Photoelectron spectroscopy studies on group IV semiconductor clusters and novel binary clusters

Clusters consisting of a few to a few hundred atoms (~ 2 nm) cover a critical size range, in which the finite-sized systems evolve from molecular-like species to nanoparticles. Photoelectron spectroscopy of size-selected cluster anions in gas phase is a powerful technique to investigate their electronic structure and follow the evolution from discrete molecular features to bulk band structures. Advances in our laboratory to improve the photoelectron energy resolution and to control cluster temperatures have enabled us to obtain well-resolved photoelectron spectra for a wide range of gas-phaseatomic clusters. This dissertation mainly focuses on studies of group IV semiconductor (Si, Ge, Sn) clusters. Several binary cluster systems, such as hydrogenated aluminum clusters and alkali and coinage metal alloy clusters were also investigated. We have confirmed a prolate-to-spherical structural transition with the increase of size for silicon clusters. A semiconductor-to-metal transition was elucidated for tin clusters as a function of size. More importantly, we discovered a stable 12-atom tin cluster Sn12 2-, which has ahighly symmetric icosahedral structure and is named stannashperene for its high stability, high symmetry and π-bonding characters. This icosahedral cage has a size comparable to that of C60 and can be considered as an inorganic analog of the fullerenes. Subsequently, we have synthesized a series of endohedral cage clusters M@Sn12 2-, where M is a transition metal atom. The doped atom in M@Sn12 - keeps its quasi-atomic nature with large magnetic moments. These endohedral cage clusters might thus be viewed as “superatoms”, yielding a rich class of new building blocks for cluster-assembled materials with tunable magnetic, electronic, and chemical properties

Comprehensive evaluation of wheat operation during COVID-19 outbreak in Pakistan.

The whole world is confronting under extreme danger from COVID-19 pandemic. Which spread rapidly including an agro-based developing state like Pakistan. Right now this year "Rabi" crop season has safely ended during this pandemic. Wheat-crop operations are depended on environmental conditions and different operational safety measures. Farmworkers are the key individuals, as they are exposed to various environmental, health, safety, biological, and respiratory hazards. Due to COVID-19, there are about more than three thousand (3000) mortalities and one hundred eight thousand (18, 0000) plus persons have been effected, however this number increases further rapidly. The key purpose of this review-study is to highlight the timely adopted safe strategies and their impacts on the yield of wheat along with farmworkers under some Standard Operational Procedures (SOPs) during wheat operations, enabling food security, self-sustainably and securing of farmers in the context of COVID-19. Various actions have been taken worldwide, but a developing state like Pakistan with minimum resources, has made well-organized planning and strategies to sustain the production of wheat with public awareness. We highlighting government efforts to-combat this fatal pandemic, where it has directly impacted the crop yield and also the economy of the state. Whereas, especially during this period, uplifting of economy through agriculture sector, needs to overcome the same management deficiencies from other sectors. Pakistani Government has adopted and implemented different key steps for fighting against COVID-19 include: i. Government command along with incentive approach, ii. Mutual coordination among stakeholders, local governments, and farmers, iii. Continuous inspection setup, and iv. Provide adequate personal protective equipment (PPE)

Evaluating Modules in Graph Contrastive Learning

The recent emergence of contrastive learning approaches facilitates the research on graph representation learning (GRL), introducing graph contrastive learning (GCL) into the literature. These methods contrast semantically similar and dissimilar sample pairs to encode the semantics into node or graph embeddings. However, most existing works only performed model-level evaluation, and did not explore the combination space of modules for more comprehensive and systematic studies. For effective module-level evaluation, we propose a framework that decomposes GCL models into four modules: (1) a sampler to generate anchor, positive and negative data samples (nodes or graphs); (2) an encoder and a readout function to get sample embeddings; (3) a discriminator to score each sample pair (anchor-positive and anchor-negative); and (4) an estimator to define the loss function. Based on this framework, we conduct controlled experiments over a wide range of architectural designs and hyperparameter settings on node and graph classification tasks. Specifically, we manage to quantify the impact of a single module, investigate the interaction between modules, and compare the overall performance with current model architectures. Our key findings include a set of module-level guidelines for GCL, e.g., simple samplers from LINE and DeepWalk are strong and robust; an MLP encoder associated with Sum readout could achieve competitive performance on graph classification. Finally, we release our implementations and results as OpenGCL, a modularized toolkit that allows convenient reproduction, standard model and module evaluation, and easy extension

Selection of suitable reference genes for abiotic stress-responsive gene expression studies in peanut by real-time quantitative PCR

Background: Because of its strong specificity and high accuracy, real-time quantitative PCR (RT-qPCR) has been a widely used method to study the expression of genes responsive to stress. It is crucial to have a suitable set of reference genes to normalize target gene expression in peanut under different conditions using RT-qPCR. In this study, 11 candidate reference genes were selected and examined under abiotic stresses (drought, salt, heavy metal, and low temperature) and hormone (SA and ABA) conditions as well as across different organ types. Three statistical algorithms (geNorm, NormFinder and BestKeeper) were used to evaluate the expression stabilities of reference genes, and the comprehensive rankings of gene stability were generated. Results: The results indicated that ELF1B and YLS8 were the most stable reference genes under PEG-simulated drought treatment. For high-salt treatment using NaCl, YLS8 and GAPDH were the most stable genes. Under CdCl2 treatment, UBI1 and YLS8 were suitable as stable reference genes. UBI1, ADH3, and ACTIN11 were sufficient for gene expression normalization in low-temperature experiment. All the 11 candidate reference genes showed relatively high stability under hormone treatments. For organs subset, UBI1, GAPDH, and ELF1B showed the maximum stability. UBI1 and ADH3 were the top two genes that could be used reliably in all the stress conditions assessed. Furthermore, the necessity of the reference genes screened was further confirmed by the expression pattern of AnnAhs. Conclusions: The results perfect the selection of stable reference genes for future gene expression studies in peanut and provide a list of reference genes that may be used in the future