Recent Advances in BiVO4- and Bi2Te3-Based Materials for High Efficiency-Energy Applications

This chapter provides recent progress in developments of BiVO4- and Bi2Te3-based materials for high efficiency photoelectrodes and thermoelectric applications. The self-assembling nanostructured BiVO4-based materials and their heterostructures (e.g., WO3/BiVO4) are developed and studied toward high efficiency photoelectrochemical (PEC) water splitting via engineering the crystal and band structures and charge transfer processes across the heteroconjunctions. In addition, crystal and electronic structures, optical properties, and strategies to enhance photoelectrochemical properties of BiVO4 are presented. The nanocrystalline, nanostructured Bi2Te3-based thin films with controlled structure, and morphology for enhanced thermoelectric properties are also reported and discussed in details. We demonstrate that BiVO4-based materials and Bi2Te3-based thin films play significant roles for the developing renewable energy

OPERATING SYSTEM FOR WIRELESS SENSOR NETWORKS AND AN EXPERIMENT OF PORTING CONTIKIOS TO MSP430 MICROCONTROLLER

Wireless Sensor Networks (WSNs) consist of a large number of sensor nodes, and are used for various applications such as building monitoring, environment control, wild-life habitat monitoring, forest fire detection, industry automation, military, security, and health-care. Each sensor node needs an operating system (OS) that can control the hardware, provide hardware abstraction to application software, and fill in the gap between applications and the underlying hardware. In this paper, researchers present OS for WSNs and an experiment of porting contikiOS to MSP430 microcontroller which is very popular in many hardware platforms for WSNs. Researchers begin by presenting the major issues for the design of OS for WSNs. Then, researchers examine some popular operating systems for WSNs including TinyOS, ContikiOS, and LiteOS. Finally, researchers present an experiment of porting ContikiOS to MSP430 microcontroller. Wireless Sensor Networks (WSNs) terdiri dari sejumlah besar sensor nodes, dan digunakan untuk berbagai aplikasi seperti pemantauan gedung, pengendalian lingkungan, pemantauan kehidupan habitat liar, deteksi kebakaran hutan, otomatisasi industri, militer, keamanan, dan kesehatan. Setiap sensor nodememerlukan sistem operasi (SO) yang dapat mengontrol hardware, menyediakan abstraksi hardware untuk aplikasi perangkat lunak, dan mengisi kesenjangan antara aplikasi dan hardware. Dalam penelitian ini, peneliti menyajikan SO untuk WSNs dan percobaan dari port contikiOS untuk MSP430 mikrokontroler yang sangat populer di platformhardware untuk WSNs. Peneliti memulai dengan menghadirkan isu utama yaitu desain SO untuk WSNs. Lalu, penelitimemeriksa beberapa sistem operasi populer untuk WSNs, termasuk TinyOS, ContikiOS, dan LiteOS. Akhirnya penelitimenyajikan sebuah percobaan dari port ContikiOS untuk MSP430 mikrokontroler

Endangered fish species and seed release strategies in Vietnam

World economic growth has led to considerable changes in the ecosystem in many places and has raised concerns on global resource management particularly aquatic animal resources and their living environment. In Vietnam, aquatic animal resources play an important role in the national economy and are one of the targets for economic development. However, under high population pressure, high demand for seafood has resulted in unfavorable living environment. Aquatic animal resource has been over-exploited and in some places reported to be declining; hence some species have become extinct or endangered. This paper provides a list some endangered freshwater, brackishwater, and marine species. Moreover, the seed production activities and the release strategies for resource conservation of the government of Vietnam are also presented

Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback

A key technology for the development of large language models (LLMs) involves instruction tuning that helps align the models' responses with human expectations to realize impressive learning abilities. Two major approaches for instruction tuning characterize supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), which are currently applied to produce the best commercial LLMs (e.g., ChatGPT). To improve the accessibility of LLMs for research and development efforts, various instruction-tuned open-source LLMs have also been introduced recently, e.g., Alpaca, Vicuna, to name a few. However, existing open-source LLMs have only been instruction-tuned for English and a few popular languages, thus hindering their impacts and accessibility to many other languages in the world. Among a few very recent work to explore instruction tuning for LLMs in multiple languages, SFT has been used as the only approach to instruction-tune LLMs for multiple languages. This has left a significant gap for fine-tuned LLMs based on RLHF in diverse languages and raised important questions on how RLHF can boost the performance of multilingual instruction tuning. To overcome this issue, we present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages. Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research. We also present benchmark datasets to enable the evaluation of generative LLMs in multiple languages. Our experiments demonstrate the advantages of RLHF for multilingual instruction over SFT for different base models and datasets. Our framework and resources are released at https://github.com/nlp-uoregon/Okapi

CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages

The driving factors behind the development of large language models (LLMs) with impressive learning capabilities are their colossal model sizes and extensive training datasets. Along with the progress in natural language processing, LLMs have been frequently made accessible to the public to foster deeper investigation and applications. However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX.Comment: Ongoing Wor

Comparative analysis of root transcriptomes from two contrasting drought-responsive Williams 82 and DT2008 soybean cultivars under normal and dehydration conditions

The economically important DT2008 and the model Williams 82 (W82) soybean cultivars were reported to have differential drought-tolerant degree to dehydration and drought, which was associated with root trait. Here, we used 66K Affymetrix Soybean Array GeneChip to compare the root transcriptomes of DT2008 and W82 seedlings under normal, as well as mild (2h treatment) and severe (10h treatment) dehydration conditions. Out of the 38172 soybean genes annotated with high confidence, 822 (2.15%) and 632 (1.66%) genes showed altered expression by dehydration in W82 and DT2008 roots, respectively, suggesting that a larger machinery is required to be activated in the drought-sensitive W82 cultivar to cope with the stress. We also observed that long-term dehydration period induced expression change of more genes in soybean roots than the short-term one, independently of the genotypes. Furthermore, our data suggest that the higher drought tolerability of DT2008 might be attributed to the higher number of genes induced in DT2008 roots than in W82 roots by early dehydration, and to the expression changes of more genes triggered by short-term dehydration than those by prolonged dehydration in DT2008 roots vs. W82 roots. Differentially expressed genes (DEGs) that could be predicted to have a known function were further analyzed to gain a basic understanding on how soybean plants respond to dehydration for their survival. The higher drought tolerability of DT2008 vs. W82 might be attributed to differential expression in genes encoding osmoprotectant biosynthesis-, detoxification- or cell wall-related proteins, kinases, transcription factors and phosphatase 2C proteins. This research allowed us to identify genetic components that contribute to the improved drought tolerance of DT2008, as well as provide a useful genetic resource for in-depth functional analyses that ultimately leads to development of soybean cultivars with improved tolerance to drought

Effect of boron and vanadium addition on friction-wear properties of the coating AlCrN for special applications

Cutting tools have long been coated with an AlCrN hard coating system that has good mechanical and tribological qualities. Boron (B) and vanadium (V) additions to AlCrN coatings were studied for their mechanical and tribological properties. Cathodic multi-arc evaporation was used to successfully manufacture the AlCrBN and AlCrVN coatings. These multicomponent coatings were applied to the untreated and plasma-nitrided surfaces of HS6-5-2 and H13 steels, respectively. Nanoindentation and Vickers micro-hardness tests were used to assess the mechanical properties of the materials. Ball-on-flat wear tests with WC-Co balls as counterparts were used to assess the friction-wear capabilities. Nanoindentation tests demonstrated that AlCrBN coating has a higher hardness (HIT 40.9 GPa) than AlCrVN coating (39.3 GPa). Steels’ wear resistance was significantly increased by a hybrid treatment that included plasma nitriding and hard coatings. The wear volume was 3% better for the AlCrBN coating than for the AlCrVN coating on H13 nitrided steel, decreasing by 89% compared to the untreated material. For HS6-5-2 steel, the wear volume was almost the same for both coatings but decreased by 77% compared to the untreated material. Boron addition significantly improved the mechanical, tribological, and adhesive capabilities of the AlCrN coating. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Department of Mechanical Engineering, University of Defence in Brno [SV20-216]; Project for the Development of the Organization "DZRO Military autonomous and robotic systems"; Slovak Research and Development AgencySlovak Research and Development Agency [APVV-15-0710]Agentúra na Podporu Výskumu a Vývoja, APVV: APVV-15-071

Function of KAI2 signaling in plant drought adaptation

Drought causes substantial reductions in crop yields worldwide. Therefore, we set out to identify new chemical and genetic factors that regulate drought resistance in Arabidopsis thaliana. Karrikins (KARs) are a class of butenolide compounds found in smoke that promote seed germination, and have been reported to improve seedling vigor under stressful growth conditions. Here, we discovered that mutations in KARRIKIN INSENSITIVE2 (KAI2), encoding the proposed karrikin receptor, result in hypersensitivity to water deprivation. We performed transcriptomic, physiological and biochemical analyses of kai2 plants to understand the basis for KAI2-regulated drought resistance. We found that kai2 mutants have increased rates of water loss and drought-induced cell membrane damage, enlarged stomatal apertures, and higher cuticular permeability. In addition, kai2 plants have reduced anthocyanin biosynthesis during drought, and are hyposensitive to abscisic acid (ABA) in stomatal closure and cotyledon opening assays. We identified genes that are likely associated with the observed physiological and biochemical changes through a genome-wide transcriptome analysis of kai2 under both well-watered and dehydration conditions. These data provide evidence for crosstalk between ABA- and KAI2-dependent signaling pathways in regulating plant responses to drought. A comparison of the strigolactone receptor mutant d14 (DWARF14) to kai2 indicated that strigolactones also contributes to plant drought adaptation, although not by affecting cuticle development. Our findings suggest that chemical or genetic manipulation of KAI2 and D14 signaling may provide novel ways to improve drought resistance

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License