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Abstract The relationship between green and grey urban infrastructure, local meteorological conditions, and traffic-related air pollution is complex and dynamic. This case study examined the effect of evolving morphologies around a city square park in Dublin and explores the twin impacts of local urban development (grey) and maturing parks (green) on particulate matter (PM) pollution. A fixed air quality monitoring campaign and computational fluid dynamic modelling (ENVI-met) were used to assess current (baseline) and future scenarios. The baseline results presented the distribution of PM in the study area, with bimodal (PM2.5) and unimodal (PM10) diurnal profiles. The optimal vegetation height for air quality within the park also differed by wind direction with 21 m vegetation optimal for parallel winds (10.45% reduction) and 7 m vegetation optimal for perpendicular winds (30.36% reduction). Increased building heights led to higher PM2.5 concentrations on both footpaths ranging from 25.3 to 37.0% under perpendicular winds, whilst increasing the height of leeward buildings increased PM2.5 concentrations by up to 30.9% under parallel winds. The findings from this study provide evidence of the importance of more in-depth analysis of green and grey urban infrastructure in the urban planning decision-making process to avoid deteriorating air quality conditions around city square parks.

Abstract Background Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting over 300,000 people worldwide. It is characterized by the progressive decline of the nervous system that leads to the weakening of muscles which impacts physical function. Approximately, 15% of individuals diagnosed with ALS have a known genetic variant that contributes to their disease. As therapies that slow or prevent symptoms continue to develop, such as antisense oligonucleotides, it is important to discover novel genes that could be targets for treatment. Additionally, as cohorts continue to grow, performing analyses in ALS subtypes, such as primary lateral sclerosis (PLS), becomes possible due to an increase in power. These analyses could highlight novel pathways in disease manifestation. Methods Building on our previous discoveries using rare variant association analyses, we conducted rare variant burden testing on a substantially larger multi-ethnic cohort of 6,970 ALS patients, 166 PLS patients, and 22,524 controls. We used intolerant domain percentiles based on sub-region Residual Variation Intolerance Score (subRVIS) that have been described previously in conjunction with gene based collapsing approaches to conduct burden testing to identify genes that associate with ALS and PLS. Results A gene based collapsing model showed significant associations with SOD1, TARDBP, and TBK1 (OR = 19.18, p = 3.67 × 10–39; OR = 4.73, p = 2 × 10–10; OR = 2.3, p = 7.49 × 10–9, respectively). These genes have been previously associated with ALS. Additionally, a significant novel control enriched gene, ALKBH3 (p = 4.88 × 10–7), was protective for ALS in this model. An intolerant domain-based collapsing model showed a significant improvement in identifying regions in TARDBP that associated with ALS (OR = 10.08, p = 3.62 × 10–16). Our PLS protein truncating variant collapsing analysis demonstrated significant case enrichment in ANTXR2 (p = 8.38 × 10–6). Conclusions In a large multi-ethnic cohort of 6,970 ALS patients, collapsing analyses validated known ALS genes and identified a novel potentially protective gene, ALKBH3. A first-ever analysis in 166 patients with PLS found a candidate association with loss-of-function mutations in ANTXR2.

Injuries of the posterior root of the medial meniscus can be accompanied by damage to the anterior cruciate ligament or often occur independently in cases of degenerative meniscal injury in older individuals. Anchor suture repair can achieve favorable biomechanical effects and clinical outcomes. However, anchor placement is technically challenging and requires a posterior medial approach, which increases the risk of iatrogenic injury. To address these issues, we have utilized the reverse anchor technique to repair the posterior root of the medial meniscus. This technique offers advantages such as reduced surgical time, simplified operation, and reduced risk of the “bungee effect” and iatrogenic injury.

Vehicle electrification has been proven to be an efficient way to reduce carbon dioxide emissions and solve the energy crisis. Lithium-ion batteries (LiBs) are considered the dominant energy storage medium for electric vehicles (EVs) owing to their high energy density and long lifespan. To maintain a safe, efficient, and stable operating condition for the battery system, we must monitor the state of the battery, especially the state-of-charge (SOC) and state-of-health (SOH). With the development of big data, cloud computing, and other emerging techniques, data-driven machine learning (ML) techniques have attracted attention for their enormous potential in state estimation for LiBs. Therefore, this paper reviews the four most studied types of ML algorithms for SOC and SOH estimation, including shallow neural network (NN), deep learning (DL), support vector machine (SVM), and Gaussian process regression (GPR) methods. The basic principles and uniform flowcharts of different ML algorithms are introduced. Then, the applications of each ML algorithm for state estimation within recent years are comprehensively reviewed and compared in terms of used datasets, input features, hyperparameter selection, performance metrics, advantages, and disadvantages. Based on the investigation, this review discusses the current challenges and prospects from four aspects, aiming to provide some inspiration for developing advanced ML state estimation algorithms.

AbstractThe high accurate detection of blood glucose level (BGL) is very important for non-invasive monitoring of diabetes mellitus. In this work, the optoacoustic (OA) quantitative in vitro detection of diabetes mellitus involving the comprehensive impacts of multiple factors (irradiation energy, concentration, temperature, flow rate and vessel depth) was firstly studied. To achieve this aim, a set of OA in vitro detection system of blood glucose with the comprehensive influence of five factors was constructed. The real-time OA signals of 625 rabbit whole blood were obtained at the characteristic wavelength of 750 nm, as well as peak-to-peak values (PPVs). Results show that the accurate detection of BGL was very difficult due to the complicated OA signals. To accurately predict the BGL under the comprehensive impacts of five factors, wavelet neural network (WNN) was employed to train BGL of 500 training set blood. The mean square error (MSE) of BGL for 125 testing set blood was 6.5782 mmol/L. To decrease the MSE, WNN optimized by quantum particle swarm optimization (QPSO), i.e., QPSO-WNN algorithm was utilized. The MSE of BGL based on QPSO-WNN was 0.37485 mmol/L, which was superior to 0.48005 mmol/L of PSO-WNN. Particularly, to further decrease MSE, a novel nonlinear dynamic shrinkage coefficient (DSC) strategy was proposed, and compared with other four kinds of DSC strategies and the fixed one. With the optimal parameters, the MSE of BGL was decreased to 0.3088 mmol/L. Comparison results of seven algorithms and research works demonstrate that OA technology combined with QPSO-WNN algorithm and the novel nonlinear DSC strategy has excellent performance in the quantitative detection of diabetes mellitus involving in the comprehensive impacts.

Abstract The precipitate and the coacervate are two aggregated states in the polyelectrolyte complexes (PECs). The precipitate‐to‐coacervate transition and glass transition in PECs have been widely reported in the past. In many cases, the two phenomena are studied independently, although both of them are apparently affected by water and small ions. Here, utilizing a PEC system consisting of poly(acrylic acid) (PAA) and a cationic bolaamphiphile (DBON), we explore the states of PECs as a function of salt, temperature, and the molecular weight of PAAs. By a combination of microscopic observation, time‐resolved fluorescence measurements, and differential scanning calorimetry, we identify salt/temperature driven precipitate‐to‐coacervate transitions of the complexes. The thermally induced morphology transformation from the precipitate to coacervate occurs around the glass transition temperature, indicating a strong correlation between the two processes. As the molecular weight of the PAA increases, the thermal transition temperature becomes higher. This finding offers new insights on the mechanistic interactions that dictate the aggregated states of PECs. Based on the photothermal effect of DBON, we also develop a UV light‐induced strategy to mediate the precipitate‐to‐coacervate transition, providing a fantastic platform to create functional PEC materials.

A highly accurate classification of diabetes mellitus (DM) with the synthetic impacts of several variables is first studied via optoacoustic technology in this work. For this purpose, an optoacoustic measurement apparatus of blood glucose is built, and the optoacoustic signals and peak–peak values for 625 cases of in vitro rabbit blood are obtained. The results show that although the single impact of five variables are obtained, the precise classification of DM is limited because of the synthetic impacts. Based on clinical standards, different levels of blood glucose corresponding to hypoglycaemia, normal, slight diabetes, moderate diabetes and severe diabetes are employed. Then, a wavelet neural network (WNN) is utilized to establish a classification model of DM severity. The classification accuracy is 94.4 % for the testing blood samples. To enhance the classification accuracy, particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are successively utilized to optimize WNN, and accuracy is enhanced to 98.4 % and 100 %, respectively. It is demonstrated from comparison between several algorithms that optoacoustic technology united with the QPSO-optimized WNN algorithm can achieve precise classification of DM with synthetic impacts.

Many countries and regions across the world have been actively implementing their commitments to achieve carbon neutrality in response to the 2030 Agenda for Sustainable Development and the Paris Agreement on Climate Change (United Nations, 2021). This paper highlights some evolutions and revolutions currently experienced by the transportation industry in different contexts with respect to some common carbon neutrality and environmental sustainability goals. The evolutionary and revolutionary changes are powered by the adoption and implementation of emerging technologies and policies on three aspects: Low-carbon transportation infrastructure, transportation electrification and decarbonization, and intelligent transportation systems management. It is hoped that the experience and lessens we learned from these accumulative and adaptive practices can help us more precisely select and adjust the technological and policy pathways for achieving carbon–neutral transportation.

Air pollution is a growing concern in metropolitan areas worldwide, and Shanghai, as one of the world’s busiest ports, faces significant challenges in local air pollution control. Assessing the contribution of a specific port to air pollution is essential for effective environmental management and public health improvement, making the analysis of air pollution contributions at a selected port in Shanghai a pertinent research focus. This study aims to delve into the distribution patterns of atmospheric pollutants in port areas and their influencing factors, utilizing a data-driven approach to unveil the relationship between pollution sources and dispersion. Through a comparative analysis of pollution levels in the port’s interior, surrounding regions, and urban area concentrations, we ascertain that carbon monoxide (CO) and nitric oxide (NO) are the primary pollutants in the port, with concentrations significantly exceeding those of the surrounding areas and urban area levels. These two pollutants exhibit an hourly pattern, with lower levels during the day and higher concentrations at night. Employing a random forest model, this study quantitatively analyzes the contribution rates of different factors to pollutant concentrations. The results indicate that NO concentration is primarily influenced by operational intensity and wind speed, while CO concentration is mainly affected by meteorological factors. Further, an orthogonal experiment reveals that maintaining daily operational vehicle numbers within 5000 effectively controls NO pollution, especially at low wind speeds. Additionally, humidity and temperature exhibit similar trends in influencing NO and CO, with heightened pollution occurring within the range of 75% to 90% humidity and 6 °C to 10 °C temperature. Severe pollution accumulates under stagnant wind conditions with wind speeds below 0.2 m/s. The results help to explore the underlying mechanisms of port pollution further and use machine learning for early pollution prediction, aiding timely warnings and emission reduction strategy formulation.

The entropy coefficient (dUOC/dT) is an important parameter for predicting the heat generation of lithium-ion batteries (LiBs), especially under low-rate conditions. The potentiometric method is generally used to measure the entropy coefficient, which is accurate but time-consuming. Therefore, an efficient and accurate entropy measurement method is urgently needed. In this paper, a fast and accurate improved potentiometric method based on a positive adjustment (PA) is proposed, which reduces the relaxation time to 10 min for the first time. Commercial 18650 lithium-ion cells are used to validate the PA method. Comparing two entropy profiles measured by PA method and conventional potentiometric method (CPM), the result clearly shows that the PA method maintains comparable accuracy to CPM with an average error of ±0.01 mV/K. Even compared with other new methods proposed in recent years, the PA method still has certain advantages in measurement efficiency.