Integration of smart cities and Building Information Modeling (BIM) for a sustainability oriented business model to address Sustainable Development Goals

   The construction industry, business models, and smart cities are recognized as pivotal domains with profound implications for fostering sustainability, prompting extensive research endeavors. However, there remains a dearth of interdisciplinary integration within this sphere aimed at fostering sustainable development. Nevertheless, current studies suggest that research in this area could provide theoretical and practical guidance for the sustainable transformation of society and make a positive contribution to the realization of the Sustainable Development Goals (SDGs). Therefore, this paper aims to utilize an innovative mixed research approach combining macro-quantitative bibliometric analysis with subsequent micro-qualitative content examination based on the SDGs to explore the relationship between BIM and smart cities in promoting a sustainability-oriented business model, which provides a comprehensive understanding of the overall situationand development of research topics in the field and contributes to the improvement of the SDGs. The results show that, during the last 13 years (from the year 2011 to 2023), the period from the year 2011 to 2016 was the initial stage of the field, followed by a rapid growth after the year 2018, of which “BIM”, “Smart City”, “Business Model”, “Building Life Cycle”, “Urban Management”, and “Business Model Innovation” are the keywords representing the current research hotspots. The circular economy model that has been developed since 2021 has contributed to life cycle stages, including “briefing stages” and “procurement stages”. As such, the “whole life cycle”, “strategic urban planning frameworks”, and sustainable business models” have become future research trends, whilst real-world applications such as “smart tourism”, “e-government”, and “green building” have emerged. Further, the key partnerships of “city managers”, “corporate enterprises”, and “public participation” for smart cities contribute to the chievement of SDGs 8 and 17 in terms of integrating urban information technology and urban infrastructure, policy regulation, knowledge-sharing, improving economic efficiency, and promoting sustainable economic growth.</p

Urban-rural inequality regarding drug prescriptions in primary care facilities - a pre-post comparison of the National Essential Medicines Scheme of China

Objective: To assess the impact of the National Essential Medicines Scheme (NEMS) with respect to urban-rural inequalities regarding drug prescriptions in primary care facilities. Methods: A stratified two-stage random sampling strategy was used to sample 23,040 prescriptions from 192 primary care facilities from 2009 to 2010. Difference-in-Difference (DID) analyses were performed to test the association between NEMS and urban-rural gaps in prescription patterns. Between-Group Variance and Theil Index were calculated to measure urban-rural absolute and relative disparities in drug prescriptions. Results: The use of the Essential Medicines List (EML) achieved a compliance rate of up to 90 % in both urban and rural facilities. An overall reduction of average prescription cost improved economic access to drugs for patients in both areas. However, we observed an increased urban-rural disparity in average expenditure per prescription. The rate of antibiotics and glucocorticoids prescription remained high, despite a reduced disparity between urban and rural facilities. The average incidence of antibiotic prescription increased slightly in urban facilities (62 to 63 %) and reduced in rural facilities (67 % to 66 %). The urban-rural disparity in the use of parenteral administration (injections and infusions) increased, albeit at a high level in both areas (44 %-52 %). Conclusion: NEMS interventions are effective in reducing the overall average prescription costs. Despite the increased use of the EML, indicator performances with respect to rational drug prescribing and use remain poor and exceed the WHO/INRUD recommended cutoff values and worldwide benchmarks. There is an increased gap between urban and rural areas in the use of parenteral administration and expenditure per prescription

Co3Se4 quantum dots encapsulated with nitrogen-doped porous nanocarbon as ultrastable electrode material for water-based all-solid asymmetric supercapacitors

Co3Se4 quantum dots encapsulated with nitrogen-doped porous nanocarbon as ultrastable electrode material for water-based all-solid asymmetric supercapacitor

Impact of roasting on the phenolic and volatile compounds in coffee beans

Phenolic compounds present in coffee beans could generate flavor and bring benefits to health. This study aimed to evaluate the impacts of commercial roasting levels (light, medium, and dark) on phenolic content and antioxidant potential of Arabica coffee beans (Coffea arabica) comprehensively via antioxidant assays. The phenolic compounds in roasted samples were characterized via liquid chromatography–electrospray ionization quadrupole time-of-flight mass spectrometry (LC-ESI-QTOF-MS/MS). Furthermore, the coffee volatile compounds were identified and semi-quantified by headspace/gas chromatography–mass spectrometry (HS-SPME-GC-MS). Generally, for phenolic and antioxidant potential estimation, light roasted samples exhibited the highest TPC (free: 23.97 ± 0.60 mg GAE/g; bound: 19.32 ± 1.29 mg GAE/g), DPPH, and FRAP. The medium roasted beans performed the second high in all assays but the highest ABTS+ radicals scavenging capacity (free: 102.37 ± 8.10 mg TE/g; bound: 69.51 ± 4.20 mg TE/g). Totally, 23 phenolic compounds were tentatively characterized through LC-ESI-QTOF-MS/MS, which is mainly adopted by 15 phenolic acid and 5 other polyphenols. The majority of phenolic compounds were detected in the medium roasted samples, followed by the light. Regarding GC-MS, a total of 20 volatile compounds were identified and semi-quantified which exhibited the highest in the dark followed by the medium. Overall, this study confirmed that phenolic compounds in coffee beans would be reduced with intensive roasting, whereas their antioxidant capacity could be maintained or improved. Commercial medium roasted coffee beans exhibit relatively better nutritional value and organoleptic properties. Our results could narrow down previous conflicts and be practical evidence for coffee manufacturing in food industries

X-ray properties of two transient ULX candidates in galaxy NGC 7090

We report the X-ray data analysis of two transient ultraluminous X-ray sources (ULXs, hereafter X1 and X2) located in the nearby galaxy NGC 7090. While they were not detected in the 2004 XMM-Newton and 2005 Chandra observations, their 0.3-10 keV X-ray luminosities reached $>3\times10^{39}\,\mathrm{erg\,s^{-1}}$ in later XMM-Newton or Swift observations, showing increases in flux by a factor of $>80$ and $>300$ for X1 and X2, respectively. X1 showed indications of spectral variability: at the highest luminosity, its X-ray spectra can be fitted with a powerlaw ($\Gamma=1.55\pm0.15$), or a multicolour disc model with $T_{\mathrm{in}}=2.07^{+0.30}_{-0.23}$ keV; the X-ray spectrum became softer ($\Gamma=2.67^{+0.69}_{-0.64}$), or cooler ($T_\mathrm{in}=0.64^{+0.28}_{-0.17}$ keV) at lower luminosity. No strong evidence for spectral variability was found for X2. Its X-ray spectra can be fitted with a simple powerlaw model ($\Gamma=1.61^{+0.55}_{-0.50}$), or a multicolour disc model ($1.69^{+1.17}_{-0.48}$ keV). A possible optical counterpart for X1 is revealed in HST imaging. No optical variability is found, indicating that the optical radiation may be dominated by the companion star. Future X-ray and optical observations are necessary to determine the true nature of the compact object

Modeling and analysis of compressive properties of porous NiTi shape memory alloy using artificial neural network

Artificial neural network (ANN) is an intriguing data processing technique. Over the last decade, it was applied widely in the chemistry field, but there were few applications in the porous NiTi shape memory alloy (SMA). In this paper, 32 sets of samples from thermal explosion experiments were used to build a three-layer BP (back propagation) neural network model. According to the registered BP model, the effect of process parameters including heating rate (v), green density(D) and particle size of Ti ( d ) on compressive properties of reacted products including ultimate compressive strength (σ ) and ultimate compressive strain (ε ) was analyzed. The predicted results agree with the actual data within reasonable experimental error, which shows that the BP model is a practically very useful tool in the properties analysis and process parameters design of the porous NiTi SMA prepared by thermal explosion method

Heat and moisture transfer in fabric consisting of fibers with low hygroscopicity

The heat and moisture transfer through dry and wet fabric made up of hydrophobic fibers was modeled and experimentally verified. In the modeling, the governing equations are obtained with consideration of the energy balance and the mass balance of the liquid water, where the specific initial and boundary conditions were set according to measurement of the cooling property of fabric. By comparing temperature changes at the fabric inner surface, the model validity was confirmed by the agreements between the experimental results and numerical solutions. Thus, this simulation was able to predict the cooling performance of fabrics. The effects of internal and external factors on heating/cooling performances of fabrics were analyzed by this model. With an increasing evaporation rate, thermal conductivity and water content of fabric, the cooling capability of fabric was improved. Surrounding conditions with lower temperature, lower relative humidity and higher air velocity demonstrated a positive effect on fabric thermal and moisture transfer performances, which benefits the thermal comfort of the human body after excessive sweating

Tesia: A Trusted Efficient Service Evaluation Model in IoT Based on Improved Aggregation Signature

Service evaluation model is an essential ingredient in service‐oriented Internet of things (IoT) architecture. Generally, traditional models allow each user to submit their comments with respect to IoT services individually. However, these kind of models are fragile to resist various attacks, like comment denial attacks, and Sybil attacks, which may decrease the comments submission rate. In this article, we propose a new aggregation digital signature scheme to resolve the problem of comments aggregation, which may aggregate different comments into one with high efficiency and security level. Based on the new aggregation digital signature scheme, we further put forward a new service evaluation model named Tesia allowing specific users to submit the comments as a group in IoT networks. More specifically, they aggregate comments and assign one user as a submitter to submit these comments. In addition, we introduce the synchronization token mechanism into the new service evaluation model, to assure that all users in the group may sign their comments one by one, and the last one who receives the token is assigned as the final submitter. Tesia has more acceptable robustness and can greatly improve the comments submission rate with rather lower submission delay time

A Comparative and Comprehensive Characterization of Polyphenols of Selected Fruits from the Rosaceae Family

The present research presents a comprehensive characterization of polyphenols from peach, pear, and plum using liquid chromatography coupled with electrospray ionization quadrupole-time-of-flight-mass spectrometry (LC-ESI-QTOF-MS/MS), followed by the determination of their antioxidant potential. Plums showed the highest total phenolic content (TPC; 0.62 mg GAE/g), while peaches showed the highest total flavonoid content (TFC; 0.29 mg QE/g), also corresponding to their high scavenging activities (i.e., DPPH, ABTS, FRAP, and TAC). In all three fruit samples, a total of 51 polyphenolic compounds were tentatively identified and were mainly characterized from hydroxybenzoic acids, hydroxycinnamic acids, hydroxyphenylpentanoic acids, flavanols, flavonols, and isoflavonoids subclasses. Twenty targeted phenolic compounds were quantified using high-performance liquid chromatography with photodiode array detection (HPLC-PDA). The plum cultivar showed the highest content of phenolic acids (chlorogenic acid, 11.86 mg/100 g), whereas peach samples showed the highest concentration of flavonoids (catechin, 7.31 mg/100 g), as compared to pear. Based on these findings, the present research contributes and complements the current characterization data of these fruits presented in the literature, as well as ensures and encourages the utilization of these fruits in different food, feed, and nutraceutical industries

Underwater object detection using Invert Multi-Class Adaboost with deep learning

Abstract—In recent years, deep learning based methods have achieved promising performance in standard object detection. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) Objects in real applications are usually small and their images are blurry, and (2) images in the underwater datasets and real applications accompany heterogeneous noise. To address these two problems, we first propose a novel neural network architecture, namely Sample-WeIghted hyPEr Network (SWIPENet), for small object detection. SWIPENet consists of high resolution and semantic-rich Hyper Feature Maps which can significantly improve small object detection accuracy. In addition, we propose a novel sample-weighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the in-fluence of noise on the proposed SWIPENet. Experiments on two underwater robot picking contest datasets URPC2017 andURPC2018 show that the proposed SWIPENet+IMA framework achieves better performance in detection accuracy against several state-of-the-art object detection approaches.</p