Betwixt agency and accountability: re-visioning street-level bureaucrats

This paper presents a critical assessment of the much-discussed tension between bureaucratic accountability and the contextual discretion of ‘street-level bureaucrats’ (i.e. front-line public sector workers). Based on an extensive literature review, the paper outlines the implications of the exercise of agency by street-level bureaucrats in everyday settings. It also looks at the challenges this agency engenders: loss of accountability and divergence from stated policy goals. The paper underlines the need for future research on institutional structures and organisational cultures around street-level bureaucracy. It suggests possible lines of enquiry to steer the debate in new, and hopefully productive, directions

Factor XII deficiency - A rare coagulation disorder

Severe coagulation factor XII (FXII) deficiency is a very rare, mysterious, and not well-known inherited condition. Unlike other coagulation factor deficiencies, it is usually asymptomatic in most of the cases. Congenital FXII deficiency is the most common cause of an isolated prolongation of the activated partial thromboplastin time in a non-bleeding child or adult; consequently, most patients are detected during a routine pre-operative coagulation study. Surprisingly, it does not lead to abnormal bleeding, but some cases of severe FXII deficiency experiences thrombotic events in their lifetime. There are only a few reports of FXII deficiency in literature. We are reporting a case of congenital FXII deficiency in a 7-month-old child

Application of Actinobacteria in Agriculture, Nanotechnology, and Bioremediation

“Actinobacteria” are of significant economic value to mankind since agriculture and forestry depend on their soil system contribution. The organic stuff of deceased creatures is broken down into soil, and plants are able to take the molecule up again. Actinobacteria can be used for sustainable agriculture as biofertilizers for the improvement of plant growth or soil health by promoting different plant growth attributes, such as phosphorus and potassium solubilization, production of iron-chelating compounds, phytohormones, and biological nitrogen attachment even under the circumstances of natural and abiotic stress. Nanotechnology has received considerable interest in recent years due to its predicted impacts on several key fields such as health, energy, electronics, and the space industries. Actinobacterial biosynthesis of nanoparticles is a dependable, environmentally benign, and significant element toward green chemistry, which links together microbial biotechnology and nanobiology. Actinobacterial-produced antibiotics are common in nearly all of the medical treatments, and they are also recognized to aid in the biosynthesis of excellent surface and size properties of nanoparticles. Bioremediation using microorganisms is relatively safe and more efficient. Actinobacteria use carbon toxins to synthesize economically viable antibiotics, enzymes, and proteins as well. These bacteria are the leading microbial phyla that are beneficial for deterioration and transformation of organic and metal substrates

TSTR^\mathrm{R}: Target Similarity Tuning Meets the Real World

Target similarity tuning (TST) is a method of selecting relevant examples in natural language (NL) to code generation through large language models (LLMs) to improve performance. Its goal is to adapt a sentence embedding model to have the similarity between two NL inputs match the similarity between their associated code outputs. In this paper, we propose different methods to apply and improve TST in the real world. First, we replace the sentence transformer with embeddings from a larger model, which reduces sensitivity to the language distribution and thus provides more flexibility in synthetic generation of examples, and we train a tiny model that transforms these embeddings to a space where embedding similarity matches code similarity, which allows the model to remain a black box and only requires a few matrix multiplications at inference time. Second, we show how to efficiently select a smaller number of training examples to train the TST model. Third, we introduce a ranking-based evaluation for TST that does not require end-to-end code generation experiments, which can be expensive to perform.Comment: Accepted for EMNLP-Findings, 202

SIMULATION OF ELECTRONICS CIRCUITS USING SOFTWARE

This paper describes the design and simulation of clipper circuits using Multisim software. Multisim is developed by National Instruments and it is a powerful tool for simulation and design of circuits virtually to save valuable time and money. Multisim is one of the best electronics design software which includes a wide variety of simulation tools and it has large component library for to quickly build the circuits. This paper utilizes the design of various clipper circuits (biased and unbiased, combination) and its simulation. The ideal diode as well as silicon diode (1N4007) have been used to design the clipper circuits. The simulation result has been shown in oscilloscope and input is given by function generator in Multisim software. We found that the simulation result obtained using Multisim software is very much similar to the Ideal result

Simulation Of Electronics Circuits Using Software

This paper describes the design and simulation of clipper circuits using Multisim software. Multisim is developed by National Instruments and it is a powerful tool for simulation and design of circuits virtually to save valuable time and money. Multisim is one of the best electronics design software which includes a wide variety of simulation tools and it has large component library for to quickly build the circuits. This paper utilizes the design of various clipper circuits (biased and unbiased, combination) and its simulation. The ideal diode as well as silicon diode (1N4007) have been used to design the clipper circuits. The simulation result has been shown in oscilloscope and input is given by function generator in Multisim software. We found that the simulation result obtained using Multisim software is very much similar to the Ideal result

Music Feature Extraction And Recommendation Using CNN Algorithm

In this age of technological advancements, it has become considerably easier for an individual to access a variety of music from a significant number of sources. Today, there are a multitude of songs of varying diversity available to users. Therefore, it becomes difficult for users to manually discover new music that may suit their liking. Thus arises the need for a system that will help the music streaming applications to recommend new music to their users that will befit their music taste, based on some predetermined criteria. With the ever-expanding user and song database, the system must also be dynamic and its recommendations must be up-to-date and accurate. Therefore, there is a strong demand for a well-qualified music recommendation system. The proposed system focuses of technical features of audio. The main purpose of this systems is to classify songs in different genre using Deep Learning Algorithm. There are two main approaches for implementing these system, viz, Feature Extraction and Content Based Filtering