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Background: Hypertension is frequently studied in epidemiological studies that have been conducted using retrospective observational data, either as an outcome or a variable. However, there are few validation studies investigating the accuracy of hypertension phenotyping algorithms in aggregated electronic health record (EHR) data. Methods: Utilizing a centralized repository of inpatient EHR data from Singapore for the period of 2019–2020, a new algorithm that incorporates both diagnostic codes and medication details (Diag+Med) was devised. This algorithm was intended to supplement and improve the diagnostic code-only model (Diag-Only) for the classification of hypertension. We computed various metrics (sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)) to assess the algorithm’s effectiveness in identifying hypertension on 2813 chart-reviewed records. This pool was composed of two patient cohorts: a random sampling of all inpatient admissions (Random Cohort) and a targeted group with atrial fibrillation diagnoses (AF Cohort). Results: The Diag+Med algorithm was more sensitive at detecting hypertension patients in both cohorts compared to the Diag-Only algorithm (83.8 and 87.6% vs. 68.2 and 66.5% in the Random and AF Cohorts, respectively). These improvements in sensitivity came at minimal costs in terms of PPV reductions (88.2 and 90.3% vs. 91.4 and 94.2%, respectively). Conclusion: The combined use of diagnosis codes and specific antihypertension medication exposure patterns facilitates a more accurate capture of patients with hypertension in a database of aggregated EHRs from diverse healthcare institutions in Singapore. The results presented here allow for the bias correction of risk estimates derived from observational studies involving hypertension.

This paper presents an Enhanced Particle Swarm Optimisation (EPSO) algorithm to improve multi-robot path planning by integrating a new path planning scheme with a cubic Bezier curve trajectory smoothing algorithm. Traditional PSO algorithms often result in suboptimal paths with numerous turns, necessitating frequent stops and higher energy consumption. The proposed EPSO algorithm addresses these issues by generating smoother paths that reduce the number of turns and enhance the efficiency of multi-robot systems. The proposed algorithm was evaluated through simulations in two scenarios, and its performance was compared against the basic PSO algorithm. The results demonstrated that EPSO consistently produced shorter, smoother paths with fewer directional changes, albeit with slightly longer execution times. This improvement translates to more efficient navigation, reduced energy consumption, and enhanced overall performance of multi-robot systems. The findings underscore the potential of EPSO in applications requiring precise and efficient path planning, highlighting its contribution to advancing the field of robotics.

Background: Identifying patients with diabetes mellitus (DM) is often performed in epidemiological studies using electronic health records (EHR), but currently available algorithms have features that limit their generalizability. Methods: We developed a rule-based algorithm to determine DM status using the nationally aggregated EHR database. The algorithm was validated on two chart-reviewed samples (n = 2813) of (a) patients with atrial fibrillation (AF, n = 1194) and (b) randomly sampled hospitalized patients (n = 1619). Results: DM diagnosis codes alone resulted in a sensitivity of 77.0% and 83.4% in the AF and random hospitalized samples, respectively. The proposed algorithm combines blood glucose values and DM medication usage with diagnostic codes and exhibits sensitivities between 96.9% and 98.0%, while positive predictive values (PPV) ranged between 61.1% and 75.6%. Performances were comparable across sexes, but a lower specificity was observed in younger patients (below 65 versus 65 and above) in both validation samples (75.8% vs. 90.8% and 60.6% vs. 88.8%). The algorithm was robust for missing laboratory data but not for missing medication data. Conclusions: In this nationwide EHR database analysis, an algorithm for identifying patients with DM has been developed and validated. The algorithm supports quantitative bias analyses in future studies involving EHR-based DM studies.

Spontaneous adverse events reporting systems are used internationally to flag new or unexpected adverse drug reactions (ADRs). Disproportionality analysis is a recognised technique, but false alarms may arise. We aimed to determine whether these new ADR signals had subsequently been followed-up with detailed hypothesis-testing studies. We searched PubMed to identify published studies (years 2017–2021) where the authors reported findings of new ADR signals from disproportionality analyses. We used PubMed and forward citation tracking (Google Scholar) to identify any subsequent confirmatory studies of these ADR signals. We screened 414 titles and abstracts and checked the full-text articles of 57 studies. We found signals for 56 suspected new ADRs from 24 drugs. Google Scholar showed that the ADR studies had been cited a median of seven times (range 0–61). However, none of the suspected new ADRs had undergone detailed evaluation in the citing literature. Similarly, our PubMed search did not find any confirmation studies for the 56 suspected new ADRs. Although many suspected new ADR signals have been identified through disproportionality analysis, most signals have not been further verified as being either genuine ADRs or false alarms. Researchers must focus on follow-up studies for these new signals.

Commercial buildings in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling systems. Despite the pressing need for effective day-ahead cooling load predictions, current methodologies have not fully harnessed the potential of advanced deep-learning techniques. This paper aims to address this gap by investigating the application of innovative deep-learning models in day-ahead hourly cooling load prediction for commercial buildings in tropical climates. A range of multi-output deep learning techniques, including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs), are employed to enhance prediction accuracy. Furthermore, these individual deep learning techniques are synergistically integrated to create hybrid models, such as CNN-LSTM and Sequence-to-Sequence models. Experiments are conducted to choose the time horizons from the past that can serve as input to the models. In addition, the influence of various categories of input parameters on prediction performance has been assessed. Historical cooling load, calendar features, and outdoor weather parameters are found in decreasing order of influence on prediction accuracy. This research focuses on buildings located in Singapore and presents a comprehensive case study to validate the proposed models and methodologies. The sequence-to-sequence model provided better performance than all the other models. It offered a CV-RMSE of 7.4%, 10%, and 6% for SIT@Dover, SIT@NYP, and the simulated datasets, which were 2.3%, 3%, and 1% less, respectively, than the base Deep Neural Network model.

One of the research directions in Internet of Things (IoT) is the field of Context Management Platforms (CMPs) which is a specific type of IoT middleware. CMPs provide horizontal connectivity between vertically oriented IoT silos resulting in a noticeable difference in how IoT data streams are processed. As these context data exchanges can be monetised, there is a need to model and predict the context metrics and operational costs of this exchange to provide relevant and timely context in a large-scale IoT ecosystem. In this paper, we argue that caching all transient context information to satisfy this necessity requires large amounts of computational and network resources, resulting in tremendous operational costs. Using Service Level Agreements (SLAs) between the context providers, CMP, and context consumers, where the level of service imperfection is quantified and linked to the associated costs, we show that it is possible to find efficient caching and prefetching strategies to minimize the context management cost. So, this paper proposes a novel method to find the optimal rate of IoT data prefetching and caching. We show the main context caching strategies and the proposed mathematical models, then discuss how a correctly chosen proactive caching strategy and configurations can help to maximise the profit of CMP operation when multiple SLAs are defined. Our model is accurate up to 0.0016 in Root Mean Square Percentage Error against our simulation results when estimating the profits to the system. We also show our model is valid using the t-test value tending to 0 for all the experimental scenarios.

This paper proposes a policy management framework which we call the SANIJO framework. This framework comprises three different types of policy rules that are applicable to smart devices for managing their multiuser–multidevice interactions in IoT collectives, from a socio-ethical perspective. We developed a policy language to help regulate and manage the interaction behaviors of smart internet-connected devices that are being deployed at an increasing rate around the world. The policy rules are classified into Authorization, Obligation, and Prohibition rules and are prototyped in the SANIJO system. We implemented our framework as a collection of mobile apps (running on smartphones) and a robot app (running on the robot). We then illustrate its operation based on an aged care center scenario.

Fruit juice is an essential food product that has received significant acceptance among consumers. Harmonized concentration, preservation of nutritional constituents, and heat-responsive sensorial of fruit juices are demanding topics in food processing. Membrane separation is a promising technology to concentrate juice at minimal pressure and temperatures with excellent potential application in food industries from an economical, stable, and standard operation view. Microfiltration (MF) and ultrafiltration (UF) have also interested fruit industries owing to the increasing demand for reduced pressure-driven membranes. UF and MF membranes are widely applied in concentrating, clarifying, and purifying various edible products. However, the rising challenge in membrane technology is the fouling propensity which undermines the membrane’s performance and lifespan. This review succinctly provides a clear and innovative view of the various controlling factors that could undermine the membrane performance during fruit juice clarification and concentration regarding its selectivity and permeance. In this article, various strategies for mitigating fouling anomalies during fruit juice processing using membranes, along with research opportunities, have been discussed. This concise review is anticipated to inspire a new research platform for developing an integrated approach for the next-generation membrane processes for efficient fruit juice clarification.

Quorum sensing (QS) in Pseudomonas aeruginosa plays an essential role in virulence factors, biofilm formation as well as antibiotic resistance. Approaches that target virulence factors are known to be more sustainable than antibiotics in weakening the infectivity of bacteria. Although honey has been shown to exert antipseudomonal activities, the enhancement of such activity in ginger-enriched honey is still unknown. The main objective of this study was to determine the impacts of honey and ginger-enriched honey on the QS virulence factors and biofilm formation of antibiotic resistant P. aeruginosa clinical isolates. Outcomes showed honey and/or ginger-enriched honey significantly reduced the protease activity, pyocyanin production and exotoxin A concentration of the isolates. The swarming and swimming motility together with biofilm formation in all clinical isolates were also significantly inhibited by both honey samples. Notable morphological alteration of bacterial cells was also observed using scanning electron microscopy. A principal component analysis (PCA) managed to distinguish the untreated group and treatment groups into two distinct clusters, although honey and ginger-enriched honey groups were not well differentiated. This study revealed the effectiveness of honey including ginger-enriched honey to attenuate QS virulence factors and biofilm formation of P. aeruginosa.

The Internet of Things is emerging as a vast, inter-connected space of devices and things surrounding people, many of which are increasingly capable of autonomous action, from automatically sending data to cloud servers for analysis, changing the behaviour of smart objects, to changing the physical environment. A wide range of ethical concerns has arisen in their usage and development in recent years. Such concerns are exacerbated by the increasing autonomy given to connected things. This paper reviews, via examples, the landscape of ethical issues, and some recent approaches to address these issues concerning connected things behaving autonomously as part of the Internet of Things. We consider ethical issues in relation to device operations and accompanying algorithms. Examples of concerns include unsecured consumer devices, data collection with health-related Internet of Things, hackable vehicles, behaviour of autonomous vehicles in dilemma situations, accountability with Internet of Things systems, algorithmic bias, uncontrolled cooperation among things, and automation affecting user choice and control. Current ideas towards addressing a range of ethical concerns are reviewed and compared, including programming ethical behaviour, white-box algorithms, black-box validation, algorithmic social contracts, enveloping IoT systems, and guidelines and code of ethics for IoT developers; a suggestion from the analysis is that a multi-pronged approach could be useful based on the context of operation and deployment.

Making internet-of-things (IoT)-based applications context-aware demands large amounts of raw data to be collected, interpreted, stored, and reused or repurposed if needed from many domains and applications. Context is transient but interpreted data can be distinguished from IoT data in many aspects. Managing context in cache is a novel area of research that has been given very little attention. Performance metric-driven adaptive context caching (ACOCA) can have a profound impact on the performance and cost efficiency of context-management platforms (CMPs) when responding to context queries in realtime. Our paper proposes an ACOCA mechanism to maximize both the cost and performance efficiency of a CMP in near realtime. Our novel mechanism encompasses the entire context-management life cycle. This, in turn, distinctively addresses the problems of efficiently selecting context for caching and managing the additional costs of context management in the cache. We demonstrate that our mechanism results in long-term efficiencies for the CMP that have not been observed in any previous study. The mechanism employs a novel, scalable, and selective context-caching agent implemented using the twin delayed deep deterministic policy gradient method. It further incorporates an adaptive context-refresh switching policy, a time-aware eviction policy, and a latent caching decision management policy. We point out in our findings that the additional complexity of adaptation introduced to the CMP through ACOCA is significantly justified, considering the cost and performance gains achieved. Our algorithm is evaluated using a real-world inspired heterogeneous context-query load and a data set based on parking-related traffic in Melbourne, Australia. This paper presents and benchmarks the proposed scheme against traditional and context-aware caching policies. We demonstrate that ACOCA outperforms the benchmarks in both cost and performance efficiency, i.e., up to 68.6%, 84.7%, and 67% more cost efficient compared to traditional data caching policies to cache context, redirector mode, and context-aware adaptive data caching under real-world-like circumstances.

Light-emitting diodes are increasingly used as artificial light sources in Haematococcus pluvialis cultivation due to the fact of their energy advantages. The immobilized cultivation of H. pluvialis in pilot-scale angled twin-layer porous substrate photobioreactors (TL-PSBRs) was initially performed with a 14/10 h light/dark cycle and showed relatively low biomass growth and astaxanthin accumulation. In this study, the illumination time with red and blue LEDs at a light intensity of 120 µmol photons m−2 s−1 was increased to 16–24 h per day. With a light/dark cycle of 22/2 h, the biomass productivity of the algae was 7.5 g m−2 day−1, 2.4 times higher than in the 14/10 h cycle. The percentage of astaxanthin in the dry biomass was 2%, and the total amount of astaxanthin was 1.7 g m−2. Along with the increase in light duration, adding 10 or 20 mM NaHCO3 to the BG11-H culture medium over ten days of cultivation in angled TL-PSBRs did not increase the total amount of astaxanthin compared with only CO2 addition at a flow rate of 3.6 mg min−1 to the culture medium. Adding NaHCO3 with a 30–80 mM concentration inhibited algal growth and astaxanthin accumulation. However, adding 10–40 mM NaHCO3 caused algal cells to accumulate astaxanthin at a high percentage in dry weight after the first four days in TL-PSBRs.

The present study utilized response surface methodology (RSM) and Bayesian neural network (BNN) to predict the characteristics of a diesel engine powered by a blend of biodiesel and diesel fuel. The biodiesel was produced from waste cooking oil using a biocatalyst synthesized from vegetable waste through the wet impregnation technique. A multilevel central composite design was utilized to predict engine characteristics, including brake thermal efficiency (BTE), nitric oxide (NO), unburned hydrocarbons (UBHC), smoke emissions, heat release rate (HRR), and cylinder peak pressure (CGPP). BNN and the logistic–sigmoid activation function were used to train the experimental data in the artificial neural network (ANN) model, and the errors and correlations of the predicted models were calculated. The study revealed that the biocatalyst was capable of producing a maximum yield of 93% at 55 °C under specific reaction conditions, namely a reaction time of 120 min, a stirrer speed of 900 rpm, a catalyst loading of 7 wt.%, and a molar ratio of 1:9. Further, the ANN model was found to exhibit comparably lower prediction errors (0.001–0.0024), lower MAPE errors (3.14–4.6%), and a strong correlation (0.984–0.998) compared to the RSM model. B100-80%-20% was discovered to be the best formulation for emission property, while B100-90%-10% was the best mix for engine performance and combustion at 100% load. In conclusion, this study found that utilizing the synthesized biocatalyst led to attaining a maximum biodiesel yield. Furthermore, the study recommends using ANN and RSM techniques for accurately predicting the characteristics of a diesel engine.

The increase in global temperature calls for ambitious action to reduce the release of greenhouse gases into the atmosphere. The transportation sector contributes up to 25% of the total emissions released, mainly from the burning of vehicle fuel. Therefore, scientists from all around the world are focusing on finding a sustainable alternative to conventional vehicle fuel. Biofuel has attracted much attention, as it shows great potential for the replacement of traditional fossil fuels. However, the main bottlenecks of biofuel are the ongoing controversial conflict between food security with biofuel production. Therefore, this study focuses on a sustainable extraction of lipids from microalgae for the production of biofuel using a liquid biphasic flotation system coupled with sugaring-out method. This is the first study to combine the methods of liquid biphasic flotation system with the sugaring-out technique. It represents a holistic study of optimum and effective conditions needed to extract lipids from the system and to understand the reliability of sugar solution as the agent of cell disruption. At the 15-min flotation time, 150 g/L of fructose solution with a 1:2 mass separating agent-acetonitrile ratio successfully extracted up to 74% of lipid from Chlorella sorokiniana CY-1. Two types of fatty acid methyl esters were recovered from the study, with C5:0 being the main component extracted.

Philosophical analysis is of vital importance for addressing the controversies in science and theology. This article evaluates the analyses concerning God and the beginning of the universe offered by a number of philosophers. It is shown that, while Linford is correct in that establishing that physical reality has a finite past is not sufficient for establishing that physical reality had a beginning, the objections which Linford, Schmid, Oberle, and Wielenberg have offered against the Cosmological Argument for the existence of God can be rebutted. The examination of the objections and rebuttals demonstrates how a more careful philosophical analyses of the issues concerning the argument against infinite causal regress, personal identity, timelessness, the definition and reducibility of time, and the causal principle can contribute to the interaction between science and theology.

The Agrobacterium-mediated transient gene expression system is a rapid and efficient method for heterologous recombinant protein expression in plants. The fermentation of genetically modified Agrobacterium tumefaciens is an important step in increasing the efficiency of recombinant protein production in plants. However, the limitation of this system that makes it economically non-competitive for industrial-scale applications is the Agrobacterium suspension production cost. In this study, the utilization of sugarcane molasses as an alternative low-cost source of carbon at a concentration of 8.7 g/L and nitrogen at a concentration of 2.4 g/L for Agrobacterium cultivation was investigated. Molasses pretreatment using sulfuric acid (SA) was applied before fermentation, and it resulted in a maximum specific growth rate of 0.232 ± 0.0063 h−1 in the A. tumefaciens EHA105 culture. The supplementation of antibiotics in the molasses-based medium was shown to be unnecessary for plasmid maintenance during fermentation in both Agrobacterium strains, which helped to reduce the production cost. We evaluated recombinant protein production using an Agrobacterium culture without antibiotic supplementation in the growth media by demonstrating green fluorescent protein expression in wild-type Nicotiana benthamiana leaves. In the evaluation of the culture medium cost, the molasses-based medium cost was 6.1 times lower than that of LB. Finally, this study demonstrated that the newly developed molasses-based medium for Agrobacterium fermentation is a feasible and effective medium for transient recombinant protein production in plant tissues.

Currently, many governments are gearing up to promote the development of smart cities in their countries. A smart city is an urban area using different types of sensors to collect data, which will then be used to manage assets and resources efficiently. Through smart technology, the quality of living and performance of urban services are enhanced. Recent works addressed a set of platforms aimed to support the development of smart city applications. It seems that most of them involved dealing with collecting, managing, analyzing, and correlating data to extract new information useful to a city, but they do not integrate a diversified set of services and react to events on the fly. Moreover, the application development facilities provided by them seem to be limited and might even increase the complexity of this task. We propose an event-based architecture with components that meet important requirements for smart city platforms, supporting increased demand for scalability, flexibility, and heterogeneity in event processing. We implement such architecture and data representation models, handling different data formats, and supporting a semantics-based data model. Finally, we discuss the effectiveness of a S mart Event-based Middleware (SEMi) and present empirical results regarding a performance evaluation of SEMi.

With the proliferation of IoT applications, more and more smart, connected devices will be required to communicate with one another, operating in situations that involve diverse levels of range and cost requirements, user interactions, mobility, and energy constraints. Wireless technologies that can satisfy the aforementioned requirements will be vital to realise emerging market opportunities in the IoT sector. Bluetooth Mesh is a new wireless protocol that extends the core Bluetooth low energy (BLE) stack and promises to support reliable and scalable IoT systems where thousands of devices such as sensors, smartphones, wearables, robots, and everyday appliances operate together. In this article, we present a comprehensive discussion on current research directions and existing use cases for Bluetooth Mesh, with recommendations for best practices so that researchers and practitioners can better understand how they can use Bluetooth Mesh in IoT scenarios.

The control and monitoring of the environmental conditions in mushroom cultivation has been a challenge in the mushroom industry. Currently, research has been conducted to implement successful remote environmental monitoring, or, in some cases, remote environmental control, yet there is not yet a combination of both these systems providing live stream images or video. As a result, this research aimed to design and develop an Internet of things (IoT)-based environmental control and monitoring system for mushroom cultivation, whereby the growth conditions of the mushrooms, such as temperature, humidity, light intensity, and soil moisture level, are remotely monitored and controlled through a mobile and web application. Users would be able to visualize the growth of the mushroom remotely by video and images through the Internet. The respective sensors are implemented into the mushroom cultivation process and connected to the NodeMCU microcontroller, which collects and transfers the data to the cloud server, enabling remote access at any time through the end device with internet connection. The control algorithm regulates the equipment within the cultivational chamber autonomously, based on feedback from the sensors, in order to retain the optimum environment for the cultivation of mushrooms. The sensors were tested and compared with manual readings to ensure their accuracy. The implementation of IoT toward mushroom cultivation would greatly contribute to the advancement of the current mushroom industry which still applies the traditional cultivation approach.

Algal biodiesel is of growing interest in reducing carbon emissions to the atmosphere. The production of biodiesel is affected by many process parameters. Although many research works have been conducted, the influence of each parameter on biodiesel production is not well understood when considering a complete system. Therefore, the experimental data from literature sources related to types of algae, methanol-to-algal-oil ratio, temperature, and time on the biodiesel production rate were reviewed and introduced into a neural-network-inspired correlation (N2IC) model to study the rate of transesterification. The developed N2IC model optimized for biodiesel production is based on the studied variables, specifically reaction time, temperature, methanol-to-algal-oil ratio, and type of algae. It was found from ANN analysis that the reaction time is the most significant parameter with 87% importance, followed by temperature (85%), alcohol-to-oil-molar ratio (75%), and type of algae (62%). Using error analysis, the results from the proposed N2IC model show excellent agreement with the experimentally obtained values with an overall 5% error. The results show that the N2IC model can be utilized effectively to solve the problem of industrial biodiesel production when various operating data are readily available.

Red chili is a climacteric fruit that still undergoes respiration after harvest. During storage, it is susceptible to mechanical, physical, and physiological damage and decay incidence, therefore a method is needed to protect it so that the quality losses can be minimized. One way this can be achieved is by applying edible coatings that can be made from hydrocolloids, lipids, or composites of both, in addition to antimicrobial agents that can also be added to inhibit microbial growth. In this study, we detail the application of an edible coating made of gelatin composite from tilapia fish skin, which has a transparent color and good barrier properties against O2, CO2, and lipids. To increase its physicochemical and functional qualities, it must be modified by adding composite elements such as pectin as well as hydrophobic ingredients such as garlic essential oil. This study was conducted to determine the effect of a gelatin–pectin composite edible coating (75:25, 50:50, 25:75), which was incorporated with garlic essential oil (2% and 3%) on the physicochemical properties of red chili at room temperature (±29 °C), RH ± 69%) for 14 days. The best treatment was the 50–50% pectin–gelatin composite, which was incorporated with garlic essential oil with a concentration of 2 and 3%. This treatment provided a protective effect against changes in several physicochemical properties: inhibiting weight loss of 36.36 and 37.03%, softening of texture by 0.547 and 0.539 kg/84 mm2, maintaining acidity of 0.0087 and 0.0081%, maintaining vitamin C content of 2.237 and 2.349 mg/gr, anti-oxidant activity (IC50) 546.587 and 524.907; it also provided a protective effect on chili colors changing to red, and retains better total dissolved solid values.

The persistent fight against global environmental threats and energy catastrophe is currently a major concern for the economic development of bioenergy across the entire country. Hence, traditional fuel-based reserves are overburdened to cope with the rapid energy crisis, necessitating an urgent need for an innovative carbon-neutral green-energy resource. In order to address these critical bottlenecks, microalgae with incredible metabolic versatility have paved the way for a pivotal attention towards sustainable biofuel production. However, due to high operational costs and low lipid productivity, the microalgae-based biofuel resource is still in its infancy. As a result, this problem can be overcome by incorporating engineered microalgal strains which can pave the way for significant lipid augmentation for biofuel production. Thus, our current review depicts an in-depth understanding of a multi-omics approach to microalgae, the broad scope of self-sustaining microalgae cultivation, lipid-extraction strategies, and conversion processes to improve economic commercialization in the bioenergy framework. The present review also provides a detailed analysis of the international and national status of bioenergy development by several federal agencies.

Promising results in clinical studies have been demonstrated by the utilization of electrothermal agents (ETAs) in cancer therapy. However, a difficulty arises from the balance between facilitating the degradation of ETAs, and at the same time, increasing the electrothermal performance/stability required for highly efficient treatment. In this study, we controlled the thermal signature of the MoS2 by harnessing MoS2 nanostructures with M13 phage (MNM) via the structural assembling (hydrophobic interaction) phenomena and developed a combined PANC-1 cancer cell–MNM alternating current (AC)-stimulus framework for cancer cell ablation and electrothermal therapy. A percentage decrease in the cell viability of ~23% was achieved, as well as a degradation time of 2 weeks; a stimulus length of 100 μs was also achieved. Molecular dynamics (MD) simulations revealed the assembling kinetics in integrated M13 phage–cancer cell protein systems and the structural origin of the hydrophobic interaction-enabled increase in thermal conduction. This study not only introduced an ‘ideal’ agent that avoided the limitations of ETAs but also provided a proof-of-concept application of MoS2-based materials in efficacious cancer therapy.

Implementation of outdoor photobioreactors has been challenged by an extremely oversaturated daily peak of solar irradiance. This study aims to understand the role of column size and paranet shading as well as to investigate the most convenient light control in outdoor cyanobacterial culture. The photobioreactor (PBR) consisted of plastic columns with a diameter of 12.74 cm (PBRd-20) and 31.85 cm (PBRd-50) laid outdoors and inclined at 158.22° upwards against solar radiation, while paranet shading was provided at 0%, 50%, 70%, and 90% shading capacity. A semi-continuous culture of cyanobacterium Arthrospira (Spirulina) platensis was conducted for 6 weeks with weekly monitoring of the growth parameter as well as the proximate and pigments content, while the daily irradiance and culture maximum temperature were recorded. The result shows that the column diameter of 12.74 cm had a lethal risk of 44.7% and this decreased to 10.5% by widening the column diameter to 31.85 cm. This lethal risk can be eliminated by the application of a paranet at a 50% reduction level for the column diameter of 31.85 cm and a 70% reduction level for the column diameter of 12.74 cm. The highest culture productivity of 149.03 mg/(L·day) was achieved with a PBRd-20 with 50% shading treatment, but a PBRd-50 with 90% shading treatment led to an increase in the protein and phycocyanin content by 66.7% and 14.91%, respectively.

The cultivation of Plectonema terebrans BERC10 in wastewater and integrating the wastewater-derived biomass followed by its processing for multiple products in a biorefinery could help in achieving environmental sustainability and cost effectiveness. This study evaluated the resource recovery potential of the cyanobacterium Plectonema terebrans BERC10 from urban wastewater followed by the cascading processing of the biomass into multiple bioproducts. The annual biomass productivity ranged from 0.035–0.064 gL−1d−1 and contained 40–46% lipids and 20–38% protein. The cascading processing of the biomass resulted in multiple products, including 53 mgg−1 of high-value pigments and high-quality biodiesel in accordance with American and European standards. The pigment-free and de-fatted residual biomass was used as a sole feedstock (30–70 gL−1) to produce enzymes and mycoproteins via fungal fermentation employing Aspergillus niger and Aspergillus oryzae. Interestingly, A. oryzae produced 28 UmL−1 of α-amylase and the final residues were mycoproteins after 96 h. Furthermore, the strain removed 80–90% of total phosphorous, 90–99% of total nitrogen, and significantly lowered the COD, BOD, and TDS of urban wastewater. The data demonstrated that P. terebrans has substantial potential for resource recovery and could become a candidate for a wastewater-derived algal biorefinery.

Nowadays, since large amounts of energy are consumed for a variety of applications, more and more emphasis is placed on the conservation of energy. Recent investigations have experienced the significant advantages of using metaheuristic algorithms. Given the importance of the thermal loads’ analysis in energy-efficiency buildings, a new optimizer method, i.e., the teaching–learning based optimization (TLBO) approach, has been developed and compared with alternative techniques in the present paper to predict the heating loads (HLs). This model is applied to the adaptive neuro–fuzzy interface system (ANFIS) in order to overcome its computational deficiencies. A literature-based dataset acquired for residential buildings is used to feed these models. According to the results, all the applied models can appropriately predict and analyze the heating load pattern. Based on the value of R2 calculated for both testing and training (0.98933, 0.98931), teaching–learning-based optimization can help the adaptive neuro–fuzzy interface system to enhance the results’ correlation. Also, the high R2 value means that the model has high accuracy in the HL prediction. In addition, according to the estimated RMSE, the training error of TLBO–ANFIS in the testing and training stages was 0.07794 and 0.07984, respectively. The low value of root–mean–square error (RMSE) indicates that the TLBO–ANFIS method acts favorably in the estimation of the heating load for residential buildings.

Non-terrestrial networks (NTNs) have recently attracted elevated levels of interest in large-scale and ever-growing wireless communication networks through the utilization of flying objects, e.g., satellites and unmanned aerial vehicles/drones (UAVs). Interestingly, the applications of UAV-assisted networks are rapidly becoming an integral part of future communication services. This paper first overviews the key components of NTN while highlighting the significance of emerging UAV networks where for example, a group of UAVs can be used as nodes to exchange data packets and form a flying ad hoc network (FANET). In addition, both existing and emerging applications of the FANET are explored. Next, it provides key recent findings and the state-of-the-art of FANETs while examining various routing protocols based on cross-layer modeling. Moreover, a modeling perspective of FANETs is provided considering delay-tolerant networks (DTN) because of the intermittent nature of connectivity in low-density FANETs, where each node (or UAV) can perform store-carry-and-forward (SCF) operations. Indeed, we provide a case study of a UAV network as a DTN, referred to as DTN-assisted FANET. Furthermore, applications of machine learning (ML) in FANET are discussed. This paper ultimately foresees future research paths and problems for allowing FANET in forthcoming wireless communication networks.

Merkel cell polyomavirus (MCPyV) is the only human polyomavirus currently known to cause human cancer. MCPyV is believed to be an etiological factor in at least 80% of cases of the rare but aggressive skin malignancy Merkel cell carcinoma (MCC). In these MCPyV+ MCC tumors, clonal integration of the viral genome results in the continued expression of two viral proteins: the viral small T antigen (ST) and a truncated form of the viral large T antigen. The oncogenic potential of MCPyV and the functional properties of the viral T antigens that contribute to neoplasia are becoming increasingly well-characterized with the recent development of model systems that recapitulate the biology of MCPyV+ MCC. In this review, we summarize our understanding of MCPyV and its role in MCC, followed by the current state of both in vitro and in vivo model systems used to study MCPyV and its contribution to carcinogenesis. We also highlight the remaining challenges within the field and the major considerations related to the ongoing development of in vitro and in vivo models of MCPyV+ MCC.

With the rapid development of the economy and productivity, an increasing number of citizens are not only concerned about the nutritional value of algae as a potential new food resource but are also, in particular, paying more attention to the safety of its consumption. Many studies and reports pointed out that analyzing and solving seaweed food safety issues requires holistic and systematic consideration. The three main factors that have been found to affect the food safety of algal are physical, chemical, and microbiological hazards. At the same time, although food safety awareness among food producers and consumers has increased, foodborne diseases caused by algal food safety incidents occur frequently. It threatens the health and lives of consumers and may cause irreversible harm if treatment is not done promptly. A series of studies have also proved the idea that microbial contamination of algae is the main cause of this problem. Therefore, the rapid and efficient detection of toxic and pathogenic microbial contamination in algal products is an urgent issue that needs to be addressed. At the same time, two other factors, such as physical and chemical hazards, cannot be ignored. Nowadays, the detection techniques are mainly focused on three major hazards in traditional methods. However, especially for food microorganisms, the use of traditional microbiological control techniques is time-consuming and has limitations in terms of accuracy. In recent years, these two evaluations of microbial foodborne pathogens monitoring in the farm-to-table chain have shown more importance, especially during the COVID-19 pandemic. Meanwhile, there are also many new developments in the monitoring of heavy metals, algal toxins, and other pollutants. In the future, algal food safety risk assessment will not only focus on convenient, rapid, low-cost and high-accuracy detection but also be connected with some novel technologies, such as the Internet of Things (artificial intelligence, machine learning), biosensor, and molecular biology, to reach the purpose of simultaneous detection.

Distillation has relatively low thermodynamic efficiency, so it is a prime target for process intensification studies. The current research aims to study exergy losses in various heat-integrated distillation columns. A conventional industrial-scale i-butane/n-butane fractionator has been selected as a case study for the comparison of the performances of various heat-integrated designs. The Aspen Plus® process simulator is used to perform steady-state simulations and exergy analyses of the conventional distillation column (CDC), internally heat-integrated distillation column (iHIDiC), externally heat-integrated double distillation columns (EHIDDiC), and vapor recompression (VRC) systems. The results of these exergy analyses show that a modified VRC system (ηE = 10.69%) is the most efficient design for this separation. The exergy efficiency of the conventional VRC system is the same as that of the CDC (ηE = 9.27%). The EHIDDiC system (ηE = 9.77%) is somewhat better than the CDC, whereas iHIDiC shows poor exergy efficiency (ηE = 8.09%), even lower than the CDC.

Lipoxygenases have proven to be a potential biocatalyst for various industrial applications. However, low catalytic activity, low thermostability, and narrow range of pH stability largely limit its application. Here, a lipoxygenase (LOX) gene from Enterovibrio norvegicus DSM 15893 (EnLOX) was cloned and expressed in Escherichia coli BL21 (DE3). EnLOX showed the catalytic activity of 40.34 U mg−1 at 50 °C, pH 8.0. Notably, the enzyme showed superior thermostability, and wide pH range stability. EnLOX remained above 50% of its initial activity after heat treatment below 50 °C for 6 h, and its melting point temperature reached 78.7 °C. More than 70% of its activity was maintained after incubation at pH 5.0–9.5 and 4 °C for 10 h. In addition, EnLOX exhibited high substrate specificity towards linoleic acid, and its kinetic parameters of Vmax, Km, and Kcat values were 12.42 mmol min−1 mg−1, 3.49 μmol L−1, and 16.86 s−1, respectively. LC-MS/MS analysis indicated that EnLOX can be classified as 13-LOX, due to its ability to catalyze C18 polyunsaturated fatty acid to form 13-hydroxy fatty acid. Additionally, EnLOX could improve the farinograph characteristics and rheological properties of wheat dough. These results reveal the potential applications of EnLOX in the food industry.

Embedding ethical concepts into smart Internet-connected devices and making them behave in a more human-centred manner, i.e., ethically and in a socially acceptable manner, has received significant attention in the software industry. To make smart devices behave in more human-centered manners, it is important to develop a methodology for defining smart devices’ key roles and mapping them with socio-ethical and administrative policies. This paper proposes a policy development methodology for making smart devices more human-centred by following its four phases i.e., concept development, defining and mapping policies, implementing the processing of policies, and deploying the devices. The suggested methodology may be used in a variety of situations where smart devices interact with people. For illustration, the proposed methodology has been applied to three different settings, including a supermarket, a children’s hospital, and early learning centers, where each phase defined in the methodology has been followed. The application of the methodology to smart internet-connected devices, including robots, smart cameras, and smart speakers, has shown significant results. It has been observed that the devices behave in more human-centric ways while performing their core functions, adhering to socio-ethical policies.

In this study, reactive green 19 dye from wastewater was immobilized on the functionalized chitosan nanofiber membranes to treat soluble microbial proteins in biological wastewater. Polyacrylonitrile nanofiber membrane (PAN) was prepared by the electrospinning technique. After heat treatment, alkaline hydrolysis, and chemically grafted with chitosan to obtain modified chitosan nanofibers (P-COOH-CS), and finally immobilized with RG19 dye, dyed nanofibers were generated (P-COOH-CS-RG19). The synthesis of P-COOH-CS and P-COOH-CS-RG19 are novel materials for protein adsorption that are not deeply investigated currently, with each of the material functions based on their properties in significantly improving the adsorption efficiency. The nanofiber membrane shows good adsorption capacity and great recycling performance, while the application of chitosan and dye acts as the crosslinker in the nanofiber membrane and consists of various functional groups to enhance the adsorption of protein. The dyed nanofibers were applied for the batch adsorption of soluble protein (i.e., lysozyme), and the process parameters including chitosan’s molecular weight, coupling pH, chitosan concentration, dye pH, dye concentration, and lysozyme pH were studied. The results showed that the molecular weight of chitosan was 50 kDa, pH 5, concentration 0.5%, initial concentration of dye at 1 mg/mL dye and pH 12, lysozyme solution at 2 mg/mL at pH 8, and the maximum adsorption capacity was 1293.66 mg/g at a temperature of 318 K. Furthermore, thermodynamic, and kinetic studies suggested that the adsorption behavior of lysozyme followed the Langmuir adsorption isotherm model and the pseudo-second-order kinetic model. The optimal adsorption and desorption conditions based on batch experiments were directly applied to remove lysozyme in a continuous operation. This study demonstrated the potential of dyed nanofibers as an efficient adsorbent to remove approximately 100% of lysozyme from the simulated biological wastewater.

Helminthiasis disease is one of the most serious health problems in the world and frequently occurs in children, especially in unhygienic conditions. The manual diagnosis method is time consuming and challenging, especially when there are a large number of samples. An automated system is acknowledged as a quick and easy technique to assess helminth sample images by offering direct visibility on the computer monitor without the requirement for examination under a microscope. Thus, this paper aims to compare the human intestinal parasite ova segmentation performance between machine learning segmentation and deep learning segmentation. Four types of helminth ova are tested, which are Ascaris Lumbricoides Ova (ALO), Enterobious Vermicularis Ova (EVO), Hookworm Ova (HWO), and Trichuris Trichiura Ova (TTO). In this paper, fuzzy c-Mean (FCM) segmentation technique is used in machine learning segmentation, while convolutional neural network (CNN) segmentation technique is used for deep learning. The performance of segmentation algorithms based on FCM and CNN segmentation techniques is investigated and compared to select the best segmentation procedure for helminth ova detection. The results reveal that the accuracy obtained for each helminth species is in the range of 97% to 100% for both techniques. However, IoU analysis showed that CNN based on ResNet technique performed better than FCM for ALO, EVO, and TTO with values of 75.80%, 55.48%, and 77.06%, respectively. Therefore, segmentation through deep learning is more suitable for segmenting the human intestinal parasite ova.

Antioxidants are currently utilized to prevent the occurrence of liver cancer in non-alcoholic fatty liver disease (NAFLD) patients. Clinacanthus nutans possesses anti-oxidative and anti-inflammatory properties that could be an ideal therapy for liver problems. The objective of this study is to determine the potential antioxidative compounds from the C. nutans leaves (CNL) and stems (CNS). Chemical- and cell-based antioxidative assays were utilized to evaluate the bioactivities of CNS and CNL. The NMR metabolomics approach assisted in the identification of contributing phytocompounds. Based on DPPH and ABTS radical scavenging activities, CNL demonstrated stronger radical scavenging potential as compared to CNS. The leaf extract also recorded slightly higher reducing power properties. A HepG2 cell model system was used to investigate the ROS reduction potential of these extracts. It was shown that cells treated with CNL and CNS reduced innate ROS levels as compared to untreated controls. Interestingly, cells pre-treated with both extracts were also able to decrease ROS levels in cells induced with oxidative stress. CNL was again the better antioxidant. According to multivariate data analysis of the 1H NMR results, the main metabolites postulated to contribute to the antioxidant and hepatoprotective abilities of leaves were clinacoside B, clinacoside C and isoschaftoside, which warrants further investigation.

In recent years, pasture production changed from conventional tilled single pastures to minimum tilled mixed pastures in the Tsitsikamma region, South Africa. However, storage of soil organic matter (SOM) under minimum tilled mixed pastures is not yet quantified. This study evaluated SOM indices in the upper 60 cm soil of six-year-old mixed pastures in the Upper (UT) and Lower (LT) Tsitsikamma regions. Soil samples were collected at 0–15, 15–30, 30–45, and 45–60 cm soil layers of five farms (F1, F2, F3, F4, and F5) treated with different rates of fertilizer (NPK) alone and in combination with dairy effluent (DE) and/or poultry manure (PM). Results of this study indicated that there were no significant differences in bulk density, total nitrogen (N), and rate of potentially mineralizable N (PMN) between farms in the UT region. In the LT region, NPK, DE, and PM combinations improved soil C accumulation relative to the soil application of NPK. Higher C/N ratios in the LT region suggested adequate C for microbial energy and maintenance. Integrating manure into minimum tilled pasture mixtures as a replacement for synthetic fertilizers seems to be a feasible option to promote SOM storage, but remains only feasible by applying site-specific management strategies.

The smart grid is one of the core technologies that enable sustainable economic and social developments. In recent years, various cyber attacks have targeted smart grid systems, which have led to severe, harmful consequences. It would be challenging to build a real smart grid system for cybersecurity experimentation and validation purposes. Hence, analytical techniques, with simulations, can be considered as a practical solution to make smart grid cybersecurity experimentation possible. This paper first provides a literature review on the current state-of-the-art in smart grid attack analysis. We then apply graphical security modeling techniques to design and implement a Cyber Attack Analysis Framework for Smart Grids, named GridAttackAnalyzer. A case study with various attack scenarios involving Internet of Things (IoT) devices is conducted to validate the proposed framework and demonstrate its use. The functionality and user evaluations of GridAttackAnalyzer are also carried out, and the evaluation results show that users have a satisfying experience with the usability of GridAttackAnalyzer. Our modular and extensible framework can serve multiple purposes for research, cybersecurity training, and security evaluation in smart grids.

The first part of the current review highlights the evolutionary nuances and research hotspots in the field of nanoparticles in low carbon fuels. Our findings reveal that contribution to the field is largely driven by researchers from Asia, mainly India. Of the three biofuels under review, biodiesel seems to be well studied and developed, whereas studies regarding vegetable oils and alcohols remain relatively scarce. The second part also reviews the application of nanoparticles in biodiesel/vegetable oil/alcohol-based fuels holistically, emphasizing fuel properties and engine characteristics. The current review reveals that the overall characteristics of the low carbon fuel–diesel blends improve under the influence of nanoparticles during combustion in diesel engines. The most important aspect of nanoparticles is that they act as an oxygen buffer that provides additional oxygen molecules in the combustion chamber, promoting complete combustion and lowering unburnt emissions. Moreover, the nanoparticles used for these purposes exhibit excellent catalytic behaviour as a result of their high surface area-to-volume ratio—this leads to a reduction in exhaust pollutants and ensures an efficient and complete combustion. Beyond energy-based indicators, the exergy, economic, environmental, and sustainability aspects of the blends in diesel engines are discussed. It is observed that the performance of the diesel engine fuelled with low carbon fuels according to the second law of efficiency improves under the influence of the nano-additives. Our final part shows that despite the benefits of nanoparticles, humans and animals are under serious threats from the highly toxic nature of nanoparticles.

High energy demand from the market due to the rapid increment of the human population worldwide has urged society to explore alternatives to replace non-renewable energy. Renewable diesel produced from biomass could be the next potential energy source for its high stability, long-term storage, and comparable performance with diesel fuels. In producing renewable diesel, the application of catalyst is essential, and the catalyst support is synthesized with the catalyst to enhance the reaction rate and catalytic properties. In this review, the type of catalyst support will be reviewed along with a brief introduction to biodiesel and renewable diesel production, especially focusing on zeolites as the catalyst support. The enhancement of catalyst support will be critically discussed to improve the catalytic performance of support in renewable diesel production and important aspects such as the stability and recyclability of the supported catalyst are included. The application of the supported catalyst in increasing the selectivity and yield of renewable diesel is significant, in which the catalytic properties depend on the interaction between catalyst and catalyst support. The supported catalyst as a favorable substance to assist in enhancing renewable diesel yield could lead to a sustainable and greener future for the biofuel industry in Malaysia.

Satisfying a context consumer’s quality of context (QoC) requirements is important to context management platforms (CMPs) in order to have credibility. QoC indicates the contextual information’s quality metrics (e.g., accuracy, timeliness, completeness). The outcomes of these metrics depend on the functional and quality characteristics associated with all actors (context consumers (or) context-aware applications, CMPs, and context providers (or) IoT-data providers) in context-aware IoT environments. This survey identifies and studies such characteristics and highlights the limitations in actors’ current functionalities and QoC modelling approaches to obtain adequate QoC and improve context consumers’ quality of experience (QoE). We propose a novel concept system based on our critical analysis; this system addresses the functional limitations in existing QoC modelling approaches. Moreover, we highlight those QoC metrics affected by quality of service (QoS) metrics in CMPs. These recommendations provide CMP developers with a reference system they could incorporate, functionalities and QoS metrics to maintain in order to deliver an adequate QoC.

Ionic liquid (IL) pretreatment of lignocellulose is an efficient method for the enhancement of enzymatic saccharification. However, the remaining residues of ILs deactivate cellulase, therefore making intensive biomass washing after pretreatment necessary. This study aimed to develop the one-pot process combining IL pretreatment and enzymatic saccharification by using low-toxic choline acetate ([Ch][OAc]) and IL-tolerant bacterial cellulases. Crude cellulases produced from saline soil inhabited Bacillus sp. CBD2 and Brevibacillus sp. CBD3 were tested under the influence of 0.5–2.0 M [Ch][OAc], which showed that their activities retained at more than 95%. However, [Ch][OAc] had toxicity to CBD2 and CBD3 cultures, in which only 32.85% and 12.88% were alive at 0.5 M [Ch][OAc]. Based on the specific enzyme activities, the sugar amounts produced from one-pot processes using 1 mg of CBD2 and CBD3 were higher than that of Celluclast 1.5 L by 2.0 and 4.5 times, respectively, suggesting their potential for further application in the biorefining process of value-added products.

Electrospun polyacrylonitrile (PAN) nanofiber membrane was functionalized with chitosan and proteins for use in the treatment of dye-containing wastewater. The PAN nanofiber membrane was subjected to alkaline hydrolysis, before being grafted with chitosan and subsequently the proteins from chicken egg white. The resultant nanofiber membrane (P-COOH-CS-CEW) was comprehensively characterized using thermogravimetric analysis, Fourier-transform infrared spectroscopy, and scanning electron microscopy. The efficiency of P-COOH-CS-CEW in removing cationic dye toluidine blue O (TBO) and anionic dye acid orange 7 (AO7) in aqueous solution was evaluated. Based on the performance of model fitting, Langmuir and pseudo-second-order kinetic model could be used to describe the performance of P-COOH-CS-CEW in the removal of TBO (pH 10) and AO7 (pH 2) from the dye solutions. The adsorbed TBO and AO7 dyes can be completely desorbed by an elution solution made of 50% (v/v) ethanol and 1 M sodium chloride. After five consecutive adsorption-desorption cycles, the efficiency of dye removal by P-COOH-CS-CEW was maintained above 97%.

This study investigates the mechanical properties of aluminium 2024 (Al-2024) and aluminium 7075 (Al-7075) cold-sprayed materials and coatings for repairs. It aims to determine the acceptable data needed to meet regulatory requirement when substantiating cold spray repairs. The study focuses on repairs of non-principal structural element (PSE) structures such as skin and panels that are prone to corrosion and wear. For cold spray repair of such components, the microstructure, tensile, peel, bearing, and bending strength from the repair process and powder materials of Al-2024 and Al-7075, were identified and investigated in accordance with MIL-STD-3021. Results show an average coating porosity of

Bone supports animal bodies, is the place where blood is produced, and is essential for the immune system, among other important functions. The dominant inorganic component in bone is hydroxyapatite (Hap), the structure and dynamics of which still pose many unsolved puzzles. An updated understanding of HAp is of great significance to osteology, dentistry, and the development of artificial bone and other biomaterials. In this work, HAp nanoparticles were synthesized with the wet chemical precipitation method and their structure and morphologies were controlled by varying pH and adding fluoride ions by two different routes: (1) fluoride ions were added during synthesis, and (2) fluoride ions were introduced after the samples were synthesized by soaking the samples in solutions with fluoride ions. XRD and HRTEM were employed to confirm the composition and structure, while various multinuclear (1H, 19F, 31P) solid-state nuclear magnetic resonance (NMR) methods including 1D single pulse, cross-polarization under magic-angle spinning (CPMAS), and 2D heteronuclear correlation (HETCOR) were used to characterize the structure, morphology, and dynamics, validating the general core-shell morphology in these F-HAp samples. It was found that all hydroxide ions were substituted when the fluoride ion concentration was above 0.005 M. An NMR peak corresponding to water structure emerged and the bulk water peak was shifted upfield, indicating that fluoride substitution modifies both the crystalline core and the amorphous shell of F-HAp nanoparticles. With the second route of fluoride substitution, increases in soaking time or fluoride ion concentration could increase fluoride substitution in HAp, but could not achieve complete substitution. Finally, with 1H-31P CPMAS and HETCOR, it was established that there are two types of phosphorous, one in the crystalline core (PO43−) and the other in the amorphous shell (HPO42−). These results are valuable for clarifying the fluoride substitution mechanism in HAp in biomaterials or in organisms, and provide insights for developing next generation replacement materials for bone, tooth, or coating films, drug delivery systems, etc.

Accelerated urbanisation has replaced many natural shorelines with coastal defences, resulting in the loss of natural habitats. However, structures such as seawalls can support some biotic assemblages, albeit of lower species richness. Ecological engineering techniques such as coral transplantation can enhance biodiversity on these artificial structures, but its success is circumscribed by high costs. Little is known about the fusion of discrete coral colonies that could potentially improve coral transplantation success on seawalls, particularly for the slow-growing massive species that are generally well-adapted to living on seawalls. Here, we investigated the feasibility and cost-effectiveness of transplanting Platygyra sinensis on seawalls by comparing the survivability and growth of fragments transplanted adjoining with those transplanted further apart. Fragments (approximately 3 cm diameter; n = 24) derived from three individuals were randomly grouped into two treatments, transplanted at 0.5 cm and 5 cm apart. Fragments in the former treatment came into contact with each other after three months. We observed that in all cases, the contact zones were characterised by a border of raised skeletal ridges without tissue necrosis, often termed nonfusion (=histoincompatible fusion). The adjoining transplants showed better survival (75 vs. 43%) and grew at a rate that was significantly higher than fragments transplanted 5 cm apart (3.7 ± 1.6 vs. 0.6 ± 1.1 cm2 month−1). Our projections demonstrated the possibility of reducing transplantation cost (USD cm−2) by 48.3% through nonfusion. These findings present nonfusion as a possible strategy to increase the overall cost-effectiveness of transplanting slow-growing massive species on seawalls.

Recent scientific and technological advancements driven by the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI), distributed computing and data communication technologies have opened up a vast range of opportunities in many scientific fields—spanning from fast, reliable and efficient data communication to large-scale cloud/edge computing and intelligent big data analytics. Technological innovations and developments in these areas have also enabled many opportunities in the space industry. The successful Mars landing of NASA’s Perseverance rover on 18 February 2021 represents another giant leap for humankind in space exploration. Emerging research and developments of connectivity and computing technologies in IoT for space/non-terrestrial environments is expected to yield significant benefits in the near future. This survey paper presents a broad overview of the area and provides a look-ahead of the opportunities made possible by IoT and space-based technologies. We first survey the current developments of IoT and space industry, and identify key challenges and opportunities in these areas. We then review the state-of-the-art and discuss future opportunities for IoT developments, deployment and integration to support future endeavors in space exploration.

The most effective methods of preventing COVID-19 infection include maintaining physical distancing and wearing a face mask while in close contact with people in public places. However, densely populated areas have a greater incidence of COVID-19 dissemination, which is caused by people who do not comply with standard operating procedures (SOPs). This paper presents a prototype called PADDIE-C19 (Physical Distancing Device with Edge Computing for COVID-19) to implement the physical distancing monitoring based on a low-cost edge computing device. The PADDIE-C19 provides real-time results and responses, as well as notifications and warnings to anyone who violates the 1-m physical distance rule. In addition, PADDIE-C19 includes temperature screening using an MLX90614 thermometer and ultrasonic sensors to restrict the number of people on specified premises. The Neural Network Processor (KPU) in Grove Artificial Intelligence Hardware Attached on Top (AI HAT), an edge computing unit, is used to accelerate the neural network model on person detection and achieve up to 18 frames per second (FPS). The results show that the accuracy of person detection with Grove AI HAT could achieve 74.65% and the average absolute error between measured and actual physical distance is 8.95 cm. Furthermore, the accuracy of the MLX90614 thermometer is guaranteed to have less than 0.5 °C value difference from the more common Fluke 59 thermometer. Experimental results also proved that when cloud computing is compared to edge computing, the Grove AI HAT achieves the average performance of 18 FPS for a person detector (kmodel) with an average 56 ms execution time in different networks, regardless of the network connection type or speed.

Wax is an organic compound found on the surface of lignocellulose biomass to protect plants from physical and biological stresses in nature. With its small mass fraction in biomass, wax has been neglected from inclusion in the design of the biorefinery process. This study investigated the interfering effect of wax in three types of lignocellulosic biomass, including rice straw (RS), Napier grass (NG), and sugarcane bagasse (SB). In this study, although small fractions of wax were extracted from RS, NG, and SB at 0.57%, 0.61%, and 1.69%, respectively, dewaxing causes changes in the plant compositions and their functional groups and promotes dissociations of lignocellulose fibrils. Additionally, dewaxing of biomass samples increased reducing sugar by 1.17-, 1.04-, and 1.35-fold in RS, NG, and SB, respectively. The ethanol yield increased by 1.11-, 1.05-, and 1.23-fold after wax removal from RS, NG, and SB, respectively. The chemical composition profiles of the waxes obtained from RS, NG, and SB showed FAME, alcohol, and alkane as the major groups. According to the conversion rate of the dewaxing process and ethanol fermentation, the wax outputs of RS, NG, and SB are 5.64, 17.00, and 6.00 kg/ton, respectively. The current gasoline price is around USD 0.903 per liter, making ethanol more expensive than gasoline. Therefore, in order to reduce the cost of ethanol in the biorefinery industry, other valuable products (such as wax) should be considered for commercialization. The cost of natural wax ranges from USD 2 to 22 per kilogram, depending on the source of the extracted wax. The wax yields obtained from RS, SB, and NG have the potential to increase profits in the biorefining process and could provide an opportunity for application in a wider range of downstream industries than just biofuels.

In recent years, downstream bioprocessing industries are venturing into less tedious, simple, and high-efficiency separation by implementing advanced purification and extraction methods. This review discusses the separation of proteins, with the main focus on amylase as an enzyme from agricultural waste using conventional and advanced techniques of extraction and purification via a liquid biphasic system (LBS). In comparison to other methods, such as membrane extraction, precipitation, ultrasonication, and chromatography, the LBS stands out as an efficient, cost-effective, and adaptable developing method for protein recovery. The two-phase separation method can be water-soluble polymers, or polymer and salt, or alcohol and salt, which is a simpler and lower-cost method that can be used at a larger purification scale. The comparison of different approaches in LBS for amylase purification from agricultural waste is also included. Current technology has evolved from a simple LBS into microwave-assisted LBS, liquid biphasic flotation (LBF), thermoseparation (TMP), three-phase partitioning (TPP), ultrasound-assisted LBS, and electrically assisted LBS. pH, time, temperature, and concentration are some of the significant research parameters considered in the review of advanced techniques.

MicroRNAs (miRNAs) are important non-coding, single-stranded RNAs possessing crucial regulating roles in human body. Therefore, miRNAs have received extensive attention from various disciplines as the aberrant expression of miRNAs are tightly related to different types of diseases. Furthermore, the exceptional stability of miRNAs has presented them as biomarker with high specificity and sensitivity. However, small size, high sequence similarity, low abundance of miRNAs impose difficulty in their detection. Hence, it is of utmost importance to develop accurate and sensitive method for miRNA biosensing. Electrochemical biosensors have been demonstrated as promising solution for miRNA detection as they are highly sensitive, facile, and low-cost with ease of miniaturization. The incorporation of nanomaterials to electrochemical biosensor offers excellent prospects for converting biological recognition events to electronic signal for the development of biosensing platform with desired sensing properties due to their unique properties. This review introduces the signal amplification strategies employed in miRNA electrochemical biosensor and presents the feasibility of different strategies. The recent advances in nanomaterial-based electrochemical biosensor for the detection of miRNA were also discussed and summarized based on different types of miRNAs, opening new approaches in biological analysis and early disease diagnosis. Lastly, the challenges and future prospects are discussed.