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The availability of fossil energy is dwindling, so renewable fuels are the alternative choices, one of which is bioethanol. To increase the purity of the ethanol produced via the fermentation process, activated carbon (AC) was made from durian (Durio zibethinus) peel. The steps for making AC consist of carbonization (300 °C and 400 °C), chemical activation using phosphoric acid (10–40%), pyrolysis (700 °C and 800 °C), and neutralization. The results showed that the maximum surface area (326.72 m2/g) was obtained from 400 °C carbonization, 800 °C pyrolysis, and activation using a 40% phosphoric acid solution. Other characteristics are the surface area of 326.72 m2/g, pore radius of 1.04 nm, and total pore volume of 0.17 cc/g with phosphate residue in the form a P2O5 molecule of 3.47% by weight, with COOH, OH, CO, C=C, C=O, P-OC, and Fe-O groups with wavenumbers (cm−1), respectively, of 3836, 3225, 2103, 1555, 1143, and 494. The AC also demonstrated the highest number of carbon (86.41%) upon detection using EDX, while XRF analysis verified an average carbon content of 94.45 wt%. The highest ethanol adsorption efficiency (%) and the lowest yield (%) of AC (%) were 90.01 ± 0.00 and 23.26 ± 0.01. This study shows that durian peel has great potential as the raw material for the activated carbon manufacture of ethanol adsorbents.

Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average F1 score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.

Travel time is a measure of time taken to travel from one place to another. Global Positioning System (GPS) navigation applications such as Waze and Google Maps are easily accessible presently and allow users to plan a route based on travel time from one place to another. However, these applications can only estimate general travel time based on a vehicle’s total distance and average safe speed without considering route curvature. A parametric cubic curve has shown a potential result in travel-time estimation through geometric properties. In this paper, travel time has been estimated using the curvature value obtained from the Hermite Interpolation curve fitted to each section of the selected road. Design speed is determined from the curvature value, and thus an algorithm for travel-time estimation incorporating initial driving information is developed. The proposed method’s accuracy was compared to the existing method’s accuracy using a real-life driving test. This comparison demonstrated that the proposed method estimates travel time more accurately than Google Maps and Waze. Future study can further improve the estimation by embedding traffic data into the algorithm.

The sustainability, performance, and cost of production in the plywood industry depend on wood adhesives and the hot-pressing process. In this study, a cold-setting plywood adhesive was developed based on polyvinyl alcohol (PVOH), high-purity lignin, and hexamine. The influence of lignin content (10%, 15%, and 20%) and cold-pressing time (3, 6, 12, and 24 h) on cohesion, adhesion, and formaldehyde emission of plywood were investigated through physical, chemical, thermal, and mechanical analyses. The increased lignin addition level lowered the solids content, which resulted in reduced average viscosity of the adhesive. As a result, the cohesion strength of the adhesive formulation with 10% lignin addition was greater than those of 15% and 20% lignin content. Markedly, the adhesive formulation containing a 15% lignin addition level exhibited superior thermo-mechanical properties than the blends with 10% and 20% lignin content. This study showed that 10% and 15% lignin content in the adhesive resulted in better cohesion strength than that with 20% lignin content. However, statistical analysis revealed that the addition of 20% lignin in the adhesive and using a cold-pressing time of 24 h could produce plywood that was comparable to the control polyurethane resins, i.e., dry tensile shear strength (TSS) value of 0.95 MPa, modulus of rupture (MOR) ranging from 35.8 MPa, modulus of elasticity (MOE) values varying from 3980 MPa, and close-to-zero formaldehyde emission (FE) of 0.1 mg/L, which meets the strictest emission standards. This study demonstrated the feasibility of fabricating eco-friendly plywood bonded with PVOH–lignin–hexamine-based adhesive using cold pressing as an alternative to conventional plywood.

Japonica rice has been considerably impacted from climate change, mainly regarding temperature variations. Adjusting the crop management practices based on the assessment of adaptability mechanisms to take full advantage of climate resources during the growing season is an important technique for japonica rice adaptation to climate changed conditions. Research based on the adaptability mechanisms of japonica rice to temperature and other environmental variables has theoretical and practical significance to constitute a theoretical foundation for sustainable japonica rice production system. A contrived study was arranged with method of replacing time with space having four different japonica cultivars namely Longdao-18, Longdao-21, Longjing-21, and Suijing-18, and carried out in Harbin and Qiqihar during the years 2017–2019 to confer with the adaptability mechanisms in terms of growth, yield and quality. The formation of the grain-filling material for superior and inferior grains was mainly in the middle phase which shared nearly 60% of whole grain-filling process. Maximum yield was noticed in Longdao-18 at Harbin and Qiqihar which was 9500 and 13,250 kg/ha, respectively. The yield contributing components fertile tillers, number of grains per panicle, and 1000-grain weight were higher at Qiqihar; therefore, there was more potential to get higher yield. The data for grain-filling components demonstrated that the filling intensity and duration at Qiqihar was contributive to increase the grain yield, whereas the limiting agents to limit yield at Harbin were the dry weights of inferior grains. The varietal differences in duration and time of day of anthesis were small. Across all cultivars and both study sites, nearly 85% of the variation of the maximum time of anthesis could be justified with mean atmospheric temperature especially mean minimum temperature. Mean onset of anthesis was earliest in Longdao-21 at Harbin, whereas it was latest in Longdao-18 at Qiqihar. The maximum time to end anthesis and the longest duration of anthesis were taken by Longdao-18, i.e., 9.0 hasr and 4.2 h, respectively. Chalkiness and brown rice percentages were elevated at Qiqihar showing Harbin produced good quality rice. This study investigated the adaptability mechanisms of japonica rice under varying temperature conditions to distinguish the stress tolerance features for future sustainability and profitability in NEC. It was concluded that there is an adaptive value for anthesis especially regarding Tmin and, moreover, earlier transplantation may produce tall plants. The results demonstrated that high temperature at the onset of anthesis at the start of the day enhanced the escape from high temperature later during the day. Early transplantation is recommended in NEC because earlier anthesis during humid days rendered for potential escape from high ambient temperature later during that day. Temperature influenced japonica rice significantly and coherently, whereas the influence of growing season precipitation was not significant. Daily mean sunshine influenced the japonica rice significantly, but the impact was less spatially coherent. The results foregrounded the response of the japonica rice to external driving factors focusing climate, but ignored socioeconomic suggesting emphasis on both driving factors to target future research and render important insights into how japonica rice can adapt in mid-high-latitude regions.

Falls are the main cause of susceptibility to severe injuries in many humans, especially for older adults aged 65 and over. Typically, falls are being unnoticed and interpreted as a mere inevitable accident. Various wearable fall warning devices have been created recently for older people. However, most of these devices are dependent on local data processing. Various algorithms are used in wearable sensors to track a real-time fall effectively, which focuses on fall detection via fuzzy-as-a-service based on IEEE 1855–2016, Java Fuzzy Markup Language (FML) and service-oriented architecture. Moreover, several approaches are used to detect a fall using machine learning techniques via human movement positional data to avert any accidents. For fuzzy logic web services, analysis is performed using wearable accelerometer and gyroscope sensors, whereas in machine learning techniques, k-NN, decision tree, random forest and extreme gradient boost are used to differentiate between a fall and non-fall. This study aims to carry out a comparative analysis of real-time fall detection using fuzzy logic web services and machine learning techniques and aims to determine which one is better for real-time fall detection. Research findings exhibit that the proposed fuzzy-as-a-service could easily differentiate between fall and non-fall occurrences in a real-time environment with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively, while the random forest algorithm of machine learning achieved 99.19%, 98.53% and 99.63%, respectively.

Colonic diverticular disease, especially diverticulitis constitutes a major cause of hospitalization and an economic burden in developed countries. Proton pump inhibitors (PPIs) are among the commonest drugs used to treat several diseases affecting the upper gastrointestinal tract. A few studies have reported that the use of Proton Pump Inhibitors PPIs caused dysbiosis. In this study, we searched for a relationship between PPI use and the onset and severity of diverticulitis in patients with colonic diverticulosis. In a retrospective study, patients who were hospitalized for documented diverticulitis were enrolled as cases and compared with a control group of patients with uncomplicated diverticulosis. Overall, 613 patients who had a diagnosis of diverticulosis were included in the study, 217 of whom had diverticulitis. After multivariate analysis, the non-modifiable risk factors associated with diverticulitis included: age (p < 0.0001), hypertension (p < 0.0001), chronic renal failure (p = 0.007), diabetes mellitus (p < 0.0001), and left colon location (p = 0.02). However, among the modifiable factors, only PPI use (p < 0.0001) showed a significant association. Advanced disease severity (according to Hinchey classification of diverticulitis stages II–IV) was associated with aspirin use (p = 0.0004) and pan-colonic location (p = 0.02). PPI use was the only modifiable factor significantly associated with diverticulitis, but not with its severity, among patients with diverticulosis. This observation should be confirmed in future multicenter prospective studies.

This paper presents anomaly detection in activities of daily living based on entropy measures. It is shown that the proposed approach will identify anomalies when there are visitors representing a multi-occupant environment. Residents often receive visits from family members or health care workers. Therefore, the residents’ activity is expected to be different when there is a visitor, which could be considered as an abnormal activity pattern. Identifying anomalies is essential for healthcare management, as this will enable action to avoid prospective problems early and to improve and support residents’ ability to live safely and independently in their own homes. Entropy measure analysis is an established method to detect disorder or irregularities in many applications: however, this has rarely been applied in the context of activities of daily living. An experimental evaluation is conducted to detect anomalies obtained from a real home environment. Experimental results are presented to demonstrate the effectiveness of the entropy measures employed in detecting anomalies in the resident’s activity and identifying visiting times in the same environment.

Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors. Research related to HAR has devoted particular attention to monitoring and recognizing the human activities of a single occupant in a home environment, in which it is assumed that only one person is present at any given time. Recognition of the activities is then used to identify any abnormalities within the routine activities of daily living. Despite the assumption in the published literature, living environments are commonly occupied by more than one person and/or accompanied by pet animals. In this paper, a novel method based on different entropy measures, including Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn), is explored to detect and identify a visitor in a home environment. The research has mainly focused on when another individual visits the main occupier, and it is, therefore, not possible to distinguish between their movement activities. The goal of this research is to assess whether entropy measures can be used to detect and identify the visitor in a home environment. Once the presence of the main occupier is distinguished from others, the existing activity recognition and abnormality detection processes could be applied for the main occupier. The proposed method is tested and validated using two different datasets. The results obtained from the experiments show that the proposed method could be used to detect and identify a visitor in a home environment with a high degree of accuracy based on the data collected from the occupancy sensors.

Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques.

In this paper, a novel approach to the container loading problem using a spatial entropy measure to bias a Monte Carlo Tree Search is proposed. The proposed algorithm generates layouts that achieve the goals of both fitting a constrained space and also having “consistency” or neatness that enables forklift truck drivers to apply them easily to real shipping containers loaded from one end. Three algorithms are analysed. The first is a basic Monte Carlo Tree Search, driven only by the principle of minimising the length of container that is occupied. The second is an algorithm that uses the proposed entropy measure to drive an otherwise random process. The third algorithm combines these two principles and produces superior results to either. These algorithms are then compared to a classical deterministic algorithm. It is shown that where the classical algorithm fails, the entropy-driven algorithms are still capable of providing good results in a short computational time.

Physical activities have tremendous benefit to older adults. A report from the World Health Organization has mentioned that lack of physical activity contributed to around 3.2 million premature deaths annually worldwide. Research also shows that regular exercise helps the older adults by improving their physical fitness, immune system, sleep and stress levels, not to mention the countless health problems it reduces such as diabetes, cardiovascular disease, dementia, obesity, joint pains, etc. The research reported in this paper is introducing a Socially Assistive Robot (SAR) that will engage, coach, assess and motivate the older adults in physical exercises that are recommended by the National Health Services (NHS) in the UK. With the rise in the population of older adults, which is expected to triple by 2050, this SAR will aim to improve the quality of life for a significant proportion of the population. To assess the proposed robot exercise trainer, user’s observational evaluation with 17 participants is conducted. Participants are generally happy with the proposed platform as a mean of encouraging them to do regular exercise correctly.

Human activity recognition algorithms based on information obtained from wearable sensors are successfully applied in detecting many basic activities. Identified activities with time-stationary features are characterised inside a predefined temporal window by using different machine learning algorithms on extracted features from the measured data. Better accuracy, precision and recall levels could be achieved by combining the information from different sensors. However, detecting short and sporadic human movements, gestures and actions is still a challenging task. In this paper, a novel algorithm to detect human basic movements from wearable measured data is proposed and evaluated. The proposed algorithm is designed to minimise computational requirements while achieving acceptable accuracy levels based on characterising some particular points in the temporal series obtained from a single sensor. The underlying idea is that this algorithm would be implemented in the sensor device in order to pre-process the sensed data stream before sending the information to a central point combining the information from different sensors to improve accuracy levels. Intra- and inter-person validation is used for two particular cases: single step detection and fall detection and classification using a single tri-axial accelerometer. Relevant results for the above cases and pertinent conclusions are also presented.