Building a text collection for Urdu information retrieval

Urdu is a widely spoken language in the Indian subcontinent with over 300 million speakers worldwide. However, linguistic advancements in Urdu are rare compared to those in other European and Asian languages. Therefore, by following Text Retrieval Conference standards, we attempted to construct an extensive text collection of 85 304 documents from diverse categories covering over 52 topics with relevance judgment sets at 100 pool depth. We also present several applications to demonstrate the effectiveness of our collection. Although this collection is primarily intended for text retrieval, it can also be used for named entity recognition, text summarization, and other linguistic applications with suitable modifications. Ours is the most extensive existing collection for the Urdu language, and it will be freely available for future research and academic education.     

Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language

Because of using traditional hand-sign segmentation and classification algorithm,many diversities of Bangla language including joint-letters,dependent vowels etc.and representing 51 Bangla written characters by using only 36 hand-signs,continuous hand-sign-spelled Bangla sign language(BdSL)recognition is challenging.This paper presents a Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language which consists of two phases.First phase is designed for hand-sign classification and the second phase is designed for Bangla language modeling algorithm(BLMA)for automatic recognition of hand-sign-spelled Bangla sign language.In first phase,we have proposed two step classifiers for hand-sign classification using normalized outer boundary vector(NOBV)and window-grid vector(WGV)by calculating maximum inter correlation coefficient(ICC)between test feature vector and pre-trained feature vectors.At first,the system classifies hand-signs using NOBV.If classification score does not satisfy specific threshold then another classifier based on WGV is used.The system is trained using 5,200 images and tested using another(5,200×6)images of 52 hand-signs from 10 signers in 6 different challenging environments achieving mean accuracy of 95.83%for classification with the computational cost of 39.972 milliseconds per frame.In the Second Phase,we have proposed Bangla language modeling algorithm(BLMA)which discovers all"hidden characters"based on"recognized characters"from 52 hand-signs of BdSL to make any Bangla words,composite numerals and sentences in BdSL with no training,only based on the result of first phase.To the best of our knowledge,the proposed system is the first system in BdSL designed on automatic recognition of hand-sign-spelled BdSL for large lexicon.The system is tested for BLMA using hand-sign-spelled 500 words,100 composite numerals and 80 sentences in BdSL achieving mean accuracy of 93.50%,95.50%and 90.50%respectively.     

A new nonparametric double exponentially weighted moving average control chart

There are many practical situations where the underlying distribution of the quality characteristic either deviates from normality or it is unknown. In such cases, practitioners often make use of the nonparametric control charts. In this paper, a new nonparametric double exponentially weighted moving average control chart on the basis of the signed-rank statistic is proposed for monitoring the process location. Monte Carlo simulations are carried out to obtain the run length characteristics of the proposed chart. The performance comparison of the proposed chart with the existing parametric and nonparametric control charts is made by using various performance metrics of the run length distribution. The comparison showed the superiority of the suggested chart over its existing parametric and nonparametric counterparts. An illustrative example for the practical implementation of the proposed chart is also provided by using the industrial data set.     

Highly porous and thermally stable tribopositive hybrid bimetallic cryogel to boost up the performance of triboelectric nanogenerators

There have been tremendous efforts made to investigate various materials to enhance the electrical performance of triboelectric nanogenerators (TENGs) but there is still demand for some techniques to further enhance the performance of tribomaterials. Therefore, we fabricated a bimetallic hybrid cryogel via cheap and facile UV-radiation as well as in situ reduction method. Fabricated TENG device made up of porous hybrid bimetallic cryogel film containing silver and gold nanoparticles as tribopositive material and poly dimethyl siloxane (PDMS) as a tribonegative layer with dimension of 1 × 2 cm² has the ability to produced output voltage of 262.14 V with current density of 27.52 mA/m² and 7.44 W/m² peak power density, which was sufficient to light up more than 120 white light emitting-diodes (LEDs). Porous and rough structure, interaction of nanoparticles was the reason behind the performance enhancement of tribopositive material. Thus, this study introduces a very stable and easily synthesized bimetallic hybrid cryogel as a tribopositive material to enhance the performance of tribomaterials to design high performance TENG devices.     

Numerical solution of 2D and 3D elliptic-type interface models with regular interfaces

Engineering with Computers - In the current paper, two numerical methods are proposed for the numerical solution of two- and three-dimensional elliptic partial differential equations (PDEs) with...     

2D Metal Oxyhalide‐Derived Catalysts for Efficient CO2 Electroreduction

Electrochemical reduction of CO₂ is a compelling route to store renewable electricity in the form of carbon‐based fuels. Efficient electrochemical reduction of CO₂ requires catalysts that combine high activity, high selectivity, and low overpotential. Extensive surface reconstruction of metal catalysts under high productivity operating conditions (high current densities, reducing potentials, and variable pH) renders the realization of tailored catalysts that maximize the exposure of the most favorable facets, the number of active sites, and the oxidation state all the more challenging. Earth‐abundant transition metals such as tin, bismuth, and lead have been proven stable and product‐specific, but exhibit limited partial current densities. Here, a strategy that employs bismuth oxyhalides as a template from which 2D bismuth‐based catalysts are derived is reported. The BiOBr‐templated catalyst exhibits a preferential exposure of highly active Bi () facets. Thereby, the CO₂ reduction reaction selectivity is increased to over 90% Faradaic efficiency and simultaneously stable current densities of up to 200 mA cm^?2 are achieved—more than a twofold increase in the production of the energy‐storage liquid formic acid compared to previous best Bi catalysts.     

Comparative Assessment of Ambient Air Quality of Major Cities of Pakistan

The deterioration of ambient air quality is one of the emerging environmental problems in developing countries of South Asia. Unplanned urbanization, population growth, degradation of vegetation cover and industrial and vehicular emissions, particularly in urban areas, have resulted in substantial rise in the level of air pollutants and emission sources. This study focused on monitoring of different cities as per traffic volume and flow. Air quality monitoring was conducted on hourly basis to determine the major parameters; i.e. PM₁₀, NO_x, SO₂, CO by using fixed station for 8 h from 1:30 pm to 9:30 pm. All the measuring values were then compared with the National Environment Quality Standards (NEQS) and Air Quality Index (AQI). Results revealed that the concentration of PM₁₀ at the selected areas of these cities ranged from 156 to 390 μg/m³, CO ranged from 1.18 to 6.01 mg/m³, and NO_x ranged from 32.65 to 129.47 μg/m³. It was evident that all these concentration had been higher than the permissible limits of NEQS, whereas only SO₂ was found within the permissible limits (15.60–110.52 μg/m³⁾. Air Quality Index (AQI) of all the designated points of cities was also assessed, and most of the vehicular and commercial areas had shown unhealthy and severe conditions ranging from 191 to 320, respectively.

     

Autonomous Eyewitness Identification by Employing Linguistic Rules for Disaster Events

Social networking platforms provide a vital source for disseminating information across the globe, particularly in case of disaster. These platforms are great mean to find out the real account of the disaster. Twitter is an example of such platform, which has been extensively utilized by scientific community due to its unidirectional model. It is considered a challenging task to identify eyewitness tweets about the incident from the millions of tweets shared by twitter users. Research community has proposed diverse sets of techniques to identify eyewitness account. A recent state-of-the-art approach has proposed a comprehensive set of features to identify eyewitness account. However, this approach suffers some limitation. Firstly, automatically extracting the feature-words remains a perplexing task against each feature identified by the approach. Secondly, all identified features were not incorporated in the implementation. This paper has utilized the language structure, linguistics, and word relation to achieve automatic extraction of feature-words by creating grammar rules. Additionally, all identified features were implemented which were left out by the state-of-the-art model. A generic approach is taken to cover different types of disaster such as earthquakes, floods, hurricanes, and wildfires. The proposed approach was then evaluated for all disaster-types, including earthquakes, floods, hurricanes, and fire. Based on the static dictionary, the Zahra et al. approach was able to produce an F-Score value of 0.92 for Eyewitness identification in the earthquake category. The proposed approach secured F-Score values of 0.81 in the same category. This score can be considered as a significant score without using a static dictionary.     

Degradable Bottlebrush Polypeptides and the Impact of their Architecture on Cell Uptake,Pharmacokinetics, and Biodistribution In Vivo

Bottlebrush polymers are highly promising as unimolecular nanomedicines due to their unique control over the critical parameters of size, shape and chemical function. However, since they are prepared from biopersistent carbon backbones, most known bottlebrush polymers are non-degradable and thus unsuitable for systemic therapeutic administration. Herein, we report the design and synthesis of novel poly(organo)phosphazene-g-poly(α-glutamate) (PPz-g-PGA) bottlebrush polymers with exceptional control over their structure and molecular dimensions (Dh ≈ 15–50 nm). These single macromolecules show outstanding aqueous solubility, ultra-high multivalency and biodegradability, making them ideal as nanomedicines. While well-established in polymer therapeutics, it has hitherto not been possible to prepare defined single macromolecules of PGA in these nanosized dimensions. A direct correlation was observed between the macromolecular dimensions of the bottlebrush polymers and their intracellular uptake in CT26 colon cancer cells. Furthermore, the bottlebrush macromolecular structure visibly enhanced the pharmacokinetics by reducing renal clearance and extending plasma half-lives. Real-time analysis of the biodistribution dynamics showed architecture-driven organ distribution and enhanced tumor accumulation. This work, therefore, introduces a robust, controlled synthesis route to bottlebrush polypeptides, overcoming limitations of current polymer-based nanomedicines and, in doing so, offers valuable insights into the influence of architecture on the in vivo performance of nanomedicines.