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A series of novel side-chain liquid crystalline (SCLC) copolymers were synthesized by attaching two distinct mesogenic units, namely a chiral cholesteryl-based monomer (M1) and an achiral biphenyl-based monomer (M2), to a poly(3-mercaptopropylmethylsiloxane) (PMMS) backbone via thiol-ene click chemistry. The influence of side chain composition on the self-assembly behavior and phase structures of these SCLC copolymers was systematically investigated using different instrument. Results indicate that three distinct liquid crystalline phases and four unique molecular configurations were identified within the polymer series, with the emergence of the liquid crystalline phase being a synergistic outcome of the two distinct side chains. This study underscores the critical influence of side chain dimensions, rigidity, and spatial volume on the self-assembly structures and phase characteristics of liquid crystalline polymers, providing valuable insights for the rational design and development of advanced functional materials with tailored properties. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) materials have gained significant attention in the machining industry due to their exceptional strength-to-weight ratio and vibration characteristics. In the poor rigidity machining tool system, such as plunge milling, deep drilling, and boring, the large overhanging shaft increases the requirement of chatter stability. In this study, a solution of carbon fiber reinforced shaft was proposed for the poor rigidity machining tool system. The vibration characteristic model was established by theoretical calculation, FE analysis, and modal testing. The relationship between fiber arrangement and vibration characteristics was precisely described, and the optimization scheme of fiber reinforcement was proposed considering both the theoretical analysis and procedure technique. The machining performance of the optimal HSS-CFRP shaft was compared with that of the traditional HSS shaft in the same tool system. The main aspects that significantly affect the machining performance were thoroughly evaluated, including tool weight, energy consumption, dynamic characteristics, and chatter stability. The effect of fiber reinforcement was fully discussed, which provided a reliable solution for the poor rigidity machining system and other potential applications with large overhanging shaft. [ABSTRACT FROM AUTHOR]

This article explores the role of energy-efficient buildings in addressing climate change and reducing carbon emissions. It highlights the significant impact that buildings have on global energy consumption and greenhouse gas emissions, emphasizing the need for sustainable construction materials and design techniques. The article also discusses the potential of digital technologies, such as Building Information Modeling (BIM), IoT sensors, and Digital Twins, to optimize energy use and sustainability throughout the building lifecycle. However, challenges such as cost concerns and operational inefficiencies need to be addressed for widespread adoption of these technologies. The article proposes a research roadmap to overcome these barriers and promote the use of digital tools in the building sector. [Extracted from the article]

Cognitive impairment is recognized as a risk factor for suicide in schizophrenia (SZ) patients. Despite empathy being an important aspect of social cognition, the association between suicidal behavior and empathy has received little attention. We aimed to compare empathy and neurocognition in SZ patients with and without suicide attempts (SAs), and to explore the relationship between empathy, neurocognition, and clinical symptoms in SZ patients with and without SAs. Data on SAs and socio-demographic characteristics were collected from 628 chronic SZ patients. The patients' symptomatology was measured by the Positive and Negative Syndrome Scale (PANSS). Empathy and neurocognition were assessed with the Interpersonal Reactivity Index (IRI) and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), respectively. Patients with SAs performed better on all IRI domains (except for Perspective Taking) and total scores. Regression results showed that negative symptoms, positive symptoms, and duration of illness were independently associated with IRI total score in patients without SAs (adjusted R2 = 0.048). In patients without SAs, negative symptoms, general psychopathology, education, age, and sex were independently associated with RBANS total score (adjusted R2 = 0.265), while in patients with SAs, education, PANSS total score, and age at onset were independently associated with RBANS total score (adjusted R2 = 0.456). Our results show that SZ patients with SAs may have better empathic performance than patients without SAs. In chronic SZ patients, negative and positive symptoms may have different effects on cognition in the SAs and non-SAs groups. [ABSTRACT FROM AUTHOR]

Poly(butylene succinate) (PBS)-based nanocomposites, reinforced and toughened with ZnO-coated multi-walled carbon nano-tubes (MWCNT-ZnO), demonstrate significantly enhanced properties, making them ideal for potential applied in food packaging applications. This study explores the effects of varying proportions of MWCNT-ZnO on the overall characteristics of these composites. The addition of 0.1 parts per hundred (phr) MWCNT-ZnO optimizes the nanocomposites' mechanical properties, crystallinity, melting temperature, thermal stability, and barrier performance. Specifically, the composite exhibits a 22% increase in tensile strength, a 28.4% rise in yield strength, and a remarkable 95.7% enhancement in the material's elongation at break, compared to the pure PBS matrix. Moreover, these nanocomposites exhibit excellent antibacterial properties, crucial for food preservation and safety. The soil burial test indicates that, except for the addition of 0.1phr which is lower than pure PBS, the biodegradation rate increases with the increasing addition of MWCNT-ZnO. This further suggests that a low nanoparticle filler content can enhance structural compactness, thereby improving the mechanical stability. The study also reveals notable preservation benefits for vegetables. When used for beef packaging, this composite material successfully extends the meat's freshness period, substantially curtails bacterial proliferation, and ensures the beef remains within safe consumption parameters. The combination of enhanced mechanical, thermal, barrier, and antibacterial properties makes PBS/MWCNT-ZnO nanocomposites promising candidates for sustainable and efficient food packaging materials. [ABSTRACT FROM AUTHOR]

Variable-exponent weakly singular Volterra integral equations of the second kind with integral kernels of the form (t - s) - α (t) are considered. In Liang and Stynes (IMA J Numer Anal 19:drad072, 2023) it is shown that a typical solution of such an equation exhibits a weak singularity at the initial time t = 0 , similarly to the case where α (t) is constant. Our paper extends this analysis further by giving a decomposition for the exact solution. To solve the problem numerically, a fractional polynomial collocation method is applied on a graded mesh. The convergence of the collocation solution to the exact solution is analysed rigorously and it is proved that specific choices of the fractional polynomials and mesh grading yield optimal-order convergence of the computed solution. Superconvergence properties of the iterated collocation solution are also analysed. Numerical experiments illustrate the sharpness of our theoretical results. [ABSTRACT FROM AUTHOR]

Highlights: Asymmetrically coordinated Co–N4–O sites on N-doped carbon nanofiber were prepared. Co–O coordination along the axial direction led to enhanced dielectric polarization loss. Multifunctional films were developed for practical application in harsh environments. Single-atom materials have demonstrated attractive physicochemical characteristics. However, understanding the relationships between the coordination environment of single atoms and their properties at the atomic level remains a considerable challenge. Herein, a facile water-assisted carbonization approach is developed to fabricate well-defined asymmetrically coordinated Co–N4–O sites on biomass-derived carbon nanofiber (Co–N4–O/NCF) for electromagnetic wave (EMW) absorption. In such nanofiber, one atomically dispersed Co site is coordinated with four N atoms in the graphene basal plane and one oxygen atom in the axial direction. In-depth experimental and theoretical studies reveal that the axial Co–O coordination breaks the charge distribution symmetry in the planar porphyrin-like Co–N4 structure, leading to significantly enhanced dielectric polarization loss relevant to the planar Co–N4 sites. Importantly, the film based on Co–N4–O/NCF exhibits light weight, flexibility, excellent mechanical properties, great thermal insulating feature, and excellent EMW absorption with a reflection loss of − 45.82 dB along with an effective absorption bandwidth of 4.8 GHz. The findings of this work offer insight into the relationships between the single-atom coordination environment and the dielectric performance, and the proposed strategy can be extended toward the engineering of asymmetrically coordinated single atoms for various applications. [ABSTRACT FROM AUTHOR]

Big data present new opportunities for modern society while posing challenges for data scientists. Recent advancements in sensor networks and the widespread adoption of the Internet of Things (IoT) have led to the collection of physical-sensor data on an enormous scale. However, significant challenges arise in conducting high-quality data analytics within the realm of big data. To uncover big data challenges and enhance data quality, it is essential to quantitatively unveil data characteristics. Furthermore, the existing studies lack analysis of the specific time-related characteristics of physical-sensor data. Enhancing the efficiency and precision of data analytics through the big data lifecycle requires a comprehensive understanding of data characteristics to address the hidden big data challenges. To fill in the research gap, this paper proposes a systematic data characteristic framework based on a 6Vs model. The framework aims to unveil the data characteristics in terms of data volume, variety, velocity, veracity, value, and variability through a set of statistical indicators. This model improves the objectivity of data characteristic understanding by relying solely on data-driven indicators. The indicators related to time-related characteristics in physical-sensor data are also included for the analysis of temporal aspects in the physical-sensor data. Furthermore, the big data challenges are linked to each dimension of the 6Vs model to gain a quantitative understanding of the data challenges. Finally, a pipeline is developed to implement the proposed framework, and two case studies are conducted to illustrate the process of understanding the physical-sensor data characteristics and making recommendations for data preprocessing to address the big data challenges. The proposed framework is able to analyze the characteristics of all physical-sensor data, therefore, identifying potential challenges in subsequent analytics, and providing recommendations for data preprocessing. Furthermore, the characteristic indicators can be used to analyze other types of big data. [ABSTRACT FROM AUTHOR]

The median urinary iodine concentration (UIC) of school-aged children has been commonly used as a surrogate to assess iodine status of a population including pregnant women. However, pregnant women have higher iodine requirements than children due to increased production of thyroid hormones. The aim of the study was to evaluate the iodine status of pregnant women and children as well as their household salt iodine concentration (SIC) in Quzhou, Zhejiang Province, China. Eligible pregnant women and children from all six counties of Quzhou in 2021 were recruited into the study. They were asked to complete a socio-demographic questionnaire and provide both a spot urine and a household table salt sample for the determination of UIC and SIC. A total of 629 pregnant women (mean age and gestation weeks of 29.6 years and 21.6 weeks, respectively) and 1273 school-aged children (mean age of 9 years and 49.8% of them were females) were included in the study. The overall median UIC of pregnant women and children in our sample was 127 (82, 193) μg/L and 222 (147, 327) μg/L, respectively, indicating sufficient iodine status in children but a risk of mild-to-moderate iodine deficiency in pregnant women. Distribution of iodine nutrition in children varied significantly according to their sex and age (P < 0.05). The rate of adequately household iodised salt samples (18–33 mg/kg) provided by pregnant women and children was 92.4% and 90.6%, respectively. In conclusion, our results indicated a risk of insufficient iodine status in pregnant population of China, but iodine sufficiency in school-aged children. Our data also suggested that median UIC of children may not be used as a surrogate to assess iodine status in pregnant women. [ABSTRACT FROM AUTHOR]

The Vlasov–Poisson–Fokker–Planck (VPFP) system is a fundamental model in plasma physics that describes the Brownian motion of a large ensemble of particles within a surrounding bath. Under the high-field scaling, both collision and field are dominant. This paper introduces two Asymptotic-Preserving Neural Network (APNN) methods within a physics-informed neural network (PINN) framework for solving the VPFP system in the high-field regime. These methods aim to overcome the computational challenges posed by high dimensionality and multiple scales of the system. The first APNN method leverages the micro–macro decomposition model of the original VPFP system, while the second is based on the mass conservation law. Both methods ensure that the loss function of the neural networks transitions naturally from the kinetic model to the high-field limit model, thereby preserving the correct asymptotic behavior. Through extensive numerical experiments, these APNN methods demonstrate their effectiveness in solving multiscale and high dimensional uncertain problems, as well as their broader applicability for problems with long time duration and non-equilibrium initial data. [ABSTRACT FROM AUTHOR]

Aim: To analyze the level of knowledge, attitudes, practices, and risk perception regarding COVID-19 among Chinese residents 1.5 years after the pandemic. Subject and methods: A cross-sectional study was carried out with both online and paper questionnaires. We included a variety of covariates that were characteristic-related factors such as age, gender, education level, and retirement status, as well as those closely associated with risk perception regarding COVID-19. Results: Participants (n = 3588), 53.49 ± 18.88 years old, from two provinces of China, of which 44.7% were male and 52.03% had a high school or greater level of education, answered the questions. More than 90% of participants had adequate background knowledge about COVID-19 and agreed or even strongly agreed with many attitude items related to the government's role in diagnosis, treatment, and dealing with COVID-19 infections. About three fifths of the participants reported fear of contracting COVID-19, but only a minority (18.63%) felt they were more susceptible than others. Respondents aged 45 years or younger were more likely to fear contracting the virus than those older than 45 years (adjusted OR = 1.464, 95% CI 1.196 to 1.794, P = 0.0002). High education level (adjusted OR = 1.503, 95% CI 1.187 to 1.904, P = 0.0007) and non-retired status (adjusted OR = 1.679, 95% CI 1.354 to 2.083, P < 0.0001) were associated with a higher perception of susceptibility to infection than others. Moreover, respondents who were not retired had a significantly reduced practice score (adjusted OR = 1.554, 95% CI 1.261 to 1.916, P < 0.0001). Age, retirement status, and education level were also associated with knowledge, attitude, and practice level. Conclusion: Our findings suggest that the public generally has trust in the COVID-19 vaccine and the government with regard to COVID-19 in China. We recommend that high-risk groups of communities, such as elders and patients with chronic diseases, be given greater consideration in the outbreaks. Health education campaigns combined with workplace preventive intervention should be aimed at improving COVID-19 knowledge and beliefs in order to encourage more optimistic attitudes and to maintain safe practices. [ABSTRACT FROM AUTHOR]

In the porthole extrusion of complex profiles, the die structure plays a significant role in the formability of the profile. In this study, numerical simulation was conducted to optimize the porthole extrusion die for a complex cross-sectional profile, with the standard deviation of velocity (SDV) as the evaluation criterion. The optimization involved the introduction of pseudo-mandrels, adjustments to the shape of the 2nd-step welding chamber, and modification of the bearing height. The rationality of the optimized die structure was verified through porthole extrusion experiments, and the microstructure and mechanical properties of the profile at different locations were investigated. The results show that the optimized die effectively controlled the metal flow velocity. The actual profiles obtained from the experiments exhibited improved surface quality and avoided defects such as distortion and dimensional errors. The grain size is uniform at different positions of the profile, and the mechanical properties are comparable. The profile meets the performance requirements. This research provides guidance for the practical production of the profiles. [ABSTRACT FROM AUTHOR]

Ultrasonic vibration-assisted (UVA) cutting is an advanced technique to improve the machinability and productivity of difficult-to-machine materials. This paper aims to assess the cutting performance of ultrasonic vibration-assisted milling (UVAM) technique and conventional milling (CM) in side milling of Ti–6Al–4 V. Tool trajectory and instantaneous chip thickness are calculated considering tool runout, vibration, and deflection. The geometric-kinematic-dynamic surface topography matrix and its corresponding material elastic recovery height matrix are reconstructed based on tool trajectory and cutting thickness, which are then summed to obtain the geometric-kinematic-dynamic-physical surface topography matrix. Finally, roughness parameters Ra and Rz are predicted based on the final reconstructed physical surface topography matrix. Experimental results show that Ra and Rz of UVAM are on average 26 and 39% greater, respectively, compared to CM due to the presence of high-frequency ultrasonic vibration-induced texture patterns in the feed grooves. The average prediction error for Ra and Rz is 23%, proving the validity of the prediction model. UVAM reduces the radial and tangential cutting force coefficients and ploughing force coefficients compared to CM due to the separation and impact effect of ultrasonic vibration. However, UVAM leads to an increase in the axial ploughing force coefficient because ultrasonic vibration introduces relative motion and friction between the tool and the workpiece in the axial direction. [ABSTRACT FROM AUTHOR]

A collocation method for the numerical solution of Volterra integro-differential equations with weakly singular kernels, based on piecewise polynomials of fractional order, is constructed and analysed. Typical exact solutions of this class of problems have a weak singularity at the initial time t = 0 . A rigorous error analysis of our method shows that, with an appropriate choice of the fractional-order polynomials and a suitably graded mesh, one can attain optimal orders of convergence to the exact solution and its derivative, and certain superconvergence results are also derived. In particular, our analysis shows that on a uniform mesh our method attains a higher order of convergence than standard piecewise polynomial collocation. Numerical examples are presented to demonstrate the sharpness of our theoretical results. [ABSTRACT FROM AUTHOR]

Software-in-the-Loop (SIL) testing is an approach used for verification and validation in the energy sector. However, there is no comprehensive overview of the application, potential, and challenges of SIL within this sector. Therefore, this paper conducts a thorough scoping review of the existing literature within the scope of SIL and related in-the-loop approaches in the energy sector. A total of 88 full-text articles from four significant databases ACM, IEEE Xplore, Scopus, and Web of Science are analyzed and categorized to map the purpose, methods, architecture, interoperability and protocols, technologies, challenges, and limitations. The results present a grand perspective of in-the-loop across several domains followed by an analysis of SIL in the energy sector. The application domains carry characteristics from complex systems, systems-of-systems, cyber-physical systems, critical systems, real-time systems, and sociotechnical systems. The energy sector and the automotive industry are amongst the most applied domains. Within energy- and electricity systems, hardware-based in-the-loop paradigms are mostly applied for testing low-level signaling, and SIL is used for control strategy testing, optimization, dispatching, and experimentation. The examined SIL architectures have distributed-, real-time, and closed-loop properties, and are constrained by specialized simulation power hardware. Future research should address how to systematically develop SIL testing environments with guiding principles to support application development for the future digitalized energy system. [ABSTRACT FROM AUTHOR]

Social recognition encompasses encoding social information and distinguishing unfamiliar from familiar individuals to form social relationships. Although the medial prefrontal cortex (mPFC) is known to play a role in social behavior, how identity information is processed and by which route it is communicated in the brain remains unclear. Here we report that a ventral midline thalamic area, nucleus reuniens (Re) that has reciprocal connections with the mPFC, is critical for social recognition in male mice. In vivo single-unit recordings and decoding analysis reveal that neural populations in both mPFC and Re represent different social stimuli, however, mPFC coding capacity is stronger. We demonstrate that chemogenetic inhibitions of Re impair the mPFC-Re neural synchronization and the mPFC social coding. Projection pathway-specific inhibitions by optogenetics reveal that the reciprocal connectivity between the mPFC and the Re is necessary for social recognition. These results reveal an mPFC-thalamic circuit for social information processing. How the brain distinguishes familiar individuals from unfamiliar ones is not fully understood. Here, the authors show that neurons in the medial prefrontal cortex and thalamus encode social information and the two brain areas interact with each other to promote social recognition. [ABSTRACT FROM AUTHOR]

The aims of the study were to degrade the anti-nutritional factors (ANFs) such as phytic acid, glycinin, and β-conglycinin and improve the values of soybean meal (SBM). Firstly, in this study, a strain PY-4B which exhibited the best enzymatic activities of protease (403.3 ± 17.8 U/mL) and phytase (62.9 ± 2.9 U/mL) was isolated and screened among the isolates. Based on the analysis of physiological and biochemical characteristics and 16S rDNA sequence, the strain PY-4B was identified and named as Pseudomonas PY-4B. Next, Pseudomonas PY-4B was applied to fermentation of SBM. The results showed that the contents of glycinin and β-conglycinin were decreased by 57–63%, and the phytic acid was remarkably degraded by 62.5% due to the fermentation of SBM by Pseudomonas PY-4B. The degradation of glycinin and β-conglycinin resulted in increase of contents of water-soluble proteins and amino acids in fermented SBM. Moreover, Pseudomonas PY-4B exhibited no hemolytic activity and slight inhibitory effect on the growth of pathogen Staphylococcus aureus and the wide range of pH tolerance (3 to 9). In summary, our study indicates that isolated strain Pseudomonas PY-4B is a safe and applicable strain and has the ability to effectively degrade the ANFs (phytic acid, glycinin, and β-conglycinin) in SBM by fermentation. [ABSTRACT FROM AUTHOR]

Background: Benign prostatic hyperplasia (BPH) is a prevalent and chronic progressive disease in aging males with lower urinary tract symptoms. Shikonin is known as traditional herb extracts, which have the capacity of anti-oxidation, anti-inflammation, and antitumor. However, little is known about the effect of shikonin on BPH. Objective: The BPH animal model was established by orchiectomy and testosterone propionate injection. After the end of animal model, the weight of prostate and the histologic structure of prostate tissues were examined. ELISA and immunoblotting were performed to detect the levels of steroid hormones, inflammatory cytokines, and oxidative factors as well as proteins. TUNEL was utilized to detect apoptotic cells in prostate tissues. Results: The administration of shikonin in BPH rats reduced the weight gain of prostate tissues, decreased the levels of DHT, testosterone, and PSA, and also restored the histologic change of prostate tissues. Also, in BPH rats, the IL-6, IL-1β, TNF-α, and MDA levels of prostate tissues were higher than control rats, while shikonin treatment reduced these biochemical changes. There were a lower apoptosis rate and lower expression of Bcl-2 in BPH rats, which was reversed by shikonin treatment. An increase in Nrf2, NQO1, and HO-1 of BPH prostate tissues was induced by shikonin supplement. The NF-κB pathway was activated in BPH prostate tissues, which was then inhibited by shikonin treatment. Conclusion: Our data revealed that shikonin treatment had a beneficial effect on the inflammation response, apoptotic process, and oxidative stress of BPH via the Nrf2-ARE and NF-κB pathways. [ABSTRACT FROM AUTHOR]

Background: This study constructed and validated a prognostic model to evaluate long-term cancer-specific survival (CSS) in middle-aged patients with early gastric cancer (EGC). Methods: We extracted clinicopathological data from relevant patients between 2004 and 2015 from Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided the patients into a training group (N = 688) and a validation group (N = 292). In addition, 102 Chinese patients were enrolled for external validation. Univariate and multivariate Cox regression models were used to screen for independent prognostic factors, and a nomogram was constructed to predict CSS. We used the concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the predictive performance of the model. Results: Univariate and multivariate COX regression analyses showed that tumor location, differentiation grade, N stage, chemotherapy, and number of regional nodes examined were independent risk factors for prognosis, and these factors were used to construct the nomogram. The C-index of the model in the training cohort, internal validation cohort, and external validation cohort was 0.749 (95% CI 0.699–0.798), 0.744 (95% CI 0.671–0.818), and 0.807 (95% CI 0.721–0.893), respectively. The calibration curve showed that the model had an excellent fit. The DCA curve showed that the model had good predictive performance and practical clinical value. Conclusion: This study developed and validated a new nomogram to predict CSS in middle-aged patients with EGC. The prediction model has unique and practical value and can help doctors carry out individualized treatment and judge prognosis. [ABSTRACT FROM AUTHOR]

Background and aims: Skeletal muscle strength decline is strongly associated with inflammation. While previous research has confirmed that diet can modulate chronic inflammation, little is known about the relationship between an anti-inflammatory dietary pattern and muscle strength. The aim of this study was to investigate the association between an anti-inflammatory dietary pattern and handgrip strength decline in a large-scale adult population. Methods: During a median follow-up period of 3 years, this prospective cohort study was carried out between 2013 and 2018 and included 2840 participants (60.2% men). Dietary intake was assessed using a validated food frequency questionnaire at baseline, and handgrip strength was measured annually with a handheld digital dynamometer. The dietary inflammatory potential score was calculated using white blood cell count and hypersensitive C-reactive protein as inflammatory markers, and was determined using reduced rank regression and stepwise linear regression. Multivariable linear regression models were used to estimate the association between the dietary inflammatory potential score and the annualized change in handgrip strength and weight-adjusted handgrip strength. Results: After adjusting for multiple confounding factors, significant associations between the dietary inflammatory potential score and the annualized change in handgrip strength and weight-adjusted handgrip strength in women, with values of − 0.8322 kg (95% confidence interval [CI] − 1.6405, − 0.0238; P < 0.0408) and − 0.0171 kg/kg (95% CI − 0.0310, − 0.0032; P < 0.0158), respectively. However, no significant differences were observed between the dietary inflammatory potential score and the annualized change in handgrip strength and weight-adjusted handgrip strength in men, with values of 0.1578 kg (95% CI − 0.6107, 0.9261; P < 0.6874) and − 0.0014 kg/kg (95% CI − 0.0115, 0.0088; P < 0.7933), respectively. Conclusion: Our findings suggest that consuming an anti-inflammatory dietary pattern could be a protective strategy against the decline in skeletal muscle strength in women. [ABSTRACT FROM AUTHOR]

The transposon mutagenesis strategy has been employed to generate random insertion mutants and analyze the correlation between genes and secondary metabolites in the genus Streptomyces. In this study, our primary objective was to identify an unknown gene involved in rimocidin biosynthesis and elucidate its role in rimocidin production in Streptomyces rimosus M527. To achieve this, we established a random mutant library of S. rimosus M527 using a Tn5 transposon-mediated random mutagenesis strategy. Among the 137 isolated mutants, M527-G10 and M527-W5 exhibited the most significant variations in antagonistic activity against the plant pathogenic fungus Fusarium oxysporum f. sp. cucumerinum. Specifically, M527-G10 displayed a 72.93% reduction, while M527-W5 showed a 49.8% increase in rimocidin production compared to the wild-type (WT) strain S. rimosus M527. Subsequently, we employed a plasmid rescue strategy to identify the insertion loci of the transposon in the genomes of mutants M527-G10 and M527-W5, revealing a response regulator transcription factor (rrt) and a hypothetical protein (hyp), respectively. The roles of rrt and hyp in rimocidin biosynthesis were determined through gene deletion, overexpression in the WT strain, and complemented expression in the transposon mutants. Notably, the gene-deletion mutants M527-ΔRRT and M527-ΔHYP exhibited similar behavior in rimocidin production compared to the corresponding transposon mutants M527-G10 and M527-W5, suggesting that transposon insertions in genes rrt and hyp led to alterations in rimocidin production. Furthermore, both gene deletion and overexpression of rrt and hyp had no discernible effects on cell growth. These results reveal that genes rrt and hyp have positive and negative impacts on rimocidin production in S. rimosus M527, respectively. [ABSTRACT FROM AUTHOR]

The transition to green energy systems is vital for addressing climate change, with a focus on renewable sources like wind and solar. This change requires substantial investment, societal adaptations, and managing a complex energy ecosystem. However, no existing evaluation methods support this purpose. The "energy metaverse" is proposed as a digital platform that mirrors the energy ecosystem, enabling the design, trial, and assessment of new technologies, business models, and value chains before real-world deployment. Drawing from State-of-the-Art technologies and methodologies, this paper introduces a conceptual framework for the energy metaverse, comprising five essential components: a versatile energy ecosystem data space, an interoperable virtual ecosystem living lab, an energy system models and artificial intelligent algorithms sandbox, a circular value chain co-design toolbox, and an ecosystem lifecycle evaluation software tool. This paper also suggests specific methods and technologies to develop each of these five components of the energy metaverse. [ABSTRACT FROM AUTHOR]

Modulation of magnetic properties through voltage-driven ion motion and redox processes, i.e., magneto-ionics, is a unique approach to control magnetism with electric field for low-power memory and spintronic applications. So far, magneto-ionics has been achieved through direct electrical connections to the actuated material. Here we evidence that an alternative way to reach such control exists in a wireless manner. Induced polarization in the conducting material immersed in the electrolyte, without direct wire contact, promotes wireless bipolar electrochemistry, an alternative pathway to achieve voltage-driven control of magnetism based on the same electrochemical processes involved in direct-contact magneto-ionics. A significant tunability of magnetization is accomplished for cobalt nitride thin films, including transitions between paramagnetic and ferromagnetic states. Such effects can be either volatile or non-volatile depending on the electrochemical cell configuration. These results represent a fundamental breakthrough that may inspire future device designs for applications in bioelectronics, catalysis, neuromorphic computing, or wireless communications. Conventional voltage control of magnetism relies on making direct electrical contacts to target samples. Here, wireless converse magnetoelectric actuation through bipolar electrochemistry is reported in magnetoionic transition metal nitride films. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) participate in the regulation of biological processes by binding to specific proteins and thus influence transcriptional processes. In recent years, circRNAs have become an emerging hotspot in RNA research. Due to powerful learning ability, the various deep learning frameworks have been used to predict the binding sites of RNA-binding protein (RPB) on circRNAs. These methods usually perform only single-level feature extraction of sequence information. However, the feature acquisition may be inadequate for single-level extraction. Generally, the features of deep and shallow layers of neural network can complement each other and are both important for binding site prediction tasks. Based on this concept, we propose a method that combines deep and shallow features, namely CRBP-HFEF. Specifically, features are first extracted and expanded for different levels of network. Then, the expanded deep and shallow features are fused and fed into the classification network, which finally determines whether they are binding sites. Compared to several existing methods, the experimental results on multiple datasets show that the proposed method achieves significant improvements in a number of metrics (with an average AUC of 0.9855). Moreover, much sufficient ablation experiments are also performed to verify the effectiveness of the hierarchical feature expansion strategy. [ABSTRACT FROM AUTHOR]

Platinum-based nanocomposites have been considered as one of the most promising catalysts for methanol oxidation reactions (MORs), which yet still suffer from low electrochemical activity and electron-transfer properties. Apart from van-der-Waals heterostructures, herein, we report a novel nanocomposite with the structure of Pt–Ru bimetallic nanoparticles covalently-bonded onto multi-walled carbon nanotubes (MWCNTs) (Pt–Ru@MWCNT), which have been successfully fabricated via a facile and green synthesis method. It is demonstrated that the Pt–Ru@MWCNT nanocomposite possesses much enhanced electrocatalytic activity with the electrochemical active surface area (ECSA) of 110.4 m2·g−1 for Pt towards MOR, which is 2.67 and 4.0 times higher than those of 20wt% commercial Pt@C and Pt-based nanocomposite prepared by other method, due to the improved electron-transfer properties originated from M–O–C covalent bonds. This work provides us a new strategy for the structural design of highly-efficient electrocatalysts in boosting MOR performance. [ABSTRACT FROM AUTHOR]

The cooperative formation tracking control is a key problem for the cooperative work of multiple agricultural machines on farmland. In view of the problem, this paper proposes an observer-based robust cooperative formation tracking control method, and the cooperative harvesting system of the combine harvester group is chosen to verify the effectiveness of the proposed method. Firstly, a second-order model is used to describe the combine harvester, and both matched and mismatched disturbances are taken into account. The disturbances are then observed using an observer with a cascade structure that combines NDO and ESO. On this basis, the observer-based robust cooperative formation tracking controller is designed based on multiple agent theory and the SMC method. In addition, the APF method is also employed to achieve the goal of preventing collisions among combine harvesters or between the combine harvesters and obstacles during the collaborative harvesting process. The results demonstrate that the observer-based robust tracking control method proposed in this paper can successfully achieve the cooperative formation tracking control of the combine harvester group without a collision. Moreover, the disturbance compensation method reduces the tracking errors of the combine harvester's working trajectory and increases the robustness of the cooperative formation harvesting system. [ABSTRACT FROM AUTHOR]

In this work, the effects of different ZnO-decorated graphene (Graphene-ZnO) proportions on the comprehensive properties of Poly(butylene succinate) (PBS) nanocomposites were investigated. The results show that the nanocomposites have stronger mechanical properties, higher thermal stability, greater crystallinity, better barrier ability, and excellent antibacterial property due to the influence of Graphene-ZnO. The tensile strength, crystallinity, melting temperature, thermal stability and barrier performance of the nanocomposites reached the maximum when Graphene-ZnO with a content of 0.2 phr was added. Compared with pure PBS, when Graphene-ZnO was added to PBS at 0.2 phr, the tensile strength, yield strength, elongation at break and moisture barrier performance of PBS were significantly increased by 22.5%, 31.34% ,62.84% and 42.57% respectively. This improvement in barrier performance might be attributed to the presence of Graphene-ZnO extended the penetration path of molecules and the coordination from PBS and Graphene-ZnO, so as to narrow the H2O transmission path. The results of the buried soil test show that the weight loss rate of the nanocomposite increased with increase Graphene-ZnO content, which may be caused by boundary defects between nanofiller and matrix, and poor compatibility at high content of Graphene-ZnO. [ABSTRACT FROM AUTHOR]

This study aimed to determine the common latent patterns of geographical distribution of health-related minerals across the USA and to evaluate the real-world cumulative effects of these patterns on overall population health. It was an ecological study using county-level data (3080 contiguous counties) on the concentrations of 14 minerals (i.e., aluminum, arsenic, calcium, copper, iron, lead, magnesium, manganese, mercury, phosphorus, selenium, sodium, titanium, zinc) in stream sediments (or surface soils), and the measurements of overall health including life expectancy at birth, age-specific mortality risks and cause-specific (summarized by 21 mutually exclusive groups) mortality rates. Latent class analysis (LCA) was employed to identify the common clusters of life expectancy-related minerals based on their concentration characteristics. Multivariate linear regression analyses were then conducted to examine the relationship between the LCA-derived clusters and the health measurements, with adjustment for potential confounding factors. Five minerals (i.e., arsenic, calcium, selenium, sodium and zinc) were associated with life expectancy and were analyzed in LCA. Three clusters were determined across the USA, the 'common' (n = 2056, 66.8%), 'infertile' (n = 739, 24.0%) and 'plentiful' (n = 285, 9.3%) clusters. Residents in counties with the 'infertile' profile were associated with the shortest life expectancy, highest mortality risks at all ages, and highest mortality rates for many reasons including the top five leading causes of death: cardiovascular diseases, neoplasms, neurological disorders, chronic respiratory conditions, and diabetes, urogenital, blood and endocrine diseases. Results remained statistically significant after confounding adjustment. Our study brings novel perspectives regarding environmental geochemistry to explain health disparities in the USA. [ABSTRACT FROM AUTHOR]

Incorporating distiller's grains (DG) into poly(ethylene terephthalate) (PET) has not been investigated because DG is not suitable for processing at high temperatures. Hence, in this study, DG was treated with methylenediphenyl diisocyanate (MDI) to prepare reinforced DG (RDG), which was then used as a biological filler that was melt-mixed with a PET resin to produce PET/DG and PET/RDG composites. The composite mechanical properties were investigated. Compared with PET/DG composites, PET/RDG composites exhibited improved mechanical properties. When the RDG content was 12.5%, the elongation at break reached the maximum. Scanning electron microscopy was used to observe the structure of composites filled with MDI-modified DG at the tensile section, and the compatibility between RDG fillers and the PET matrix was analyzed; RDG dispersed and adhered well in the matrix. The FTIR results showed the appearance of new characteristic peaks, indicating the possibility of DG reacting with MDI after the blending with PET, as well as providing clues about the probable reaction mechanism. Through X-ray diffraction, thermogravimetric analysis, differential scanning calorimetry, and water absorption tests, the crystallinity, thermal stability, and water absorption for the composites were analyzed. The results of TGA showed that RDG had a higher thermal stability than DG. Adding DG could significantly increase the crystallinity and rate of crystallization of PET, and at the same time, it could also improve the water absorption performance of the composites. But in the case of DG modification or treatment with MDI, the water absorption was slightly reduced. The results of contact angle analysis showed that the surface of PET/RDG composites had higher hydrophobicity than that of PET/DG composites because of the tighter structure. Relative to DG, RDG also effected improvement in the thermal stability of PET. [ABSTRACT FROM AUTHOR]

Cycloxaprid is a novel cis-neonicotinoid, mainly acting on the nicotinic acetylcholine receptor; however, it is not clear whether cycloxaprid can act on voltage-gated ion channels. In this study, the effects of cycloxaprid on the sodium, calcium and potassium channels in central neurons acutely dissociated from Helicoverpa armigera Hübner (Lepidoptera: Noctuidae) were investigated by the whole-cell patch clamp technique. With the application of cycloxaprid, the half voltage (V0.5) of activation and inactivation of sodium channels exhibited an obvious hyperpolarizing shift around 4–16 mV and 4–14 mV, respectively. The window currents of sodium channels increased by 35.04–88.89%. The time course of recovery from inactivation was also significantly prolonged by 0.25–0.43 ms. The V0.5 of activation and inactivation of calcium channels exhibited a marked hyperpolarizing shift around 6–9 mV and 13–19 mV, respectively. The window currents of calcium channels increased by 13.82–28.97%. The time course of recovery from inactivation for calcium channels was prolonged by 0.76–16.85 ms, although not significantly. Comparatively, the peak currents and the V0.5 of activation of potassium channels showed no significant change. These results indicate that sodium and calcium channels of H. armigera are potential target sites of cycloxaprid. [ABSTRACT FROM AUTHOR]

Low-frequency magnetic fields exert a significant inhibitory effect on tumor growth and have been developed as a therapeutic modality. However, the effect of a low-frequency magnetic field on the interaction between cells is still poorly understood. This study aimed to preliminarily evaluate the direct effect of magnetic field ditectely on cultured cells and indirect effect mediated by cell-environment (conditioned medium). 293 T cells, Hepg2 cells, A549 cells have been cultured at 37 ± 0.18 °C in presence of an extremely low-frequency magnetic field of 20 Hz, 5-mT. The adherent tumor cells were more sensitive to magnetic field inhibition in the original environment (conditioned medium) with adherence inhibition rate for Hepg2 and A549 estimated at 18% and 30% respectively. The inhibition effect was suppressed when the suspended cells separated or clump density at a low density. The nontumor cell lines showed no inhibitory effect on exposure to a low-frequency magnetic field. The intracellular ion fluorescence (IIF) showed that the magnetic field significantly altered the membrane potential, indicating hyperpolarization of the adherent cells (ΔIIF 293 T cells: − 25%, ΔIIF Hepg2 cells: − 20% and ΔIIF A549 cells: − 13%) and depolarization of the suspended cells (ΔIIF Raji cells: + 9%). In addition, the conditioned media collected after magnetic field exposure acted on unexposed tumor cells and caused inhibition. Our findings might provide a basis for the mechanism of magnetic field interaction between cells and cell environment in the future. [ABSTRACT FROM AUTHOR]

In this paper, we propose an hp -version fractional collocation method for solving second kind Volterra integro-differential equations with weakly singular kernels. We derive hp -version error estimates in a weighted H 1 -norm for the method on arbitrary meshes. The results show that for any given mesh partition, exponential rates of convergence can be achieved for certain weakly singular solutions by linearly increasing the degrees of piecewise fractional polynomials. The results also imply that in the case of uniform mesh, the method has no ( h -version) order barrier for weakly singular solutions, which is different from classical polynomial collocation methods. The method is easy to implement and has the same computational complexity as polynomial collocation methods. Numerical experiments are presented to demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we develop a collocation method for solving third-kind Volterra integral equations. In order to achieve high order convergence for problems with nonsmooth solutions, we construct a collocation scheme on a modified graded mesh using a basis of fractional polynomials, depending on a certain parameter λ . For the proposed method, we derive an error estimate in the L ∞ -norm, which shows that the optimal order of global convergence can be obtained by choosing the appropriate parameter λ and modified mesh, even when the exact solution has low regularity. Numerical experiments confirm the theoretical results and illustrate the performance of the method. [ABSTRACT FROM AUTHOR]

In this paper we develop a neural network for the numerical simulation of time-dependent linear transport equations with diffusive scaling and uncertainties. The goal of the network is to resolve the computational challenges of curse-of-dimensionality and multiple scales of the problem. We first show that a standard Physics-Informed Neural Network (PINN) fails to capture the multiscale nature of the problem, hence justifies the need to use Asymptotic-Preserving Neural Networks (APNNs). We show that not all classical AP formulations are directly fit for the neural network approach. We construct a micro-macro decomposition based neural network, and also build in a mass conservation mechanism into the loss function, in order to capture the dynamic and multiscale nature of the solutions. Numerical examples are used to demonstrate the effectiveness of this APNNs. [ABSTRACT FROM AUTHOR]

Scene text with occlusions is common in the real world, and occluded text recognition is important for many machine vision applications. However, corresponding techniques are not well explored as public datasets cannot represent the situation well, and methods designed for occluded text are still scarce. In this work, we discuss different kinds of occlusions and propose an occluded scene text enhancing network to improve recognition performance. The network is based on generative adversarial networks, and we design accretion blocks to help the network generate the occluded image regions. The model is independent of the recognition networks, so it can be readily used in different frameworks and can be easily trained without the annotations of text content. We also refine the training objective to improve the framework. Experiments on several public benchmarks demonstrate that the proposed method effectively enhances occluded text images, improving recognition accuracy by over 10% on several state-of-the-art frameworks. Meanwhile, the network has no severe impact on the text images without occlusions. [ABSTRACT FROM AUTHOR]

To enable sound scientific research in future energy informatics projects, it is necessary to obtain an overview of the current state of the research field to identify unaddressed gaps and challenges. Therefore, this paper aims to investigate the research trends and achievements within energy informatics in a Danish context within the last three decades. This paper reviews 207 energy informatics projects collected until the second quarter of 2022. Quantitative analysis results show that most projects have focused on applying energy informatics through energy-aware control of end-user consumption. The qualitative review finds an emphasis on data usage and end-users which aligns with the quantitative review. Furthermore, it tends to focus on specific end-users, e.g., buildings and heat pumps. Four overall recommendations are established: (I) Increased emphasis on research for sector coupling to aid in unlocking energy system flexibility, (II) project data value chain output, focusing on structuring and managing the data to make it applicable for future re-use, (III) utilizing industrial loads and incorporating an end-user perspective, (IV) inclusion of research institutions for the improved overall output of the projects through interdisciplinary solutions. [ABSTRACT FROM AUTHOR]

Due to large energy consumption and equipped building control systems, the majority of campus buildings have the potential to increase energy efficiency and provide energy flexibility. Among the three main types of occupants in the campus buildings (researchers/teachers, students, administration/management), students spend most of their time on learning activities in the campus buildings, and the energy performance can influence their learning performance. Therefore, this paper conducts a questionnaire targeting student occupants at a large engineering faculty in a Danish university to investigate occupants' experience of energy control, indoor comfort, and options of energy flexibility in campus buildings. In total, 267 fully completed and usable questionnaires were received. The dataset is available in.xlsx format, and the questionnaire that was used to collect the data is also provided together. [ABSTRACT FROM AUTHOR]

Understanding the local needs and challenges is critical for technology adoption in the energy sector. However, it is still a big challenge for most ecosystem stakeholders. Furthermore, technology adoption theories have mainly focused on the technology itself, and the business ecosystem perspective has been neglected. Therefore, this paper proposes an ecosystem-driven business opportunity identification method, a systematic approach for ecosystem stakeholders to conduct business opportunity analysis and evaluation based on the CSTEP ecosystem analysis and evaluation method. This method includes four correlated steps: Step 1: Identify the five CSTEP dimensions of the business ecosystem; Step 2: Identify potential changes in the business ecosystem; Step 3: Identify future ecosystem trends and timeline; Step 4: Select business opportunities; and Step 5: Potential solution identification. A web-based tool called opportunity identifier is developed for implementing the proposed method. A case study of the electric vehicle (EV) home charging energy ecosystem in Denmark is applied and demonstrates the application of the proposed method and the implementation of the developed web-based tool. Three value propositions are identified in the case study: (1) EV users can have optimal EV charging cost and optimal CO2 emission consumption with the intelligent EV charging algorithms that consider electricity prices, tariffs, and CO2 emission; (2) DSOs can avoid grid overloads and postpone the grid upgrade by applying intelligent EV charging algorithms; (3) Independent aggregators can aggregate EVs and participate in the ancillary service market or provide Vehicle-to-Grid services by using intelligent EV charging algorithms. Moreover, three feasible decentralized EV charging strategies (Real Time Pricing, Time-of-Use Pricing, and Timed charging) are identified as the potential solutions targeting the first value proposition. [ABSTRACT FROM AUTHOR]

Natural disasters threat the resilience of the electricity system. However, little literature has investigated the electricity system's recovering process and progress after natural disasters' hit which strongly influence the system operators' planning and quality of the security of supply for the electricity customers. To fill the research gap, this paper applies an unsupervised machine learning method, the k means clustering algorithm, to investigate the normal/abnormal electricity load profiles, identify natural disaster- and electrical fault-impacted electricity load profiles with a case study of the Lombok electricity system, Indonesia, and ½-hourly electricity load data from 2015 until 2021. The results show that electricity consumption in Lombok has increased over the years, which match the installed production capacity of Lombok. The results prove that the disturbance-induced electricity load patterns and especially natural disaster-impacted load profiles can be identified by the k means clustering algorithm. Especially, the pre-, during, and post-natural disaster impacted load patterns can be portrayed. Furthermore, the investigation results regarding the impacts of natural disasters and electrical faults on the performance of the electricity system, show that the natural disaster-induced load reductions and electrical fault-induced load reductions differ from the short and long-term perspectives. Moreover, the results can facilitate the electricity system operators to better understand the load patterns, predict ND strikes' impact on the electricity system and conduct better long-term energy management strategies. [ABSTRACT FROM AUTHOR]

The role of consumers as price-sensitive participants in electricity markets is considered essential to ensure efficient and secure operations of electricity systems. Yet the uncertain or unknown consequences of active market participation remain a large barrier for active consumer-side market participation. Simulations are a powerful tool to reduce this uncertainty by giving consumers an insight on the potential benefits and costs of market participation. However, the simulation setup must be adapted to each market context and each consumer market participation strategy. To simplify the simulation development process and improve the comparability of simulation results, this paper proposes a modular yet systematic electricity market modelling framework. The framework applies object-oriented programming concepts for business ecosystem modelling presented in previous works to develop an agent-based model of a consumer-centric electricity market ecosystem. The market ecosystem is represented by a multitude of interacting submarkets with their own logic. Within submarkets, context-independent and context-dependent elements are distinguished to provide model abstraction which can be adapted to different contexts. This framework is illustrated by applying it to three different submarkets in the Western Danish electricity market context: the Nordpool day-ahead market, the Nordpool intraday market, and the Frequency Containment Reserve market. The submarket role abstractions allow to benefit from the commonalities between the analysed submarkets during model implementation, while the role parametrisations allow to quickly adapt the roles to each market context. The implementation of the modelling framework in the Nordic context highlights the benefits of a modular approach in a liberalised and unbundled market context. [ABSTRACT FROM AUTHOR]

Digital twin technologies have become popular in wind energy for monitoring and what-if scenario investigation. However, developing a digital representation of the wind is challenging, especially due to the digital twin platform constraints. Game engines might be possible to solve this issue, especially since game engines have been used for product design, testing, prototyping, and also digital twins. Therefore, this study investigates the potential of developing a digital twin of wind power in the Unreal game engine. A case study of two types of wind turbines (Vestas V164-8 and Enercon E-126 7.580) and one location (Esbjerg, Denmark) is chosen for this study. The digital twin includes the environment with historical wind data and the visual representation of the wind turbine with a wind power production model and the estimated production in the given wind conditions of the area. The results show that game engines are viable for building entire digital twins where a realistic graphical user interface is required. Unreal Engine 5 provides the tools for modelling the landscape, surrounding water, and lighting. In addition, the Unreal Engine ecosystem provides vast amounts of content, such as 3D assets and game logic plugins, easing the digital twin development. The results prove that digital twins built in Unreal Engine 5 have great potential development of digital twins and user interfaces for communicating with a digital twin. The developed digital twin allows for further extension to benefit future digital twins utilizing wind turbines. [ABSTRACT FROM AUTHOR]

Energy systems face challenges due to climate change, distributed energy resources, and political agenda, especially distribution system operators (DSOs) responsible for ensuring grid stability. Accurate predictions of the electricity load can help DSOs better plan and maintain their grids. The study aims to test a systematic data identification and selection process to forecast the electricity load of Danish residential areas. The five-ecosystem CSTEP framework maps relevant independent variables on the cultural, societal, technological, economic, and political dimensions. Based on the literature, a recurrent neural network (RNN), long-short-term memory network (LSTM), gated recurrent unit (GRU), and feed-forward network (FFN) are evaluated and compared. The models are trained and tested using different data inputs and forecasting horizons to assess the impact of the systematic approach and the practical flexibility of the models. The findings show that the models achieve equal performances of around 0.96 adjusted R2 score and 4–5% absolute percentage error for the 1-h predictions. Forecasting 24 h gave an adjusted R2 of around 0.91 and increased the error slightly to 6–7% absolute percentage error. The impact of the systematic identification approach depended on the type of neural network, with the FFN showing the highest increase in error when removing the supporting variables. The GRU and LSTM did not rely on the identified variables, showing minimal changes in performance with or without them. The systematic approach to data identification can help researchers better understand the data inputs and their impact on the target variable. The results indicate that a focus on curating data inputs affects the performance more than choosing a specific type of neural network architecture. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) have attracted great attention in the energy sector. Game engines have been suggested to model DTs of their physical counterparts because they provide realistic graphics, lighting-, fluid- and physics engines that simulate the real world. However, the application of game engines to develop DTs for photovoltaics (PVs) has not yet been discussed in the literature. Therefore, this paper assesses the built-in game engine features' ability to support the DT development of PVs with Unreal Engine 5. This paper mainly focuses on visual representation because the surrounding environment significantly impacts PV deployment, and the existing software tools do not allow the study of the environmental factors at the early planning phase of a project's lifecycle. Furthermore, this paper investigates the position of the sun, shadows and reflections from nearby objects that influence the PVs' power output, and if the built-in light engine can be used for planning the deployment of PVs. The result shows that in-game objects in the environment can be used to affect the simulated PV output estimate over a year. It also indicates that applying Unreal Engine 5 to model PV systems that rely on mirroring real-world behaviour is promising if accurate data is used in the modelling. Real data and mathematical PV models are necessary since Unreal Engine 5's Lumen subsystem cannot provide realistic solar radiance on PVs for a given location on earth. [ABSTRACT FROM AUTHOR]

Worldwide, buildings consume about 40 percent of the overall energy resources and contribute to an average of 30 percent of the global carbon emission. Hence, technologies for improving the energy efficiency of buildings play an essential role in the global fight against climate change. The CELSIUS project aims to improve the energy efficiency and indoor climate of medium to large sized commercial and public buildings by developing an integrated system solution that consists of (1) an IoT-enabled and cloud-based platform for monitoring and diagnostics of building energy performance and indoor climate quality, (2) a middleware software platform for cost-effective large-scale deployment of wireless sensors and gateways, and (3) an IoT network management platform for cost-efficient life-cycle maintenance of sensors and gateways. The integrated system solution will be deployed and demonstrated in a 6000 m2 building in Aarhus, Denmark, and an 18,000 m2 building in Kuala Lumpur, Malaysia. By choosing buildings located in different climate zones on different continents allows the developed system solution to be tested under realistic conditions for the international export market. [ABSTRACT FROM AUTHOR]

Severe outcome particularly death is the largest burden of COVID-19. Clinical observations showed preliminary data that deficiency in certain trace elements, essential for the normal activity of immune system, may be associated with worse COVID-19 outcome. Relevant study of environmental epidemiology has yet to be explored. We investigated the geographical association between concentrations of Se, Zn, Fe and Cu in surface soils and case fatality rate of COVID-19 in USA. Two sets of database, including epidemiological data of COVID-19 (including case fatality rate, from the University of John Hopkinson) and geochemical concentration data of Se, Zn, Fe and Cu in surface soils (from the National Geochemical Survey), were mapped according to geographical location at the county level across conterminous USA. Characteristics of population, socio-demographics and residential environment by county were also collected. Seven cross-sectional sampling dates, with a 4-week interval between adjacent dates, constructed an observational investigation over 24 weeks from October 8, 2020, to March 25, 2021. Multivariable fractional (logit) outcome regression analyses were used to assess the association with adjustment for potential confounding factors. In USA counties with the lowest concentration of Zn, the case fatality rate of COVID-19 was the highest, after adjustment for other influencing factors. Associations of Se, Fe and Cu with case fatality rate of COVID-19 were either inconsistent over time or disappeared after adjustment for Zn. Our large study provides epidemiological evidence suggesting an association of Zn with COVID-19 severity, suggesting Zn deficiency should be avoided. [ABSTRACT FROM AUTHOR]

A series of 2-(4-phenylpiperazin-1-yl)pyrimidine-5-carboxamide derivatives as acetylcholinesterase inhibitors (AChEIs) were designed and synthesized for the treatment of Alzheimer's disease (AD). Their bioactivities were evaluated by the Ellman's method, and the results showed that most of synthesized compounds displayed moderate acetylcholinesterase inhibitory activities in vitro. Among them, compound 6g exhibited the most potent inhibitory activity against AChE with IC50 of 0.90 μM, and poor inhibitory activity against butyrylcholinesterase (BuChE) with IC50 of 7.53 μM, which indicated that compound 6g was a selective AChE inhibitor, and compound 6g as a selective AChE inhibitor was confirmed by the molecular docking studies of compound 6g with AChE and BuChE. Furthermore, the mechanism of inhibition of compound 6g against AChE was analyzed by the kinetic study, and the result indicated that compound 6g was the mixed-type inhibitor of competitive inhibition and non-competitive inhibition. All the above showed that compound 6g could be considered as a lead compound for the development of AD drugs. [ABSTRACT FROM AUTHOR]