68 research outputs found
Synthesis and anti-Aphid Aphis gossypii (Glover) activity of some new quinoline derivatives
A series of quinoline derivatives have been elaborated fromreaction of 2-(m- and p-acetylanilino)-quinolines 6a-b withaldehydes under Claisen-Schmidt conditions followed by cyclization with phenyl hydrazine and hydroxylamine. Reaction of  6a and 6b with hydrazine, semicarbazide and thiosemicarbazide, cyanoacetylhydrazide and subsequent some condensation reactions led to diverse quinoline derivatives. Anti aphid  Aphis gossypii that harm cotton cropin Egypt was screened. Compound  12 showed an LC50 value of 19429E-10 ppm which is very more active than Marshal (Carbosulfan), one of the broad spectrum insecticides widely used in this field
Dolphin: A Challenging and Diverse Benchmark for Arabic NLG
We present Dolphin, a novel benchmark that addresses the need for an
evaluation framework for the wide collection of Arabic languages and varieties.
The proposed benchmark encompasses a broad range of 13 different NLG tasks,
including text summarization, machine translation, question answering, and
dialogue generation, among others. Dolphin comprises a substantial corpus of 40
diverse and representative public datasets across 50 test splits, carefully
curated to reflect real-world scenarios and the linguistic richness of Arabic.
It sets a new standard for evaluating the performance and generalization
capabilities of Arabic and multilingual models, promising to enable researchers
to push the boundaries of current methodologies. We provide an extensive
analysis of Dolphin, highlighting its diversity and identifying gaps in current
Arabic NLG research. We also evaluate several Arabic and multilingual models on
our benchmark, allowing us to set strong baselines against which researchers
can compare
Friedel-Crafts chemistry: Part 41. A new facile synthesis of indeno[1,2-c] pyrazoles, 2H-benzo[g]indazoles and benzo[6,7]cyclohepta[1,2-c]pyrazoles via Friedel-Crafts ring closures
Expedient and novel alternative synthesis of some fused heteropolycycles containing pyrazole moiety is described. A series of indeno[1,2-c]pyrazoles, 2H-benzo[g]indazoles and benzo[6,7] cyclohepta[1,2-c]pyrazoles were prepared by Friedel-Crafts ring closure of suitable synthesized carboxylic acids and alkanols in the presence of AlCl3/CH3NO2 or P2O5 or PPA (polyphosphoric acid) catalysts. The precursor acids were obtained by utilizing KMnO4 oxidation of the corresponding aldehyde, alkylations of diethyl malonates and Perkin type approaches, whereas starting alkanols were smoothly obtained by reaction of carboxylic acid ester with Grignard reagent. To exemplify, cyclizations of 5-chloro-1,3-diphenyl-1H-pyrazole-4-carboxylic acid (6) were accomplished using the above catalysts afforded 3-chloro-2-phenylindeno[1,2-c]pyrazol-4(2H)-one (1b). Similarly, cyclization of the 2-(5-chloro-1,3-diphenyl-1H-pyrazol-4-yl)propan-2-ol (8) by a Friedel–Crafts-type ring closure afforded the 3-chloro-2,4-dihydro-4,4-dimethyl-2-phenylindeno[1,2-c]pyrazole (1a)
ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting
Bilingual Lexicon Induction (BLI), where words are translated between two
languages, is an important NLP task. While noticeable progress on BLI in rich
resource languages using static word embeddings has been achieved. The word
translation performance can be further improved by incorporating information
from contextualized word embeddings. In this paper, we introduce ProMap, a
novel approach for BLI that leverages the power of prompting pretrained
multilingual and multidialectal language models to address these challenges. To
overcome the employment of subword tokens in these models, ProMap relies on an
effective padded prompting of language models with a seed dictionary that
achieves good performance when used independently. We also demonstrate the
effectiveness of ProMap in re-ranking results from other BLI methods such as
with aligned static word embeddings. When evaluated on both rich-resource and
low-resource languages, ProMap consistently achieves state-of-the-art results.
Furthermore, ProMap enables strong performance in few-shot scenarios (even with
less than 10 training examples), making it a valuable tool for low-resource
language translation. Overall, we believe our method offers both exciting and
promising direction for BLI in general and low-resource languages in
particular. ProMap code and data are available at
\url{https://github.com/4mekki4/promap}.Comment: To appear in IJCNLP-AACL 202
Arabic Fine-Grained Entity Recognition
Traditional NER systems are typically trained to recognize coarse-grained
entities, and less attention is given to classifying entities into a hierarchy
of fine-grained lower-level subtypes. This article aims to advance Arabic NER
with fine-grained entities. We chose to extend Wojood (an open-source Nested
Arabic Named Entity Corpus) with subtypes. In particular, four main entity
types in Wojood, geopolitical entity (GPE), location (LOC), organization (ORG),
and facility (FAC), are extended with 31 subtypes. To do this, we first revised
Wojood's annotations of GPE, LOC, ORG, and FAC to be compatible with the LDC's
ACE guidelines, which yielded 5, 614 changes. Second, all mentions of GPE, LOC,
ORG, and FAC (~44K) in Wojood are manually annotated with the LDC's ACE
sub-types. We refer to this extended version of Wojood as WojoodF ine. To
evaluate our annotations, we measured the inter-annotator agreement (IAA) using
both Cohen's Kappa and F1 score, resulting in 0.9861 and 0.9889, respectively.
To compute the baselines of WojoodF ine, we fine-tune three pre-trained Arabic
BERT encoders in three settings: flat NER, nested NER and nested NER with
subtypes and achieved F1 score of 0.920, 0.866, and 0.885, respectively. Our
corpus and models are open-source and available at
https://sina.birzeit.edu/wojood/
TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties
Despite the purported multilingual proficiency of instruction-finetuned large
language models (LLMs) such as ChatGPT and Bard, the linguistic inclusivity of
these models remains insufficiently explored. Considering this constraint, we
present a thorough assessment of Bard and ChatGPT (encompassing both GPT-3.5
and GPT-4) regarding their machine translation proficiencies across ten
varieties of Arabic. Our evaluation covers diverse Arabic varieties such as
Classical Arabic (CA), Modern Standard Arabic (MSA), and several country-level
dialectal variants. Our analysis indicates that LLMs may encounter challenges
with dialects for which minimal public datasets exist, but on average are
better translators of dialects than existing commercial systems. On CA and MSA,
instruction-tuned LLMs, however, trail behind commercial systems such as Google
Translate. Finally, we undertake a human-centric study to scrutinize the
efficacy of the relatively recent model, Bard, in following human instructions
during translation tasks. Our analysis reveals a circumscribed capability of
Bard in aligning with human instructions in translation contexts. Collectively,
our findings underscore that prevailing LLMs remain far from inclusive, with
only limited ability to cater for the linguistic and cultural intricacies of
diverse communities.Comment: ArabicNLP 202
CRT-303 NonInvasive Assessment of Plaque Characteristics with Dual Source Multislice Computed Tomography Coronary Angiography in Symptomatic Controlled Diabetic Patients
Plant growth-promoting microorganisms as biocontrol agents of plant diseases: Mechanisms, challenges and future perspectives
Plant diseases and pests are risk factors that threaten global food security. Excessive chemical pesticide applications are commonly used to reduce the effects of plant diseases caused by bacterial and fungal pathogens. A major concern, as we strive toward more sustainable agriculture, is to increase crop yields for the increasing population. Microbial biological control agents (MBCAs) have proved their efficacy to be a green strategy to manage plant diseases, stimulate plant growth and performance, and increase yield. Besides their role in growth enhancement, plant growth-promoting rhizobacteria/fungi (PGPR/PGPF) could suppress plant diseases by producing inhibitory chemicals and inducing immune responses in plants against phytopathogens. As biofertilizers and biopesticides, PGPR and PGPF are considered as feasible, attractive economic approach for sustainable agriculture; thus, resulting in a “win-win” situation. Several PGPR and PGPF strains have been identified as effective BCAs under environmentally controlled conditions. In general, any MBCA must overcome certain challenges before it can be registered or widely utilized to control diseases/pests. Successful MBCAs offer a practical solution to improve greenhouse crop performance with reduced fertilizer inputs and chemical pesticide applications. This current review aims to fill the gap in the current knowledge of plant growth-promoting microorganisms (PGPM), provide attention about the scientific basis for policy development, and recommend further research related to the applications of PGPM used for commercial purposes
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