Miracle-dz/UniFAR-FTUs huggingface dataset:
Light Scattering by Particles in Water: Theoretical and Experimental Foundations

1. Introduction 2. Basic principles of interaction of light with matter 3. Optical properties of pure water, pure sea water, and natural waters 4. General features of light scattering by particles in water: theory 5. Light scattering by particles in water: measurements and approximations 6. The particle size distribution: measurements and approximations 7. Compositions, shapes, and structures of particles in water
Tradeoff Curves for Target Costing of Assembly Processes

Tradeoff curves are a knowledge management tool used in Lean Product and Process Development (LPPD) to illustrate the known relationship of two design characteristics relative to each other. These characteristics are usually in conflict, whereby increasing one characteristic results in a decrease of the other, and the tradeoff curve defines the feasible limit for the design options. Target Costing is a practice used by firms to ensure that a future product meets and retains desired profit levels throughout its lifespan in the market. The allowable Direct Cost for the product is composed of Direct Material and Direct Assembly. In assembly process development, engineers face a tradeoff between labor and capital investment. A tradeoff curve can be used to illustrate the relationship between these two characteristics for given market conditions. This paper describes the development of a model that can be used to generate tradeoff curves to map the feasible solution space for assembly process alternatives in different economic environments with potentially different target costs. Understanding the tradeoffs and limits in advance of detailed development enables process designers to consider several solution sets to support better decisions and achieve desired levels of profitability.
Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics

Metaproteomics has been increasingly utilized for high-throughput molecular characterization in complex environments and has been demonstrated to provide insights into microbial composition and functional roles in soil systems. Despite its potential for the study of microbiomes, significant challenges remain in data analysis, including the creation of a sample-specific protein sequence database as the taxonomic composition of soil is often unknown. Almost all metaproteome analysis tools require this database and their accuracy and sensitivity suffer when the database is incomplete or contains extraneous sequences from organisms which are not present. Here, we leverage a de novo peptide sequencing approach to identify sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data, and trained it on 5 million peptide-spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified unsequenced soil isolates directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S ribosomal RNA sequencing of the soil samples and also uncovered several additional species which were strongly represented only in proteomic data. Our pipeline offers an alternative and complementary method for metaproteomic data analysis by creating a protein database directly from proteomic data, thus removing the need for metagenomic sequencing. Significance Statement Proteomic characterization of environmental samples, or metaproteomics, reveals microbial activity critical to our understanding of climate, nutrient cycling and human health. Metaproteomic samples originate from diverse environs, such as soil and oceans. One option for data analysis is a de novo interpretation of the mass spectra. Unfortunately, the current generation of de novo algorithms were primarily trained on data originating from human proteins. Therefore, these algorithms struggle with data from environmental samples, limiting our ability to analyze metaproteomics data. To address this challenge, we trained a new algorithm with data from dozens of diverse environmental bacteria and achieved significant improvements in accuracy across a broad range of organisms. This generality opens proteomics to the world of natural isolates and microbiomes.
Haematological studies of African catfish (Clarias gariepinus Burchell, 1822) exposed to sublethal concentrations of atrazine

The effects of atrazine on haematological parameters, physicochemical parameters and behavioural responses of Clarias gariepinus juveniles were examined under laboratory conditions after exposure to sublethal concentrations for 8 weeks. A sublethal examination was conducted on blood parameters of Clarias gariepinus exposed to atrazine using concentrations 0.15mg/L, 0.29mg/L, 0.44mg/L, 0.59mg/L, 0.74mg/L and 0.00mg/L as control. An indication of subtle but rapid deterioration of life due to the effects atrazine exerts on fish was evident in the blood parameters. Haemoglobin, Red Blood Cells, White Blood Cells, Packed Cell Volume, Mean Corpuscular Volume, Mean Corpuscular Haemoglobin and Mean Corpuscular Haemoglobin Concentrations in all the treatments varied significantly (P<0.05) from the control. It is however imperative to note that the life of an organism is in the blood, so the enormous impact of atrazine on the blood characteristics posed an adverse damage to the treated fish.
Low Power True Random Number Generator through Ring Oscillator for IoT and Smart Card Applications

: Tremendous developments in integrated circuit technology, wireless communication systems, and personal assistant devices have fuelled growth of Internet of Things (IoT) applications and smart cards. The security of these devices completely depends upon the generation of random and unpredictable digital data streams through random number generator. Low quality, low throughput, and high processing time are observed in software-based pseudo-random number generator due to interrelated data or programs and serial execution of codes respectively. In this paper, FPGA implementation of low power true random number generator through ring oscillator for IoT applications and smart cards is presented. Ring oscillators based on higher jitter and sampling techniques were exploited to present true random number generator. Further statistical parameters of the generated data streams are enhanced through feedback mechanism and post-processing technique. The presented true random number generator technique does not depend on the characteristics of a particular FPGA. The presented technique consumes low power, requires low hardware footprints and passes the entire National Institute of Standards & Technology (NIST) 800-22 statistical test suite. The presented low power and area true random number generator with enhanced security through post-processing unit may be applied for encryption/decryption of data in IoT and smart cards.
Analytical Hierarchy Process (AHP) for Selecting Freight Forwarder Services to Get Minimum Shipping Cost for Export Goods

The selection of freight forwarders in the delivery of exported goods is carried out with a direct appointment system. This can affect the level of customer satisfaction, and the cost of shipping and export of goods issued by the company increases. This study aims to minimise the cost of shipping export products. The method used to select freight forwarders is the Analytical Hierarchy Process (AHP) Method. Using the AHP method, the calculation of freight forwarder services can be used to determine th type of freight forwarder services that have the highest weight and calculation of shipping costs for export goods outlined by the company. Then, it is possible to compare the costs incurred by the company of several types of freight forwarding services, using one type of goods shipment which has the highest weight in the calculation of the AHP Method. There are four steps to choosing freight forwarder services, using the AHP method. Pairwise comparisons are matrices that can compare the weights between criteria and between alternatives. Companies, in choosing freight forwarding services, are advised to use the AHP method as it can be implemented to produce an initial solution that is feasible regarding the performance of the freight forwarding company.
WEB 2.0 TECHNOLOGIES’ INFLUENCE ON CUSTOMER RELATIONSHIP MARKETING

The use of new technology always brings with it questions as to its usefulness. In other words, does the new technology actually work and to what extent? This paper examines Web 2.0 technology and its impact on customer attraction and retention. It was found that the use of Web 2.0 can have a positive influence on customer attraction and retention that leads to stronger business-to-customer relationships. However, research also identified drawbacks such as: risk of abuse, misleading information, outdated information, information overload, privacy breaches and fear of being offended issues. Recommendations are given for the use of Web 2.0 in marketing. There is also a warning about the need for dedicated staff to minimize the amount of misleading and outdated information
Sliding Mode and Terminal Sliding Mode Control of Cascaded Doubly Fed Induction Generator

Classical structure of Doubly Fed Induction Generators (DFIGs) is not completely adapted in high-speed regions due to their brushes and slip rings. So in the Cascaded DFIGs (CDFIGs), the rotor windings of a given DFIG are supplied by another wound rotor induction machine leading to a complete brushless structure. This paper presents and compares Sliding Mode Control (SMC) and Terminal Sliding Mode Control (TSMC) methods to control the output voltage of CDFIG. The SMC and TSMC methods are identified as strong controllers with large stability and robustness margins. In this paper, the SMC and TSMC methods are evaluated and compared to the conventional Voltage Oriented Control (VOC) in terms of output voltage change, prime over speed’s variation, and nonlinear load. Simulation and experimental results using a TMS320F28335 based prototype system show that the SMC and TSMC techniques are more robust against parameter variations and uncertainties, and TSMC offers improved dynamic response.
A global Bayesian temperature calibration for lacustrine brGDGTs

Abstract Despite widespread use of branched glycerol dialkyl glycerol tetraethers (brGDGTs) for paleo-temperature reconstruction, no global calibration for their application in lakes has been generated since improved analytical methods have allowed for the separation of the structural isomers. This is a substantial obstacle for the application of this tool as soil calibrations underestimate temperature values when applied to lake sediments. Here, we present a comprehensive dataset ( N = 272 ) of lacustrine brGDGT distributions, consisting of both new and previously reported samples, spanning a wide range of geographical locations, air temperatures, and lakewater pH values. Empirical Orthogonal Function (EOF) analysis on the fractional abundance of brGDGTs indicates that temperature exerts a strong control (explaining 58 % of the variance) on brGDGT distribution. The influence of water chemistry is weaker, with pH and conductivity explaining 24 % of the variance. We use our dataset to generate a new Bayesian calibration to the mean temperature of Months Above Freezing (MAF), which has an R 2 = 0.82 and RMSE = 2.9 ° C. Application of the new temperature calibration to a previously published lake core record spanning the last 900 years demonstrates that it generates values comparable to instrumental observations of MAF. Our new calibration facilitates the use of lacustrine brGDGTs to reconstruct continental temperatures, a vital piece of information for understanding past climates.
Tree Rotations for Dependency Trees: Converting the Head-Directionality of Noun Phrases

Corresponding Author: Ika Alfina Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia Email: ika.alfina@cs.ui. ac.id Abstract: To overcome the lack of NLP resources for the low-resource languages, we can utilize tools that are already available for other highresource languages and then modify the output to conform to the target language. In this study, we proposed an approach to convert an Indonesian constituency treebank to a dependency treebank by utilizing an English NLP tool (Stanford CoreNLP) to create the initial dependency treebank. Some annotations in this initial treebank did not conform to Indonesian grammar, especially noun phrases’ head-directionality. Noun phrases in English usually have head-final direction, while in Indonesian is the opposite, head-initial. We proposed a variant of tree rotations algorithm named headSwap for dependency trees. We used this algorithm to convert the head-directionality for noun phrases that were initially labeled as a compound. Moreover, we also proposed a set of rules to rename the dependency relation labels to conform to the recent guidelines. To evaluate our proposed method, we created a gold standard of 2,846 tokens that were annotated manually. Experiment results showed that our proposed method improved the Unlabeled Attachment Score (UAS) with a margin of 32.5% from 61.6 to 94.1% and the Labeled Attachment Score (LAS) with a margin of 41% from 44.1 to 85.1%. Finally, we created a new Indonesian dependency treebank that converted automatically using our proposed method that consists of 25,416 tokens. The dependency parser model built using this treebank has UAS of 75.90% and LAS of 70.38%.
Turbulent Mixing and Chemical Reactions

Introduction-Mixing and Reaction. Bulk Flow Patterns and the Selectivity of Slow Reactions. Fast Reactions and the Limits of Classical Methods. The Nature and Definition of Turbulence. Theory of Turbulence and Models of Turbulent Flow. Mixing of Non-Reacting Materials: From Turbulent Diffusion to Mixing on Molecular Scale. Analysis and Modelling of Mixing of Reactive Materials. Mixing and Chemical Reaction in a Lagrangian Framework-Mechanistic Models. The Evaluation of Mixing Models: General Considerations. Mechanisms and Kinetics of Some Test Reactions. Model-Experiment Comparisons when Micromixing Controls. Model-Experiment Comparisons when Micro- and Mesomixing Control. Characteristics of Various Mixers. Precipitation. Further Applications. Index.
BebopNet: Deep Neural Models for Personalized Jazz Improvisations

A major bottleneck in the evaluation of music generation is that music appreciation is a highly subjective matter. When considering an average appreciation as an evaluation metric, user studies can be helpful. The challenge of generating personalized content, however, has been examined only rarely in the literature. In this paper, we address generation of personalized music and propose a novel pipeline for music generation that learns and optimizes user-specific musical taste. We focus on the task of symbol-based, monophonic, harmony-constrained jazz improvisations. Our personalization pipeline begins with BebopNet, a music language model trained on a corpus of jazz improvisations by Bebop giants. BebopNet is able to generate improvisations based on any given chord progression. We then assemble a personalized dataset, labeled by a specific user, and train a user-specific metric that reflects this user's unique musical taste. Finally, we employ a personalized variant of beam-search with BebopNet to optimize the generated jazz improvisations for that user. We present an extensive empirical study in which we apply this pipeline to extract individual models as implicitly defined by several human listeners. Our approach enables an objective examination of subjective personalized models whose performance is quantifiable. The results indicate that it is possible to model and optimize personal jazz preferences and offer a foundation for future research in personalized generation of art. We also briefly discuss opportunities, challenges, and questions that arise from our work, including issues related to creativity.
Constrained optimal control for complex systems Analysis and applications

Many applications in control engineering, involve solving a constrained optimal control problem. Especially for the case of complex systems, the presence of constraints gives rise both to theoretical and computational challenges. Developing a general theoretical framework is crucial to capture all features and interdependencies among the components of the underlying system, but numerical computations for such detailed models might not be tractable. Therefore, a trade-off between the generality of the developed theory and the scale of applications that can be addressed numerically needs to be reached. This dissertation deals with three of these problems, all of which resulting in a constrained optimal control formulation. The first problem concentrates on the development of an optimal control framework to deal with state constrained reachability problems for continuous and hybrid systems. The main objective is to design suitable controllers to steer or keep the state of the system in a “safe” part of the state space. The synthesis of such safe controllers relies on the ability to solve target problems for the case where state constraints are also present, giving rise to reach-avoid calculations. We first focus on continuous time systems and show how such reach-avoid problems can be related to the viscosity solutions of certain optimal control problems. We then extend our framework to reachability/viability problems for hybrid systems and provide a complete characterization based entirely on optimal control, logic theorems and the definition of executions of hybrid automata. Hybrid automata with nonlinear continuous dynamics and bounded competing inputs can be captured by the proposed framework. The theoretical results are illustrated on a 4D trajectory management problem in air traffic control and various benchmark examples. The second problem involves applying model predictive control for nonlinear, but feedback linearizable systems, with input constraints. The challenge is that after feedback linearization, even though the systems dynamics are rendered linear, the initial input constraints are mapped to a set of state dependent, and in general nonlinear and possibly non con-
A Year of Automated Anomaly Detection in a Datacenter

—Anomaly detection based on Machine Learning can be a powerful tool for understanding the behavior of large, complex computer systems in the wild. The set of anomalies seen, however, can change over time: as the system evolves, is put to different uses, and encounters different workloads, both its ‘typical’ behavior and the anomalies that it encounters can change as well. This naturally raises two questions: how effective is automated anomaly detection in this setting, and how much does anomalous behavior change over time? In this paper, we examine these question for a dataset taken from a system that manages the lifecycle of servers in datacenters. We look at logs from one year of operation of a datacenter of about 500 servers. Applying state-of-the art techniques for ﬁnding anomalous events, we ﬁnd that there are a ‘core’ set of anomaly patterns that persist over the entire period studied, but that in to track the evolution of the system, we must re-train the detector periodically. Working with the administrators of this system, we ﬁnd that, despite these changes in patterns, they still contain actionable insights.
Expression and clinical significance of PD-1 /PD-L1 in gastric cancer tissues

[基金项目] 河北省自然基金课题资助项目(No. H2015206461)。 Project supported by Natural Science Foundation of Hebei Province(No. H2015206461) [作者简介] 张风宾(1981-), 男, 硕士, 主治医师, 主要从事消化系统肿瘤内科治疗研究,E-mail:zhangfengbin1981@163.com;乔静雪(1989-),女, 硕士,主要从事消化系统肿瘤内科治疗研究,E-mail:1220155682@qq.com。 △共同第一作者 [通信作者] 张瑞星(ZHANG Ruixing,corresponding author),博士,教授,硕士生导师,主要从事消化系统肿瘤内科及生物免疫治疗研究,E-mail: zrx@medmail.com. cn PD-1/PD-L1在胃癌组织中的表达及其临床意义
Emergence of Green Business Engendering New Avenues for Augmentation of Green Entrepreneurship and Sustainable Development through Indian MSMEs

Manifestation of Green slant to the business is prevailing in the contemporary global scenario that provides practical solution for the unprecedented increase in the exploitation of natural and non-renewable resources. The perceptible rise in the levels of environment pollution in the form of emission of poisonous gases, solid/liquid waste and e-waste is resulting in environmental degradation and drastic climate change. Along with this the population explosion and employment challenges are causing severe threats to sustainable life on earth. A paradigm shift in the economy towards a clean technological mission is an eye-opener for sustainable development that can be guaranteed through mainstreaming environment into economic growth. The idea of green business emerged at the close of 20th century by embracing on green spectrum of business operations. Green entrepreneurship in manufacturing and service sectors are striving for sustainable development through different forms of economic activities bringing synergy in growth through maintaining proper equilibrium between rural and urban needs and requirements of local and international communities. Micro Small and Medium Enterprises (MSMEs), one of the key stakeholders in the transition of economies worldwide are great emissaries of sustainable growth as they are run on small scale basis, causing low environmental hitches, concentrating on wide range of products and services especially in the rural and less industrialised areas. The article attempts to bring to light the emergence of green business that is opening up novel avenues for supplementing green entrepreneurship in India. Research methodology consists of use of secondary data drawn from annual reports, journals, magazines, newspapers etc. Findings reveal that green entrepreneurship or ecopreneurship is gaining momentum across India decoupling economic and environmental performances. In addition, it is also adding social values by developing innovative technologies and new fangled business models thereby rejuvenating the prevailing economy. Green entrepreneurship triggers sustainable development by finely-tuning environmental challenges into business opportunities to create environmental value through innovative eco-designs thereby reducing the negative impacts on the environment. The study explores the increasing scope for ecopreneurial talents in young India especially in the State of Kerala where female entrepreneurs are on the rise that can facilitate sustainable, balanced and inclusive development in upcoming years. A number of government initiatives and policies are helping to hasten the growth of green entrepreneurs and more emphasis should be given to the effective implementation and proper follow ups to bring buoyancy in transforming the nation into a green economy.