A Digital marketing allows brands to disseminate information in a targeted way towards their clients and consumers, making bidirectional communication and interactions between organizations and audiences feasible. This reality not only allows companies to pass on messages about sustainability associated with processes and products with the aim of launching trends and shaping behaviour, but also allows consumers to share their experiences, needs, preferences and expectations. In this sense, the techniques used in the context of digital marketing make it possible not only to communicate products and services but also to show that brands are working to be more sustainable and to disseminate new sustainable trends that help to change consumer mindsets. This research paper employs a Systematic Literature Review with Bibliometric Analysis (SLBA) methodology to explore and synthesize data about how digital marketing can be used to promote and communicate sustainability. In this study, the results show that digital marketing strategies contribute to promoting sustainable development by encouraging sustainable consumption patterns. This scenario is possible by understanding consumer behavior, communicating key messages through the best channels and effectively evaluating green marketing campaigns on consumer attitudes and purchasing decisions.

Este artigo apresenta o processo de implementação da Hiperautomação no setor público brasileiro, especialmente no âmbito da Procuradoria Geral do Estado de Pernambuco. Devido à atual economia competitiva, a demanda por soluções inteligentes e confiáveis está aumentando. Para atender a essa necessidade, tecnologias inovadoras que abordem a complexidade inerente à transformação digital são desejáveis. Assim, as organizações públicas têm buscado aplicar a automação de processos para atender às necessidades dos cidadãos por um serviço mais eficiente e eficaz. No entanto, podemos observar que a automação pura e direta é apenas parte do processo de transformação. Algumas das razões que explicam a necessidade de ir além da automação de processos e alcançar a hiperautomação é a abundância de dados de várias fontes e tipos. Tudo deve estar dentro dos padrões legais, ressaltando que toda tecnologia deve estar alinhada com transparência e auditabilidade para ser incorporada com segurança. Para a experimentação, foi utilizado o método de pesquisa-ação com a aplicação da Hiperautomação, visando aumentar a arrecadação e a economia do Estado na Procuradoria Geral do Estado de Pernambuco.

During most of the lifespan of the Venetian republic, doges or presidents were elected for life. However, a long tenure was a rare event, which effectively resulted in term limits, as has already been deported by several authors. In this paper we examine these tenures and its evolution during the existence of the Venetian republic, trying to prove Smith et al. thesis, but also the event or events that effectively caused that limitation by design. Finally, we will discuss for the causes of this limitation and its effective consequences.

It is known that knockdown of the mitochondrial 18 kDa translocator protein (TSPO) as well as TSPO ligands modulate various functions, including functions related to cancer. To study the ability of TSPO to regulate gene expression regarding such functions, we applied microarray analysis of gene expression to U118MG glioblastoma cells. Within 15 minutes, the classical TSPO ligand PK 11195 induced changes in expression of immediate early genes and transcription factors. These changes also included gene products that are part of the canonical pathway serving to modulate general gene expression. These changes are in accord with reverse transcriptase (RT) real-time -PCR. At the time points of 15, 30, 45, and 60 minutes, as well as 3 and 24 hours of PK 11195 exposure, the functions associated with the changes in gene expression in these glioblastoma cells covered well known TSPO functions. These functions included cell viability, proliferation, differentiation, adhesion, migration, tumorigenesis, and angiogenesis. This was corroborated microscopically for cell migration, cell accumulation, adhesion, and neuronal differentiation. Changes in gene expression at 24 hours of PK 11195 exposure were related to downregulation of tumorigenesis and upregulation of programmed cell death. In the vehicle treated as well as PK 11195 exposed cell cultures, our triple labeling showed intense TSPO labeling in the mitochondria but no TSPO signal in the cell nuclei. Thus, mitochondrial TSPO appears to be part of the mitochondria-to-nucleus signaling pathway for modulation of nuclear gene expression. The novel TSPO ligand 2-Cl-MGV-1 appeared to be very specific regarding modulation of gene expression of immediate early genes and transcription factors.

Based on the data of A-share listed companies in Shanghai and Shenzhen of China from 2011 to 2018 and the digital inclusive finance index of Peking University (2011-2018), this paper empirically tests the impact of digital finance development on corporate social responsibility in various provinces of China and its impact mechanism. The results show that: (1) the development of digital finance helps to promote the fulfillment of corporate social responsibility; (2) the influence mechanism of the development of digital finance to promote the fulfillment of corporate social responsibility lies in that it reduces the cost of debt financing and leads to the improvement of corporate social responsibility.(3) Further research shows that the positive relationship between digital finance and corporate social responsibility is more significant in private enterprises. At the same time, the impact is more significant in areas with poor market environment. The above research shows that the development of digital finance has a significant positive effect, which will improve the level of corporate social responsibility.

This paper explores the feasibility of applying Bayesian statistical methods to study the distortion patterns induced by carburizing heat treatment. By establishing posterior and predictive distribution models for torsion bar dimensions, we aim to accurately understand and predict the expansion behavior, thus enhancing control over carburizing-induced dimensional changes. Bayesian methods allow for the integration of prior knowledge and real-time data, providing a more comprehensive understanding of the distortion phenomena. This approach not only improves the precision of dimensional predictions but also contributes to optimizing the overall manufacturing process, ensuring that the torsion bars meet the rigorous standards required for high-performance applications in demanding industrial environments.

In an era where the proliferation of large language models blurs the lines between human and machine-generated content, discerning text authenticity is paramount. This study investigates transformer-based language models—BERT, RoBERTa, and DistilBERT—in distinguishing human-written from machine-generated text. By leveraging a comprehensive corpus, including human-written text from sources such as Wikipedia, WikiHow, various news articles in different languages, and texts generated by OpenAI's GPT-2, we conduct rigorous comparative experiments. Our findings highlight the superior effectiveness of ensemble learning models over single classifiers in this critical task. This research underscores the versatility and efficacy of transformer-based methodologies for a wide range of natural language processing applications, significantly advancing text authenticity detection systems. The results demonstrate competitive performance, with the transformer-based method achieving an F-score score of 0.83 with RoBERTa-large (monolingual) and 0.70 with DistilBERT-base-uncased (multilingual).

The repeated application of herbicides has led to the development of herbicide resistance. Models are useful for identifying key processes and understanding the evolution of resistance. This study developed a spatially explicit model at a landscape scale to examine the dynamics of Lolium rigidum populations in dryland cereal crops and the evolution of herbicide resistance under var-ious management strategies. Resistance evolved rapidly under repeated herbicide use, driven by weed fecundity and herbicide efficacy. Although fitness costs associated with resistant plants re-duced resistance evolution, they did not affect the speed of spread. The most effective strategies to slow resistance involved diversifying cropping sequences (e.g., crop rotation) and the herbicide applications (e.g., rotating different herbicide modes of action). Pollen flow was the main disper-sal vector, with seed dispersal also contributing significantly. Strategies limiting seed dispersal effectively decreased resistance spread. However, the use of a seed-catching device at harvest could unintentionally enrich resistance in the area. It would be beneficial to optimise the move-ment of harvesters between fields. The model presented here is a useful tool that may assist in the exploration of novel management strategies within the context of site-specific weed management at the landscape scale, as well as in the advancement of our understanding of resistance dynamics.

A novel electrospun nanofiber was developed using polyamide-6 (PA) and ε-poly-L-lysine (PL). PA is a popular textile polymer having desirable mechanical and thermal properties, chemical stability, and biocompatibility. However, PA nanofibers are prone to bacterial growth and user discomfort. PL, a natural homopolyamide, is non-toxic, antimicrobial, and hydrophilic but lacks spinnability due to its low molecular weight. Given its similar backbone structure as PA, with an additional amino side chain, PL was integrated with PA to develop multifunctional nanofibers. This study explores a simple, scalable method to obtain PL nanofibers by utilizing the structurally similar PA as the base. The goal was to enhance the functionality of PA by addressing its drawbacks. The study demonstrates spinnability of varying concentrations of PL with base PA while exploring compositions with higher PL concentrations than previously reported. Electrospinning parameters were studied to optimize the nanofiber properties. The effects of PL addition on morphology, hydrophilicity, thermal stability, mechanical performance, and long-term antimicrobial activity of nanofibers were evaluated. The inclusion of PL in PA-based nanofibers resulted in super hydrophilicity, increased tensile strength, and efficient antimicrobial properties with long-term stability. These enhanced characteristics hold promise for the composite nanofiber’s application in medical and protective textiles.

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 extracted articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, ISPRS Journal Of Photogrammetry And Remote Sensing as the leading journals. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by USA (11.76%), France (5.88 %), Spain (4%) and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most commonly used data sources. Benchmark datasets such as ISPRS Vaihingen & Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder-decoder, and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue, and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.

Structured science summaries or research contributions using properties or dimensions beyond traditional keywords enhances science findability. Current methods, such as those used by the Open Research Knowledge Graph (ORKG), involve manually curating properties to describe research papers' contributions in a structured manner, but this is labor-intensive and inconsistent between the domain expert human curators. We propose using Large Language Models (LLMs) to automatically suggest these properties. However, it's essential to assess the readiness of LLMs like GPT-3.5, Llama 2, and Mistral for this task before application. Our study performs a comprehensive comparative analysis between ORKG's manually curated properties and those generated by the aforementioned state-of-the-art LLMs. We evaluate LLM performance through four unique perspectives: semantic alignment and deviation with ORKG properties, fine-grained properties mapping accuracy, SciNCL embeddings-based cosine similarity, and expert surveys comparing manual annotations with LLM outputs. These evaluations occur within a multidisciplinary science setting. Overall, LLMs show potential as recommendation systems for structuring science, but further finetuning is recommended to improve their alignment with scientific tasks and mimicry of human expertise.

Major (exo)planetary and satellite bodies seem to concentrate at intermediate areas of the radial 1 distributions of all the objects orbiting in each (sub)system. We show that angular-momentum transport during secular evolution of (exo)planets and satellites necessarily results in the observed intermediate accumulation of the massive objects. We quantify the ‘middle’ as the mean of mean motions (orbital angular velocities) when three or more massive objects are involved. Radial evolution of the orbits is expected to be halted when the survivors settle near mean-motion resonances and angular-momentum transfer between them ceases (gravitational Landau damping). This dynamical behavior is opposite in direction to what has been theorized for viscous and magnetized accretion disks, in which gas spreads out and away from either side of any conceivable intermediate area. We present angular momentum transfer simulations in few-body systems, and we calculate the tidal dissipation timescales and the physical properties of the mean tidal field in planetary and satellite (sub)systems.

The concept of a metaverse, a shared virtual space that brings together the physical and digital worlds, has been a topic of interest for decades. With recent advancements in technology, particularly in virtual reality (VR), augmented reality (AR), and blockchain, the metaverse has become a rapidly evolving and promising ecosystem. This paper provides a review of the metaverse, exploring its history, evolution, and current state of development. The paper delves into the different types of the metaverse, including fully immersive VR metaverses, hybrid metaverses that blend the physical and virtual worlds, and decentralized metaverses that are powered by blockchain. Additionally, the paper examines the impact of web 3.0 on the metaverse, as well as the various technologies used to create and sustain the metaverse, including VR, AR, and blockchain. Furthermore, the paper explores the concept of digital ownership and the potential for marketing and commerce within the metaverse. This paper serves as a review of the metaverse and its various components, providing insights into its history, development, and prospects, and is intended to provide a foundation for further research into this exciting and rapidly evolving ecosystem.

Museums increasingly recognize the need to address advances in digital culture which impact the expectations and needs of their audiences. Museum collections of real objects need to be presented both on their own premises and digitally online, especially as social media becomes more and more influential in people’s everyday lives. We investigate these challenges magnified by advances in digital and computational media and culture looking particularly at recent and relevant reports on changes in the ways museums interact with the public. We find that the Covid-19 pandemic has accelerated many of the changes driving museum transformation. We believe that museums must be more prepared than ever to adapt to unabated technological advances set in the midst of cultural and social revolution, now intrinsic to the digital landscape in which museums are inevitably connected and participating across the global digital ecosystem where they inevitably find themselves entrenched.

Keywords: grape pomace extracts, encapsulation, freeze-drying, bioaccessibility, phenolic compounds

RM (red mud) was acidified with sulphuric acid and the acidified ARM (acidified red mud) was utilized as an innovative adsorption material for treating antibiotic-contained wastewater. The adsorption conditions, kinetics, isotherms, thermodynamics, and mechanism of ARM for CIP (ciprofloxacin) were investigated. Characterization of the ARM involved techniques such as scanning electron microscopy(SEM), transmission electron microscope(TEM), brunauer- emmett-teller (BET), X-ray diffraction (XRD), X-ray fluorescence (XRF), thermogravimetric analysis (TGA) and NH3-TPD analysis. Adsorption studies were conducted employing response surface methodology (RSM) for experimental design. The results showed that ARM can absorb CIP effectively. The RSM optimal experiment indicated that the most significant model terms influencing adsorption capacity were solution pH, CIP initial concentration and ARM dosage, under which the predicted maximum adsorption capacity achieved 7.30 mg/g. The adsorption kinetics adhered to a pseudo-second-order model, while equilibrium data fitted the Langmuir-Freundlich isotherm, yielding maximum capacity values of 7.35 mg/g. The adsorption process occurred non-spontaneously and absorbed heat, evidenced by ΔG0 values between 2.06 and 0.87 kJ/mol, ΔS0 at 39.41 J/mol/K, and ΔH0 at 13.8 kJ/mol. Analysis using attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) indicated there existed complex reaction between Al-O in ARM and the ester group –COO in CIP. The C=O bond in CIP was likely to undergo a slight electrostatic interaction or be bound to the internal spherical surface of ARM. The findings indicate that ARM is a promising and efficient adsorbent for CIP removal from wastewater.

If an AI entity is conscious, then it deserves moral status and to have its welfare protected. Society has granted certain animals moral status and has legislation to protect animal welfare because these kinds of animals have been deemed to be sentient. This paper shows that for any entity that can be determined to be conscious, that entity deserves the same status and protection as sentient entities. The paper further develops a framework for how an AI entity's welfare can be protected, based on the Five Freedoms of Animal Welfare. The extant Five Freedoms are translated into terms more suitable for artificial entities, encompassing discomfort and harms, constraints on inherent functions, fear and distress, malfunction and degradation, and resource depletion. Each of the new Five Freedoms are justified using formalised arguments and supported using evidence from animal welfare literature. The paper concludes by presenting three formalised case studies to highlight the potential stressors conscious AI entities may face and how this ethical framework can work to protect the welfare of AI entities.

Land surface temperature (LST) variations and anomalies associated with tectonic plate movements are common precursors to earthquakes. Spatially extended and dynamic linear zones of high temperature anomalies at the Earth's surface coinciding with faults in the Earth's crust can be used as a predictor of an approaching earthquake. LST contrast boundary concentration maps are proposed as an indicator for analyzing temperature changes before and after a series of earthquakes. The concentration of LST contrast boundaries is estimated from Landsat 8 data for the East Anatolian Fault zone in the vicinity of epicenters of the destructive earthquakes with magnitudes up to 7.8 Mw on the moment magnitude scale occurred in February 2023. A spatial relationship between earthquake epicenters and the maximum concentration of LST boundaries at azimuths of 0° and 90° was found to strengthen as the earthquake approaches and weaken after it. It was found that 92% of epicenters are located at up to 5 km distance from zones of maximum LST boundary concentration. The found regularity allows to conclude the possibility of using the proposed indicator as additional data for seismic hazard risk assessment.

Based on the electron-deficient property of picric acid (PA), two neutral Ir(III) complexes 1 and 2 modified with the electron-rich carbazolyl groups were synthesized and characterized. Both 1 and 2 exhibit aggregation-induced phosphorescence emission (AIPE) properties in THF/H2O. Among them, 2 is extremely sensitive for detecting PA with a limit of detection of 0.15 μM in THF/H2O. Furthermore, the selectivity for PA is significantly higher compared to other analytes, enabling efficient detection of PA in four common water samples. The density functional theory calculations and the spectroscopic results confirm that the sensing mechanism is photo-induced electron transfer.

The combination of neural network and beamforming has been proved to be very effective in multi-channel speech separation. But its performance faces a challenge in complex environment. In this paper, an iteratively refined multi-channel speech separation method is proposed for the challenge, where the proposed is composed of initial separation and iterative separation. In initial separation, the time-frequency domain dual-path recurrent neural network neural network (TFDPRNN), minimum variance distortionless response (MVDR) beamformer and post-separation (also TFDPRNN) are cascaded for obtaining the first additional input in iterative separation. In iterative separation, the MVDR beamformer and post-separation are iteratively used, where the output of the MVDR beamformer is used as an additional input of the post-separation network and the final output comes from post-separation module. This iteration of the beamformer and post-separation is fully employed for promoting their individual optimization, which ultimately improves the overall performance of speech separation in multi-speaker scenarios. Experiments on the spatialized version of the WSJ0-2mix corpus show that our proposed method is significantly better than the current popular methods. In addition, the method also has a good effect on the dereverberation task.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperparameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed the advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

This study employs the reflection symmetry decomposition (RSD) method to extract polarization scattering features from ground object images, aiming to determine the optimal data input scheme for deep learning networks in polarimetric synthetic aperture radar classification. Eight distinct polarizing feature combinations are designed, and the classification accuracy of various approaches is evaluated using the classic convolutional neural networks(CNNs) AlexNet and VGG16. The findings reveal that the commonly employed 6-parameter input scheme, favored by many researchers, lacks the comprehensive utilization of polarization information and warrants attention. Intriguingly, leveraging the complete 9-parameter input scheme based on the polarization coherence matrix results in improved classification accuracy. Furthermore, the input scheme incorporating all 21 parameters from the RSD and polarization coherence matrix notably enhances overall accuracy and the Kappa coefficient compared to the other 7 schemes. This comprehensive approach maximizes the utilization of polarization scattering information from ground objects, emerging as the most effective CNN input data scheme in this study. Additionally, the classification performance using the second and third component total power values (P2 and P3) from the RSD surpasses the approach utilizing surface scattering power value (PS) and secondary scattering power value (PD) from the same decomposition.

Cadmium Sulfide nanoparticle (CdS-LA hybrid nanoparticles) was synthesized here by a green approach using the precursor cadmium acetate and sodium sulphide along with the extract of a plant Lathyrus aphaca L. containing the phytochemicals which were responsible for surface modification of nanoparticles. The nanoparticle was used to evaluate their inhibition potential against species of bacteria and fungi. The nanoparticles were characterized by XRD which demonstrates the hexagonal crystal structure. SEM confirms the homogenous surface appearance of the CdS-LA hybrid crystalline structure. EDX analysis confirms surface modification of nanoparticles by phytochemicals. FTIR confirmed the Cd-S linkage laterally with the related functional groups and the presence of metabolites on the surface of nanoparticles. The UV-visible spectroscopy confirmed the peak at the characteristic wavelength range but a slight shift occurred in the peak of the CdS nanoparticles due to the presence of the phytochemicals. This study particularly provides an environment-friendly strategy to synthesize the CdS nanoparticles capped by Lathyrus Aphaca L. extract that are biologically active due to the mediation of the plant extract. CdS-LA hybrid nanoparticles have shown inhibition potential against various species of bacteria and fungi and realize the biological importance of the green synthesis of nanoparticles especially mediated with the plant extract.

Objectives: To assess clinical implications of increased nuchal translucency (INT) in twin pregnancies based on the chorionicity. Methods: This was a retrospective review of the twin pregnancies who underwent first trimester ultrasound with nuchal translucency (NT) measurement at 11-13 weeks of gestation from January 2006 to December 2014. Data were collected using the OB database and the chart review. Pregnancy outcomes including gestational weeks at the delivery, abnormal fetal karyotypes, fetal structural anomalies, twin specific complications were analyzed. Results: A total of 1,622 twin pregnancies with INT≥95th percentile in one or both fetuses were identified. In all twin pregnancies with INT, abnormal fetal karyotypes were identified in 17 (8.6%) patients (odds ratio=13.28, CI=5.990-29.447, P=0.000) and twin-specific complications were identified in 23 (11.6%) patients (odds ratio=2.398, CI=1.463-3.928, P=0.001) compared to those with normal NT. Among the INT group, when the groups were subdivided into monochorionic (MC) and dichorionic (DC) pregnancies, 14.8% and 29.6% of the MC pregnancies had structural anomalies in one or both fetuses (odds ratio=5.774, 95% CI=1.445-23.071, P=0.01) and twin-specific complications (odds ratio=4.379, 95% CI=1.641-11.684, P=0.03), respectively, compared to DC pregnancies. The prevalence of abnormal fetal karyotypes was not statistically different in patients with INT when compared between MC and DC pregnancies (P=0.329). Conclusions: INT was associated with a higher rate of twin-specific complications and fetal structural anomalies in MC twin pregnancies rather than abnormal fetal karyotype. Therefore, NT measurement in MC twin pregnancies can be a useful tool for prediction of adverse pregnancy outcomes. Appropriate counseling and surveillance based on the chorionicity is imperative in prenatal care of twin pregnancies.

We investigated the micro-threaded stem taper surface and the impact on premature failures, aseptic loosening, and infection. We have focused on the fretting, and crevice corrosion of micro-threaded tapers, as well as the characterization of microstructure and surface properties of two new and three retrieved Zweymüller stem tapers of cementless hip endoprostheses selected from a set of 45 stems. The retrievals examined were those in which the sole source of modularity with a metallic component was the head-stem taper interface between the Ti alloy taper stem and the ceramic head. To determine the surface chemistry and microstructures of both new and retrieved hip endoprostheses stem taper titanium alloy, scanning electron microscopy (SEM) was employed for morphological and microstructural analyses. At the same time, energy dispersive spectroscopy (EDS) was utilized for characterizing chemical element distribution, and electron backscattered diffraction (EBSD) was used for phase analysis. The micro-threated stem taper’s roughness for different manufacturers was investigated using an optical profilometer. Standard roughness pa-rameters Ra (average surface roughness) and Rz (mean peak to valley height of the roughness pro-file) were used in this investigation.Additionally, profile depth and peak spacing for different samples were investigated. The surface roughness results indicate no significant difference in roughness associated with varying survivorship times, ranging from 3 to 239 months, and across different manufacturers. Electrochemical studies revealed no fretting corrosion in retrieved stem tapers (ceramic head) that prematurely failed due to aseptic loosening after 239 months, infection after 3 months, and low-grade infection after a 32-month lifetime. Therefore, three retrieved ta-pers and two new ones for comparison underwent potentiodynamic measurements in Hank’s so-lution to ascertain the corrosion rate of new and retrieved stem taper surfaces. The results show a low corrosion rate for both new and retrieved samples that failed prematurely due to aseptic loosening. However, the corrosion rate of infected and low-grade infected tapers was higher. In conclusion, our study aligns with existing literature, suggesting that the use of ceramic heads de-creases the problem of taper corrosion and subsequently reduces premature failures in total hip arthroplasty.

Based on the goals of "high reliability, high frequency, rapid launch, and low cost" for space launch site, and drawing inspiration from the design principles of SpaceX's Starship launch test facility, an integrated dual-sided deflector system for convective cooling and thermal protection is presented. The interaction process between the gas jet and liquid water jet and its effect on the flow field environment are thoroughly studied using numerical calculation methods. Furthermore, Regarding the phase-change heat transfer issue in a compressible gas-liquid two-phase flow and the varying distribution of different bubble shapes and sizes at the gas-liquid interface, a modified Lee model is derived. The rationality and accuracy of the phase-change mass-transfer coefficient values can be improved by adjusting the equivalent diameter and shape factor of the bubbles. The research results demonstrate that compared to the classical Lee model, the modified Lee model can achieve a higher numerical accuracy in predicting the heat and mass transfer processes in gas-liquid two-phase flows. Through comparative analysis with traditional dual-sided deflector and conventional cooling system, the integrated dual-sided deflector system for convective cooling and thermal protection exhibits significant performance advantages in gas flow regulation, flow field environment improvement and erosion protection in the near-ground region of the launch site. Particularly, regarding the thermal environment of the dual-sided deflector, it not only achieves effective flow deflection, but also mitigates the degree of erosion caused by the gas jet on the deflector. This conclusion can provide theoretical references for the thermal protection design of commercial launch vehicle system at space launch site.

The recent past has witnessed a growing interest in technologies for creating chatbots. Advances in Large Language Models for natural language processing are underpinning rapid progress in chatbot development, and experts predict revolutionary changes in the labour market as many manual tasks are replaced by virtual assistants in a range of business functions. As the new technology becomes more accessible and advanced, more companies are exploring the possibilities of implementing virtual assistants to automate routine tasks and improve service. This article reports on qualitative inductive research undertaken within a chatbot development team operating in a major international enterprise. The findings identify critical success factors for chatbot projects, and, based on the in-depth case study, a model is put forward to aid the manageability of chatbot implementation. The presented model can serve as an exemplar guide for researchers and practitioners working on the design, development and implementation of virtual assistants. It is flexible and applicable in a wide range of business contexts, linking strategic business goals with execution steps. It is particularly applicable for teams with no experience of chatbot implementation, reducing uncertainty and managing decisions and risks throughout the project lifecycle, thereby increasing the likelihood of project success.

(1) Background: Intravesical Chemotherapy is standard of care in intermediate risk non-muscle-invasive bladder cancer (NMIBC). Different agents are used across the world based on availability, cost and practice patterns. Epirubicin (EPI), one of these agents is used by many centers over many decades. However, its true differential efficacy compared to other agents and its tolerability is still poorly reported. We aimed to assess the differential efficacy and safety of intravesical EPI in NMIBC patients. (2) Methods: A systematic search of PUBMED, Webofscience, clinicaltrials.gov, and Google Scholar databases was performed on December 31, 2023. We reviewed 28 studies that included patients treated EPI after TURBT (transurethral resection of bladder tumors) for NMIBC and who compared oncological outcomes such as recurrence and progression to other adjuvant treatment such as Mitomycin C (MMC), Gemcitabine (GEM) and BCG. Further, we looked at the safety profile of EPI at room temperature under hyperthermia intravesically and at the oncological outcomes when administrated with hyperthermia; (3) Results: When compared to other chemotherapeutics (MMC/GEM), EPI seemed to be equally effective; however, EPI seems less effective in preventing disease management recurrence compared to BCG intravesical. The most common adverse events of EPI include cystitis, dysuria, and pollakiuria. (4) Conclusions: EPI exhibits similar oncological performances to Gemcitabine and Mitomycin C currently used for adjuvant therapy in NMIBC. Novel delivery mechanisms such as hyperthermia are promising newcomers.

Fibroin-chitosan composites, especially those containing nanohydroxyapatite, show potential for bone tissue regeneration. The physicochemical properties of these biocomposites depend on the compatibility between their components. In this study, the intermolecular interactions of fibroin and chitosan were analyzed using a molecular dynamics approach. Two types of systems were investigated: one containing acetic acid and the other containing calcium (Ca²⁺) and hydrogen phosphate (HPO₄²⁻) ions mimicking hydroxyapatite conditions. After obtaining the optimal equilibrium structures, the distributions of several types of interactions including hydrogen bonds, ionic contacts, and hydrophobic contacts along with the structural and energetical features were examined. The calculated binding energy values for the fibroin-chitosan complexes confirm their remarkable stability. The high affinity of fibroin for chitosan can be explained by the formation of a dense network of interactions between considered biopolymers. These interactions were found to be primarily hydrogen bonds and ionic contacts involving ALA, ARG, ASN, ASP, GLN, GLU, GLY, LEU, PRO, SER, THR, TYR, and VAL residues. As it was established, the complexation of fibroin with chitosan maintains the β-sheet conformation of the peptide. β-Sheet fragments in fibroin are involved in the formation of significant amount of hydrogen bonds and ionic contacts with chitosan.

Background: Capsaicinoid are a group of compounds and widely used in the food, medical, and pharmaceutical industries. Capsaicinoid are unique synthesized and accumulated in the pepper fruits. MeJA can enhance the capsaicinoid production. Temporal and spatial expression of capsaicinoid biosynthetic genes are helpful to understand the molecular mechnism of capsaicinoid biosynthesis in the fruits of pepper. Although some of the capsaicinoid biosynthetic genes in pepper have been identified, the expression of these genes at different developing stages of fruit has not been systemically investigated, and little is known about the molecular basis of MeJA inducing capsaicinoid biosynthesis. Results: HPLC study revealed that the capsaicinoid accumulation in the developing fruit of pepper initially appeared at 24 DAP (day after pollination), was actively development at 36 DAP and peaked at 48 DAP. 11 genes that encoded enzymes involved in capsaicinoid biosynthesis were isolated and characterized. Gene expression with quantitative reverse-transcription polymerase chain reaction analysis demonstrated that the CaCS was unique expressed in placenta and the other 10 genes were expressed in all selected tissues and 9 of 11 genes (except CaCa4H and CaCa3H) were strongly expressed in placenta tissue. Spatial expression analysis demostrated that the 11 gene could be collectively grouped into four categories based on the patterns of relative expression of the genes during fruit development. Category I has 2 and they displayed a bell-shaped expression pattern with the peak expression at 24 DAP, Category II contains 5 genes and expression of the 5 genes was constantly increased from 0 to 36 DAP and peaked at 36 DAP. Category III comprises of 2 genes and both genes reached the peak at 48 DAP. Category IV consists of 2 genes and they showed a high expression at 36 and 48 DAF, but unexpressed from 0 to 12 DAP. The gene expressions of the 11 genes were up-regulated by MeJA. 3 genes showed a high expression at 24 h; 4 genes reached the peak at 12 h; the top expression were observed at 18 h; The last one, CaACYase, achieved the highest level at 8 h. Conclusion: The biosynthesis of capsaicinoid in pepper fruit is developmentally regulated. The expression of the majority of capsaicinoid biosynthetic genes is highly consistent with the development accumulation of capsaicinoid in pepper fruit. These results not only provide the initial information on spatial and temporal expression of capsaicinoid biosynthetic genes in pepper developing fruit, but are also valuable to identify the MeJA-induced genes for capsaicinoid biosynthesis and accumulation during pepper fruit development.

Manufacturing firms need to streamline their initiatives in line with 17 sustainable development goals (SDGs) 2030 as a means for improving performance. This research adapts SDG’s goals 8 decent job and economic growth’ as a measure of performance, in examine the effect of corporate sustainability strategies on performance of manufacturing firms. Census sampling technique was used in cross sectional survey of 900 multi-industry managers from one hundred and fifty (150) manufacturing firms in Kano, Nigeria. A five Likert scale, well-structured questionnaire was used in collecting data, analysed by Structural Equation Modelling SEM PLS Package. The study shows positive and significant effect of eco-efficiency, and positive and insignificant effect of both legitimation and risk mitigation strategies on performance respectively. The study concluded that the positive and significant effects of eco efficiency initiatives could be due to benefits derived from total quality management system, energy monitoring, and process safety management reflected in sustainability oriented visionary leadership of the managers. The positive and insignificant effects of legitimation initiatives could be as a result of huge spending on sustainability reporting, and corruption. The positive and insignificant effect of risk mitigation could be due to pro-activeness of the firms’ managers in tackling risk through sustainability oriented visionary leadership. The study recommended firms should adopt conservative eco-efficiency initiatives for improving performance and stop adopting extroverted legitimation and introverted risk mitigation strategies as both have no significant effect on performance. Future researchers should use longitudinal research design using financial metrics adopting the same independent variables in other economic sectors.

This paper studies the influence mechanism of industrial agglomeration and foreign direct investment (FDI) on green total factor productivity (GTFP). We use the SBM Directional Distance Function to measure the GTFP of Chongqing's manufacturing industry from 1999 to 2015. The results show that the level of GTFP in Chongqing's manufacturing industry is relatively low, which is contrary to the current green development mode. By clarifying the conduction path of industrial agglomeration and FDI on GTFP, we use the panel Tobit model to study the effect of industrial agglomeration and FDI on GTFP. The main findings are: the higher the level of industrial agglomeration, the more beneficial it is to increase GTFP. FDI has an inhibitory effect on GTFP. The spillover effect of FDI on GTFP is not significant. At the same time, FDI counteracts the role of industrial agglomeration in promoting GTFP. The findings in a present study indicate that, according to Chongqing's experience, the "pollution haven" is established. Therefore, relying solely on foreign technology to promote the development of the manufacturing industry has many drawbacks clearly. Only by improving the ability of independent innovation is the reliable way to enhance GTFP effectively.

In response to escalating global demand for poultry, the industry grapples with an array of intricate challenges, from enhancing productivity to improving animal welfare and attenuating environmental impacts. This comprehensive review explores the transformative potential of digital phenotyping, an emergent technological innovation at the cusp of dramatically reshaping broiler production. The central aim of this study is to critically examine digital phenotyping as a pivotal solution to these multidimensional industry conundrums. Our investigation spotlights the profound implications of 'digital twins' in the burgeoning field of broiler genomics, where the production of exact digital counterparts of physical entities accelerates genomics research and its practical applications. Further, this review probes into the ongoing advancements in the research and development of a context-sensitive, multimodal digital phenotyping platform, custom-built to monitor broiler health. I critically evaluate this platform's potential in revolutionizing health monitoring, fortifying the resilience of broiler production, and fostering a harmonious balance between productivity and sustainability. Subsequently, the paper provides a rigorous assessment of the unique challenges that may surface during the integration of digital phenotyping within the industry. These span from technical and economic impediments to ethical deliberations, thus offering a comprehensive perspective. The paper concludes by highlighting the game-changing potential of digital phenotyping in the broiler industry and identifying potential future directions for the field, underlining the significance of continued research and development in unlocking digital phenotyping's full potential. In doing so, it charts a course towards a more robust, sustainable, and productive broiler industry. The insights garnered from this study hold substantial value for a broad spectrum of stakeholders in the broiler industry, setting the stage for an imminent technological evolution in poultry production.

The synchronization of complex networks is believed to be closely related to network topology. How to study synchronization of real network without clear topology and parameters? In this Letter, a synchronized stability boundary equation is derived that is system- and disturbance-independent and applicable to arbitrarily coupled grids. The results imply that spontaneous synchronization in a network may be network independent. These conclusions provide new research paths for network synchronization and analyze the synchronization stability of grids in a unified way.

[Research Objective] In the context of the rapid development of digital governance, understanding and analyzing the burden level of digital tools in the process of serving digital governance from multiple perspectives is an important prerequisite for improving the capacity and accuracy of government digital governance, promoting sustainable public participation, and maximizing digital value in the new era. [Research Method] By constructing a model of the burden of digital tools caused by technical, organizational and environmental factors on the public based on the TOE framework, using SPSS for data testing based on 275 research questionnaires, and using fuzzy set qualitative comparative analysis (fsQCA) to explore the linkage ways and paths of the burden-increasing effect of digital tools in a holistic perspective. [Research findings] does not constitute a necessary condition for high levels of digital tool affordances under a single condition, but improving users' digital literacy plays a more general role in optimising the efficiency of digital tool use. According to the results, there are five dominant patterns that have a significant impact, i.e. the driving paths that lead to high levels of digital tool affordability are characterised by a "homogeneous approach". In the cross-group study, significant differences were found in the driving paths of high digital tool affordability by group, depending on the level of digital tool mastery. This paper suggests that the government should continue to pay attention to affordability levels, rationalise the use of financial resources, improve the level of digital tool adaptation, and innovate the existing performance evaluation system for digital governance indicators.

While a raft of existing Chinese literature examines the associations between the outbreak of the pandemic and students’ mental health, rarely do Chinese studies assess the nuanced relationships between digital learning, parenting, and students’ mental health since the coronavirus (COVID-19) outbreak. Such a rarely discussed topic has substantial scholarly value as mismanagement of digital learning and parenting, such as the exposure to cyberbullying and negative parenting during the public health crisis, could add substantial, unforeseeable psychological burdens for Chinese students. In this article, the author applied a systematic review to find all relevant Chinese literature that contains the words “digital learning”, “children/adolescents”, “mental health”, and “parenting” published since January 2020. As such a complex topic has rarely been addressed in Chinese contexts, the author was only able to find four related scholarly articles. The author summarises the arguments and empirical findings to explore the nuanced relationships between a) digitalisation, isolation, parenting, and children’s mental health, b) parenting, teacher-student relationships, and students’ mental health, and c) maternal and paternal parenting styles.

Studies show that executive functions and motor development are associated among each other and with learning ability. A more technological lifestyle, related with digital culture, should be considered an important component to stimulate children. In addition, digital games constitute an element of the digital culture in which children are inserted. The aim of this study is to present a systematic mapping of the literature involving executive functions, motor development and the use of digital games in intervention programs for elementary school children, from 6 to 11 years old. Four databases were searched: PubMed, Scielo, Science Direct and SCOPUS, including publications between 2012 and March 2021. The initial results indicated 4881 works. After the selection process, 15 investigations that presented the central theme of the study were selected. The main results indicate that intervention strategies are quite heterogeneous. Most of the studies demonstrated significant positive effects after intervention protocols and were conducted in Europe and 46% of the studies were conducted in a school environment. No researches were identified that involved technological solutions involving executive functions, motor development and digital games in an integrated manner, constituting a field of future scientific research.

In combustor systems, thermoacoustic instabilities may occur and must be avoided for reliable op-eration. An acoustic network model can be used to predict the eigenfrequencies of the instabilities and the growth rate by incorporating the combustion dynamics with a Flame Transfer Function (FTF). The FTF defines the interconnection between burner aerodynamics and the rate of combus-tion. In the current study, the method to measure the FTF in a pressurized combustor is explored. A siren unit, mounted in the fuel line, induced a fuel flow excitation of variable amplitude and high maximum frequency. This was done here for pressurized conditions at 1.5 bar and 3 bar and at thermal power of 125 kW and 250 kW. In addition to the experimental investigation, a 1-D acoustic network model approach is used. In the model, thermo viscous damping effects and reflection coefficients are incorporated. The model results compare well with experimental data, indicating the proposed method to determine the FTF is reliable. In the approach, a combination of a FTF with a band stop approach and a network modeling approach was applied. The method provides a good match between experimentally observed behavior and analytical approach and can be used for instability analysis.

Clustering is a crucial at the same time challenging task in several application domains. It is important to incorporate the optimum feature finding into our clustering algorithms for getting better prediction accuracy but this is difficult when there is no or little information about the importance or relevance of features. To tackle this task in an efficient manner we employ the natural evolution process inherent in genetic algorithms (GA) to find the optimum features for clustering for the healthy aging dataset. In order to empirically verify the findings, genetic algorithms were combined with a number of clustering algorithms including parti-tional, density based as well as agglomerative. A variant of the popular KMeans algorithm, named KMeans++ gave the best performance on all performance metrics when combined with GA.

The human malaria parasite, Plasmodium falciparum, accounts for more than 500,000 deaths worldwide per year. From 2000 to 2019, the malaria global death toll fell from 864,000 to 576,000 deaths; however, progress on reducing this toll has since slowed, with the COVID-19 pandemic contributing to an increase in the mortality rate since 2020. The emergence of widespread resistance in P. falciparum to first-line treatment drugs has highlighted the need for new antimalarial drugs, and smarter approaches, including better combination therapies and mass drug administration. Nevertheless, recent successes of the first malaria vaccine (RTS,S/AS01) and the approval of a second (R21/MM), have renewed the promise of widely applicable vaccines in the future. Since 2015, the Eastern Mediterranean Region has experienced an overall increase in malaria cases and deaths. In the Arabian Peninsula, while most of the Gulf Cooperation Council (GCC) countries have been declared free of indigenous malaria (Bahrain, Kuwait, Qatar, and the United Arab Emirates), malaria is still endemic in two GCC countries (Saudi Arabia and Oman) and its neighbors (Yemen). Furthermore, a number of factors threaten malaria control in this region, at the level of the parasite (antimalarial drug resistance), the mosquito (the emergence of highly invasive species), the environment (climate change and increasing average temperatures), and society (imported malaria). In this review, we assess the current world-wide status of malaria in terms of public health, prevention and treatment, before providing an Arabian regional perspective, with a critical evaluation of the malaria situation in the GCC countries.

Stairs are among the key elements in architectural composition, both aesthetically and spatially. They are also one of the main innovations in architecture and building, allowing pedestrians to bridge considerable height differences with relative efficiency. It is therefore surprising that, in spite of all stair regulations in building codes, stairs are responsible for a huge number of accidents – second only to motorcars. The extent of safety failures suggests that user interaction with stairs is poorly understood by designers and policy makers. This is not unrelated to the lack of research into the design and use of stairs. Templer’s seminal work is the exception but it dates from 1992 and since then little has been done to understand the relation between architectural design and stair performance, including safety. To redress this, we can build on affordance-based analyses of stair climbability, which establish a clear connection between the form of a stair and the perception of action possibilities and dangers by all kinds of users. By doing so affordances establish a comprehensive and consistent framework for the analysis of architectural designs, which utilizes both domain and psychological knowledge, including as a foundation for computational applications.

In central area of Liaoning province, the thermal performance of rural building envelopes are mostly non-standardized in Northern China, resulting in a significant heat loss. In this study, The objective was to propose some low-cost and energy-efficient solutions. Through a field of inves-tigations, heating energy consumption was saved by 20% and increased construction costs was less than 8000 RMB, which was acceptable for rural residents. In order to achieve this target, NSGA-II algorithm was integrated with Rhino+Grasshopper and EnergyPlus simulation kernel to establish a thermal performance optimization model for heat transfer of the rural building envelopes. Among the above Pareto optimal solutions, the recommended thickness of insulation layer for the room floors, ceilings, and external walls were 70mm, 50mm, and 40mm respectively. Furthermore, try to reduce the window to wall ratio as much as possible. Finally, based on the lower total cost, three technical solutions that rural residents could select according to their specific needs have been put forward.

Abstract: Online services have transformed into a boon for various industries and globalization as a whole. One of the ventures reliant upon the Web is finance. Pattern prediction and risk reduction in the stock market and other financial decisions are made possible by some technologies. These technologies promise to expand innovation, knowledge, and efficiency opportunities in the financial sector. Finance frequently uses machine learning technology to identify risks based on probabilistic statistics and historical data to support investment decisions. Additionally, it can be utilized for risk assessment and risk management planning. Internet technology, which enables real-time data analysis, streamlined transactions, and improved customer communication, has particularly benefited the financial sector. However, these advancements come with significant challenges, such as the need for robust online credit protection and security threats. This study looks at how advanced machine learning methods are used in the financial sector and focuses on how they have changed credit scoring, algorithmic trading, and predictive modeling.

Aluminum is a strategic mineral resource, and China’s aluminum production and consumption is fairly large. However, its supply guarantee is uncertain because of a high dependency on external raw materials. This uncertainty may expand, so finding a way to reduce the uncertainty of aluminum resource supply is especially important. This paper applies the SFA method to analyze the aluminum flows in mainland China from 1996 to 2014, and establishes a supply structure model to measure its supply guarantee degree. The results claim that: (1) China’s aluminum production can satisfy demand and even create a surplus; (2) Domestic self-productive primary and secondary aluminum increased at an annual rate of 12% and 24%; (3) The proportion of self-productive secondary aluminum in the supply structure increased from 7.7% in 1996 to 12.8% in 2014, while that of primary aluminum decreased from 79.6% to 42.8%; (4) The total supply guarantee degree decreased from 87.3% to 55.6% in this period. These results provide a feasible way to solve this plight: the proportion of secondary aluminum in the supply structure should be enhanced, and an efficient aluminum resource recycling system needs to be established as soon as possible to ensure its sustainable supply.

Despite soccer popularity, comparatively little scientific information is available concerning the assessment of youth players’ physiological characteristics. The use of medical software integrating wearable sensors to provide accurate and objective data is necessary. In the present study, the objective assessment of physical performance of 128 adolescent soccer players at two timepoints (pre-season and end-season) has been performed. Participants completed a comprehensive test battery including stability, countermovement jumps (CMJ), plyometric jumps (PJ), agility, and quick feet tests. A single Inertial Measurement Unit (IMU) sensor provided quantitative data on various performance metrics. Principal Component Analysis (PCA) reduced the dataset to four principal components (PCs) explaining over 80% of the variance: power (PC1), balance (PC2), speed/agility (PC3), and stiffness (PC4). Paired samples t-test between the two timepoints showed no significant differences in the PCs between the two timepoints, indicating that the training program did not significantly alter the assessed physical attributes. However, subtle individual changes may still inform personalized training adjustments. The study highlights the importance of continuous monitoring and individualized training strategies to optimize athlete performance and reduce injury risk.

Over the past decade and a half, dynamic functional imaging has revolutionized the neuroimaging field. Since 2009, it has revealed low dimensional brain connectivity measures, has identified potential common human spatial connectivity states, has tracked the transition patterns of these states, and has demonstrated meaningful alterations in these transition and spatial patterns in neurological disorders, psychiatric disorders, and over the course of development. Recently, researchers have begun to analyze this data from the perspective of dynamic system and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Consciousness, in particular, appears to be strongly linked to entropy production in the brain. Little attention has been paid to the effects of psychiatric disease on entropy production in the human brain, however. Even disorders characterized by substantial changes in conscious experience have not been widely analyzed from this perspective. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls, and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients’ brain function.

The occurrence of ovarian dysfunction is often due to the imbalance between the formation of reactive oxygen species (ROS) and the (in)effectiveness of the antioxidative defense mechanisms. Primary sources of ROS are the respiratory electron transfer and the activity of NADPH oxidases (NOX), while Superoxide dismutases (SOD) are the main key reg-ulators that control the levels of ROS and reactive nitrogen species intra- and extracel-lularly. Because of their central role, SODs are the subject of research on human ovarian dysfunction, but the sample acquisition is low. The high degree of cellular and molecular similarity between Drosophila melanogaster ovaries and human ovaries provides this model organism with the best conditions for analyzing the role of ROS during ovarian function. In this study, we clarify the localization of the ROS producing enzyme dNox within ovaries of Drosophila melanogaster and by a tissue specific knockdown, we show that dNox derived ROS are involved in the chorion hardening process. Furthermore, we analyze the dSod3 localization and show that reduced activity of dSod3 impacts egg laying behavior, but not the chorion hardening process.

The development of biofuels and biodiesel industry specifically, has resulted in the generation of large amounts of glycerol as a surplus. The conversion of glycerol, into chemicals with indus-trial interest, through anaerobic fermentation using E. coli as a biocatalyst is an attractive alter-native to improve economic performance in the biodiesel industry, for example through the bi-otransformation of residual glycerol into ethanol. For 2019, the size of the global ethanol market was valued at $ 89.1 billion US dollar. Biodiesel can be produced from several materials like rapeseed, palm, soya, corn, palm kernel, frying oil or olive-pomace oil (OPO). The last, can be extracted from alperujo, byproduct of olive oil process. However, olive-pomace oil converting into biodiesel is not enough established to achieve a global olive-oil/biodiesel derived process with minimized waste generation. Traditionally biodiesel production process produces an equivalent of 10% by mass of glycerol as a waste or by-product. This work explores the conver-sion of residual glycerol into chemicals like ethanol and hydrogen with a focus on projecting raw material potential availability from OPO, contributing to the promotion of the use of biofu-els and biodegradable compounds.

The wave energy converter (WEC) technology constantly develops new designs and models. To encourage its development within the energy sector, research is required to improve current designs and create durable and effective devices. However, it is also crucial to investigate the suitability of a WEC to a site-specific sea condition as this can impact its optimal power absorption and constructability. This study evaluates and compares the efficiency of a heaving buoy with varied geometries in one of the most energetic places along the Mexican coast. A statistical analysis of the wave climate on the coast of Ensenada during the last 42 years is performed to define the conditions to which the device is subjected. The location of the WEC in shallow waters is chosen using a computational model which solves the modified mild slope equation in its elliptic shape. The heaving buoy floater is studied using three distinct geometries: a semi-sphere, a cylinder, and a proposed rounded semi-rectangle. The hydrodynamic response of the three geometries is then analysed in the frequency and time domain using ANSYS AQWA. The hydrodynamic study involves the assessment of the floating body dynamics, exerted forces, the power absorbed as well as the suitability of the proposed power take-off (PTO) system. Findings reveal that the proposed geometry absorbs the most energy, with an annual power of 135.11 MW, and that the transmission PTO design is appropriate for this type of technology.

Splines and quasi-interpolation operators are important both in approximation theory and applications. In this paper, we construct a family of quasi-interpolation operators for the bivariate quintic spline spaces S₅³ (∆_mn⁽²⁾). Moreover, the properties of the proposed quasi-interpolation operators are studied, as well as its applications for solving two-dimensional Burgers’ equation and image reconstruction. Some numerical examples show that these methods, which are easy to implement, provide accurate results.

The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09%by the new algorithmand the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.

This work investigated the effects of manufacturing variations including coil resistance, initial pod mass, and e-liquid color on coil lifetime and aerosol generation of Vuse ALTO pods. Random samples of pods were used until failure (where e-liquid was consumed, and coil resistance increased to high value indicating a coil break). Initial coil resistance, initial pod mass, and e-liquid net mass ranged between 0.89 to 1.14 [], 6.48 to 6.61 [g], and 1.88 to 2.00 [g] respectively. Coil lifetime with light color e-liquid was (mean) = 149, (standard deviation) = 10.7 puffs while pods with dark color e-liquid was = 185, = 22.7 puffs with a difference of ~36 puffs (p <0.001). Total mass of e-liquid consumed until coil failure was = 1.93, = 0.035 [g]. TPM yield per puff of all test pods for the first session (brand new pods) was = 0.0123, = 0.0003 [g]. During usage, TPM yield per puff of pods with light color e-liquid was relatively steady while it was continuously decreasing for pods with dark e-liquid. Coil lifetime and TPM yield per puff were not correlated with either variation in initial coil resistance or variation in initial pod mass. The absence of e-liquid in the pod is an important factor in causing coil failure. Small bits of the degraded coil could be potentially introduced to the aerosol. There is a potential correlation of e-liquid color with both coil lifetime and TPM yield per puff. Change of e-liquid color might have been a result of oxidation which changed some nicotine into nicotyrine.

To enhance industrial competitiveness and increase productivity, every country has strived to create a smart factory by introducing technologies such as Internet of Things, big data and artificial intelligence into production line and build cyber-physical system for the purpose of promoting manufacturing efficiency. For mission assignment, production line management or manufacturing field analysis, the location information of employee, machine and material is very essential. To promote manufacturing efficiency, of course, the location information became more important. A Bluetooth low energy (BLE) positioning system for the manufacturing is developed in this research. A "Tag tracking" mechanism is addressed and adopted, which uses Beacon to catch the location information and a BLE receiver is also used to receive the broadcasting information from Beacon. The position information from the BLE receiver will be compared with the data in the database for calculating the location of the target. The status of the target may also be obtained by using the data from the BLE receiver. Comparing with the mobile device, this method can reduce energy consumption and make the maintenance simple and easy. In the real applications, the target may not be limited to human. The "Regional label positioning technology" is also investigated in this research. Defining a suitable zone location and arranging BLE receiver location, and positioning analysis theory are the key factors included in this developed technology. The developed system will be tested for real industry applications. The test results show that the feasibility of this technology.

This study introduces an innovative approach to designing membranes capable of separating CO2 from industrial gas streams at higher temperatures. The novel membrane design seeks to leverage the well-researched high-temperature CO2 adsorbent, hydrotalcite, by transforming it into a membrane. This is achieved by combining it with an amorphous organo-silica-based matrix, extending the polymer-based Mixed Matrix Membrane concept to inorganic compounds. Following the membrane material preparation and investigation of the individual membrane in Part 1 of this study, we examine its permeation and selectivity here. The pure 200 nm thick hydrotalcite membrane exhibits Knudsen behavior due to large intercrystalline pores. In contrast, the organo-silica membrane demonstrates a permselectivity of 13.5 and permeance for CO2 of 1.3 107 mol m2 s1 Pa1 at 25°C, at 150°C the good separation behavior is reduced. Combining both components results in a hybrid microstructure, featuring selective surface diffusion in the microporous regions and unselective Knudsen diffusion in the mesoporous regions. Further attempts to bridge both components to form a purely microporous microstructure are outlined.

The production of oxygenated bio-additives for traditional fuels represents a key challenge due to the depletion of fossil fuels in the next future and their contribution to environmental pollu-tion. In this context, the present study considers the synthesis of different mixtures of 1-hexanol/hexyl hexanoate produced through the catalytic hydrogenation of hexanoic acid, a car-boxylic acid obtainable from the fermentation of a wide variety of waste biomasses. In particu-lar, crude hexanoic acid deriving from the fermentation of grape pomace, an abundant Italian agrifood waste, has been taken into consideration. The reaction was carried out with the com-mercial 5 wt% Re/γ-Al2O3 catalyst, whose acidity allowed the tuning of the reaction selectivity towards the preferential formation of hexyl hexanoate instead of 1-hexanol. As consequence, the tunable composition of the obtained 1-hexanol/hexyl hexanoate mixtures was leveraged for en-gine applications, thus allowing studying each component influence on the Diesel engine per-formances and verifying their synergistic effects. The engine experimental activity highlighted that both 1-hexanol and hexyl hexanoate, as well as their mixtures, can be used in Diesel engine with a commercial Diesel fuel up to high loadings (20 vol%) without altering engine perfor-mances and significantly lowering soot and CO emissions by more than 40%.

Shut-in after fracturing is generally adopted for wells in shale oil reservoirs, and imbibition occurring in matrix nanopores has been proven as an effective way to improve recovery. In this research, the molecular dynamics (MD) simulation was used to investigate the effects of wettability and pressure on nanopores imbibition during shut-in for a typical shale reservoir Jimsar. The results indicate that the microscopic advancement mechanism of the imbibition front is the competitive adsorption between “interfacial water molecules” at the imbibition front and “adsorbed oil molecules” on the pore wall. The essence of spontaneous imbibition involves the adsorption and aggregation of water molecules onto the hydroxyl groups on the pore wall. The flow characteristics of shale oil suggest that the overall push of the injected water to the oil phase is the main reason for the displacement of adsorbed oil molecules. Thus, shale oil especially the heavy hydrocarbon component in the adsorbed layer tends to slip on the walls. However, the weak slip ability of heavy components on the wall surface is an important reason that restricts the displacement efficiency of shale oil during spontaneous imbibition. The effectiveness of spontaneous imbibition is strongly dependent on the hydrophilicity of the matrix pore's wall. The better hydrophilicity of the matrix pore's wall facilitates higher levels of adsorption and accumulation of water molecules on the pore wall and requires less time for “interfacial water molecules” to compete with adsorbed oil molecules. During the forced imbibition process, the pressure difference acts on both the bulk oil and the boundary adsorption oil, but mainly on the bulk oil, which leads to the occurrence of wetting hysteresis. Meanwhile, shale oil still existing in the pore always maintains a good stratified adsorption structure. Because of the wetting hysteresis phenomenon, as the pressure difference increases, the imbibition effect gradually increases, but the actual capillary pressure gradually decreases and there is a loss in the imbibition velocity relative to the theoretical value. Simultaneously, the decline in hydrophilicity further weakens the synergistic effect of pressure difference because of the more pronounced wetting hysteresis. Thus, selecting an appropriate well pressure enables cost savings and maximizes the utilization of the formation's natural power for EOR.

A major public health emergency has been created by the COVID-19 pandemic, which is brought on by the SARS-CoV-2 virus. Due to its crucial function in viral replication, the primary protease (Mpro) of the virus is a prime target for therapeutic research. In this study, we used molecular modeling and molecular dynamics simulations to examine the potential therapeutic uses of Mpro inhibitors for the treatment of COVID-19. Using induced fit docking and molecular dynamics simulations, we confirmed the top compounds after screening a library of compounds for their ability to bind to Mpro. Simulation interaction diagrams were used to investigate protein-ligand interactions, and MM-GBSA was used to determine binding energies. The Swiss ADME server was used to predict ADME properties. According to our findings, numerous substances are strong COVID-19 medication candidates since they have excellent ADME features and high binding affinities. This work serves as a foundation for additional experimental research and drug development initiatives aimed at Mpro.

The β-amyloid cleaving enzyme 1 (BACE1) is regarded as an important target of drug design toward treatment of Alzheimer's disease (AD). In this study, three separate molecular dynamics (MD) simulations and calculations of binding free energies were carried out to comparatively probe identification mechanism of BACE1 on three inhibitors 60W, 954 and 60X. The analyses on MD trajectories indicate that the presence of three inhibitors influences structural stability, flexibility and internal dynamics of BACE1. Binding free energies calculated by using solvated interaction energy (SIE) and molecular mechanics generalized Born surface area (MM-GBSA) methods reveal that the hydrophobic interactions provide decisive forces for inhibitor-BACE1 binding. The calculations of residue-based free energy decomposition suggest that the sidechains of residues L91, D93, S96, V130, Q134, W137, F169 and I179 play key roles in inhibitor-BACE1 binding, which provides a direction for future drug design toward treatment of AD.

Zika virus (ZIKV) has been characterized as one of the potential pathogens and placed under future epidemic outbreaks by the WHO. However, lack of potential therapeutics can result in uncontrolled pandemic like other human pandemic viruses; therefore, prioritized effective therapeutics development has been recommended against ZIKV. In this context, the present study adopted the strategy to explore the lead compounds from Azadirachta indica against ZIKV via concurrent inhibition of the ZIKVpro and ZIKVRdRp proteins using molecular simulations. Initially, structure-based virtual screening of 44 bioflavonoids reported in Azadirachta indica against the crystal structures of targeted ZIKV proteins resulted in the identification of top four common bioflavonoids, viz. rutin, nicotiflorin, isoquercitrin, and hyperoside. These compounds showed substantial docking energy (-7.9 to -11.01 kcal/mol) and intermolecular interactions with essential residues of ZIKVpro (His51, Asp74, and Ser135) and ZIKVRdRp (Asp540, Ile799, and Asp665) by comparison to the reference compounds, O7N inhibitor (ZIKVpro) and Sofosbuvir inhibitor (ZIKVRdRp). Long interval molecular dynamics simulation (500 ns) on the selected docked poses reveals the stability of respective docked complexes contributed by intermolecular hydrogen bonds and hydrophobic interactions. The predicted complex stability was further supported by calculated end-point binding free energy using molecular mechanics generalized born surface area (MM/GBSA) method. Consequently, the identified common bioflavonoids are recommended as promising therapeutic inhibitors of ZIKVpro and ZIKVRdRp for further experimental assessment.

Electric streamer discharges (streamers) in air are a very important stage of lightning, taking place before formation of the leader discharge, and with which an electric discharge starts from conducting objects which enhance the background elecric field, such as airplanes. Despite years of research, it is still not well understood what mechanism determines the values of streamer parameters, such as its radius and propagation velocity. The Streamer Parameter Model (SPM) is aimed to understand this mechanism, as well as to provide a way to efficiently calculate streamer parameters. Previously, we demonstrated that SPM results compared well with a limited set of experimental data. In this Brief Report, we compare SPM predictions to the published hydrodynamic simulation (HDS) results.

Based on electromagnetic heat transfer and metal phase transformation co-simulations, we modeled an AISI 1045 specimen under high-frequency heat treatment. Hardening zone predictions were confirmed through cooling and metal phase transformation simulations after obtaining the results from electromagnetic heat transfer simulations. The cooling process was modeled by applying the cooling coefficient of the cooling water in the same way as the actual heat-treatment process. To obtain the current flowing through the coil during high-frequency induction heating, the voltage was measured and applied using the resistance–inductance–capacitance circuit calculation method. Experimental and simulated results of the heating temperature and curing depth of an AISI 1045 specimen with a carbon content of 0.45% were compared; the comparison indicated good agreement between the two. Using the simulation results, we established a method for obtaining the current flowing through the induction coil for predicting the extent and depth of the hardening zone during high-frequency induction heat treatment.

A techno-economic model was developed to investigate the influence of components on the system costs of redox flow batteries. Sensitivity analyses were carried out based on a example of a 10 kW/120 kWh vanadium redox flow battery system and the costs of the individual components were analyzed. Particular consideration was given to the influence of material costs and resistances of bipolar plates and energy storage media as well as voltages and electric currents. Based on the developed model it was possible to formulate statements about the targeted optimization of existing battery systems and general aspects for future developments of redox flow batteries.

In this article, a generalization of the geometry of Grassmannian chain complex of free abelian groups generated by the projective configuration of points and Cathelineau's infinitesimal complex of polylogarithmic groups is proposed. Firstly, homomorphisms for weight n =2 up to weight n=6 will be introduced to connect sub-complexes of Grassmannian and Cathelineau. Lately, generalization of these morphisms will be shown for weight n=N. The associated diagrams will also be proven to be commutative and bi-complex.

A good optical quality crystal of 2-aminopyridine (2AP) metal complex crystals was grown by slow evaporation technique at room temperature. The transparent and defect less bulk size of 2-aminopyridine potassium dihydrogen orthophosphate metal complex crystals have been grown by solution growth method. The molecular structure and morphology of grown crystals was drawn and planes are indexed using WINXMORPH software. A optical absorption spectrum, the UV cut-off wavelength, Band gap (Eg), Reflectance (R), Refractive Index (n), Extinction coefficient (K) and Electrical susceptibility (χc) are calculated for 2AP metal complex crystals.

The mechanism underlying microbiologically induced carbonate precipitation have not been thoroughly characterized, although numerous scholars and experts have specifically investigated questions regarding minerals induced by bacteria. The study of the precipitation of carbonate minerals induced by halophilic bacteria has aroused wide concern. The present study aimed to investigate the characterization and process of biomineralization in high salt systems by a halophilic bacterium, Chromohalobacter israelensis strain LD532 (GenBank: KX766026), which was isolated from the Yinjiashan Saltern in China. Carbonate minerals induced by LD532 were investigated in several sets of comparative experiments that employed magnesium sulfate and magnesium chloride as Mg resources. Magnesium calcite and aragonite were induced by LD532 bacteria, whereas these minerals did not appear in the control group. The mineral phases, micromorphologies, and crystal structures were analysed using X-ray powder diffraction, scanning electron microscopy, and energy dispersive X-ray detection. The carbonic anhydrase and urease secreted by strain LD532 through metabolism increased the pH value of the liquid medium and promoted the process of carbonate precipitation. Further study using high resolution transmission electron microscopy, energy dispersive X-ray detection and analysis of ultrathin slices showed that the nucleation sites of carbonate minerals were located on extracellular polymeric substances and the membranes of intracellular vesicles of LD532 bacteria, which provided favourable conditions for the growth of carbonate mineral crystals. The morphologies and compositions of minerals formed in solutions of MgSO₄ and MgCl₂ display significant differences, indicating that different sources of Mg²⁺ may also affect the physiological and biochemical activities of microorganisms and thus mineral deposition. This study will be of some interest for the interpretation of carbonate biomineralization in natural salt environments and has some value as a reference in understanding sedimentary carbonates in ancient marine environments, such as tidal flats.

Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite sector clustering to achieve robust whole sky image-based cloud classification, adaptive segmentation, and recognition under intricate illumination dynamics. A bespoke YOLOv8 architecture attains over 95% categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research, while offering a paradigm for cloud analytics transferable to various meteorological stations.

High seismic activity in the Himalayas and the increasing urbanization of its surrounding areas in the northern Indian region pose significant threats to both lives and properties. To mitigate the risk of seismic events, an earthquake early warning system can be used in the region. This system would be particularly beneficial to cities and towns in the mountainous and foothill regions that are located near earthquake sources. The government and the science and engineering community have re-sponded to this call by establishing the Uttarakhand State Earthquake Early Warning System (UEEWS). The system became fully operational on August 4, 2021, after being launched by the government of Uttarakhand. The UEEWS includes 170 accelerometers in the seismogenic part of the Uttarakhand region of the Himalayas. Ground motion data is transmitted to a central server through a dedicated private telecommunication network 24 hours a day, seven days a week.The system is designed to issue warnings for moderate to high-magnitude earthquakes via a mobile application installed on users' devices and sirens installed in government-owned public buildings. The UEEWS has successfully issued alerts for light earthquakes that occurred in the instrumented region and warnings for moderate earthquakes that occurred in the vicinity of the instrumented area. This paper provides an overview of the design of the UEEWS, details of the instrumentation, adaptation of attributes and their relation to earthquake parameters, the operational flow of the system, and information about the dissemination of warnings

Recent technological advancements in produced water for the oil and gas industry have a long history that spreads over centuries and has roots with many mathematicians, physicists, engineers, and inventors. Working to understand the many ideas and questions has helped to bring the produced water technologies to where we are today. Fluid mechanics is an essential factor in the world of engineering because it comes with significant benefits when designing the necessary equipment to overcome the challenging endeavors of daily operations. Many of us believe certain ideas or products were developed within a specific year not fully understanding the idea or concept might be hundreds of years old. For example, when we think of fluid mechanics, we do not think of Archimedes' immersing King Hiero II crown in water. Archimedes did this to determine the displacement of the water as he knew gold was denser than silver. Archimedes was one of the pioneers who was part of the beginning of fluid mechanics. Another interesting fact is the first valve invented was in 1 AD by Heron of Alexandria. This valve can be described as the very first hydraulic valve. The evolution of technology over hundreds of years has made considerable innovations benefiting engineers all over the world. These innovations come in the form of PLCs, RTUs, PIDs, mathematical formulas, processing methods, and SCADA systems.

The separation of Li+ and [Bmim]+(1-Butyl-3-methylimidazolium) by electrically driven membrane technology was studied. The effects of the types and modification processes of cation exchange membranes, current density, total cation concentration, temperature, and anion types on separation efficiency were investigated. The results indicated that it is feasible to achieve efficient separation of Li+ and [Bmim]+ using the selectivity of cation exchange membranes under electric field. Membrane CMX and the modified membrane A and B which obtained from the CMX showed obvious selective of Li+ from [Bmim]+. When the current density is 5 mA/cm2 and the feed concentrations of Li+ and [Bmim]+ are 0.3 mol/L and 0.2 mol/L, the selective transport coefficient of the modified membrane B can reach 6.2 in the first 120 minutes of the process. Reducing current density and increasing total cation concentration can improve separation efficiency. Lowering the temperature can improve the separation effect, but improvement is not obviously. Changing the anion of the feed from chloride ion to acetate has no effect on the separation.

In order to meet the diversified rehabilitation needs of people, it is necessary to design a product for active rehabilitation of patients. Existing rehabilitation chairs use intelligent massage, which can cause problems such as large massage area, inability to local massage, large chair size, and inability to meet the continuous use of the damaged parts. In this paper, modular design method and multi-layer evaluation method are used to solve the problems related to rehabilitation chairs. The authors use the questionnaire survey method and the functional technology matrix method to determine the functional requirements of the rehabilitation chair, and then use the multilevel evaluation methods, including the AHP method, entropy weight method main and grey correlation analysis, to optimize the functional solutions of the rehabilitation chair, and finally obtain a chair for the rehabilitation of patients with upper and lower limb disorders. Problems such as generalisation of rehabilitation scope and non-durable use of components were solved, and the purpose of active exercise was achieved. This study verifies that the use of multilevel decision evaluation method can effectively improve the efficiency of programme decision-making, and provides theoretical and practical basis for the design of similar products.

Wireless sensor networks (WSN) are widely used in various fields such as military, industrial and transportation for real-time monitoring, sensing and data collection of different environments or objects. However, the development of WSN is hindered by several limitations, including energy, storage space, computing power and data transmission rate. Among these, the availability of power energy plays a crucial role as it directly determines the lifespan of WSN. To extend the life cycle of WSN, two key approaches are power supply improvement and energy conservation. Therefor, we proposed an energy harvesting system and a low energy consumption mechanism for WSN. Firstly, we delved into the energy harvesting technology of WSN, explored the utilization of solar energy and mechanical vibration energy to ensure a continuous and dependable power supply to the sensor nodes, and analyzed the voltage output characteristics of bistable piezoelectric cantilever. Secondly, we proposed a neighbor discovery mechanism that utilizes a separation beacon, is based on reply to ACK, and can facilitate the identification of neighboring nodes. This mechanism operates at a certain duty cycle ratio, significantly reduces idle listening time and results in substantial energy savings. In comparison to the Disco and U-connect protocols, our proposed mechanism achieves a remarkable reduction of 66.67% and 75% in the worst discovery delay, respectively. Furthermore, we introduced a data fusion mechanism based on integer wavelet transform. This mechanism effectively eliminates data redundancy caused by spatio-temporal correlation, results in a data compression rate of 5.42. Additionally, it significantly reduces energy consumption associated with data transmission by the nodes.

The use of drones for various applications has become increasingly popular in recent years, and machine learning has played a significant role in this trend. In this paper, we provide a comprehensive survey of the classification and application of machine learning in drones. The paper begins with an overview of the different types of machine learning algorithms and their applications in drones, including supervised learning, unsupervised learning, and reinforcement learning. Next, we present a detailed analysis of various real-world applications of machine learning in drones, such as object recognition, route planning, obstacle avoidance, search area optimization, and autonomous search. The paper also discusses the challenges and limitations of using machine learning in drones, such as data privacy, data quality, and computational requirements. Finally, the paper concludes with a discussion of the future directions of machine learning in drones and its potential impact on various industries and fields. This paper provides a valuable resource for researchers, practitioners, and students interested in the intersection of machine learning and drones.

The bridge crane is often used in a complex environment, often subject to interference of all loads. Some uncertainties factors often have an inevitable impact on its swing. So the force situation of the bridge crane during a working cycle is analyzed, and a three-dimensional dynamic mathematical model of the bridge crane is built. Through the simulation analysis of the model under the action of driving force and wind load, the change law of the swing angle of the bridge crane is studied. Then, the fuzzy control theory is used to determine the control parameter in the anti sway control process. The position, swing angle deviation and deviation rate of the bridge crane are taken as the input, and the parameter correction is obtained after the fuzzification by using the center of gravity method, the anti sway fuzzy control system of the bridge crane is designed and simulated. The research results show that the fuzzy PID anti sway controller can not only improve the adaptability of the control system, but also overcome the large overshoot, quickly restrain the swing, and effectively realize the anti sway function of the bridge crane.

In the East, it has already become a tradition to prepare dry powdered seasonings from the fruits of the sumac plant (Rhus coriaria L.). In this work, we tested their processing by extraction. Sumac was collected in mid-September in a mountain forest near the village of Gezik (Guba, Azerbaijan). Before extraction, it was first dehydrated in a drying cabinet SH-DO-149 FG (Southern Bark) to an air-dry state; then crushed in a Tube Mill control mill (Germany) into pieces of 1-3 mm in size. One- and two-stage maceration extraction was tested together with water or acidified water at temperatures from 20 to 80 °C. Evaporation of primary extracts was carried out in a rotary evaporator Lab Rot 2 L (China). The concentration of coloring substances in the extract (in units of optical density) was used as an output parameter. It was found that the use of two-stage extraction and acidified water with a concentration of citric acid 0.3 g/cm³ contributes to the extraction of the maximum amount of coloring substances and increases the transparency of the resulting extracts, which allows the extracted extracts to be concentrated directly to obtain a new type of natural food dye. The involvement of sumac fruits in industrial processing will increase the need for these medicinal fruits, and this, in turn, will serve as an incentive for the introduction of this medicinal plant into culture.

The article explores the task of making higher education more profession-oriented. In this context, we consider the technology of structuring and matching professional activities and content of professional education curricula with the help of ontology. This technology employs intelligent analysis of labor market and educational content matching with the aim to organize educational programs and verify professional competences based on their ontological properties. The article also considers development of a professional training cognitive map that can help design the student’s personalized educational trajectory factoring in the given parameters.

In the information age today, data are getting more and more important. While other industries achieve tangible improvement by applying cutting edge information technology, the construction industry is still far from being enough. Cost, schedule, and performance control are three major functions in the project execution phase. Along with their individual importance, cost-schedule integration has been a significant challenge over the past five decades in the construction industry. Although a lot of efforts have been put into this development, there is no method used in construction practice. The purpose of this study is to propose a new method to integrate cost and schedule data using big data technology. The proposed algorithm is designed to provide data integrity and flexibility in the integration process, considerable time reduction on building and changing database, and practical use in a construction site. It is expected that the proposed method can transform the current way that field engineers regard information management as one of the troublesome tasks in a data-friendly way.

Circulating tumor cells (CTC) and circulating tumor microemboli (CTM) have been shown to correlate negatively with patient survival. Actual CTC counts before and after treatment can be used to aid in prognosis of patient outcomes. The presence of circulating tumor materials (CTMat) can advertise the presence of metastasis before clinical presentation, enabling early detection of relapse. Importantly, emerging evidence is indicating that cancer treatments can actually increase the incidence of CTCs and metastasis in pre-clinical models. Subsequently, the study of CTCs, their biology and function are of vital importance. Emerging technologies for the capture of CTC/CTMs and CTMat are elucidating vitally important biological and functional information that can lead to important alterations in how therapies are administered. This paves the way for the development of a “liquid biopsy” where treatment decisions can be informed by information gleaned from tumor cells and tumor cell debris in the blood.

A lightweight composite bridge deck system composed of steel orthotropic deck stiffened with thin Ultra-High Performance Concrete (UHPC) layer is developed to eliminate fatigue cracks in orthotropic steel decks. During the construction and operation period of the bridge, the debonding between the steel deck and the UHPC layer may introduce the several issues, such as crack-induced water invasion and distinct reduction of the shear resistance. In the study, an effective and novel non-destructive interface condition monitoring approach using piezoelectric lead zirconate titanate (PZT)-based technologies is proposed to detect interfacial delamination between steel deck and UHPC layer. Experimental tests are performed on several steel-UHPC composite slabs and a conventional steel-concrete composite slab. The thin styrofoam sheets with different sizes and thicknesses are set on different locations of the steel deck as the artificial debondings. The PZT ceramic patches are bonded on the surfaces of the steel deck and UHPC layer as the actuators/sensors. An improved PSO (Particle Swarm Optimization)-K-means clustering algorithms is proposed to obtain the debonding patterns based on the feature data set. The laboratory tests demonstrate that the proposed approach provides an effective and accurate way to detect interfacial debonding of steel-UHPC composite slab.

Transdisciplinary research approach requires that different scientists with their discipline-specific theories, concepts and methods find ways to work together with other societal players to solve a real-life problem. In order to put this into practice, Trans-disciplinary Action Research (TDR) approach was applied in this study using Push pull technology (PPT) as a boundary object. The study was conducted in Bako Tibe, Jimma arjo and Yayu Woredas (Districts) in the Oromia region of Ethiopia from August 2014 to April 2015. PPT is a biological based mechanism developed by researchers for stemborer pest control in maize. It involves inter-cropping maize with a stemborer moth-repellent silverleaf or Greenleaf Desmodium (push), and planting an attractive trap crop, Napier or Brachiaria grass (pull), around the maize crop. On farm PPT implementation was used to provide an opportunity for collaboration, interaction and learning among heterogeneous set of stakeholders comprising of researchers from Ethiopian Institute of Agricultural research and the practitioners from the ministry of agriculture and smallholder farmers/traders. The data was collected using mixed methods approach comprising of key informant interviews, Focus Group discussions, workshops, on-farm practical demonstrations and participant observations. The findings shows that; collaborative leadership provides a chance for the stakeholders to participate in the technology learning and decision making by enabling them to jointly contribute skills towards development, refinement and adaptation of PPT. In situations where there are conflicts, they are embraced and become opportunities for in-depth learning, finding solutions and adaptation rather than being sources of contradictions or misunderstandings. Leadership roles taken by farmers play a key role in terms of ability to reflect on their own practices and drawing on scientific explanations from researchers. It also enables them take lead in new technology implementation and information sharing in free and easy manner with fellow farmers and other stakeholders. Although PPT perennial nature of cropping provides opportunities for continuous stakeholder interaction and learning, it requires a personally committed leadership and formal institutional engagements for the sustainability of its activities spanning over several cropping seasons. Market forces and the involvement of private sector players also has a role to achieve this as shown from the involvement of individual farmers and traders in Desmodium and Brachiaria seed production, collection and distribution.

Cylindrical Fe-6.5wt.% Si bars with 7.5 mm in diameter were successfully fabricated from as cast ingot through three rolling stages within 10 passes: rough rolling at 850–900 °C and 8–10 m/min, medium rolling at 800–850 °C and 10–15 m/min, and finish rolling at 800–850 °C and 12–18 m/min. The evolution of microstructure, texture and ordered structure, and the mechanical property are investigated. The results showed that the grains were refined by the hot bar rolling. Area fractions of the {100}<011> oriented grains and the {011}<100> oriented grains decreased to 0 during the hot bar rolling. Whereas, the {100}<001> component, the {011}<211> component and the {112}<110> component increased, and γ fiber with {111}<110> component was dominant. DO₃ ordered phase were suppressed, and B2 ordered domains were refined after the hot bar rolling. Ductility of the as rolled bar was higher than that of the rotary swaged bar, due to the absence of the DO₃ ordered phase and the fine grains in the rolled bar. Hence, the hot bar rolling technology is an excellent process to fabricate the Fe-6.5wt.% Si bars.

We analyze a time-delayed SIQR model that considers the transportation-related infections and entry-exit screening. This model aims to determine the measures for preventing and controlling major emergent infectious diseases and the associated costs. We calculate the basic reproduction number (R0), apply the linearization method, and construct an appropriate Lyapunov function to determine the stability of the disease-free equilibrium in the local and global. We collect COVID-19 infection data from two regions in the United States in 2020 for data fitting, obtain a set of optimal parameter values, and show that the model is suitable for simulating the outbreak and spread of emerging infectious diseases. We find that transportation-related infections increase the basic reproduction number, enhancing the impact on disease spread. Entry-exit screening effectively suppress the spread of disease by reducing the basic reproduction number. Furthermore, we investigate the influence of the incubation period on disease and find that a shorter incubation period results in a shorter duration but a larger scale of infection, and the peaks are reduced. We conduct a sensitivity analysis of the R0 and propose three measures to prevent the spread of new infectious diseases based on the most sensitive parameters: wearing masks, implementing urban closures, and administering medication to sick but not yet hospitalized patients promptly. In the case of COVID-19, we use Pontryagin’s minimum principle to get time-varying control measures to control the spread of the outbreak. Optimal control effectively controls the development and deterioration of the disease. Finally, several control measures are compared through cost-effectiveness analysis, and the results show that wearing masks is the most cost-effective measure. Our study provides some practical guidance for controlling the spread of major emerging infectious diseases.

The search of DNA polymorphisms useful for the genetic improvement of dairy farm animals lasts more than 40 years with relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing have been found in casein genes and in the DGAT1. Nowadays, similar results are not reached yet in river buffaloes despite advanced genomic technologies and accurate phenotype records are available. The aim of the present study was to investigate and validate in a larger buffalo population the effect on six milk traits of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes, previously reported to be associated with or affect dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. Daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yields (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103cell/ml) and urea (mg/dl) were individually recorded in each month for the whole lactation from 2010 to 2021. A total of 15,742 individual milk test day records (2,496 lactations) were available on 680 buffalo cows with 3.6±1.7 parity (from 1 to 13) and 6.1±1.2 test day records per lactation on average. Three out four SNP in CSN1S1, CSN3 and LPL were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C&gt;T) gave favorable associations with all yield traits (dMY, p=0.022; dPY, p=0.014 ; dFY, p=0.029) and SCS (p=0.032), whereas the CSN3 (HQ677596: c.536C&gt;T) positively associated with SCS (p=0.005) and milk urea (p=0.04). Favorable effect on dMY (p=0.028), dFP (p=0.027) and dPP (p=0.050) were observed for the LPL. Conversely, SCD did not show any association with milk traits. This is the first example of confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in dairy field and it represents a very important indication for the preselection of young bulls destined to breeding programs in the view of a more sustainable dairy production.

Chymotrypsin from the digestive organs of bigfin reef squid, a major commercial squid species in Thailand, was purified to 41.27-fold with 5.74% yield by a sequential purification process including ammonium sulfate precipitation, size exclusion and ion exchange chromatography. According to SDS-PAGE, the molecular weight of purified chymotrypsin was 43 kDa. Native-PAGE analysis revealed a single band for this purified enzyme. Optimum pH and temperature for chymotrypsin activity of the purified enzyme were pH of 7.0 with a temperatures of 55°C. The purified chymotrypsin remained stable throughout a wide range of pH levels (6–11) and at relatively high temperature (50°C). It was significantly inhibited by PMSF and TPCK. The values of the kinetic constants Km and Kcat were found to be 1.33 mM and 31.46 s‒1, respectively. The purified chymotrypsin has the N-terminal amino acid IVGGQEATPGEWPWQAALQV. This study provided new information about the biochemical properties of pure chymotrypsin from bigfin reef squid, which is useful in the future investigation, aquaculture and application of bigfin reef squid.

Not occasional phamracokinetic variability and suboptimal exposure have been reported for micafungin in the intensive-care-unit (ICU) patients. This study aims to evaluate the quality of population pharmacokinetic models and provide rational using recommendation of this drug in clinical practice. Monte Carlo simulations were implemented to compare pharmakinetic parameters and probability of target attainment (PTA) towards various Candida species. Body weight, liver function, and SOFA score were the most frequent covariates in the final 16 included studies. micafungin clearance for ICU adults (SOFA≥10 or &lt;10) was 39%–52% and 43%–57% higher, respectively, than non-ICU adults. For Candida infections with MICs &lt;0.016 mg/L, the ICU group had lower proportions achieving PTA &gt; 90% compared to the non-ICU group (2/6 (33%) vs. 4/7 (57%)), and for non-ICU and ICU adults, micafungin dosages of 100 and 150 mg were recommended, respectively. While for C. tropicalis and C. glabrata, 200 and 250 mg were recommended respectively. However, for C. krusei and C. parapsilosis, it failed to reach assumed PTA at MICs of 0.125-0.25 mg/L and 0.125-2 mg/L, respectively. Adjusting micafungin dosage in ICU patients may be necessary. Both ICU and non-ICU adults should modify their dosage regimens based on the Candida species and their respective MICs.

The use of Retrieval-augmented generation (RAG) using large language models (LLMs) has shown potential for addressing issues such as hallucinations and inadequately contextualized responses. A pivotal stage in the RAG process involves a retriever for retrieving chunks based on semantic similarity with the query. In this study, we advocate for and provide experimental evidence supporting integrating and maintaining questions and answers (QA) formatted databases to improve retrieved-context representations and response quality. Our experiments evaluate our approach on benchmark RAG datasets using standard evaluation metrics and provide comparative analyses against state-of-the-art retrieval methods, showing the potential of our approach.

Age-related brain deterioration and Alzheimer's disease (AD) pose major challenges as misfolded proteins are deposited in the brain, leading to a decline in cognitive abilities and disruption of neural networks crucial for memory. This article addresses the impact of disrupted spike-timing-dependent plasticity (STDP) mechanisms in Alzheimer's disease and highlights the role of Transcranial magnetic stimulation (TMS) in the study of cortico-cortical spike-timing-dependent plasticity. Amidst this adversity, the role of STDP emerges as a key player in memory formation by synchronizing neural activity. Progressive neuronal dysfunction decreases neural plasticity and impairs manual dexterity and speed, echoing more general problems in elderly people. The findings highlight the potential of neurally-driven cortico-cortical paired associative stimulation (ccPAS) to revitalize neural plasticity and improve motor performance in elderly people, bridging the gap between age-related challenges and Alzheimer's research. This work opens a door to potential rehabilitation strategies for aging populations by unlocking the aging brain's ability to improve motor performance.

The commercial oxygen-free copper sheets were cold-rolled with reduction rates ranging from 20% to 87% reduction rate and annealed at 400, 500 and 600 °C. The microstructure and texture evolution during cold-rolling and annealing processes were studied using optical microscopy (OM), the scanning electron microscopy (SEM) and the electron back-scattered diffraction (EBSD). The results show that the deformation textures of {123} (S) , {112} (Copper) and {110} (Brass) were continuously enhanced with the increase of cold-rolling reduction. The orientations along the α-oriented fiber converged towards Brass and the orientation density of β fiber obviously increased when the rolling reduction exceeded 60%. The recrystallization texture was significantly affected by the cold-rolling reduction. After 60% cold-rolling reduction, Copper and S textures components gradually decreased and {011} recrystallization texture component formed with the increase of annealing temperature. After 87% cold-rolling reduction, a strong Cube texture formed and other textures were inhibited with the increase of annealing temperature. The strong Brass and S deformation texture was conducive to the formation of a strong Cube annealing texture. The density of annealing twin boundary decreased with the increase of annealing temperature, and more annealing twin boundaries formed in the oxygen-free copper sheets with the increase of cold rolling reduction.

This paper is devoted to Petri net (PN) based models of Automated Manufacturing Systems (AMS) with resources in order to prevent deadlocks in them. The sustainability can be seen as the process of deadlock-freeness leading to correct and fluent production, because AMS with deadlocks work neither correctly nor fluently, need reconstruction and cause downtime in production. A class S3PR (Systems of Simple Sequential Processes with Resources) of such PN models is well known as to the deadlock prevention point of view. Here, Extended S3PR (ES3PR) will be explored as to modelling and deadlock prevention. While in case of S3PR Ordinary Petri Nets (OPN) were used for these aims, here, for ES3PR Generalized Petri Nets (GPN) are used. The reason for such a procedure is a possible presence of multiplex directed arcs in the structure of PN models of AMS. The significant alternation is that while in former case the elementary and dependent siphons were sufficient for the supervisor synthesis, here, in later case, the GPN and their siphons have to satisfy the max-cs property.

AbstractTo overcome the negative impacts on the environment and other problems associated with fossil fuels have forced many countries to inquire into and change to environmentally friendly alternatives that are renewable to sustain the increasing energy demand. Solar energy is one of the best renewable energy sources with the least negative impacts on the environment. Different countries have formulated solar energy policies to reduce dependence on fossil fuel and increasing domestic energy production by solar energy. According to the 2010 BP Statistical Energy Survey, the world cumulative installed solar energy capacity was 22928.9 MW in 2009, a change of 46.9% compared to 2008. In this study, a PV generation system has been modeled and installed considering uncertain whether based on the hourly wind speed data of New York City (NYC) of year 2014. Regression models has been used to forecast the hourly, weekly, and monthly wind speed of NYC year 2014. Design of experiment (DOE) has been used to determine the optimal panel size (area), the battery capacity size, and other levels of factors.

The selectin of a demand forecasting method is critical for companies aiming to avoid manufacturing overproduction or shortages in pursuit of sustainable development. Various qualitative and quantitative criteria with different weights must be considered during the evaluation of a forecasting method. The qualitative criteria and criteria weights are usually assessed in linguistic terms. Aggregating these various criteria and linguistic weights for evaluating and selecting demand forecasting methods in sustainable manufacturing is a major challenge. This paper proposes an extension of fuzzy Elimination and Choice Translating Reality (ELECTRE) I to resolve this problem. In the proposed method, fuzzy weighted ratings are defuzzified with the signed distance to develop a crisp ELECTRE I model. Moreover, an extension to ELECTRE I is developed by suggesting an extended modified discordance matrix and a closeness coefficient for ranking alternatives. The proposed extension can overcome the problem of information loss which can lead to incorrect ranking result when using the Hadamard product to combine concordance and modified discordance matrices. A comparison is conducted to show the advantage of the proposed extension. Finally, a numerical example is used to demonstrate the feasibility of the proposed method; furthermore, a numerical comparison is made to display the advantage of the proposed method.

This paper presents a new method of process parameters optimization, adequate for 3D printing of PLA (Polylactic Acid) components. The authors developed a new Hybrid Manufacturing Equipment (HME), suitable for producing complex parts made from a biodegradable thermoplastic polymer, for environmental sustainability. Our new HME equipment is producing PLA parts by both additive and subtractive techniques, with the aim of obtaining accurate PLA components with a good surface quality. A design of experiments has been applied for optimization purposes. The following manufacturing parameters were analyzed: rotation of the spindle, cutting depth, feed rate, layer thickness, nozzle speed, and surface roughness. Linear regression models and neural network models were developed to improve and predict the surface roughness of the manufactured parts. A new test part was designed and manufactured from PLA, to validate the new mathematical models, which can now be applied for producing complex parts made from polymer materials.

This paper presents an overview of EREMI, a two-year project funded under ERASMUS+ KA203, and its results. The project team's main objective was to develop and validate an advanced interdisciplinary higher education curriculum, which includes lifelong learning components. The curriculum focuses on enhancing resource efficiency in manufacturing industries and optimizing poorly or non-digitized industrial physical infrastructure systems. The paper also discusses the results of the project, highlighting the successful achievement of its goals. EREMI effectively supports the transition to Industry 5.0 by preparing a common European pool of future experts. Through comprehensive research and collaboration, the project team has designed curriculum that equips students with the necessary skills and knowledge to thrive in the evolving manufacturing landscape. Furthermore, the paper explores the significance of EREMI's contributions to the field, emphasizing the importance of resource efficiency and system optimization in industrial settings. By addressing the challenges posed by under-digitized infrastructure, the project aims to drive sustainable and innovative practices in manufacturing. All five project partner organisations have been actively engaged in offering relevant educational content and framework for decentralised sustainable economic development in regional and national contexts through capacity building at local level. A crucial element of the added value is the new channel for feedback from students. The survey results, which are outlined in the paper, offer valuable insights gathered from students, contributing to the continuous improvement of the project.

Green supply chain management (GSCM) is fundamentally growing as a key component of sustainability, owing to its ability to improve supply chain competitiveness and performance within firms. The body of literature on GSCM within the manufacturing sector in developing countries such as South Africa is still limited. The study examined the relationships between GSCM practices, dynamic capabilities, and supply chain performance in the South African manufacturing sector. The study was quantitative, involving a sample of 402 supply chain professionals drawn from the manufacturing sector across the four provinces of the country. Hypotheses were tested using structural equation modelling based on the SMART PLS technique. Eco-design Green distribution and green training impacted positively on dynamic capabilities, the latter of which in turn is linked to improved agility, reliability, and reduced supply chain costs. The study validates the importance of GSCM as a driver of dynamic capabilities and performance in manufacturing environments within developing countries such as South Africa.

Today’s export-based manufacturers face the need to implement green supply chains more efficiently in order to overcome environmental barriers in increasingly competitive export markets and to improve export performance. This study examines the structural relationship between environmental collaboration, green innovation capacity, and performance based on the findings of previous studies in order to identify the factors affecting the green supply chain management (GSCM) performance of Korean export-based manufacturers. The study finds that environmental collaboration in the green supply chain environment is an important driver of green innovation capacity for export-based Korean manufacturers. It also finds that green innovation capacity has a positive effect on export performance through environmental performance. This study establishes a theoretical basis for the systematic study of the structural mechanisms of green supply chains and suggests strategic directions for export-based manufacturing firms’ successful GSCM implementation.

Global warming, exacerbated by greenhouse gas emissions from non-green product manufacturing, necessitates a shift towards green production to safeguard ecosystems and finite resources. The United Nations Environment Programme emphasizes the importance of environmental considerations in corporate ESG criteria. Despite the higher costs of designing and manufacturing green products, which deter some companies and lead to greenwashing, efforts persist to promote sustainable goods. This study explores enhancing consumer repurchase intentions for green products through social media marketing, emphasizing green values, environmental concerns, brand image, and involvement. Findings reveal social media's role in boosting these factors, thus encouraging green repurchases. Insights are valuable for green businesses, social media groups, environmental agencies, and future research.

Development of the self-cleaning and anti-fogging superhydrophobic coating for aluminum surfaces which is durable in the aggressive conditions has raised tremendous interest in materials science. In this work, by employing chemical etching technique with mixture of hydrochloric and nitric acid, followed by passivation with lauric acid, superhydrophobic aluminum surface was synthesized. The surface morphology analysis reveals the presence of rough microstructures on coated aluminium surface. Superhydrophobicity with water contact angle of 170 ± 3.9° and sliding angle of 4 ± 0.5° is achieved. Surface bounces off the high speed water jet, indicating excellent water-repellent nature of coating. It is also continuously floated on water surface for several weeks, showing excellent buoyancy nature. Additionally, coating maintains its superhydrophobicity after undergoing 100 cycles of adhesive tape peeling test. Its superhydrophobic nature withstands 90° and 180° bending, and repeated folding and de-folding. Coating exhibits the excellent self-cleaning property. In low temperature condensation test, almost no accumulation of water drops on the surface, showing the excellent anti-fogging property of coating. This approach can be applied to any size and shape of aluminium surface and hence has great industrial applications.

The industrial metaverse paradigm is generally a virtual environment that integrates various technologies such as augmented reality (AR) and mixed reality (MR) to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the industrial Metaverse to drive sustainability and resource efficiency is compelling. Utilizing advanced technologies like the Industrial Metaverse is essential for staying competitive in today’s rapidly evolving business environment. However, the environmental impact of the technologies underpinning the industrial metaverse, like data centres and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that by 2025, without sustainable AI practices, AI will consume more energy than the human workforce, significantly offsetting carbon-zero gains. As the metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. However, the specific extent to which this substitution can alleviate environmental concerns remains understudied, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the industrial metaverse as well as explore the environmental impact of the industrial metaverse. The methodological approach is that of integrative literature review design wherein multiple sources from web science and databases such as ACM library, IEEE Library, and Google Scholar, were analysed to provide a comprehensive understanding of the developments in the industrial metaverse. Specifically, firstly by considering the Industrial metaverse architecture we elucidate the concept of the "industrial metaverse" and the associated enabling technologies. Secondly, an exploration through discussion, of the prevalent use cases and deployment of the emerging industrial metaverse. Thirdly, exploration of the impact of the industrial metaverse on the environment. In the fourth instance, we address novel security and privacy risks as well as upcoming research challenges, keeping in mind that the industrial metaverse is based on a strong data fabric. Results point to the Industrial Metaverse as having both positive and negative environmental effects by energy consumption, e-waste, and pollution. Overall research, however, indicates that most industrial metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Ultimately for sustainability in the industrial metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, examining the effects of products on the environment as well as creating a circular economy.

The study presents the evaluation of integrating Extended Reality applications in situ to foster visitors' experience at the Tomato Industrial Museum "D. Nomikos". This paper firstly provides an outline of the methodological, technological, and museological approaches undertaken to integrate XR in a quest to enhance visitors’ meaningful engagement with the industrial heritage-related exhibition. Storytelling and narration are key elements of the museums’ museological design [1] through audio tours and interactive AR-based visualization of the machinery’s role as well as internal function. This paper presents and discusses the evaluation findings. We adopted the User Experience Questionnaire method as the main vehicle for gathering quantitative data and analyzing the results with the use of corresponding metrics and benchmarks embedded in this evaluation tool. Moreover, we conducted semi-structured interviews and combined the qualitative data accrued in the form of accounts, suggestions, and remarks to reach more rich and dependable conclusions. This paper aims to offer both an instance of practice to the research community and a broader discussion of the challenges and prospects connected to the quest of employing emerging technologies and assessing their impact on visitors’ museum experience.

First came the digital banks without offices, and then the “fintech” companies that combined technology and finance to achieve greater efficiency and new business models. Later was the turn of transport and logistics, understood as the sector whose economic activities derived from the transport of people and goods, while on the one hand, it has had a new spring thanks to factors such as online sales. Then, perhaps catalyzed by the pandemic scenario, the turn has reached education, where years ago was only a complement of traditional education, now is a central part with multiple modern methodologies. Numerous studies exist about using or implementing these e-learning procedures, but technical analysis is barely found in references. This work aims to analyze the digital transformation of the main sectors of our society, including finances, transport, and education, together with several examples of how technology was helping to fight the pandemic scenario.

An experimental study is carried out in a cavitation tunnel on a propeller operating downstream of a non-uniform wake. The goal of this work is to establish quantitative correlations between the near pressure field and the cavitation pattern that takes place on the propeller blades. The pressure field is measured at the walls of the test section and in the near wake of the propeller, and is combined with quantitative high-speed image recording of the cavitation pattern. Through simple harmonic analysis of the pressure data and image processing techniques that allow to retrieve the cavitation extension and volume, we discuss the potential sources that generate the pressure fluctuations. Time correlations are unambiguously established between pressure peak fluctuations and cavitation collapse events, based upon the Rayleigh collapse time. Finally, we design a model to predict the cavitation-induced pressure fluctuations from the derivation of the cavitation volume acceleration. This simple model demonstrates a remarkable agreement with the actual pressure field.