Search

Abstract Electric vehicles (EVs) and small photovoltaic (PV) installations advance residential power grids by lowering charging costs and fostering eco‐friendly operations. Yet, the variable nature of EV charging presents challenges to grid reliability. This research introduces a Monte Carlo‐based simulation for predicting EV charging loads and a systematic charging method that integrates a ‘green electricity’ pricing scheme with a joint optimization model for PV and EV management. By applying an improved ant lion optimizer (IALO) algorithm enriched with differential evolution features, an optimization strategy that markedly enhances grid performance is devised. In a park scenario, this ‘green electricity’ model reduced the mean square error of EV charging load by 11.82%, smoothed the power load curve, and improved grid stability. When compared with particle swarm optimization (PSO) and grey wolf optimizer (GWO) algorithms, the IALO algorithm boosted overall revenue by 16.8% and 12.8%, increased PV utilization by 162.3% and 37.1%, and significantly cut carbon emissions by 159.6% and 31.6%, respectively. These outcomes affirm the financial, environmental, and functional benefits of our proposed approach.

Presently, the application of big data in the construction industry encounters numerous obstacles and involves diverse stakeholders, with the intricate network of relationships between these factors and stakeholders remaining unclear. Investigating stakeholders’ management priorities and collaborative patterns can facilitate the development of BDAC. Therefore, this study employs a two-mode social network analysis to explore stakeholders’ power and attitudes toward the factors of BDAC. Firstly, the initial list of stakeholders and factors is identified based on the literature and expert interviews, followed by a questionnaire to establish stakeholder–factor relationships and construct the network. Subsequently, the adjacency matrix, centrality, core–periphery structure, and hierarchical cluster are adopted to analyze the network. The results found that (1) technical factors need to be addressed by all stakeholders due to complexity; (2) due to the low resource similarity of factors and low power similarity of stakeholders, all stakeholders should be involved in the collaboration; and (3) government, developers, and consultants, as core stakeholders, exhibit a proactive inclination towards collaborative efforts in addressing central factors, and can coordinate with peripheral stakeholders. Consequently, this study establishes a stakeholder collaboration model centered on the government–developer–consultant trio, which provides clear responsibility allocation and strategic guidance for fostering long-term, effective collaboration in BDAC.

Objective: To study the best purification method of Perilla flavone (PF), and to investigate its protective effect and potential mechanism on Paracetamol (APAP) - induced acute liver injury. Methods: Single factor experiments were used to determine the optimal conditions for the purification of PF by macroporous resin. BALB/c male mice were randomly divided into 6 groups, blank control group (CON), model group (MOD), diphenylene dibenzoate group (DDB), low dose group of PF (L-PF, 12.5 mg/kg), middle dose group of PF (M-PF, 25 mg/kg), and high dose group of PF (H-PF, 50 mg/kg). DDB and PF were given by continuous intragastric administration for 15 days according to the dosage. After the last administration for 1 h, APAP was injected intraperitoneally to establish liver injury model in each group except CON group. The serum levels of AST and ALT of mice were determined, and the levels of MDA, SOD, GSH, and GSH-PX in the liver of mice were tested. HE staining of the livers was performed to observe pathological changes. The expressions of Nrf2, Keap1 and HO-1 proteins in the livers were detected by Western Blot. Results: The optimal purification conditions were as follows: 10 g of D101 resin, 2.0 mg/mL of up-sampling solution, up-sampling volume of 30 mL, 50 mL of 80% ethanol solution elution. After purification, the flavonoid content was 70.32%±1.49%. Compared with CON group, the expressions of AST and ALT in the serum were significantly higher in MOD group (P

Abstract Background Computed tomography (CT) is the usual modality for diagnosing stroke, but conventional CT angiography reconstructions have limitations. Methods A phantom with tubes of known diameters and wall thickness was scanned for wall detectability, wall thickness, and contrast-to-noise ratio (CNR) on conventional and spectral black-blood (SBB) images. The clinical study included 34 stroke patients. Diagnostic certainty and conspicuity of normal/abnormal intracranial vessels using SBB were compared to conventional. Sensitivity/specificity/accuracy of SBB and conventional were compared for plaque detectability. CNR of the wall/lumen and quantitative comparison of remodeling index, plaque burden, and eccentricity were obtained for SBB imaging and high-resolution magnetic resonance imaging (hrMRI). Results The phantom study showed improved detectability of tube walls using SBB (108/108, 100% versus conventional 81/108, 75%, p

Peanuts play a crucial role as an oil crop, serving not only as a primary source of edible oil but also offering ample protein and vitamins for human consumption. The lateral branch angle of peanuts is the angle between the main stem and the first pair of lateral branches, which is an important agronomic trait of peanuts, significantly impacts the peg penetration into the soil, plant growth, and pod yield. It is closely intertwined with planting density, cultivation techniques, and mechanized harvesting methods. Therefore, the lateral branch angle holds substantial importance in enhancing peanut yield and facilitating mechanization. In order to conduct in-depth research on the lateral branch angle of peanuts, this research is grounded in the QTL mapping findings, specifically focusing on the QTL qGH associated with the lateral branch angle of peanuts located on chromosome B05 (142610834-146688220). By using Jihua 5 and PZ42 for backcrossing, a BC1F2 population comprising 8000 individual plants was established. Molecular markers were then developed to screen the offspring plants, recombine individual plants, conduct fine mapping. he results showed that using the phenotype and genotype of 464 recombinant individual plants selected from 8000 offspring, narrow down the localization interval to 48kb, and designate it as qLBA. The gene Arahy.C4FM6Y, responsible for the F-Box protein, was identified within qLBA through screening. Real-time quantitative detection of Arahy.C4FM6Y was carried out using M130 and Jihua 5, revealing that the expression level of Arahy.C4FM6Y at the junction of the main stem and the first lateral branch of peanuts was lower in M130 compared to Jihua 5 during the growth period of the first lateral branch from 1 to 10 centimeters. Consequently, Arahy.C4FM6Y emerges as a gene that restrains the increase in the angle of the first lateral branch in peanuts. This investigation offers novel genetic reservoirs for peanut plant type breeding and furnishes a theoretical foundation for molecular marker-assisted peanut breeding.

Cs0.33WO3 (CWO) is a widely used inorganic material in window films and glass coatings, known for its excellent near-infrared radiation (NIR) blocking property and high visible light transmittance (Tvis). However, the stability of NIR blocking and the optical properties of CWO in the process of application is an urgent and important problem, because significant changes in optical results can impact the related products, such as window films, glass coatings, and so on. In this paper, the particle sizes and optical properties of CWO are tested to study the light stability and their relative relations. The results indicate that CWO particle sizes between 130 nm and 100 nm (D90, the point where 90% of the particles have a diameter smaller than the specified value) exhibit high stability in terms of NIR blocking and visible light transmittance (Tvis). CWO particles with D90 < 100 nm experience a greater reduction in NIR blocking, though this ability significantly recovers upon exposure to sunlight, making these coatings particularly suitable for use in tropical and subtropical climates.

This research focuses on achieving sustainable development in residential buildings with energy use. Under the influence of the energy crisis and related problems, research on residential buildings for less energy use has great potential. The literature review, according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses, and including VOSviewer analysis, shows the research is increasing and meaningful. Solar Decathlon buildings are used as the main objects in this research. The fifth Solar Decathlon Europe energy use technologies are examined through onsite investigation and online searching. The Analytic Hierarchy Process method for multi-criteria decision analysis is used for sustainability assessment. Moreover, the Ladybug and ClimateStudio plugins simulated respectively the annual solar radiation and the best angle for receiving it. The main findings show that 34 kinds of technologies used in these buildings can be classified into two categories in three directions. Passive technologies should be applied and prioritized, but generating renewable energy is also important. Some infrequently used technologies are not insignificant. The research shows that the combination of technologies decides sustainability performance, but the quantity used does not. Furthermore, energy use also needs to be balanced and coordinated in combination with architectural aesthetics. This research on energy use in residential buildings is beneficial for achieving sustainable development.

Abstract To address the balancing challenge of real‐time power demand and supply in power systems caused by unpredictable output fluctuations of renewable energies, collaborative and effective scheduling of integrated energy systems is proposed with consideration for carbon restrictions. The model incorporates various energy resources, including cold, heat, electricity, and gas, and overcomes the barriers among different levels of heterogeneous energy grid systems. This makes it possible to coordinate the vertical complementarity of source‐grid‐load‐storage and the horizontal complementarity of multi‐energy while taking into account investment costs and carbon trading to achieve the economic operation of the system. This analysis shows that the proposed model can dissipate the excess of new energy generation from the grid. and achieve a balance between supply and demand, while effectively reducing the economic operation cost of the whole system.

Perception with electric neuroprostheses is sometimes expected to be simulated using properly designed physical stimuli. Here, we examined a new acoustic vocoder model for electric hearing with cochlear implants (CIs) and hypothesized that comparable speech encoding can lead to comparable perceptual patterns for CI and normal hearing (NH) listeners. Speech signals were encoded using FFT-based signal processing stages including band-pass filtering, temporal envelope extraction, maxima selection, and amplitude compression and quantization. These stages were specifically implemented in the same manner by an Advanced Combination Encoder (ACE) strategy in CI processors and Gaussian-enveloped Tones (GET) or Noise (GEN) vocoders for NH. Adaptive speech reception thresholds (SRTs) in noise were measured using four Mandarin sentence corpora. Initial consonant (11 monosyllables) and final vowel (20 monosyllables) recognition were also measured. NaÏve NH listeners were tested using vocoded speech with the proposed GET/GEN vocoders as well as conventional vocoders (controls). Experienced CI listeners were tested using their daily-used processors. Results showed that: 1) there was a significant training effect on GET vocoded speech perception; 2) the GEN vocoded scores (SRTs with four corpora and consonant and vowel recognition scores) as well as the phoneme-level confusion pattern matched with the CI scores better than controls. The findings suggest that the same signal encoding implementations may lead to similar perceptual patterns simultaneously in multiple perception tasks. This study highlights the importance of faithfully replicating all signal processing stages in the modeling of perceptual patterns in sensory neuroprostheses. This approach has the potential to enhance our understanding of CI perception and accelerate the engineering of prosthetic interventions. The GET/GEN MATLAB program is freely available athttps://github.com/BetterCI/GETVocoder.

Electric power audit text classification is one of the important research problem in electric power systems. Recently, kinds of automatic classification methods for these texts based on machine learning or deep learning models have been applied. At present, the development of computing technology makes “pre-training and fine-tuning” the newest paradigm of text classification, which achieves better results than previous fully-supervised models. Based on pre-training theory, domain-related pre-training tasks can enhance the performance of downstream tasks in the specific domain. However, existing pre-training models usually use general corpus for pre-training, and do not use texts related to the field of electric power, especially electric power audit texts. This results in that the model does not learn too much electric-power-related morphology or semantics in the pre-training stage, so that less information can be used in the fine-tuning stage. Based on the research status, in this paper, we propose EPAT-BERT, a BERT-based model pre-trained by two-granularity pre-training tasks: word-level masked language model and entity-level masked language model. These two tasks predict word and entity in electric-power-related texts to learn abundant morphology and semantics about electric power. We then fine-tune EPAT-BERT for electric power audit text classification task. The experimental results show that, compared with fully supervised machine learning models, neural network models, and general pre-trained language models, EPAT-BERT can significantly outperform existing models in a variety of evaluation metrics. Therefore, EPAT-BERT can be further applied to electric power audit text classification. We also conduct ablation studies to prove the effectiveness of each component in EPAT-BERT to further illustrate our motivations.

The temporal-limits-encoder (TLE) strategy has been proposed to enhance the representation of temporal fine structure (TFS) in cochlear implants (CIs), which is vital for many aspects of sound perception but is typically discarded by most modern CI strategies. TLE works by computing an envelope modulator that is within the temporal pitch limits of CI electric hearing. This paper examines the TFS information encoded by TLE and evaluates the salience and usefulness of this information in CI users. Two experiments were conducted to compare pitch perception performance of TLE versus the widely-used Advanced Combinational Encoder (ACE) strategy. Experiment 1 investigated whether TLE processing improved pitch discrimination compared to ACE. Experiment 2 parametrically examined the effect of changing the lower frequency limit of the TLE modulator on pitch ranking. In both experiments, F0 difference limens were measured with synthetic harmonic complex tones using an adaptive procedure. Signal analysis of the outputs of TLE and ACE strategies showed that TLE introduces important temporal pitch cues that are not available with ACE. Results showed an improvement in pitch discrimination with TLE when the acoustic input had a lower F0 frequency. No significant effect of lower frequency limit was observed for pitch ranking, though a lower limit did tend to provide better outcomes. These results suggest that the envelope modulation introduced by TLE can improve pitch perception for CI listeners.

Abstract Background Rice (Oryza sativa L.) is a food crop for humans worldwide. However, temperature has an effect during the vegetative and reproductive stages. In high-latitude regions where rice is cultivated, cold stress is a major cause of yield loss and plant death. Research has identified a group of plant-specific transcription factors, DNA binding with one zinc fingers (DOFs), with a diverse range of functions, including stress signaling and stress response during plant growth. The aim of this study was to identify Dof genes in two rice subspecies, indica and japonica, and screen for Dof genes that may be involved in cold tolerance during plant growth. Results A total of 30 rice Dofs (OsDofs) were identified using bioinformatics and genome-wide analyses and phylogenetically analyzed. The 30 OsDOFs were classified into six subfamilies, and 24 motifs were identified based on protein sequence alignment. The chromosome locations of OsDofs were determined and nine gene duplication events were identified. A joint phylogenetic analysis was performed on DOF protein sequences obtained from four monocotyledon species to examine the evolutionary relationship of DOF proteins. Expression profiling of OsDofs from two japonica cultivars (Longdao5, which is cold-tolerant, and Longjing11, which is cold-sensitive) revealed that OsDof1 and OsDof19 are cold-inducible genes. We examined the seed setting rates in OsDof1- and OsDof19-overexpression and RNAi lines and found that OsDof1 showed a response to cold stress. Conclusions Our investigation identified OsDof1 as a potential target for genetic breeding of rice with enhanced cold tolerance.

The WRKY transcription factor (TF) belongs to one of the major plant protein superfamilies. The WRKY TF gene family plays an important role in the regulation of transcriptional reprogramming associated with plant stress responses. Change in the expression patterns of WRKY genes or the modifications in their action; participate in the elaboration of numerous signaling pathways and regulatory networks. WRKY proteins contribute to plant growth, for example, gamete formation, seed germination, post-germination growth, stem elongation, root hair growth, leaf senescence, flowering time, and plant height. Moreover, they play a key role in many types of environmental signals, including drought, temperature, salinity, cold, and biotic stresses. This review summarizes the current progress made in unraveling the functions of numerous WRKY TFs under drought, salinity, temperature, and cold stresses as well as their role in plant growth and development.

Despite pitch being considered the primary cue for discriminating lexical tones, there are secondary cues such as loudness contour and duration, which may allow some cochlear implant (CI) tone discrimination even with severely degraded pitch cues. To isolate pitch cues from other cues, we developed a new disyllabic word stimulus set (Di) whose primary (pitch) and secondary (loudness) cue varied independently. This Di set consists of 270 disyllabic words, each having a distinct meaning depending on the perceived tone. Thus, listeners who hear the primary pitch cue clearly may hear a different meaning from listeners who struggle with the pitch cue and must rely on the secondary loudness contour. A lexical tone recognition experiment was conducted, which compared Di with a monosyllabic set of natural recordings. Seventeen CI users and eight normal-hearing (NH) listeners took part in the experiment. Results showed that CI users had poorer pitch cues encoding and their tone recognition performance was significantly influenced by the “missing” or “confusing” secondary cues with the Di corpus. The pitch-contour-based tone recognition is still far from satisfactory for CI users compared to NH listeners, even if some appear to integrate multiple cues to achieve high scores. This disyllabic corpus could be used to examine the performance of pitch recognition of CI users and the effectiveness of pitch cue enhancement based Mandarin tone enhancement strategies. The Di corpus is freely available online: https://github.com/BetterCI/DiTone.

Climate change and population aging are two of the most important global health challenges in this century. A 2020 study by the Environmental Protection Agency showed that average people, particularly older adults, spent 90% of their time at home. This is even more evident during the coronavirus disease 2019 (COVID-19) pandemic. Home-based care models have become a new trend. The health and comfort of the living environment profoundly impacts the wellbeing of older adults. Therefore, research on the physical environment of the family wards has become an inevitable part of promoting the health of older adults; however, current research is still lacking. Based on the study and analysis of continuous monitoring data related to elements of the physical environment (thermal comfort, acoustic quality, lighting quality, and indoor air quality) of family wards, this paper explores the living behaviors of the participants in this environmental research (open or closed windows, air conditioning, artificial lighting, and television) on the indoor physical environment. (1) While referring to the requirements of international standards for an indoor aging-friendly physical environment, we also discuss and analyze the physical environment parameter values according to Chinese standards. (2) People's life behaviors have different degrees of influence on the elements of indoor physical environments. For example, opening doors and windows can alleviate the adverse effects of indoor environmental quality on the human body better than simply turning on the air conditioner. (3) Owing to the decline in physical function, older adults need special care. Studying the status quo of physical environmental elements and proposing suitable environmental improvement measures for aging are of great significance. (4) This research aims to address global warming and severe aging and to contribute to sustainable environmental development.

Climate change has reduced the comfort of community environments, and there is an urgent need to improve the health and well-being of low-income residents through design and technical measures. Therefore, this paper conducts research in the context of an ongoing social housing renovation project in Aosta, Italy, in a cold winter and hot summer Alpine environment. The study combined interviews, field measurements, and multiple software simulations to analyze the home of an older adult experiencing energy deprivation. The study found that the indoor acoustic environment quality meets the requirements of various sound-related standards. Still, the lighting and thermal environment must be designed to reduce glare and western sun exposure, and the air quality could improve. Residents’ demand for renovation is low technology, low cost, and high comfort. Therefore, suggestions for combining active and passive transformation measures and maximizing the use of climate and resources are proposed. The lighting and thermal environment are optimized based on the green wisdom of the Haylofts building of the Walser family in the Alps: increase ventilation and reduce indoor air age to improve air quality. Overall, a comprehensive assessment of extreme climatic conditions facilitates the quantitative and qualitative study and control of social housing environments, improves occupant comfort, and decarbonizes such social building stock.

Priming is used as a method to improve plant growth and alleviate the detrimental effects of pathogens. The present study was conducted to evaluate the effects of different priming methods in the context of resistance to Aspergillus niger in wheat (Triticum aestivum L.). Here, we show that different priming treatments—viz., hydropriming, osmotic priming, halopriming, and hormonal priming techniques can induce disease resistance by improving the biochemical contents of wheat, including chlorophyll, protein, proline, and sugar. In addition, physiological parameters—such as root length, shoot length, fresh and dry root/shoot ratios, and relative water content were positively affected by these priming methods. In essence, hydropriming and osmotic priming treatments were found to be more potent for enhancing wheat biochemical contents, along with all the physiological parameters, and for reducing disease severity. Hydropriming and osmotic priming significantly decreased disease severity, by 70.59–75.00% and 64.71–88.33%, respectively. RT-PCR and quantitative real-time PCR analyses of potentially important pathogenesis-related (PR)-protein genes (Thaumatin-like protein (TLP), chitinase, and β-1,3-glucanase) in primed plants were evaluated: β-1,3-glucanase was most highly expressed in all primed plants; Chitinase and TLP exhibited higher expression in hormonal-, halo-, osmotic-, and hydro-primed plants, respectively. These results suggest that the higher expression of β-1,3-glucanase, TLP, and chitinase after hydropriming and osmotic priming may increase disease resistance in wheat. Our study demonstrates the greater potential of hydropriming and osmotic priming for alleviating stress caused by A. niger inoculation, and enhancing resistance to it, in addition to significantly improving plant growth. Thus, these priming methods could be beneficial for better plant growth and disease resistance in other plants.

In the face of the dual crisis of energy shortages and global warming, the vigorous development of renewable energy represented by wind-solar energy is a significant approach towards achieving energy transition, carbon peaking, and carbon neutrality goals. Targeting the park-level integrated energy system (PIES) with high penetration of wind-solar energy, we propose a day-ahead dispatching strategy that takes into account the flexible supply and the reward-punishment ladder-type carbon trading mechanism (RPLTCTM). Firstly, RPLTCTM and carbon capture equipment (CCE) are considered in the dispatching model, and the mechanism of coordinated operation of CCE and RPLTCTM is explored to further improve the system’s ability to restrain carbon emissions. Secondly, power-based flexibility indicators (PFIs) are adopted to quantitatively evaluate the flexibility supply, and based on the load demand response characteristics, the dispatchable resources on the load side are guided to improve the system’s operation flexibility. On this basis, a multi-objective optimal dispatching model that takes into account the carbon emission cost, energy cost, and flexibility supply are constructed, and the original problem is transformed into a mixed-integer single-objective linear problem through mathematical equivalence and flexibility cost. Finally, simulation examples validate that the economy, flexibility, and low-carbon level of the dispatching plan can be synergistically improved by the proposed strategy.

The cochlea plays a key role in the transmission from acoustic vibration to neural stimulation upon which the brain perceives the sound. A cochlear implant (CI) is an auditory prosthesis to replace the damaged cochlear hair cells to achieve acoustic-to-neural conversion. However, the CI is a very coarse bionic imitation of the normal cochlea. The highly resolved time-frequency-intensity information transmitted by the normal cochlea, which is vital to high-quality auditory perception such as speech perception in challenging environments, cannot be guaranteed by CIs. Although CI recipients with state-of-the-art commercial CI devices achieve good speech perception in quiet backgrounds, they usually suffer from poor speech perception in noisy environments. Therefore, noise suppression or speech enhancement (SE) is one of the most important technologies for CI. In this study, we introduce recent progress in deep learning (DL), mostly neural networks (NN)-based SE front ends to CI, and discuss how the hearing properties of the CI recipients could be utilized to optimize the DL-based SE. In particular, different loss functions are introduced to supervise the NN training, and a set of objective and subjective experiments is presented. Results verify that the CI recipients are more sensitive to the residual noise than the SE-induced speech distortion, which has been common knowledge in CI research. Furthermore, speech reception threshold (SRT) in noise tests demonstrates that the intelligibility of the denoised speech can be significantly improved when the NN is trained with a loss function bias to more noise suppression than that with equal attention on noise residue and speech distortion.

Rubus L. is one of the most diverse genera belonging to Rosaceae; it consists of more than 700 species with a worldwide distribution. It thus provides an ideal natural “supergenus” for studying the importance of its edible, medicinal, and phylogenetic characteristics for application in our daily lives and fundamental scientific studies. The Rubus genus includes many economically important species, such as blackberry (R. fruticosus L.), red raspberry (R. ideaus L.), black raspberry (R. occidentalis L.), and raspberry (R. chingii Hu), which are widely utilized in the fresh fruit market and the medicinal industry. Although Rubus species have existed in human civilization for hundreds of years, their utilization as fruit and in medicine is still largely inadequate, and many questions on their complex phylogenetic relationships need to be answered. In this review, we briefly summarize the history and progress of studies on Rubus, including its domestication as a source of fresh fruit, its medicinal uses in pharmacology, and its systematic position in the phylogenetic tree. Recent available evidence indicates that (1) thousands of Rubus cultivars were bred via time- and labor-consuming methods from only a few wild species, and new breeding strategies and germplasms were thus limited; (2) many kinds of species in Rubus have been used as medicinal herbs, though only a few species (R. ideaus L., R. chingii Hu, and R. occidentalis L.) have been well studied; (3) the phylogeny of Rubus is very complex, with the main reason for this possibly being the existence of multiple reproductive strategies (apomixis, hybridization, and polyploidization). Our review addresses the utilization of Rubus, summarizing major relevant achievements and proposing core prospects for future application, and thus could serve as a useful roadmap for future elite cultivar breeding and scientific studies.

The cochlea “translates” the in-air vibrational acoustic “language” into the spikes of neural “language” that are then transmitted to the brain for auditory understanding and/or perception. During this intracochlear “translation” process, high resolution in time–frequency–intensity domains guarantees the high quality of the input neural information for the brain, which is vital for our outstanding hearing abilities. However, cochlear implants (CIs) have coarse artificial coding and interfaces, and CI users experience more challenges in common acoustic environments than their normal-hearing (NH) peers. Noise from sound sources that a listener has no interest in may be neglected by NH listeners, but they may distract a CI user. We discuss the CI noise-suppression techniques and introduce noise management for a new implant system. The monaural signal-to-noise ratio estimation-based noise suppression algorithm “eVoice,” which is incorporated in the processors of Nurotron® EnduroTM, was evaluated in two speech perception experiments. The results show that speech intelligibility in stationary speech-shaped noise can be significantly improved with eVoice. Similar results have been observed in other CI devices with single-channel noise reduction techniques. Specifically, the mean speech reception threshold decrease in the present study was 2.2 dB. The Nurotron society already has more than 10,000 users, and eVoice is a start for noise management in the new system. Future steps on non-stationary-noise suppression, spatial-source separation, bilateral hearing, microphone configuration, and environment specification are warranted. The existing evidence, including our research, suggests that noise-suppression techniques should be applied in CI systems. The artificial hearing of CI listeners requires more advanced signal processing techniques to reduce brain effort and increase intelligibility in noisy settings.

Functionally graded NiTi structures benefit from the combination of the smart properties of NiTi and those of functionally graded structures. This article provides experimental data for thermomechanical deformation behaviour of microstructurally graded, compositionally graded and geometrically graded NiTi alloy components, related to the research article entitled “Functionally graded shape memory alloys: design, fabrication and experimental evaluation” (Shariat et al., 2017) [1]. Stress–strain variation of microstructurally graded NiTi wires is presented at different heat treatment conditions and testing temperatures. The complex 4-way shape memory behaviour of a compositionally graded NiTi strip during one complete thermal cycle is demonstrated. The effects of geometrical design on pseudoelastic behaviour of geometrically graded NiTi plates over tensile loading cycles are presented on the stress–strain diagrams. Keywords: Shape memory alloy (SMA), NiTi, Martensitic transformation, Functionally graded material (FGM), Pseudoelasticity, Heat treatment

Living walls are important vertical greening systems with modular prevegetated structures. Studies have suggested that living walls have many social benefits as an ecological engineering technique with notable potential for reconciliation ecology. Despite these benefits, there are currently no mature workflows or technologies for monitoring the health status and water stress of living wall systems. To partially fill the current knowledge gap related to water stress, we acquired thermal, multispectral, and hyperspectral remote sensing data from an indoor living wall in the Cloud Forest of the Gardens by the Bay, Singapore. The surface temperature (Ts) and a normalized difference vegetation index (NDVI) were obtained from these data to construct a Ts-NDVI space for applying the “triangle method”. A simple and effective algorithm was proposed to determine the dry and wet edges, the key components of the said method. The pixels associated with the dry and wet edges were then selected and highlighted to directly display the areas under water-stress conditions. Our results suggest that the proposed algorithm can provide a reasonable overview of the water-stress information of the living wall; therefore, our method can be simple and effective to monitor the health status of a living wall. Furthermore, our work confirms that the triangle method can be transferred from the outdoors to an indoor environment.

Satellite remote sensing data that lacks spatial resolution and timeliness is of limited ability to access urban thermal environment on a micro scale. This paper presents an unmanned airship low-altitude thermal infrared remote sensing system (UALTIRSS), which is composed of an unmanned airship, an onboard control and navigation subsystem, a task subsystem, a communication subsystem, and a ground-base station. Furthermore, an experimental method and an airborne-field experiment for collecting land surface temperature (LST) were designed and conducted. The LST pattern within 0.8-m spatial resolution and with root mean square error (RMSE) value of 2.63 °C was achieved and analyzed in the study region. Finally, the effects of surface types on the surrounding thermal environment were analyzed by LST profiles. Results show that the high thermal resolution imagery obtained from UALTIRSS can provide more detailed thermal information, which are conducive to classify fine urban material and assess surface urban heat island (SUHI). There is a significant positive correlation between the average LST of profiles and the percent impervious surface area (ISA%) with R2 around 0.917. Overall, UALTIRSS and the retrieval method were proved to be low-cost and feasible for studying micro urban thermal environments.

The present study aims to further demonstrate the cooling benefits of an extensive green roof (EGR) and fill the gap existing in the literature in terms of a sensitivity analysis of an EGR, especially in subtropical areas. First, onsite measurements were performed. The results indicated that the peak air temperatures in the chamber with the EGR were 4.0 °C and 1.9 °C lower, respectively, compared to those in the chamber with a bare roof on sunny and rainy days. Moreover, the EGR decreased the daily electricity consumption from air conditioning by up to 16.7% on sunny days and 6.7% on cloudy days. Second, the measured values were employed to validate the green roof module (GRM) in EnergyPlus. The results demonstrated that the GRM yielded accurate results in quantifying the cooling benefits of the EGR. Finally, we selected 16 factors of the EGR, each with four levels, to perform the sensitivity analysis. Range and variance analyses revealed that the factors that most significantly impacted the EGR performance were the R-value of roof construction, substrate (soil) thickness, the thermal conductivity of dry substrate, the leaf area index, leaf emissivity, and the solar absorptance of the substrate. These factors contributed 90.8% to the performance index.