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This study aimed to isolate and identify moulds from rotten Citrus sinensis post-harvests and to investigate the activity of antagonist and biocontrol activity moulds that cause citrus fruit rotting. A total of 12 mould isolates were obtained. Following the pathogenicity test, two representative mould isolates were selected and identified based on the sequence analyses of internal transcribed spacer (ITS) regions of the rDNA. Methods used in this study include isolation of fungal postharvest diseases, pathogenicity assay, antagonism assay, growth curve analysis, in vitro biocontrol assay, and molecular phylogenetic analysis. Two isolates of fungal postharvest diseases were determined as the most destructive pathogens. The biocontrol assay showed that isolates of Y1 and Y10 were capable to reduce the growth of fungal isolates K6 and K9 and mitigate up to 100% of the damage of sweet citrus fruits after 7 days of incubation. The moulds were identified as K6 (Aspergillus flavus sensu lato) and K9 (Aspergillus niger sensu lato). Phylogenetic analysis showed that the Y10 yeast isolate was identified as Candida orthopsilosis, whereas the Y1 isolate had a close genetic relationship with Aureobasidium pullulans and possibly belongs to a new species. Further analysis is necessary to confirm this finding.

Herein, a multifunctional binary nickel–copper-modified MXene on nickel foam was synthesized and employed as an electrode material for hydrogen evolution reaction (HER), oxygen evolution reaction (OER), overall water splitting and supercapacitors.

Today, smart cities offer many significant benefits. As a result, the smart city is usually described in highly benign terms, and it is often uncritically assumed that its implementation must lead to widescale improvements, at all levels of society. Yet, while smart cities undoubtedly offer advantages, they also carry associated risks and dangers which could outweigh the benefits. This raises the important question of to which extent the ordinary citizen is aware of these risks and dangers. This study sets out to examine this question. While the ‘downsides’ of smart cities have been the subject of research over the past few years, there have been no studies which explore the perception of smart city disadvantages among citizen-stakeholders in the GCC countries. This study seeks to fill this gap in the literature, by examining the perspectives of a representative sample of citizens from the GCC. The results show that, despite the ‘whitewashing’ effect of public messaging, the study’s participants have a range of significant concerns about smart cities which could affect their acceptance of such initiatives. The findings will inform smart city development bodies in the GCC and contribute to strategies for promoting smart cities and citizen engagement.

Dual-Mode Vehicles (DMVs) represent a new and sustainable solution to the problems of urban mobility, and energy consumption. This proposed work of a DMV can operate without a conventional battery. Instead, it uses a combination of energy management technology, renewable energy systems, and other energy sources to ensure continuous operation. The efficiency and the working statistics of such DMV could also be sufficiently monitored through semantic techniques. The user can freely choose the type of working of the vehicle based on the current inputs given by the users. These semantics serve a crucial role in establishing a reference model for seamless data exchange between the trained Bald Eagle Search (BES) algorithm and the deep neural network (DNN). This proposed work has been implemented and the performance has been analyzed concerning the State Of Charging (SOC), generator voltage, and DC-DC converter voltage. The proposed work yields higher SOC and voltage than other existing schemes.

This study aimed to evaluate the efficacy of dietary Acacia nilotica bark bioactive lipid compounds (ANBBLCs) as novel feed additives on the growth performance, carcass criteria, antioxidants, and antimicrobial activities of growing male rabbits. A total of 100 California male weanling rabbits aged 35 days were divided into four nutritional treatments, each of which contained ANBBLCs at concentrations of 0 (control group), 50, 100, and 150 mg/kg diet (n = 25 per treatment, each replication consisting of one animal). The average body weight of the animals was 613 ± 14 g. The experiments lasted for 56 days. Dietary ANBBLC levels linearly improved (p < 0.05) the body weight, body weight gain, and feed conversion ratio (FCR) of rabbits. Furthermore, with increasing concentrations of ANBBLCs, the total antioxidant capacity of blood and liver tissue was linearly (p < 0.05) enhanced. Lactobacillus increased and Staphylococcus decreased (p < 0.05) in comparison to the control group when ANBBLC levels were added to the diets of rabbits. Rabbit diets supplemented with ANBBLCs increased dressing percentages and decreased abdominal fat. This study shows that ANBBLCs can be used as a feed additive to enhance the growth performance, carcass criteria, antioxidant, and antibacterial properties of growing rabbits.

The study reported in this paper investigates the feasibility of utilizing the fluid energy,which is causing the vibrations, to suppress the flow induced vibration of a mechanical structure. A cantilever beam with uniform thickness and variable tip mass subjected to harmonic excitation is considered to simulate the oscillating structure (the primary system). Two hinged plates, as a secondary system, are attached at its free end to provide the flow induced dynamic suppression by tuning their characteristic vibration to the oscillation of the primary system. A computer program was developed to evaluate the optimum design parameters of the secondary system (absorber) at any flow speed for a given structure. Experimental verification of the response with the calculated optimum absorber at each flow speed was undertaken. The results show a good correlation between the predicted and the experimentally measured suppressed amplitudes of the primary system.

This research aims to investigate how customer relationship management capabilities (CRMCs) improve customer engagement and innovation performance. Customer engagement is also tested as a mediator between CRM capabilities and innovation performance. Furthermore, the distrust moderating role was studied towards the relationship between CRMCs and customer engagement. Data were collected from 491 owner/managers of SMEs working in Saudi Arabia. The findings revealed that CRMCs positively predict customer engagement and innovation performance. Customer engagement directly affects innovation performance and acts as a mediator as well. In addition, the findings proved that distrust moderates on the link between CRMCs and customer engagement. This research adds to the marketing management theory and practices and highlighted the role of CRMCs in achieving innovation performance. It explored, considering the data base collected from SME owners and managers, the mechanism of innovation performance achievement through CRMCs and customer engagement. CRMC improvements allow businesses to have sustainable development and a better integration in the market.

Abstract Due to environment-friendliness, renewable energy like solar power and wind power is more and more introduced to energy systems all over the world. Simultaneously, high penetrations of wind and solar generation also have brought severe curtailment of wind and solar. How to alleviate curtailment of wind and solar is a crucial problem in evaluating accommodation capability of renewable energy, which reflects the extent of utilization of renewable energy and economic benefits. The uncertainty of renewable energy brings challenges to precisely describe renewable generation, which leads to difficulty in designing effective mechanisms for accommodation capability of renewable energy. Existing work suffers from high computation overhead from frequently updated data, and low precision of describing renewable energy, which leads to less effective policies for renewable energy accommodation and underestimated accommodation capability. To make the most of renewable energy, an algorithm AccCap-DRL based on deep reinforcement learning is proposed. AccCap-DRL partitions a distribution into segments by time intervals, employs WGAN to describe distributions of renewable energy data, and employs DDPG to obtain approximate policies for renewable energy accommodation in different scenarios. Simulation results from real power generation and users’ demand data show high effectiveness of the proposed algorithm, and high efficiency of evaluating accommodation capability.