Assessment of Cu, Pb and Zn contamination in sediment of north western Peninsular Malaysia by using sediment quality values and different geochemical indices

Surface sediments were collected from the north western aquatic area (13 intertidal sites and 5 river drainages) of Peninsular Malaysia, which were suspected to have received different anthropogenic sources. These sites included town areas, ports, fishing village, industrial areas, highway sides, jetties and some relatively unpolluted sites. The present study revealed that 4.79–32.91 μg/g dry weight for Cu, 15.85–61.56 μg/g dry weight for Pb, and 33.6–317.4 μg/g dry weight for Zn based on 13 intertidal surface sediments while those based on 5 river drainage surface sediments were 10.24–119.6 μg/g dry weight for Cu, 26.7–125.7 μg/g dry weight for Pb and 88.7–484.1 μg/g dry weight for Zn. In general, the metal levels in the drainage sediments are higher than in the intertidal sediments, suggesting dilution factor in the intertidal sediment and direct effluent from point sources in the drainage sediment. In particular, the total concentrations of Cu, Pb, and Zn for the sampling site at Kuala Kurau Town exceeded the Effect Range Median values for Cu, Pb, and Zn for assessments of sediment quality values for freshwater sediment as proposed by MacDonald et al. (Arch Environ Contam Toxicol 39:20–31, 2000), thus adverse biological effects would be observed above this level. Assessment using enrichment factor (using Fe as a normalizer) and geoaccumulation index showed that the three metals at Kuala Kurau Town and Juru Industry drainage were evidenced as having more enrichment and mostly due to non-natural sources. However, caution should be exercised that the interpretation can only become valid when the ratios, indices, and sediment quality values are combined. This is due to the fact that not all the established indices are applicable and, to a certain extent, some of them should be further revised and improved to suit a different metal for Malaysian sediment. Undoubtedly, sites near drainages at Kuala Kurau Town and Juru River Basin need greater attention to mitigate the heavy metal pollution in the future

Behavioral and sensitivity responses of pomacea insularum to Cd and Cu toxicities

This chapter focuses on the toxicity test of Cd and Cu by using two different sized groups of Pomacea insularum as a test organism, with mortality as an endpoint. The findings indicate the mussel was more sensitive to Cu than Cd although the small size group was more sensitive than the large one since the small group had lower LC50 values than the large one. Results also indicated the ability of P. insularum as a potential bioindicator for acute and subacute exposures to Cu and Cd. This study has provides an important baseline information for Cd and Cu toxicities using P. insularum as a test organism which enables the comparison of the acute sensitivity of P. insularum towards Cd and Cu with other marine invertebrates

Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models

After the inception of emotion recognition or affective computing, it has increasingly become an active research topic due to its broad applications. Over the past couple of decades, emotion recognition models have gradually migrated from statistically shallow models to neural network-based deep models, which can significantly boost the performance of emotion recognition models and consistently achieve the best results on different benchmarks. Therefore, in recent years, deep models have always been considered the first option for emotion recognition. However, the debut of large language models (LLMs), such as ChatGPT, has remarkably astonished the world due to their emerged capabilities of zero/few-shot learning, in-context learning, chain-of-thought, and others that are never shown in previous deep models. In the present paper, we comprehensively investigate how the LLMs perform in emotion recognition in terms of diverse aspects, including in-context learning, few-short learning, accuracy, generalisation, and explanation. Moreover, we offer some insights and pose other potential challenges, hoping to ignite broader discussions about enhancing emotion recognition in the new era of advanced and generalised large models

Heavy metal pollution in surface sediments collected from drainages receiving different anthropogenic sources from Peninsular Malaysia

Surface drainage sediments were collected, between January and April in 2005, from 6 sampling sites in Peninsular Malaysia suspected to have received different anthropogenic sources. These drainages included sites near a petrochemical plant, an electronic factory, a metal factory and 2 townships in addition to a relatively unpolluted site (a reference site). Based on the aqua-regia method, the metal concentrations for all the sampling sites except for the agricultural site (reference site) at Sg. Benut were between 60.92-1 019 μg/g dry weight for copper (Cu), 65.32-1 266.6 μg/g dry weight for plumbum (Pb), 1.46-15.93 μg/g dry weight for cadmium (Cd), 330.10-484.14 μg/g dry weight for zinc (Zn) and 47.03-120.90 μg/g dry weight for nickel (Ni). All of these ranges were significantly (p < 0.05) higher than those (Cu: 8.77 μg/g; Pb: 57.42 μg/g; Cd: 0.96 μg/g; Zn: 68.21 μg/g; and Ni: 17.87 μg/g) of the reference site at Sg. Benut that only received agricultural disposal. The concentrations for the 5 metals from all the sites (except for the reference site) were higher than the Sediment Quality Values for Hong Kong by Chapman et al (1999) for the 5 metals. Based on the sequential extraction technique, 3 geochemical fractions [easily, freely leachable and exchangeable (EFLE), acid-reducible and oxidisable-organic] were also useful in identifying these polluted sites. The elevated metal concentrations found in the sediments might be related to point sources of industrial effluent and urban waste. Hence, the results emphasized the necessity for the treatment of effluents into this drainage area. As part of the effort to control illegal discharges and dumping into drainages, public awareness should be enhanced

Customising General Large Language Models for Specialised Emotion Recognition Tasks

The advent of large language models (LLMs) has gained tremendous attention over the past year. Previous studies have shown the astonishing performance of LLMs not only in other tasks but also in emotion recognition in terms of accuracy, universality, explanation, robustness, few/zero-shot learning, and others. Leveraging the capability of LLMs inevitably becomes an essential solution for emotion recognition. To this end, we further comprehensively investigate how LLMs perform in linguistic emotion recognition if we concentrate on this specific task. Specifically, we exemplify a publicly available and widely used LLM -- Chat General Language Model, and customise it for our target by using two different modal adaptation techniques, i.e., deep prompt tuning and low-rank adaptation. The experimental results obtained on six widely used datasets present that the adapted LLM can easily outperform other state-of-the-art but specialised deep models. This indicates the strong transferability and feasibility of LLMs in the field of emotion recognition

Experiments and simulations on interactions between 2,3-bis(hydroxymethyl)-2,3-dinitro-1,4-butanediol tetranitrate (DNTN) with some energetic components and inert materials

Abstract In order to survey the application prospects of 2,3-bis(hydroxymethyl)-2,3-dinitro-1,4-butanediol tetranitrate (DNTN, NEST-1, SMX) in high - energy solid rocket propellants and explosives, the interactionsbetween DNTN with some energetic components and inert materials were investigated by means of differential scanning calorimetry (DSC) and molecular dynamic (MD) methods, where glycidyl azide polymer (GAP), cyclotrimethylenetrinitramine (RDX), cyclotetramethylenetetranitroamine (HMX), lead 3-nitro-1,2,4-triazol-5-onate (NTO-Pb), hexanitrohexaazaisowurtzitane (CL-20), aluminum powder (Al) and magnesium powder (Mg), 3,4-dinitrofurzanfuroxan (DNTF), N-guanylurea-dinitramide (GUDN), N-butyl-N-(2-nitroxy-ethyl)nitramine (Bu-NENA), bis(2,2-dinitropropyl) acetal (BDNPA)/bis(2,2-dinitropropyl) formal (BDNPF) mixture (A3), nitrocellulose - nitroglycerine (NC-NG) and ammonium dinitramide (ADN) were used as energetic components and hydroxyl terminated polybutadiene (HTPB), polyoxytetram ethylene-co- oxyethylene (PET), addition product of hexamethylene diisocyanate and water (N-100), 2,4-toluene diisocyanate (TDI), 1,3-dimethyl-1,3-diphenyl urea (C2), carbon black (C.B.), aluminum oxide (Al2O3), lead phthalate (φ-Pb), N-nitro-dihydroxyethylamine dinitrate (DINA), cupric 2,4-dihydroxy-benzoate (β-Cu) were used as inert materials. The impact and friction sensitivities of DNTN and DNTN in combination with energetic materials were obtained. . It was concluded that the binary systems of DNTN with RDX, HMX, NTO-Pb, Al, Mg, ADN, NC-NG, HTPB, PET, C2, C.B., β-Cu and Al2O3 are compatible, whereas systems of DNTN with GAP, CL-20, A3, N-100, TDI and DINA are slightly sensitive, and those containing DNTF and GUDN are incompatible. It is demonstrated that no consequential trend between sensitivity and compatibility is found. The mechanical properties and safety performance of GAP mixtures plasticized with three plasticizers decrease in the following order: [BTTN] > [TMETN] > [Bu-NENA]

Distribution of heavy metal concentrations in the different soft tissues of the freshwater snail Pomacea insularum (DOrbigny, 1839; Gastropoda), and sediments collected from polluted and unpolluted sites from Malaysia.

Pomacea insularum were collected from polluted and unpolluted freshwater ecosystems in Malaysia. Besides the shells, the soft tissues were dissected and pooled into cephalic tentacle, foot, mantle, operculum, digestive tract, penial sac,lung sac, and remainder. These tissues were analyzed for the concentrations of Cu, Cd, Fe, Ni, and Zn. The present work resulted in three interesting findings. First, the concentrations of Cu, Cd, Fe, and Zn found in most of the different parts of P. insularum collected from the polluted Juru River were significantly (P50.05) higher than those found in the snails from the other four sites. Second, positive and significant correlation coefficients were found for sediment-lung sac for Cd, Fe, and Zn while sediment-digestive tract for Cu. These correlation results indicated that lung sacs could reflect the environmental concentrations of Cd, Fe, and Zn, while the digestive tract could do the same for Cu. Third, the different concentrations of heavy metals found in the different parts indicated different metal regulation and binding sites in these organs. The significant correlation coefficients between different tissues indicated that they might be caused by similar metal regulation and sequestration. Based on the above findings, P. insularum are a potential biomonitor of Cd, Cu, Fe, and Zn pollution in freshwater ecosystems

TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge

This paper describes the TSUP team's submission to the ISCSLP 2022 conversational short-phrase speaker diarization (CSSD) challenge which particularly focuses on short-phrase conversations with a new evaluation metric called conversational diarization error rate (CDER). In this challenge, we explore three kinds of typical speaker diarization systems, which are spectral clustering(SC) based diarization, target-speaker voice activity detection(TS-VAD) and end-to-end neural diarization(EEND) respectively. Our major findings are summarized as follows. First, the SC approach is more favored over the other two approaches under the new CDER metric. Second, tuning on hyperparameters is essential to CDER for all three types of speaker diarization systems. Specifically, CDER becomes smaller when the length of sub-segments setting longer. Finally, multi-system fusion through DOVER-LAP will worsen the CDER metric on the challenge data. Our submitted SC system eventually ranks the third place in the challenge

A Conversational Paradigm for Program Synthesis

Program synthesis strives to generate a computer program as a solution to a given problem specification. We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program space and user intent specification faced in prior approaches. Our new approach casts the process of writing a specification and program as a multi-turn conversation between a user and a system. It treats program synthesis as a sequence prediction problem, in which the specification is expressed in natural language and the desired program is conditionally sampled. We train a family of large language models, called CodeGen, on natural language and programming language data. With weak supervision in the data and the scaling up of data size and model size, conversational capacities emerge from the simple autoregressive language modeling. To study the model behavior on conversational program synthesis, we develop a multi-turn programming benchmark (MTPB), where solving each problem requires multi-step synthesis via multi-turn conversation between the user and the model. Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm. In addition, our model CodeGen (with up to 16B parameters trained on TPU-v4) outperforms OpenAI's Codex on the HumanEval benchmark. We make the training library JaxFormer including checkpoints available as open source contribution: https://github.com/salesforce/CodeGen

Knowledge networks in science-based start-ups : actors and strategies

The paper investigates the strategic choices made by young science-based firms’ regarding the selection of knowledge sources. Drawing on two streams of research – on alliances and on social networks – two different dimensions of this strategy are considered: the activation of the entrepreneurs’ social capital and the intentional inclusion of new knowledge sources. The data collected for a subset of the Portuguese biotechnology sector are analysed with a view to answer to three research questions: i) To what do extent firms’ rely on entrepreneurs’ personal networks, activating their social capital to access scientific and technological knowledge at start-up; ii) To what extent are new actors added to knowledge networks at start-up; iii) Are there differences between existing and new ties in terms of strength and formalisation. The results obtained confirm the consideration of the strategies underlying network building is vital for an understanding of the configuration of young science-based firms’ knowledge networks. They reveal the existence of different knowledge network building strategies that often combine tie persistence with search for novelty. They also suggest that differences in the network building strategies may be the behind the somewhat contradictory results presented in the literature about the network configuration that is more favourable for innovation