano type tests of predictive ability leads to a substantial power gain without worsening finite sample level distortions. An extensive evaluation on real world time-series from the M4 forecasting competition confirms the superiority of the proposed estimator and also demonstrates a substantial robustness to the violation of the underlying assumption of stationarity.
Tajima D test accurately forecasts Omicron / COVID-19 outbreak

On 26 November 2021, the World Health Organization designated the SARS-CoV-2 variant B.1. 1.529, Omicron, a variant of concern. However, the phylogenetic and evolutionary dynamics of this variant remain unclear. An analysis of the 131 Omicron variant sequences from November 9 to November 28, 2021 reveals that variants have diverged into at least 6 major subgroups. 86.3% of the cases have an insertion at amino acid 214 (INS214EPE) of the spike protein. Neutrality analysis of DH (-2.814, p<0.001) and Zeng E (0.0583, p=1.0) tests suggested that directional selection was the major driving force of Omicron variant evolution. The synonymous (Dsyn) and nonsynonymous (Dnonsyn) polymorphisms of the Omicron variant spike gene were estimated with Tajima D statistic to eliminate homogenous effects. Both D ratio (Dnonsyn/Dsyn, 1.57) and DD (Dsyn-Dnonsyn, 0.63) indicate that purifying selection operates at present. The low nucleotide diversity (0.00008) and Tajima D value (-2.709, p<0.001) also confirms that Omicron variants had already spread in human population for more than the 6 weeks than has been reported. These results, along with our previous analysis of Delta and Lambda variants, also supports the validity of the Tajima D test score, with a threshold value as -2.50, as an accurate predictor of new COVID-19 outbreaks.
The feasibility of an objective measure of the parent-child relationship in health visiting practice: assessment of the Maternal Postnatal Attachment Scale

Positive parent infant relationships are key to achieving long term child outcomes. Identifying parents who may need support is difficult because of a lack of robust assessment tools. Working in partnership with health services we piloted the Maternal Postnatal Attachment Scale (MPAS) in a deprived, multi-ethnic urban community in Bradford, UK. The pilot aimed to assess the clinical utility of MPAS to identify need for support: Was it administered to a representative group of women? Is MPAS valid for this population? Data were linked to a cohort study in the pilot area (Born in Bradfords Better Start - BiBBS). Chi Square tests assessed sample representativeness (age, ethnicity, parity, English language, education, deprivation). Exploratory factor analysis explored MPAS validity. 563 women in BiBBS were eligible, 210 (37%) completed MPAS. No differences were found between completers and non-completers, suggestive of a representative sample. In total, 336 women completed MPAS in the pilot. MPAS had ceiling effects and a satisfactory factor structure could not be identified, indicating poor psychometric properties Health visitors were successful in administering MPAS to a representative sample, but poor psychometric robustness indicates that MPAS is unsuitable for routine use in this setting. A gap for such a measure remains.
Microsurgical Tool Detection and Characterization in Intra-operative Neurosurgical Videos

Brain surgery is complex and has evolved as a separate surgical specialty. Surgical procedures on the brain are performed using dedicated micro-instruments which are designed specifically for the requirements of operating with finesse in a confined space. The usage of these microsurgical tools in an operating environment defines the surgical skill of a surgeon. Video recordings of micro-surgical procedures are a rich source of information to develop automated surgical assessment tools that can offer continuous feedback for surgeons to improve their skills, effectively increase the outcome of the surgery, and make a positive impact on their patients. This work presents a novel deep learning system based on the Yolov5 algorithm to automatically detect, localize and characterize microsurgical tools from recorded intra-operative neurosurgical videos. The tool detection achieves a high 93.2% mean average precision. The detected tools are then characterized by their on-off time, motion trajectory and usage time. Tool characterization from neurosurgical videos offers useful insight into the surgical methods employed by a surgeon and can aid in their improvement. Additionally, a new dataset of annotated neurosurgical videos is used to develop the robust model and is made available for the research community.Clinical relevance— Tool detection and characterization in neurosurgery has several online and offline applications including skill assessment and outcome of the surgery. The development of automated tool characterization systems for intra-operative neurosurgery is expected to not only improve the surgical skills of the surgeon, but also leverage in training the neurosurgical workforce. Additionally, dedicated neurosurgical video based datasets will, in general, aid the research community to explore more automation in this field.
ISOLATION OF LYSOZYME OF BLACK SEA MUSSEL MYTILUS GALLOPROVINCIALIS

The analytical review showed the promising use of lysozyme as a valuable natural antibacterial drug. The eperimental monitoring of natural sources of drugs with antibacterial activity showed the feasibility of using secondary raw materials from water resources of the Black Sea basin, obtained during the mussels processing. The isolation and chromatographic purification of lysozyme was carried out. Only the samples eluted with 0.6 M NaCl solution in 0.02 M acetate buffer, pH 5.0, were found out to exhibit lytic activity. The fraction possessinglytic activity was 54 cm3 and corresponded to the elution with 0.6 M solution of NaCl in 0.02 M acetate buffer, pH 5.0. The lysozyme under study, according to the results of gel electrophoresis in 15% PAH using the calculated calibration curve, is characterized by a molecular weight of 18 kDa, which confirms its belonging to low molecular weight proteins. A preparation of lysozyme with a specific activity of 206 u/mg with a molecular weight of 18 kDa and purification degree of 946.6 was obtained from the juice of Black Sea mussels. The antibacterial activity of the obtained lysozyme preparation against pathogens of food poisoning and food spoilage was studied. The data obtained allow us to predict the prospects of using mussels as a source of highly active lysozyme.
Nonlinear registration as an effective preprocessing technique for Deep learning based classification of disease

A number of machine learning (ML), and particularly in recent years, deep learning (DL) approaches have been proposed for automatic classification of Alzheimer’s disease (AD) using brain structural magnetic resonance imaging (MRI) data. However, the data available are limited in the case of this specific disease. Training a DL model with a large number of feature parameters on a small dataset of MRI scans will likely lead to overfitting. Overfitting reduces the generality and efficiency of the model. In this study, we show that a traditional nonlinear transformation from native space to template space, as a preprocessing stage, is effective in reducing overfitting through the reduction of spatial variations in the input data. To evaluate this effectiveness, we compare two different pre-processing approaches for DL-based AD classification task: (1) affine registration and (2) nonlinear diffeomorphic anatomical registration using exponentiated Lie algebra (DARTEL). The results show that the accuracy of the nonlinear registration based approach is much higher than the affine registration based approach. Furthermore, from the classification results obtained with noisy images, DARTEL is less susceptible to noise than affine registration. In summary, our experimental results suggest that nonlinear transformation is a preferable preprocessing step for training DL-based AD classification models on limited size datasets.
Modelling of Facial Images for Analysis of Recognition System

Face recognition is categorized as a biometric technology that employs the use of computer ability in image processing to detect and recognize human faces. Face recognition system has numerous applications for many purposes such as for access control, law enforcement and surveillance thus this system is dominant in present technology. Generally, face recognition system become more advance in term of the accuracy and implementation. However, there are a few parameters that effects the accuracy of recognition system for examples, the pose invariant, illumination effect, size of image and noise tolerance. Even though there are a number of systems were already available in the literature, the complete understanding of their performances are relatively limited. This is due to many systems focused on a narrow application band – therefore, a comprehensive analysis are needed in order to understand their performances leading to establishing the conditions for successful face recognition system. In this paper we developed a synthetic model to represent facial images to be used as a platform for performance analysis of facial recognition systems. The model includes 5 face types with the ability to vary all parameters that are affecting recognition performance – measurement noise, face size and face-background intensity differences. The model is important as it provide an avenue for performance analysis of facial recognition systems.