Comparison of antioxidant properties of pomelo [Citrus Grandis (L) Osbeck] varieties

This study aimed to compare the antioxidant content and antioxidant capacity of pulp and peel of two varieties of pomelo fruit (Tambun White and Tambun Pink). Antioxidants including total phenolic content, total flavonoid content and ascorbic acid content were determined using Folin-Ciocalteu reagent assay, aluminium chloride colorimetric assay and AOAC method, respectively. Antioxidant capacity of pomelo pulp and peel was measured using ferric reducing antioxidant potential and trolox equivalent antioxidant capacity assays. The peels of both pomelo fruits had higher antioxidant content and capacity than their pulps. Moreover, the white variety of pomelo had higher antioxidant content and capacity compared to the pink counterpart. Trolox equivalent antioxidant capacity of the samples was positively high correlated with total phenolic content (r = 0.978) and total flavonoid content (r =0.959), except for ascorbic acid. Therefore, pomelo peel from white variety possessed higher antioxidant properties and it is potentially rich sources of natural antioxidants

Screening of Rickettsia sp. in ticks (Acari: Ixodidae) collected from small mammals in three recreational forests in Selangor, Malaysia

Rickettsia is a gram-negative, non-motile, and obligate intracellular bacterium that usually associated with arthropods vectors such as ticks, fleas, lice and mites. These bacteria have the ability to cause diseases in humans, however, in Malaysia, knowledge on the prevalence and distribution of these bacteria mainly focused on humans and information of these bacteria in small mammal hosts is limited. Thus, this study aims to investigate the presence of Rickettsia sp. in the DNA of tick’s samples collected from small mammals in three different recreational forests in Selangor, Malaysia. Sampling was conducted in which 200 cage traps were set up randomly along streams and forest trails for five nights. A total of 106 fully engorged and adult ticks were collected from 23 individuals of seven small mammals host species. All samples were tested for Rickettsia bacteria based on polymerase chain reaction (PCR) using partial 17kDa antigen gene. The PCR results obtained from this study showed no infestation of Rickettsia sp. in all tick samples. Our findings revealed that none of the tick samples from these forests’ sites were infected with Rickettsia pathogen, however, more intense and extensive surveillance for Rickettsia sp. from other tick species is still necessary for greater geographical areas across Malaysia

Secure Cloud Computing based Energy Analytics Framework in Construction of Residential Buildings

The buildings are emanating a massive producer of data amidst being massive consumers of energy resources. Electrification of a region is seen as a breakthrough in fostering the economic development of the region. However, rapid urbanization has paved the way for the construction of huge buildings which is home to a large amount of population, which directly or indirectly contributes to energy consumption. Energy analytics is a form of energy conservation, especially in residential buildings, which is generally harnessed through cutting-edge computing technologies. This work proposed a comprehensive framework with five layers that collects data from the energy monitoring edge devices to build energy analytics by processing the data in the cloud platform. In addition to this, the framework uses a security score to monitor the illegitimate access of the cloud source by tracking the registered devices. This is a robust and generic framework that has the scope to include AI-based strategies that can be orchestrated in the cloud computing platform

Readiness of Malaysian on Sustainable Development in Solar Energy Application

Solar energy is a non-vanishing renewable source of energy that is eco-friendly. However, the solar energy adoption rate in Malaysia remains low due to the low readiness level among the Malaysian. This phenomenon should be emphasized where solutions need to be figured out to encourage the adoption of solar energy in buildings. Thus, this research was conducted to explore the readiness and challenges of the Malaysians in adopting solar energy. A mixed research method was adopted where a total of 200 sets of online questionnaire surveys were distributed to the public, and a semi-structured interview was carried out to obtain the opinions from the expert in the industry. Based on the findings of the questionnaires, the top 3 challenges in solar energy adoption are (1) high setting up cost of solar panels, (2) limited public awareness of solar energy and (3) efficiency of the solar panel depends on the weather. Next, the qualitative study found that 48.9% of 139 respondents were willing to adopt solar energy in the future. The findings show that that breaking down the barriers of high investment cost and long return investment would further enhance the solar energy adoption rates at the residential scale. Since this study had explored the challenges and solution to these barriers, the outcomes of this study can be used by the policy maker as the fundamental to encourage the adoption of solar energy in the existing buildings where the current adoption rate is low

Ectoparasites (ticks and mites) prevalence on small to medium-sized mammals associated with habitat condition in Kemasul, Pahang

Ectoparasites of small mammals and medium mammals are divided into two main classes which are Insecta and Arachnida. The members of the class Arachnida including order Ixodida (ticks) and Mesostigmata (mites) meanwhile class Insecta comprising Phthiraptera (lice) and Siphonaptera (fleas). This study was conducted to determine tick’s and mite’s prevalence on the small to medium-sized mammals in Kemasul Forest Reserve, Pahang. This forest has undergone rapid deforestation for agricultural purposes. Two study sites were chosen which represented by a forest remnant surrounded with different matrix of monoculture plantation; Jambu Rias (JR) (Elaeis guineensis) and Chemomoi (CM) (Acacia mangium). Three hundred wired mesh cage traps sized (28 cm × 15 cm × 12.5 cm) and forty wired mesh cage traps sized (60 cm × 40 cm × 40 cm) were deployed at the study area and ectoparasites were extracted from each host using a fine comb. Identification was based on morphology and molecular using cytochrome oxidase 1 for confirmation. Mites only represented by Laelaps sp. which shows 95% and 70% prevalence in JR and CM respectively. Ticks were represented by five species, namely Ixodes granulatus, Dermacentor atrosignatus, Rhipicephalus sanguineus, Amblyomma testudinarium and Haemaphysalis sp. JR comprise of five species while CM with two species. I. granulatus was the most common infesting the small mammals in both sites. The highest parasite load was found on small mammals which were Maxomys surifer, M. rajah and M. whiteheadi in both study sites, particularly with mites. The study indicates that habitat condition significantly affects parasite prevalence in small to medium-sized mammal population, which could be due to the resilience of an individual to persist in disturbed habitat

Interpretable rumor detection in microblogs by attending to user interactions

We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention. To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level

Driving behavior-guided battery health monitoring for electric vehicles using machine learning

An accurate estimation of the state of health (SOH) of batteries is critical to ensuring the safe and reliable operation of electric vehicles (EVs). Feature-based machine learning methods have exhibited enormous potential for rapidly and precisely monitoring battery health status. However, simultaneously using various health indicators (HIs) may weaken estimation performance due to feature redundancy. Furthermore, ignoring real-world driving behaviors can lead to inaccurate estimation results as some features are rarely accessible in practical scenarios. To address these issues, we proposed a feature-based machine learning pipeline for reliable battery health monitoring, enabled by evaluating the acquisition probability of features under real-world driving conditions. We first summarized and analyzed various individual HIs with mechanism-related interpretations, which provide insightful guidance on how these features relate to battery degradation modes. Moreover, all features were carefully evaluated and screened based on estimation accuracy and correlation analysis on three public battery degradation datasets. Finally, the scenario-based feature fusion and acquisition probability-based practicality evaluation method construct a useful tool for feature extraction with consideration of driving behaviors. This work highlights the importance of balancing the performance and practicality of HIs during the development of feature-based battery health monitoring algorithms

The Practices of Green Supply Chain Management towards Corporate Performances in Construction Industry

Green Supply Chain Management (GSCM) practices has different impact on the corporate performances while practitioners are not willing to implement practices that is irrelevant to their organization. Hence, to ensure the most appropriate investment on GSCM is implemented, this study is conducted to identify the relationship between GSCM practices and corporate performances in Malaysia construction industry. To conduct a literature review to figure the relationship between GSCM practices and corporate performances. Systematic literature review method is adopted in data collection stage. The findings of the study show that the green practices are correlated to the corporate performances and a comprehensive conceptual framework is formed from this study to describe the relationships between GSCM practices and corporate performances in Malaysia construction industry. Research on GSCM in Malaysia is still less in construction industry. However, the findings provide crucial insights for potential practitioners due to it consistency with prior studies which are related to GSCM in other industries and countries. Implementation of GSCM improves corporate performances in various dimensions. The construction industry should understand the functions and relationship of each element in GSCM to achieve best performance outcome in the form they desired. A comprehensive conceptual framework which shows the relationships of GSCM and its impact construction industry which allow the potential practitioners to understand the potential improvement in corporate performances is constructed through the systematic literature review