Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

Automated machine classifications of galaxies are necessary because the size of upcoming surveys will overwhelm human volunteers. We improve upon existing machine classification methods by adding the output of SpArcFiRe to the inputs of a machine learning model. We use the human classifications from Galaxy Zoo 1 (GZ1) to train a random forest of decision trees to reproduce the human vote distributions of the Spiral class. We prefer the random forest model over other black box models like neural networks because it allows us to trace post hoc the precise reasoning behind the classification of each galaxy. We find that, across a sample of 470,000 Sloan galaxies that are large enough that details could be seen if they were there, the combination of SpArcFiRe outputs with existing SDSS features provides a better machine classification than either one alone on comparison to Galaxy Zoo 1. We suggest that adding SpArcFiRe outputs as features to any machine learning algorithm will likely improve its performance.

Human cytomegalovirus (HCMV) is a ubiquitous herpesvirus, for which there is no vaccine or cure. This viral infection, once acquired, is life-long, residing latently in hematopoietic cells. However, latently infected individuals with weakened immune systems often undergo HCMV reactivation, which can cause serious complications in immunosuppressed and immunocompromised patients. Current anti-viral therapies target late stages of viral replication, and are often met with therapeutic resistance, necessitating the development of novel therapeutics. In this current study, we identified a naturally occurring flavonoid compound, deguelin, which inhibits HCMV lytic replication. Our findings reveal that nanomolar concentrations of deguelin significantly suppress the production of infectious virus. Further, we show that deguelin inhibits the lytic cycle during the phase of the replication cycle consistent with early (E) gene and protein expression. Importantly, our data reveal that deguelin inhibits replication of a ganciclovir-resistant strain of HCMV. Together, our findings identify a novel, naturally occurring compound that may prove useful in the treatment of HCMV replication.

This research work deals hybrid control system based integrated Cuk converter fed brushless DC motor (BLDCM) for power factor correction. In this work, moth-flame optimization (MFO) and fuzzy logic controller (FLC) has been combined and moth –flame fuzzy logic controller (MFOFLC) has been proposed. Firstly, the BLDC motor modelling is composed with power factor correction (PFC) based integrated Cuk converter and BLDC speed is regulated using variable DC-Link inverter voltage which makes low switching operation with less switched losses. Here, with the use of switched inductor, the task and execution of proposed converter is redesigned. The DBR (diode bridge rectifier) trailed by proposed PFC based integrated Cuk converter operates in discontinuous inductor conduction mode(DICM) for achievement of better power factor.MFO is exhibited for gathering of dataset from the input voltage signal. At that point separated datasets is send to FLC to improve the updating function and minimization of torque ripple. However, our main objective is to assess adequacy of proposed method, the power factor is broke down. The execution of the proposed control methodology is executed in MATLAB/Simulink working platform and the display is assessed with the existing techniques.

Neuronal cell death in the central nervous system has always been a challenging process to decipher. In physiological condition, neuronal cell death is restricted in the adult brain even as people ages. However, in pathological conditions of various neurodegenerative diseases, the cell death and shrinkage of a specific brain region represent a fundamental pathological feature across different neurodegenerative diseases. In this review, we will briefly go through the general pathways of cell death and describe the evidence of the cell deaths in the context of common neurodegenerative diseases individually, discussing our current understandings of cell death in connecting with the renowned pathogenic proteins, including tau, amyloid-beta, alpha-synuclein, huntingtin, and TDP-43.

1) Background: In service business areas, design enhances the customer experience through the elements which anticipate specific emotional responses of customers. Many service companies are keen to develop and examine design elements from the customer perspective. Furthermore, recently, having considered the significance of customers’ emotional responses by design elements, categorising design elements into manageable dimensions can facilitate the evaluation of design elements. However, design elements and dimensions of design value are not defined in the current service marketing literature, and companies need a measurement tool and managerial guideline to their innovative value creation and compelling service delivery. For these reasons, this research aims to categorise the design value dimensions and propose the managerial implication for the innovative use of design; 2) Methods: This study used the mixed methodology; two stages of interviews and SEM (Structural Equation Modelling); 3) Results: Interview analyses facilitate the identification of design elements in the service delivery process. SEM results underpin the relevance of the categorised design elements and their impact on customer loyalty by comparing different groups (country and time elapsed from experience); and 4) Conclusions: Findings are the fundamentals of developing measuring tool for design and trigger future studies for conceptualising intangible assets.

Aims TRAIL is a promising anticancer agent that has the potential to sensitize a wide variety of cancer or transformed cells by inducing apoptosis. However, resistance to TRAIL is a growing concern. Current manuscript aimed to employing combination treatment to investigate resveratrol induced TRAIL sensitization in NSCLC. Method A549 and HCC-15 cells were used in experimental design. Cell viability was determined by morphological image, crystal violet staining and MTT assay. Apoptosis was evaluated by LDH assay, Annexin V and DAPI staining. Autophagy and apoptosis indicator protein were examined by western blotting. TEM and puncta assay were carried out to evaluate the autophagy. MTP and ROS activity were evaluated by JC-1 and H₂DCFDA staining. Findings Resveratrol is a polyphenolic compound capable of activation of tumor suppressor p53 and its pro-apoptotic modulator PUMA. Herein, we showed the p53-independent apoptosis by decrease the expression of phosphorylated Akt-mediated suppression of NF-κB that is also substantiated with the downregulation of anti-apoptotic factors Bcl-2 and Bcl-xl in NSCLC, resulting in an attenuation of TRAIL resistance in combined treatment. Furthermore, apoptosis was induced in TRAIL-resistant lung cancer cells with a co-treatment of resveratrol and TRAIL assessed by the loss of MMP, ROS generations which resulting the translocation of cytochrome c from the mitochondria into the cytosol due to mitochondrial dysfunction. Moreover, autophagy flux was not affected by resveratrol-induced TRAIL-mediated apoptosis in NSCLC. Significance Overall, targeting the NF-κB (p65) pathway via resveratrol attenuates TRAIL resistance and induces TRAIL-mediated apoptosis which could be the effective TRAIL-based cancer therapy regimen.

The contents of Education for Sustainable Development (ESD) should be included in teachers’ initial and advanced training programs. In order to determine the key competences for sustainability, creating a Sustainable Consciousness is one of the main foundations. However, there are not many empirical studies that deal with consciousness from education. In this context, e-portfolio appears as a tool that promotes reflection and critical thinking, key competences for consciousness development. This work intends to propose a categorization system to extract types of consciousness and identify trainee teachers’ levels of consciousness. For this research work, of an eminently qualitative nature, we have selected twenty-five portfolios of students in the last year of the School of Education at the University of Macerata (Italy). The qualitative methodological procedure followed allowed to deduce three bases that shape trainee teachers’ consciousness: thinking, representation of reality and type of consciousness. We concluded that the attainment of a Sustainable Consciousness in teachers requires activating and developing higher levels of thinking, as well as a projective and macrostructural representation of reality.

Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network contents and the company analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector.

This paper proposes a new non-radial biennial Luenberger energy and environmental performance index (EEPI) to measure the EEP change in various Chinese cities. The sources of EEP change, in terms of technical efficiency change and technological change, are examined by Luenberger EEPI. The contributions from specific undesirable outputs and energy inputs to the EEP change are identified by means of the non-radial efficiency measure. The proposed approach is applied to evaluate the EEP of the industrial sector in 283 cities in China over 2010-2014. Factors influencing the emission abatement potential are investigated by employing geographically weighted regression (GWR) model. We find that 1) changes in EEP can be attributed to technological progress but that technological progress slows down across the study period; 2) the soot emission performance experiences a downtrend among four specific sub-performances in line with the truth that severe haze happened frequently in China; 3) the best performers begin to move from the coastal to inland cities with the less resource consumption and higher ecological equality; 4) cities with the strongest positive effect in regards to pollution intensity on emission abatement potential are located in the areas around the Bohai Gulf, where air pollution is particularly severe.

A dogma is normally considered as a principle or a belief accepted as an indisputable truth by some individuals and/or groups. Theoretically there can be no dogmas in science, but it has been demonstrated that scientific thought operates by conceptual changes. A dogma therefore can be understood as a concept present at the heart of some contemporary research programmes that need to be altered to overcome paradigms. Here we argue that two ideas relating to emergence of the biological system research need to be re-evaluated. First, is the idea that research programmes about the emergence of the biological system are the same as those of the origin of cells. Cells are strikingly important biological entities, hard core concepts for the entire field of biology. The emergence of the biological system happened much earlier than the origin of cells and thus the First Universal Common Ancestor (FUCA) should be viewed as a great-grandfather to the Last Universal Cellular Ancestor (LUCA); i.e. the latter is the first cellular life form. Second, RNA-world theories are the focus of mainstream research programmes for the origin of life stricto sensu. In the RNA-world view, self-replication of nucleic acids is seen as one of the most relevant events in the pre-biotic world. Without denying the relevance of self-replication, we argue here that the most germane event which occurred in the pre-biotic world was the crosstalk between nucleic acids and peptides. When these two macromolecules started to interact, the singularity that aggregated the complexity required to produce life emerged. Thus, comprehension of the early origins of the translation machinery and the assembly of the genetic code is key. Therefore, the relevance of cell theory and self-replication should be re-evaluated as well as the concept of life itself.

Hot rolling tests of a low-alloy steel were conducted at a rolling temperature of 850 ℃ under different lubrication conditions, including benchmarks (dry condition and water) and water-based nanolubricants containing different concentrations of nano-TiO₂ from 1.0 to 8.0 wt%. The effects of nanolubricants on rolling force, surface roughness, thickness of oxide scale and microstructure were systematically investigated through varying nano-TiO₂ concentrations. The results show that the application of nanolubricants can decrease the rolling force, surface roughness and oxide scale thickness of rolled steels, and refine ferrite grains. In particular, the nanolubricant containing an optimal concentration (4.0 wt%) of nano-TiO₂ demonstrates the best lubrication performance, owing to the synergistic effect of lubricating film, rolling, polishing and mending generated by nano-TiO₂.

Horses’ ability to adapt to new environments and to acquire new information plays an important role in handling and training. Social learning in particular would be very adaptive for horses as it enables them to flexibly adapt to new environments. In the context of horse handling, social learning from humans has been rarely investigated but could help to facilitate management practices. We assessed the impact of human demonstration on spatial problem-solving abilities in horses using a detour task. In this task, a bucket with a food reward was placed behind a double-detour barrier and horses (n = 16) received a human demonstration or no demonstration. Horses were allocated to two test groups of 8 horses each, which experienced the two treatments in a counterbalanced order. We found that horses did not solve the detour task faster with human demonstration. However, both test groups improved rapidly over trials. Our results suggest that horses prefer to use individual rather than social information when being confronted with a spatial problem-solving task.

Conservation Agriculture (CA) alters soil properties and microbial processes compared to conventional agriculture. These changes can affect soil-atmosphere greenhouse gas (GHG) fluxes. In this overview, we summarized the results of global literature and the gaps in measuring and understanding of GHG fluxes in CA systems and conventional agriculture. Some studies compared soil carbon sequestration and soil respiration in conservation agriculture and no-tillage system with conventional agriculture and the results were not consistent in all experiments. Interactions between CA pillars and soil factors such as soil moisture, temperature, texture can determine the rate of respiration rate and soil-atmosphere CO₂ fluxes. The majority of studies reported larger N₂O emissions in no-tillage treatment compared with conventional tillage while some other studies reported no difference between no-tillage and conventional tillage systems. In the majority of CA studies, there is lack of required information which is necessary to understand the mechanisms and processes that affect soil GHG fluxes. Determining factors like climate, amount of plant residues, soil type, crop types included in crop rotation and cover crops and duration of the study are not considered. Static chamber method was used for measuring soil-atmosphere GHG fluxes in the majority of studies. Spatial and temporal changes in GHG flux rates are high and missing part of highly episodic events by using static chamber method may result over- or under-estimation in flux balance calculation. Applying standard techniques for measuring continuous fluxes can help to calculating accurate GHG balance.

Background: The preexistence of obesity with nutritional anaemia is becoming an increasing problem in the Eastern Mediterranean Region (EMR). Much controversy exists around the role of continued breastfeeding in alleviating the health and survival consequences of these problems in the region. Aim: To examine trends and relationships of nutritional anaemia (NA) in women and children under-five years of age (CU5) with obesity and breastfeeding practices in the EMR. Methods: Data from regional database banks of WHO and UNICEF for anaemia in pregnant and non-pregnant women and CU5 from 22 countries in EMR were retrieved, compiled, and statistically analyzed. Breastfeeding indicators for timely first suckle (TFS), exclusive breastfeeding (EBF) and continued breastfeeding for 12 (BFC12) and 24 months (BFC24), low-birth-weight (LBW), stunting, overweight, total fertility rates (TFR) and mortality rates (MR) were correlated with nutritional anaemia and analyzed by income groups into 5 low income countries (LIC), 11 middle income countries (MIC) and 6 high income countries (HIC). Results: Trends in mean prevalence of anaemia in women showed a concave curve increasing after a short decrease in the early 2000 in all income groups (P > 0.05). Trends in anaemia in the CU5 exhibited a steep decrease from a mean of 52.59 ± 16.4 in 2000 to 37.3 ± 14.9 in 2016 at P < 0.002, the steep decline was significant in HIC (P < 0.01) and MIC (P < 0.02), but not for LIC (P > 0.05). Anaemia prevalence in women and CU5 was significantly correlated to LBW, stunting, and with overweight and obesity in adults but negatively in CU5. All age specific MRs were highly correlated with anaemia in pregnant, non-pregnant and CU5 at P < 0.001 and TFR at P < 0.05. BFC for shorter durations was negatively correlated with anaemia in pregnant women (r-0.5) at P < 0.05 and was positively correlated in MIC and HIC. In non-pregnant women, BFC12 and BFC 24 correlated positively with anaemia prevalence in all three income groups at P < 0.01. Conclusions: Completing optimal breastfeeding for two years should be promoted to protect women and CU5 from anemia and obesity. Nutritional anemia during pregnancy increases regional rates of LBW, stunting and mortality. Spacing out births by encouraging longer duration of breastfeeding, to replenish iron stores, can help alleviate many of the health and nutritional problems in the region.

The significance of arts incorporated with culture inclusion makes the arts a matter of pressing interest. The arts are vital elements of a healthy society that benefits the nations even in difficult social and economic times. Based on the previous studies this research was conducted for the first time in Pakistan to explore the historical background of public art correlated with cultural and religious ethics. Though, Pakistan has a rich cultural history yet the role of modern public art is new and often used unintentionally. Our findings of different surveys conducted in Pakistan including oldest cities such as Lahore, Peshawar and newly developed, the capital city, Islamabad concluded that Public art has a rich cultural and historical background and the local community are enthusiastically connected to it. Different community groups prefer different types of public art in their surroundings depends on the city’s profile, cultural background, and religious mindset of the local community. Overall, the sculptures and depiction of animated beings are not considering right and debatable among the Pakistani societies. On the other hand, the cultural and historical monuments are highly appreciated and welcomed by the local community of Pakistan. This study may create scope for future estimation and development of public art in Pakistan in association with Islamic laws and cultural norms of the local society.

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people use to stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches were not tested with the target population, or are not feasible to be implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We test our approach with the SisFall dataset achieving 99.4% of accuracy. Then, we validate it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.

Unlike the contiguous windthrows, the diffuse windthrows occurred as a result of wind gusts of lower speed (100-140 km/h) than in the first case (>140 km/h) are much more difficult to detect due to their much lower areas and due to their very large number, of several hundreds in the wooded mountain massifs. The objective of this research is to present a rapid procedure for the detection of the diffuse windthrows based on low cost, Landsat type images, knowing that certain sensors cannot be accessed without significant investments. Our application is based on the study of effects caused by the Xynthia storm in the Vosges Mountains in the North-East of France, on 28 February 2010. Thus, based on two sets of Landsat satellite images, we used the “dark object” approach and the Disturbance Index, as well as a classification of the images before and after the storm, resulting in a change map. Following the detection process, 257 scattered polygons were detected, totalling 229 ha. For validation purposes, high-resolution images and orthophotoplans taken before and after storm were used. The error matrix was calculated, achieving an overall accuracy of 86%, which confirms the quality of our analysis and supports this procedure for detecting diffuse windthrow based on low cost resources.

This article presents a detailed examination of the methodology and modeling tools utilized to analyze gas flow in pipelines, rooted in the fundamental principles of gas dynamics. The methodology integrates numerical simulations with modern neural network techniques, particularly focusing on the Physics-Informed Neural Network (PINN) method. This innovative approach combines artificial neural networks (ANNs) with physical equations, offering a more efficient and accurate way to model various complex processes and phenomena. The proposed mathematical model, based on the Euler equation, has been meticulously implemented using the Python language. Verification with analytical solutions ensures the accuracy and reliability of the computations. In the research, a comprehensive comparative analysis was conducted between results obtained using the PINN method and those from conventional Computational Fluid Dynamics (CFD) approaches. The analysis highlighted the advantages of the PINN method, which produced smoother pressure and velocity fluctuation profiles while reducing computation time, demonstrating its potential as a transformative modeling tool. The data derived from this study are of paramount importance for ensuring ongoing energy supply reliability and can also be used to create predictive models related to gas behavior in pipelines. The application of modeling techniques for gas flow simulation has the potential to revolutionize the integrity of our energy infrastructure and utilization of gas resources. However, it is crucial to emphasize that the effectiveness of such models relies on continuous monitoring and frequent updates to ensure alignment with real-world conditions. This research not only contributes to a deeper understanding of compressible gas flows but also underscores the crucial role of advanced modeling methodologies in the sustainable management of gas resources for both current and future generations.

Objectives: The male urethra is a complex structure that plays a critical role in genitourinary health and function. Despite its importance, histological descriptions of the penile urethra, par-ticularly its epithelial components, remain incomplete. This study offers a comprehensive his-tological analysis of the penile urethra, focusing on the epithelium across distinct anatomical regions, including the glans, distal and proximal fossa navicularis, and spongy urethra. Meth-ods: Utilizing four human penile specimens, we employed various staining techniques to eluci-date the structural characteristics of these epithelial tissues. Results: Our findings reveal notable variations in epithelial composition, such as the presence of glycogen-rich cells in the distal fos-sa navicularis and the presence of mucous glands in the spongy urethra and proximal fossa na-vicularis. Additionally, we identified a previously underreported valvule-like structure in the distal fossa navicularis in two of the specimens. Conclusions: This study provides new insights into the histological organization of the penile urethra, offering critical reference data that can enhance our understanding of urethral pathologies and improve the outcomes of surgical inter-ventions, particularly in the context of tissue engineering and reconstructive surgery.

This paper addresses the mixed convective flow and heat transfer in combustible third grade fluid through a slant channel that is filled with a permeable materials. The fluid layer in contact with the lower plate is exposed to convective heating with the ambient while at the upper plate, a constant heat flux is experienced. We employ the spectral quasilinearisation method (SQLM) to the coupled nonlinear flow governing equations. Fluid velocity and temperature profiles, local entropy generation and irreversibility ratio are computed and analysed quantitatively and qualitatively. A residual error analysis demonstrated high accuracy and convergence of the numerical method. The results on flow and thermal effects, entropy generation rate and Bejan number revealed fascinating manifestations that have profound implications in design of thermo-mechanical systems. In particular, the results of the thermal analysis are pertinent to optimal designs of systems that achieve efficient energy utilization.

As urbanization continues to accelerate, the concept of smart buildings has transitioned from a futuristic vision to an integral component of modern cities. These buildings, often seen as the building blocks of smart cities, are increasingly relying on cutting-edge technologies like 5G and cloud computing to reach unprecedented levels of energy efficiency, sustainability, and occupant comfort. This paper offers a comprehensive exploration into the marriage of smart building technologies with 5G and cloud computing. It scrutinizes the individual and combined impacts of these technologies, presents various case studies showcasing real-world applications, and anticipates future trends. Moreover, it delves into technical challenges, policy implications, and offers concrete recommendations for stakeholders.

Background: The gold standard for the diagnosis of sleep bruxism (SB) and obstructive sleep apnea (OSA) is Polysmnography (PSG). At the end of the apnea episodes there is frequently a final hy-permotor muscle activity that could act as a confusion factor in the diagnosis of SB with the elec-tromyography portable devices. The aim of this study was to compare the concordance on the number of episodes of SB in a population with OSA, between the diagnosis obtained by PSG, an-alyzed manually by a neurophysiologist and that obtained manually and automatically by a portable electromyography (EMG) and electrocardiography (EKG) device. Methods: Twenty-three subjects underwent one night of polysomnographic study with simulta-neous recording with the EMG-EKG device. The variables referring to the number of episodes and the SB index measured with both tools and analyzed in the manual and automatic modes were compared. Masticatory muscle activity was scored according to published criteria. The sample was segmented by severity of OSA according to AASM criteria. ANOVA, correlations, and the Bland–Altman plot were used to quantify the agreement between both methods. The concordance was calculated through the ICC. Results: The total events of SB per night in the PSG study were on average (8.17), lower than the one obtained with EMG-EKG manual analysis (14.13) and automatic (29.26). Both the SB PSG and Manual EMG-EKG episodes decrease from non-OSA (PSG = 16 ± 13,55, EMG-EKG = 16,83 ±11,58) to severe OSA (PSG = 3,14 ± 4,26, EMG-EKG = 9,86 ± 8,09). However, in the case of automatic EMG-EKG mode: the number of SB episodes in severe OSA doubled (41,23 ± 12,50) with respect to non OSA (24,50 ± 12,19). On average: the EMG-EKG device Automatic analysis measures 21.08 units more than PSG. The results with the manual EMG-EKG analysis improved. Conclusion: There is no concordance between the results obtained in the PSG neurophysiologic analysis and those obtained by means of the EMG-EKG device automatic and manual analysis for the diagnosis of SB in a population mostly with OSA. The OSA could act as a confusion factor in the diagnosis of SB with the electromyography portable devices, but further study is needed.

The involved components of formulated blending, CNF and CuO NPs, were individually obtained by using a biorefinery strategy for agricultural waste valorization together with an optimized chemical precipitation, assisted by ultrasounds. The optimization of synthesis parameters for CuO NPs have avoided the presence of undesirable species which usually requires of later thermal treatment with associated costs. The aerogels-based structure, obtained by conventional freeze-drying, acted as 3D support for CuO NPs providing a good dispersion within the cross-linked structure of the nanocellulose and facilitating direct contact of the antibacterial phase against undesirable microorganisms. All samples showed a positive response against Escherichia coli and Staphylococcus aureus. An increase of the antibacterial response of the aerogels, measured by agar disk diffusion test, has been observed with the increase of CuO NPs incorporated, obtaining width of the antimicrobial “halo” (nwhalo) from 0 to 0.6 and 0.35 for S. aureus and E. coli, respectively. Furthermore, the aerogels have been able to deactivate S. aureus and E. coli in less than 5 hours when the antibacterial assays have been analyzed by broth dilution method. From CNF-50CuO samples, an overlap nanoparticles effect produced a decrease of the antimicrobial kinetic.

Total Parenteral Nutrition (TPN), which uses intravenous administration of nutrients, minerals and vitamins, is essential in sustaining premature infants until they transition to enteral feeds. There is limited information on metabolomic differences between infants on TPN and enteral feeds. We performed untargeted global metabolomics on urine samples collected between 23-30 days of life from 314 infants born &lt;29 weeks gestational age from the TOLSURF and PROP cohorts. Principal component analysis across all metabolites showed a separation of infants solely on TPN as compared to infants who had transitioned to enteral feeds, indicating global metabolomic differences between infants based on feeding status. Among 913 metabolites that passed quality control filters, 609 varied in abundance between infants on TPN vs enteral feeds at p&lt;0.05. Of these, 88% were in the direction of higher abundance in the urine of infants on enteral feeds. In a subset of infants with longitudinal analysis, both concurrent and delayed changes in metabolite levels were observed with initiation of enteral feeds. Infants on enteral feeds had higher concentrations of essential amino acids, lipids, and vitamins, which are necessary for growth and development, suggesting a nutritional benefit of an enteral feeding regimen.

Tomato brown rugose fruit virus (ToBRFV) is a causal agent of severe emergent diseases in Solanaceae hosts with agronomic relevance, such as tomato and pepper. Herein, we analyzed the seed transmission rate and efficacy of different seed disinfection treatments and performed a comparison of molecular biology techniques seeking rapid and sensitive detection in seed lots. We evaluated several total RNA extraction and RT-PCR protocols using a distinct combination of PCR primers to test for the presence of the ToBRFV Mexican strain in tobacco host. Our results showed that the percentage of seed and seedlings transmission was less than 1% and that a 3% sodium hypochlorite solution was effective as a seed disinfection treatment. Finally, the most sensitive molecular method identified for virus detection consisted of a CTAB-Trizol RNA extraction followed by nested PCR using primers reported by Dovas et al. (2004). Additionally, we tested potential natural hosts in selected Cucurbitaceae and Solanaceae species. Our results showed that the ToBRFV Mexican strain is capable of experimentally infecting eggplant (Solanum melongena), tomatillo (Physalis ixocarpa), and tobacco (Nicotiana rustica).

Chronic hepatitis B (CHB) infection and the Hepatitis B virus X protein (HBx) are major risk factors associated with hepatocellular carcinoma (HCC). In CHB infection, HBx induces mitochondrial dysfunction, exhaustion and impaired function in hepatocytes. Restoring hepatocyte health along with reduction in virus replication could be an ideal treatment for CHB. Thiourea derivatives are well known for their antiviral property though their effect on mitochondrial and/ or hepatocyte health remains obscure. This study focus on the repurposing of thiourea derivatives (DSA-00, DSA-02, and DSA-09) on hepatocyte replenishment. HepG2.2.15 cells were treated with thiourea derivatives, alongside Entecavir (ETV). The proteomics analysis showed both DSA-00 and ETV were enriched with proteins associated with antiviral responses. In addition, DSA-00 additionally showed increase in proteins linked to mitochondrial response. Whereas DSA-02 exhibited association with innate immune system and citric acid cycle and DSA-09 displayed pathways similar to DSA-00 and ETV. Treated groups exhibited enhanced bio-energetic and antiviral response as compared to the untreated group. FACS analysis revealed the restoration of exhausted hepatocytes by thiourea derivatives through targeting mitochondria. Our findings suggest that thiourea derivatives hold potential as a novel therapeutic agent that seems to restore mitochondrial health along with anti-viral response in CHB.

Reduced glutathione (GSH) is an antioxidant in plants and is one of the important ways for plants to combat low-temperature stress. In this paper, Eriobotrya japonica Lindl. cv. Zaozhong No. 6 seedlings were used to study the effects of exogenous 5-aminolevulinic acid (ALA) application on glutathione synthesis and cyclic metabolism of loquat seedlings under low-temperature stress and to explore the regulatory mechanism of ALA on loquat cold tolerance. The results showed that ALA treatment could increase the content of GSH and the reduced glutathione/oxidized glutathione (GSH/GSSG) ratio in loquat leaf slices under low-temperature stress; reduce the electrolyte leakage rate and GSSG, H₂O₂ and MDA contents in leaf tissues; and alleviate the peroxidation damage caused by low temperature. ALA treatment increased the activity of γ-glutamine synthetase (γ-ECS) in loquat leaf slices under low-temperature stress and promoted the biosynthesis of reduced glutathione, thereby increasing the GSH content in leaf tissues. On the other hand, ALA treatment could also improve the activities of glutathione reductase (GR), glutathione S-transferase (GST) and glutathione peroxidase (GPX) and promote the cyclic regeneration of GSH, accordingly maintaining a high GSH/GSSG ratio, promoting the removal of reactive oxygen species (ROS), and enhancing the antioxidant capacity of leaves. The regulatory effect of ALA on enhancing the antioxidant capacity of loquat seedlings under low-temperature stress can be inhibited by L-buthionine-sulfoximine (BSO, GSH biosynthesis inhibitor). The results showed that ALA improved the antioxidant capacity of loquat seedlings under low-temperature stress, and GSH was involved in the regulation of the antioxidant effect of ALA on loquat seedlings under low-temperature stress.

Natural carotenoids are secondary metabolites that exhibit antioxidant, anti-inflammatory and anti-cancer properties. These types of compounds are in high demand by pharmaceutical, cosmetic, textile and food industries, leading to the search for new natural sources of carotenoids. In recent years, the production of carotenoids from bacteria has become of great interest for industrial applications. In addition to carotenoids with C40-skeletons, some bacteria have the ability to synthesize characteristic carotenoids with C30-skeletons. In this regard, a great variety of methodologies for the extraction and identification of bacterial carotenoids has been reported and this is the first review that condenses much of this information. To understand the diversity of these carotenoids, we present their biosynthetic origin in order to focus on the methodologies employed in their extraction and characterization. Special emphasis has been made on high-performance liquid chromatography-mass spectrometry (HPLC-MS) for the analysis and identification of bacterial carotenoids. We end up this review showing their potential commercial use of bacterial carotenoids. This review is proposed as a guide for the identification of these metabolites, which are frequently reported in new bacteria strains.

Metabolomics is now considered to be a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense response to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in molecular breeding and genome editing programs to develop superior next generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted method. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) and matrix-assisted laser desorption/ionization (MALDI) have speed up metabolic profiling in plants. Further, we deliver a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.

One of the most common and important destructive attacks on the victim system is Advanced Persistent Threats (APT)-attack. The APT attacker can achieve his hostile goals by obtaining information and gaining financial benefits regarding the infrastructure of a network. One of the solutions to detect a secret APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the network may crash because of high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods such as C5.0 decision tree, Bayesian network and deep neural network are used for timely detection and classification of APT-attacks on the NSL-KDD data set. Moreover, 10-fold cross validation method is used to experiment these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 95.64%, 88.37% and 98.85%, respectively, and also, in terms of the important criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 2.56, 10.47 and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false negative rate and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models.

Accurate measurements of tree height and diameter at breast height (DBH) in forests to evaluate the growth rate of cultivars is still a significant challenge, even when using LiDAR and 3-D modeling. We propose an integrated pipeline methodology to measure the biomass of different tree cultivars in plantation forests with high crown density which that combines unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Using a planation of Cunninghamia lanceolate, commonly known as Chinese fir, in Fujian, China, images were collected using a hyperspectral camera and orthorectified in HiSpectral Stitcher. Vegetation indices and modeling were processed in Python using decision trees, random forests, support vector machine, and eXtreme Gradient Boosting (XGBoost) third-party libraries. Tree height and DBH of 2880 samples were measured manually and clustering into three groups: “fast growth,” “median,” growth and “normal” growth group, and 19 vegetation indices from 12,000 pixels were abstracted as the input of features for the modeling. After modeling and cross-validation, the classifier generated by random forests had the best prediction accuracy compare to other algorisms (75%). This framework can be applied to other tree species to make management and business decisions.

Most of the phase change materials (PCMs) have been limited to use as functional additions or sealed in containers, and extra auxiliary equipment or supporting matrix is needed. The emergence of 3D printing technique has dramatically advanced the developments of materials and simplified production processes. This study focuses on a novel strategy to model thermal energy storage crystalline gels with three-dimensional architecture directly from liquid resin without supporting materials through light-induced polymerization 3D printing technique. A mask-projection stereolithography printer was used to measure the 3D printing test, and the printable characters of crystalline thermal energy storage P(SA-DMAA) gels with different molar ratios were evaluated. For the P(SA-DMMA) gels with small fraction of SA, the 3D fabrication was realized with higher printing precision both on mili- and micro-meter scales. As a comparison of 3D printed samples, P(SA-DMAA) gels made by other two methods, post-UV curing treatment after 3D printing and UV curing using conventional mold, were prepared. The 3D printed P(SA-DMAA) gels shown high crystallinity. Post–UV curing treatment was beneficial to full curing of 3D printed gels, but did not lead to the further improvement of crystal structure to get higher crystallinity. The P(SA-DMAA) crystalline gel having the highest energy storage enthalpy that reached 69.6 J·g⁻¹ was developed. Its good thermoregulation property in the temperature range from 25 to 40 °C was proved. The P(SA-DMAA) gels are feasible for practical applications as one kind of 3D printing material with thermal energy storage and thermoregulation functionality.

The present paper discusses an elliptic equation with Steklov boundary conditions and $(p(x),q(x))$-Laplacian. Using mountain pass theorem together with Ekeland's variational principle, we prove, under appropriate conditions on the functions involved, that the problem admits at least two solutions.

The integration of connected autonomous vehicles into the transportation system introduces significant enhancements in driving experience and convenience. Yet, it simultaneously raises important concerns regarding the security and privacy of passenger data. As individuals increasingly depend on these connected vehicles, particularly rental cars with advanced infotainment systems, safeguarding their personal information becomes a paramount imperative, presenting significant challenges that must be addressed to maintain user trust and comply with privacy regulations. This paper investigates risks associated with personal information stored in connected vehicle technology, emphasizing the importance of robust security and privacy practices. In the advent of cyber threats and the need for data protection, we examine a risk management framework (RMF) as suggested by NIST, which aims to address these quintessential concerns preemptively. Our research moves beyond the limitations of manual safeguarding methods, supporting the necessity for an advanced automated technical solution. To address these issues, we introduce and meticulously assess the efficacy of “Vehicle Inactive Profile Removal” (VIPR), an innovative technical solution conceived to actively eliminate the risks and potential oversights that stem from the prevalent tendency of renters to leave personal data unerased in the infotainment systems of rented connected vehicles. We provide a thorough evaluation of VIPR through various scenarios, including vehicle return to a rental depot, subsequent rentals, and ridesharing contexts. Our proof of concept includes an array of experiments to demonstrate how VIPR proficiently and autonomously removes previous renters’ “Inactive” profiles from a simulated infotainment system. The results highlight how VIPR constitutes a critical step towards enhancing the privacy and security of personal data, thereby promoting a safer, more responsible use of connected vehicle technology in society.

In recent years, there has been an increase in the number of illegal radio stations that exceed power limits or use illegal frequencies. There has been a radio monitoring system called the DEURAS system to control these illegal radio stations. This system monitors illegal radio stations by receiving illegal radio waves using sensor stations installed in various locations. However, these systems sometimes do not work well when there is no line of sight from the sensor stations to the illegal radio stations or when there are many reflected waves generated due to the surrounding environment. In this study, we proposed a radio wave source estimation using HAPS, an unmanned aerial vehicle flying at high altitude. The simulation results show that estimation from high altitude provides higher estimation probability and estimation accuracy than the conventional method.

A novel approach is developed to improve the convergence of Physics-Informed Neural Networks (PINNs), aiming to employ them as real-time computational models within the framework of the digital twin for manufacturing processes. This method entails the weighting of physical equations, boundary conditions, and initial conditions to ensure their comparable magnitudes, with power being the chosen quantity in this study. The approach is applied to thermal problems, which are crucial for predicting manufacturing part defects. Different configurations, including complex boundary conditions and complex physics, were tested to assess the model’s robustness. The W-PINN demonstrates good predictions and strong stability compared to the classical PINN.

Background: Excessive sun exposure is a significant risk factor for various skin conditions, including sunburn, premature aging, and skin cancer. This study aims to assess the awareness, attitudes, and practices regarding sun exposure and sunscreen use among college students in Saudi Arabia. Methodology: A cross-sectional study was conducted with 388 college students from various disciplines. Data were collected using a structured questionnaire covering demographic information, knowledge, and practices related to sun exposure and sunscreen use. Descriptive statistics, chi-square tests, and multivariate logistic regression analysis were employed to analyze the data. Results: The study comprised 53.9% females, with the majority aged 18-23 years. Formal education on sun exposure dangers was lacking for 43.0% of participants. The most recognized consequences of excessive sun exposure were sunburn (80.6%) and skin cancer (74.4%). Intermediate knowledge of sun exposure effects was most common (40.7%). Sunscreen use was inconsistent, with 27.3% never using it during peak sunlight hours. Factors influencing sunscreen use included weather conditions and healthcare provider recommendations. Males were significantly less likely to use sunscreen. First-year students and participants without formal education on sun exposure were less likely to use sunscreen regularly. Conclusion: Despite awareness of the risks associated with excessive sun exposure, sunscreen use among college students in Saudi Arabia is inconsistent and influenced by various factors, including gender and education level. These findings highlight the need for enhanced educational initiatives to promote regular sunscreen use and other sun protection behaviors to mitigate the risk of skin conditions associated with sun exposure.

Antimicrobial resistance (AMR) is a growing concern in healthcare, and the misuse of antibiotics is contributing to the problem. Antimicrobial Stewardship Programs (ASPs) have been proven to help reduce antibiotic consumption, promote appropriate use, and decrease the incidence of AMR and related side effects. In this study, we aimed to evaluate the impact of ASPs on antibiotics consumption and AMR spread in a Southern Italy. Most of the APSs consisted of applying infection control guidelines and WHO recommendations on antibiotic consumption. The results of the study showed that during the study period, the number of hospitalizations increased by 24%, while the total amount of antibiotics administered decreased from 52.1 to 38.6 defined daily doses (DDD) per 100 bed-days. There was also a significant increase in the number of rectal swabs taken to detect carbapenem-resistant Enterobacteriaceae (CRE) and methicillin-resistant Staphylococcus aureus (MRSA), while the positivity rate of these infections decreased significantly. Moreover, the hospital recorded a decrease in the incidence of several infections, including CRPsA, MRSA, PDR, and VanA infections. This study provides further evidence that ASPs can be effective in reducing antibiotic consumption and preventing the spread of AMR. By promoting the appropriate use of antibiotics and implementing infection control measures, we can help preserve the effectiveness of these life-saving drugs and promote better patient outcomes. However, more efforts are needed to improve healthcare practices and reduce related costs. In conclusion, we urge healthcare providers to implement ASPs in their institutions and help combat the growing threat of AMR. By doing so, we can ensure that antibiotics remain effective for future generations.

The construction industry, a major player in economic development, is facing increased pressure to address sustainability concerns amidst rapid population growth and urbanization. With global projections indicating a significant rise in building demand by 2050 , sustainability emerges as a crucial focus area and paradigm shift to enhance environmental friendliness, improved quality, and project outcomes. The UAE, renowned for its vibrant construction industry, offers a unique context for examining the integration of sustainable practices. The growing use of sustainable construction practices is growing in the U.A.E., where the built environment plays a key role in economic growth and environmental stewardship. The United Arab Emirates (UAE) aims to foster long-term sustainability while enhancing the standard of living for current as well as future generations by integrating social, environmental, and economic aspects within construction projects, while also reevaluating conventional sustainable development frameworks and embracing a Triple -Bottom Line approach. Through an exploration of the significance of sustainable construction materials, this researcher underscores the multifaceted benefits of locally sourced, recyclable, and renewable materials in reducing environmental impacts, fostering economic and social well-being, and improving overall project performance, and project management practices. The construction sector's role in economic development and its substantial environmental impact are discussed in, alignment with sustainable construction materials, sustainable construction practices, and the need to enhance environmental sustainability; and create healthier built environments. In the realm of sustainable construction materials, project management knowledge areas encompass a range of factors. These include the properties of materials sourced regionally;, the incorporation of recycled content;, considerations for indoor air quality, energy, and water efficiency parameters;, and how these properties relate to project scope, scheduling constraints, and challenges. Additionally, the availability of resources and competency levels, quality control standards, specifications, communication strategies, and stakeholder involvement play crucial roles. It is important to assess both the positive and negative risks associated with these elements across construction projects.

In classical broadcast models, information is disseminated in synchronous rounds under the constant communication time model, wherein a node may only inform one of its neighbors in each time unit—also known as the processor-bound model. These models assume either a coordinating leader or that each node has a set of coordinated actions optimized for each originator, which may requiring nodes to have sufficient storage, processing power, and the ability to determine the originator. This assumption is not always ideal, and a broadcast model based on the node’s local knowledge can sometimes be more effective. Messy models address these issues by eliminating the need for a leader, knowledge of the starting time, and the identity of the originator, relying solely on local knowledge available to each node. This paper investigates the Messy broadcast time and optimal scheme in a Grid graph, a structure widely used in computer networking systems, particularly in parallel computing, due to its robustness, scalability, fault tolerance, and simplicity. The focus is on scenarios where the originator is located at one of the corner vertices, aiming to understand the efficiency and performance of Messy models in such Grid structures.

This study focuses on the use of gamification, specifically the UrbanGame activity, to enhance heritage education related to the Spanish Civil War bombings in Alicante. The research aims to evaluate the effectiveness of active methodologies in raising cultural heritage awareness among university students. By employing a combination of collaborative work, meaningful learning, problem-based learning, discovery learning, concentration, and motivational strategies, the study involved 122 students from primary and secondary education programs. The methods included the design and implementation of the UrbanGame, followed by surveys to collect quantitative and qualitative data on students' perceptions, satisfaction, and learning outcomes. The results indicated that while collaborative work did not have a significant impact on content comprehension, skill acquisition, or value acquisition, meaningful learning showed a positive trend towards content comprehension. Problem-based learning had a weak but notable relationship with content comprehension, while motivation and concentration significantly influenced learning outcomes. Additionally, the UrbanGame activity promoted memory and cultural heritage by emotionally connecting students with local history. The findings suggest that an integrated approach that combines various active methodologies is essential for maximizing educational effectiveness and cultural promotion.

This paper addresses the parameter identification of a one-link flexible manipulator based on the experimental measurement of the inputs/outputs, the finite element model, and the application of evolutionary algorithms. A novel approach is proposed to find the values of inertia, stiffness, and damping parameters by minimizing the difference between the numerical model’s outputs and the testbed’s outputs, considering the joint position and acceleration of the link’s tip. The dynamic model is initially obtained using the finite element method and the Lagrange principle. A prototype of a single one-link flexible manipulator is used in the experimental application wherein the servomotor applies the input torque, and the outputs are the joint angle and the link’s tip acceleration. Then, an optimization problem minimizes the difference between numerical and experimental outputs to determine the set of parameters using evolutionary algorithms. A comparative analysis to obtain the identified parameters is established using genetic algorithms, particle swarm optimization, and differential evolution. The proposed identification approach permitted the determination of the dynamic parameters based on the complete dynamic model of the flexible-link manipulator, which is different from the approaches reported in the literature that identify a simplified model. This information is essential for the design of the motion and vibration control laws.

ABSTRACT Underwater mines may destroy friendly submarines and marine habitats, compromising nonmilitary security. Finding credible information is vital to avoiding scams and spotting hazards. This article employs Supervised Machine literacy models to appropriately categorize subsea items like mines or gems based on Sonar sounds. Python constructs three double classifier models using Gorman and Sejnowski (1988) training data. Through the extraction of key aspects from the Sonar signals, such as spectral components and intensity, the models are able to match newly obtained data with the gemstone order or mine. Delicacy decreases the chance of misidentifications in nonmilitary activities. Machine literacy offers full validation that accommodates real-world Sonar data noise and unpredictability. Based on test results, the highest performing algorithm will deliver the required ratio of accuracy to perceptivity for the bracket task. Central keywords: vaticination model, bracket algorithms, supervised machine literacy, SONAR, and aquatic mining emphasize particular foundations. Improved sophistication in spotting undersea Mines let nonmilitary defence systems respond to proven threats only, preventing friendly submarines or sea life from unnecessary damage owing to false duplicity. This work is a promising start to real-world mine detection devices. Further developments may incorporate greater training data sets, real-time testing on subsea platforms equipped with integrated sonar, and optimization for the discovery of new mine-types. Overall, machine learning and Sonar vision decrease signal fluctuation and noise, making it easier to determine accurate boundaries for non-military applications and environmental protection. The recommended automated discovery technique must balance computational efficiency, generalizability, and delicacy. Prior a real crime, new quality control and verification procedures could boost trust even further.

Abstract: Background: With the growing trends in recreational marijuana use, our study aims at analyzing the association between Acute coronary syndromes (ACS) and Ventricular Fibrillation (Vfib) and cannabis use disorder in young adults (18 – 45) Methods: National In-patient Sample was used for population analysis. Probit logistic regression was used for analyzing the associations between diagnosis of ACS/ Vfib and documented cannabis use disorder. Propensity Score matched samples were made from the ACS/Vfib admissions with Cannabis use disorder to analyze the in-hospital outcomes. Results: Statistically significant association was found between admission diagnosis of ACS/ vfib, and documented cannabis use disorder in the study population. The association remained sig-nificant after accounting for potential confounders. Documented race as black, documented di-agnosis of uncontrolled hypertension and diabetes were found to have significant association with admissions for ACS among the population with documented Cannabis use disorder. No difference in mortality were found between ACS admissions with and without documented cannabis use disorder in the propensity matched cohorts. Conclusions: Documented diagnosis of Cannabis use disorder has statistically significant associ-ation with admission diagnosis of ACS/Vfib even after accounting for confounders. Documented race as black, and co existing diagnosis of uncontrolled hypertension and diabetes had significant association with admission diagnosis of ACS in population with documented cannabis use dis-order. Keywords: Cannabis use disorder; ACS; Ventricular fibrillation

The consumption of insects in the Democratic Republic of Congo is an ancient practice. The taxonomic identification and characterization of “entomophagy” seem urgent in Kinshasa following the related anthropogenic pressures (demographic pressure, uncontrolled urbanization) leading to a food crisis. This study aims to assess the diversity of edible insects and their consumption by the population of Kinshasa. A total of 360 people were surveyed to identify the insect species consumed in Kinshasa, the ecosystems that provide them, and the preference for species in relation to ethnolinguistic groups, churches (faith) and household size. Three species of insects most consumed in Kinshasa are: Gonimbrasia jamesonii, Cirina forda and Brachytrupes membranaceus. 41.9% of these insects are consumed by the Kongo ethno-linguistic group, 23.6% by the Luba, 20.6% by the Ngala and 13.9% by the Swahili. Inhabitants of the communes of Kassa Vubu (57%), Maluku (54%), Masina (54%) and Mont-Ngafula (39%) mainly consume C. forda and G. jamesonii. Luba and Kongo ethnolinguistic groups mainly consume C. forda while the Swahili and Ngala consume G. jamesonii. Catholic, Revivalist Church, Kimbagu, Islam, Protestant and Johovah's Witness believers mainly consume C. forda and G. jamesonii. This study made it possible to understand the socio-cultural influence on the degradation (pressure) of biodiversity and; contributes to SDGs 2 and 12 by 2023..

Nitraria tangutorum Bobrov is a berry shrub with white flowers and red fruits, which grows in the deserts in the Tibetan Plateau, Mongolia, and western China. Its fruit N. tangutorum fruit (NTF) contains various bioactive compounds, with anti-fatigue, anti-inflammatory, and neuroprotective functions. However, the high saccharide content of NTF makes it unsuitable for diabetic patients. In this study, we fermented NTF to obtain N. tangutorum fermented juice (NTJ), and N. tangutorum fermented residue (NTR), which are suitable for diabetics to consume. We characterized the bioactive compounds in NTF, NTR, and NTJ, and found that fermentation increased the diversity of bioactive compounds, and greatly reduced sucrose, glucose, and fructose content while generating trehalose, which has the potential to manage blood glucose levels. Further, NTJ displays anti-diabetic potential due to various compounds anti-diabetic properties. This study provides a basis for further clinical research on NTJ's anti-diabetic function in humans.

Multilingual classrooms are becoming more common worldwide due to increasing mobility. Teaching foreign languages using the mother tongue in multilingual classrooms has become problematic; some of the significant concerns in Japanese language acquisition – such as the high contextuality of the language, vocabulary and kanji learning – make the direct teaching method confusing for many students, especially at the beginner and elementary levels. This article provides the preliminary results of a study carried out as a component of a more extensive empirical research project on instructional languages in classrooms teaching Japanese as a foreign language. It aims to gather, analyse and synthesise data from a survey conducted at a supranational level. The results of the first two sections of a questionnaire containing closed and open-ended questions were used for a quantitative analysis of the profile of Japanese language instructors with a focus on finding possible connections. The wide geographic distribution of the respondents (274 teachers from fifty-seven countries and regions) made it possible to collect data from a more representative sample and draw meaningful conclusions about the spread of multilingual classrooms that would be impossible in smaller-scale research. The study’s primary results show the proportion of multilingual classrooms among countries in different regions and across different educational stages, the distribution of Japanese language instructors in various educational stages depending on the highest level of formal education attained, and the proportion of part-time instructors working in different academic institutions.

The differential expression of transcription factors during embryonic development has been selected as the main feature to define the specific subclasses of spinal interneurons. However, recent studies based on single-cell RNA sequencing and transcriptomic experiments suggest that this approach could not be appropriate in the adult spinal cord, where interneurons show overlapping expression profiles, especially in the ventral region. This constitutes a major challenge for the identification and direct targeting of specific populations that could be involved in locomotor recovery after a traumatic spinal cord injury in adults. Current experimental therapies including electrical stimulation, training, pharmacological treatments, or cell implantation that resulted in improvements in locomotor behaviour, rely on the modulation of the activity and connectivity of interneurons located in the surroundings of the lesion core for the formation of detour circuits. However, very few publications clarify the specific identity of these cells. In this work, we review the studies where premotor interneurons were able to create new intraspinal circuits after different kinds of traumatic spinal cord injury, highlighting the difficulties encountered by researchers to classify these populations.

The objective of this work is the study the crystal structure and elemental content of braunite-hematite and braunite-psilomelane oxide manganese ores y means of X-ray diffractometry (XRD), Energy Dispersion X-ray (EDX) analysis, and X-Ray fluorescent (XRF) analysis. It was found that heat treatments at 600, 800, and 1000 °C promote the phase transitions of the manganese ores to the formation of thermostable phases hausmannite and bixbyite without change in the metallic composition. The homogeneity of the structure increases with increasing temperature, which is expressed in the decreasing of the Mn content in the EDX spectra after sintering above 800 °C as well as in a decrease of the phase number to only two phases: bixbyite and quartz. Remarkably, the hausmannite phase is obtained in the fraction of 61% after sintering the braunite-hematite ore sample at 1000 °C for 4 h. Since hausmannite is widely used to produce building materials, electrodes, catalysts, and other materials, these results are promising for developing technologies for obtaining new products based on phases of manganese minerals at high temperatures.

This review article seeks to systematically identify appropriate ways to measure managerial skills. The systematic literature review was conducted according to the criteria in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guide, and the eligibility criteria were declared through the PICOS tool (population, interventions, comparators, outcomes and study); a tool based on 6,803 records identified bibliometrically in the Web of Science database. The literature review stages determined a reduced set of articles that presented valid and reliable measurement scales covering some determinant constructs on managerial skills for employability such as fundamental, emotional, managerial, leadership, decision-making, communication and teamwork skills.

Brain-stimulation reward, also known as intracranial self-stimulation (ICSS), is a commonly used procedure for studying brain reward function and drug reward. In electrical ICSS, an electrode is surgically implanted into the medial forebrain bundle in the lateral hypothalamus or the ventral tegmental area in the midbrain. Operant lever responding leads to the delivery of electrical pulse stimulation. The alteration in the stimulation frequency-lever response curve is used to evaluate the impact of pharmacological agents on brain reward function. If a test drug induces a leftward or upward shift in the ICSS response curve, it implies a reward-enhancing or abuse-like effect. Conversely, if a drug causes a rightward or downward shift in the functional response curve, it suggests a reward-attenuating or aversive effect. A significant drawback of electrical ICSS is the lack of cellular selectivity in understanding the neural substrates underlying drug reward versus aversion. Excitingly, recent advancements in optical ICSS (oICSS) have facilitated the development of at least three cell type-specific oICSS models – dopamine-, glutamate-, and GABA-dependent oICSS. In these new models, a comparable frequency-rate response curve has been established and employed to study the substrate-specific mechanisms underlying brain reward function and a drug's rewarding versus aversive effects. In this review article, we summarize recent progress in this exciting research area. The findings in these new behavioral models have not only increased our understanding of the neural mechanisms underlying drug reward and addiction but have also introduced a novel experimental approach in preclinical medication development for treating substance use disorders.

This study was conducted on elderly over 65 years of age to verify that healing garden activities control stress in the elderly. The experimental group performed garden activities once a week for 12 weeks, 2 hours each, and the control group continued their daily lives. Each group's cumulative stress at the beginning and end, total power (TP), the standard deviation of the normal-to-normal interval (SDNN), and root mean square differences of successive R-R intervals (RMSSD) were measured using a stress index meter, u-bio MACPA. As a result of the analysis, the experimental group had a statistically significant decrease in cumulative stress compared to the control group, and TP significantly increased compared to the control group. SDNN and RMSSD of the experimental group increased, respectively, and the control group decreased, respectively, but it was not statistically significant. The fact that cumulative stress decreased, and stress evaluation indicators increased shows that daily stress can be controlled through healing garden activities. This confirms that the value of the garden can extend to healing.

Transcranial direct current stimulation (tDCS) is acknowledged for its non-invasive modulation of neuronal activity in psychiatric disorders. However, its application in insomnia research yields varied outcomes depending on different tDCS types and patient conditions. Our primary objective is to elucidate its efficiency and uncover the underlying mechanisms in insomnia treatment. We hypothesized that anodal prefrontal cortex stimulation activates glutaminergic projections from the infralimbic cortex (IL) to ventrolateral preoptic area (VLPO) to promote sleep. After administering 0.06 mA of electrical currents for 8 minutes, our results indicate significant non-rapid eye movement (NREM) enhancement in naïve mice within the initial 3 hours post-stimulation, persisting up to 16-24 hours. In the insomnia group, tDCS enhanced NREM sleep bout numbers during acute stress response and improved NREM and REM sleep duration in subsequent acute insomnia. Sleep quality, assessed through NREM delta powers, remains unaffected. Interference of the IL-VLPO pathway, utilizing designer receptors exclusively activated by designer drugs (DREADDs) with the cre-DIO system, partially blocked tDCS's sleep improvement in stress-induced insomnia. This study elucidated that the activation of the IL-VLPO pathway mediates tDCS's effect on stress-induced insomnia. These findings support the understanding of tDCS effects on sleep disturbances, providing valuable insights for future research and clinical applications in sleep therapy.

The construction sector is considered as a major consumer of virgin materials and contributor to waste generation. Therefore, it is essential to rethink current waste management practices, for example, by applying circular economy principles to building demolition, such as deconstruction. Deconstruction involves dismantling a building with the aim of maintaining the highest possible value for its materials and maximize their recovery potential. This study aims to guide the construction sector towards deconstruction to support its efforts to transform itself toward a more sustainable industry. It focuses on a regional case study in the province of Québec (Canada) presenting five buildings to be deconstructed. First, the study presents the outcomes of our analysis of the current situation. Second, it identifies the issues and obstacles encountered and proposes avenues to improve the current process based on solutions identified in the literature and the recommendations of the manager, the contractor involved in the deconstruction process, as well as experts in the construction industry. Finally, it proposes an improved deconstruction process. Our research approach is inspired from Lean thinking and follows Action Research methodology.

Chimeric antigen receptor (CAR) T-cell therapy has greatly improved outcomes for patients with relapsed or refractory hematological malignancies. However, challenges such as treatment resistance, relapse, and severe toxicity still hinder its widespread clinical application. Traditional transcriptome analysis has provided limited insights into the complex transcriptional landscape of both leukemia cells and engineered CAR-T cells, as well as their interactions within the tumor microenvironment. However, with the advent of single-cell sequencing techniques, a paradigm shift has occurred, providing robust tools to unravel the complexities of these factors. These techniques enable unbiased analysis of cellular heterogeneity and molecular patterns. These insights are invaluable for precise receptor design, guiding gene-based T cell modification, and optimizing manufacturing conditions. Consequently, this review utilizes modern single-cell sequencing techniques to clarify the transcriptional intricacies of leukemia cells and CAR-Ts. The aim of this manuscript is to discuss the potential mechanisms that contribute to the clinical failures of CAR-T immunotherapy. We will examine the biological characteristics of CAR-Ts, the mechanisms that govern clinical responses, and the intricacies of adverse events. By exploring these aspects, we hope to gain a deeper understanding of CAR-T therapy, which will ultimately lead to improved clinical outcomes and broader therapeutic applications.

Understanding how humans perceive animals is important for biodiversity conservation, however, only a few studies about this issue were carried out in South America. We selected two Brazilian cities to assess people’s perceptions of birds: Bauru (São Paulo, Brazil) and Belo Horizonte (Minas Gerais, Brazil). From the bird data we gathered, we developed a questionnaire and applied it between September 2020 and June 2021. The data obtained were analyzed by simple counts, Likert scale, and percentages. Also, open questions were placed on the Free Word Cloud Generator website. Our study confirmed that most respondents are aware of the importance of birds to ecological balance and that respondents had a generally positive attitude towards most of the bird species. However, they disliked exotic species such as the Domestic Dove and the House Sparrow, which are associated with disease, dirt, and disgust. Respondents also underestimated the number of birds that can live in urban areas and the song of birds is still a sense less explored and perceived by people. The concept of environmental perception seeks to cover aspects and understanding these human–biodiversity relationships is essential to successfully guide public policies, urban planning interventions, and environmental education activities.

Genetic diversity is the foundation of evolutionary resilience, adaptive potential, and the flourishing vitality of living organisms, serving as the cornerstone for robust ecosystems and the continuous evolution of life on Earth. The landscape of directed evolution, a powerful biotechnological tool inspired by natural evolutionary processes, has undergone a transformative shift propelled by innovative strategies for generating genetic diversity. This shift is fuelled by several factors, encompassing the utilization of advanced toolkits like CRISPR-Cas and base editors, enhanced comprehension of biological mechanisms, cost-effective custom oligo pool synthesis, and the seamless integration of artificial intelligence and automation. This comprehensive review looks into the myriad methodologies employed for constructing gene libraries, both in vitro and in vivo, categorized into three major classes: random mutagenesis, focused mutagenesis, and DNA recombination. The objectives of this review are threefold: firstly, to present a panoramic overview of recent advances in genetic diversity creation, secondly, to inspire novel ideas for further innovation in the genetic diversity generation, and thirdly, to provide a valuable resource for individuals entering the field of directed evolution.

One of the current challenges in Higher Education is the development of students’ emotional competencies, and consequently, how these competencies can be trained. Moreover, this must be addressed within the UN framework of inclusive, equitable, quality education (United Nations Agenda 2030) (SDG 4). To address this challenge, the present study set the following objectives: (1) to analyze pre-service teachers’ level of interest in EI and their perceived level of general EI knowledge, (2) to examine their self-perception of certain variables of emotional knowledge, according to their degree program and year of studies, (3) to analyze the relationship between these emotional knowledge variables and perceived emotional intelligence (EI); and (4) to study the predictive capacity of the study variables on future teachers' emotional knowledge variables. The study sample contained a total of 434 university students from three degree programs at two universities in Spain, of which 82.70% were women, and the mean age was 20.91 years. Two instruments were used: an ad hoc questionnaire, which assessed perceived emotional knowledge according to three variables (General knowledge of EI, where level of training in EI is considered; Intrapersonal knowledge of EI; and Theoretical-practical knowledge related to teaching), and the Trait Meta-Mood Scale (TMMS-24), which assesses metaknowledge of emotional states across three variables (Attention, Clarity, and Repair). Results of the study suggest that there are differences in perceived EI knowledge according to students' year of studies and according to their degree, and indicate that students present high interest, while their perceived level of knowledge is low. In addition, emotional clarity is the EI variable that was identified as being important in the emotional knowledge of future teachers. This study shows evidence of the importance of social-emotional competencies in initial teacher training.

<I>Saccharomyces cerevisiae</I> proliferates by budding, which includes the formation of a cytoplasmic protrusion called the ‘bud’, into which DNA, RNA, proteins, organelles, and other materials are transported. The transport of organelles into the growing bud must be strictly regulated for proper inheritance of organelles by daughter cells. In yeast, E3 ubiquitin ligases are involved in inheritance of mitochondria, vacuoles, and peroxisomes. These organelles are transported toward the tip of the growing bud by the myosin motor protein Myo2 along actin filaments. During organelle transport, adaptor proteins bridge the organelles and myosin. After reaching the bud, the adaptor proteins are ubiquitinated by E3 ubiquitin ligases and then degraded by the proteasome. Targeted degradation of the adaptor proteins is necessary to unload to unload the organelles from the actin–myosin machinery. Impairment of the ubiquitination of adaptor proteins results in a failure of organelle release from myosin, which in turn leads to abnormal dynamics, morphology, and function of the inherited organelles. Here, we summarize the role and regulation of E3 ubiquitin ligases during organelle inheritance in yeast.

This paper presents an approach to enhancing the clarity and intelligibility of speech in digital communications compromised by various background noises. Utilizing deep learning techniques, specifically a Variational Autoencoder (VAE) with 2D convolutional filters, we aim to suppress background noise in audio signals. Our method focuses on four simulated environmental noise scenarios: storms, wind, traffic, and aircraft. Training dataset has been obtained from public sources (TED-LIUM 3 dataset, which includes audio recordings from the popular TED-TALK series) combining with these background noises. The audio signals were transformed into 2D power spectrograms, upon which our VAE model was trained to filter out the noise and reconstruct clean audio. Our results demonstrate that the model outperforms existing state-of-the-art solutions in noise sup-pression. Although differences in noise types were observed, it was challenging to definitively conclude which background noise most adversely affects speech quality. Results have been assessed with objective methods (mathematical metrics) and subjective (listening to a set of audios by humans). Notably, wind noise showed the smallest deviation between the noisy and cleaned audio, perceived subjectively as the most improved scenario. Future work involves refining the phase calculation of the cleaned audio and creating a more balanced dataset to minimize differences in audio quality across scenarios. Additionally, practical ap-plications of the model in real-time streaming audio are envisaged. This research contributes significantly to the field of audio signal processing by offering a deep learning solution tailored to various noise conditions, enhancing digital communication quality.

Esophagogastric cancers are among the most common and deadly cancers worldwide. This review traces their chronology from 3000 BCE to the present. The first several thousand years were devoted to palliation, before advances in operative technique and technology led to the first curative surgery in 1913. Systemic therapies were introduced in 1910, and radiotherapy shortly thereafter. Operative technique improved massively over the 20th century, with operative mortality rates reducing from over 50% in 1933 to less than 5% by 1981. In addition to important roles in palliation, endoscopy became a key non-surgical curative option for patients with limited-stage disease by the 1990s. The first nonrandomized studies on combination therapies (chemotherapy +/- radiation +/- surgery) were reported in the early 1980s, with survival benefit only for subsets of patients. Randomized trials over the next decades had similar overall results, with increasing nuance. Disparate conclusions led to regional variation in global practice. Starting with the first FDA approval in 2017, multiple immunotherapies now encompass more indications and earlier lines of therapy. As standards of care incorporate these effective yet expensive therapies, care must be given to disparities and methods for increasing access.

Postmenopausal osteoporosis, a metabolic bone disease associated with aging and that affects bones throughout the body, is emerging as an urgent public health concern and imposes a substantial healthcare burden on society. The clinical application of bisphosphonate, the primary treatment for osteoporosis, is limited owing to the drug’s severe complications. Herein, we investigate the synthesis and utilization of strontium-doped mesoporous bioactive glass loaded with alendronate (ALN@Sr-MBG) as a novel therapeutic agent for osteoporosis, to explore its potential as an alternative to alendronate (ALN). Strontium-doped mesoporous bioactive glass (Sr-MBG) was synthesized using the sol-gel method, while ALN@Sr-MBG was obtained by incorporating Sr-MBG into an alendronate saturated solution. The bioactivities of ALN@Sr-MBG, including biotoxicity, inflammation inhibition, and anti-osteoclast differentiation, were investigated in vitro using CCK-8, flow cytometry, tartrate-resistant acid phosphatase (TRAP) staining, and RT-PCR assays. Animal models were established by surgically removing the ovaries from non-pregnant female C57/BL mice, followed by weekly intraperitoneal injections of ALN@Sr-MBG, ALN, or excipients. After 8 weeks, the mice femurs were extracted and analyzed through Micro-CT scanning, and hematoxylin-eosi, osteoblastic, and osteogenic staining. The in vitro findings demonstrated that ALN@Sr-MBG displayed enhanced biological efficacy compared to ALN in terms of inflammation inhibition, osteogenesis promotion, and osteoclastogenesis inhibition. Furthermore, micro-CT analysis revealed that ALN@Sr-MBG significantly augmented bone mineral density (BMD), bone volume fraction (BV/TV), trabecular number (Tb.N), and trabecular thickness (Tb.Th), while reducing trabecular separation (Tb.Sp) and structural model index (SMI) in mice with ovariectomy-induced osteoporosis. The osteoblast and osteogenic staining results indicate the enhanced bioactivities of ALN@Sr-MBG in promoting bone formation and inhibiting bone resorption compared to ALN. In vitro and in vivo assessments further confirmed that ALN@Sr-MBG exhibits superior anti-osteoporotic bioactivity compared to ALN. The study findings confirm the potential of ALN@Sr-MBG as a novel alternative therapy to ALN and drug candidate for the treatment of osteoporosis.

Adolescence is a stage of change and transition, which together with the COVID-19 situation, has been a huge source of vulnerability and facilitated the development of emotional disorders. This study aims to analyse internalising (anxiety and depression) and externalising (anger and aggression) symptoms in adolescence according to gender, as well as personal resources (self-esteem, social competence and integration, and awareness of problems) put in place as a mechanism of protection and coping after the situation of the pandemic. The study population encompassed 445 adolescents, of whom 230 were boys and 215 were girls, between the ages of 12 and 17. A self-reported Likert-type test was used. Findings indicate that girls show greater levels of internalising (anxiety and depression) and externalising problems (anger), finding no differences for the variable aggression between the two groups. Personal resources are closely linked to emotional symptomatology, providing a source of protection in both genders. The findings indicate a maladaptive pattern for adolescents that requires early intervention within the field of education.

Generative AI has gained enormous interest nowadays due to new applications like chatGPT, DALL E, Stable Difussion and Deep Fake. Particularly DALL E, Stable Difussion and others (Adobe Firefly, ImagineArt...) are able to create images from a text prompt and are also able to recreate real photographs. Due to this fact, intense research has arisen to create new image forensics applications able to distinguish between real captured images and videos and artificial ones. Detecting forgeries made with Deep Fake is one of the most researched issues. This paper is about another kind of forgery detection. The purpose of this research aims to detect photo realistic AI created images versus real photos coming from a physical camera. For this purpose, techniques that perform a pixel level feature extraction are used. First one is Photo Response Non-Uniformity (PRNU). PRNU is a special noise due to imperfections on the camera sensor that is used for source camera identification. The underlying idea is that AI images will have a different PRNU pattern. Second one is Error level analysis (ELA). This is other type of feature extraction traditionally used for detecting image editions. In fact, ELA is being used nowadays by photographers to detect manually AI created images. Both kinds of features are used to train Convolutional Neural Networks to differentiate between AI images and real photographs. Good results are obtained achieving accuracy rates over 95%. Both extraction methods are carefully assessed by computing precision/recall and F1-score measurements.

This article presents the development of an innovative Maximum Power Point Tracking (MPPT) strategy, utilizing a Particle Swarm Optimization (PSO) algorithm to improve the effectiveness of PV systems and expedite convergence. The new MPPT method incorporated a unique Swarm Self-Reinforcement Mechanism (SSRM) within the PSO algorithm, targeting quick convergence and excellent tracking accuracy. This approach enables the PSO to eliminate the fitness function that has the lowest value and subsequently reinforce it in the next iteration, revolving around the global maximum power point (GMPP). By applying this novel PSO-based method, the MPPT performance of PV systems was significantly improved, facilitating the algorithm to proficiently navigate through the solution space and quickly locate the GMPP, even in rapidly changing environmental conditions. The outcomes derived from this novel approach were contrasted with other algorithmic optimization methods, validating its superior convergence speed and tracking accuracy. Different swarm sizes were examined using SSRM, and the optimal swarm size for the system employing MPPT was identified to achieve the lowest convergence time (CT). The results showcased the impressive performance capabilities of this novel strategy, resulting in a time con-traction of up to 28% compared to the conventional PSO technique, where the optimal swarm size was found to be 5. This achievement marks a significant milestone in the evolution of PSO-based MPPT techniques, and paves the way for future advancements in this exciting field.

This study explores the implementation of an interdisciplinary curriculum in product and media design education and its impact on knowledge innovation and competency development. The curriculum is based on scaffolding theory, incorporating design proposals, workshops, digital design, marketing tests, and marketing activities guided by teachers and mentors from a branded ceramic workshop. The research findings demonstrate that the interdisciplinary curriculum facilitates knowledge innovation and fosters the development of students' professional skills, creativity, and problem-solving skills. The study also highlights the importance of stable scaffolding, including artefact, peer, and teacher support, which significantly contributes to cultivating transformational competencies, as outlined in the 2030 OECD Learning Compass. Additionally, the collaboration with Shanshing Four Seasons Celadon Studio on a patented ceramic product, "Funny Monkey," is a tangible example of the journey towards a triple-helix knowledge economy. This research underscores the importance of interdisciplinary curricula in promoting knowledge innovation and integrating transformational competencies in education. Scaffolding theory provides practical guidance for student learning and teaching strategies, presenting a sustainable roadmap for developing interdisciplinary curricula and offering a concrete and transferable pedagogical prototype for educational innovation.

This paper delves into the multifaceted domain of the aerospace industry, examining its evolution, current challenges, and imperative focus on quality management and process integration. The aerospace sector, driven by technological advancements and a burgeoning global demand for air travel and freight transport, necessitates a thorough analysis of its industrial fabric and operational intricacies. This research endeavors to analyze the dynamics of the aerospace industry, pinpoint its challenges, and propose an integrated approach to enhance efficiency, quality, and sustainability. The primary goals encompass understanding the evolving industry landscape, identifying critical challenges, and offering innovative solutions by amalgamating principles of Industry 4.0 into quality management and processes within the aerospace sector. Through an in-depth exploration of various facets, this research underscores the pivotal role of efficient processes and integrated quality management in achieving sustainable growth and competitiveness in the aerospace industry. By aligning with the paradigm of Industry 4.0, organizations can optimize their operations and contribute to the industry's advancement, delivering safer and more cost-effective aerospace products. The study adopts a multifaceted approach, incorporating extensive literature review, critical analysis of industry trends, examination of quality management frameworks, and a thorough evaluation of the integration potential of Industry 4.0 technologies. The research also involves case studies and expert insights to validate the proposed approach. The investigation reveals that by leveraging Industry 4.0 technologies and embracing an integrated approach to quality management, the aerospace industry can significantly enhance operational efficiency, product quality, and overall sustainability. The seamless integration of processes and the implementation of advanced quality frameworks pave the way for a more competitive and future-ready aerospace industry, meeting the evolving demands of a globalized world.

The cosmetic industry has a history of constant modernization, with products that are increasingly sustainable and well tolerated by the customers. In the last years, the growing understanding and knowledge of the potential of marine species, as well as the application of “blue biotechnology” have been motivating new innovative solutions in this area. Marine organisms are important sources of compounds that may be applicable in cosmetics, with biological activities that include depigmentant, anti-aging or anti-bacterial properties, specifically against acne, but also as formulation promoters and facilitators. Several methods can be used to extract these molecules, but these are not free of challenges and implications – possible biological and chemical contamination, and problems with batch reproducibility, which can be associated to the intraspecific variability of the different specimens, should be considered. Alongside these, the legislative and normative framework is increasingly developed and there is a growing concern with this situation. The application of sustainability fundamentals in the production of cosmetics must respect a life cycle approach, critically analyzing each stage. In this sense, “blue biotechnology”, in constant evolution, together with the use of invasive species or marine waste products to obtain new active ingredients, emerge as innovative and sustainable solutions for the future’s cosmetics industry. This review explores various biological properties of marine-derived molecules and discusses the challenges and implications associated with extracting these compounds. It also examines recent advancements in 'blue biotechnology' and their relevance to the sustainable development of innovative cosmetics.

The Cav3.2 T-type calcium channel is implicated in various pathological conditions, including cardiac hypertrophy, epilepsy, autism, and chronic pain. Phosphorylation of Cav3.2 by multiple kinases plays a pivotal role in regulating its calcium channel function. The calcium/calmodulin-dependent serine/threonine phosphatase, calcineurin, interacts physically with Cav3.2 and modulates its activity. However, it remains unclear whether calcineurin dephosphorylates Cav3.2, the specific spatial regions on Cav3.2 involved, and the extent of the quantitative impact. In this study, we elucidated the serine/threonine residues on Cav3.2 targeted by calcineurin using quantitative mass spectrometry. We identified six serine residues in the N-terminus, II-III loop, and C-terminus of Cav3.2 that were dephosphorylated by calcineurin. Notably, a higher level of dephosphorylation was observed in the Cav3.2 C-terminus, where calcineurin binds to this channel. Additionally, a previously known CaMKII-phosphorylated site, S1198, was found to be dephosphorylated by calcineurin. Furthermore, we also discovered that a novel CaMKII-phosphorylated site, S2137, underwent dephosphorylation by calcineurin. In CAD cells, a mouse central nervous system cell line, membrane depolarization led to an increase in the phosphorylation of endogenous Cav3.2 at S2137. Mutation of S2137 affected the calcium channel function of Cav3.2. Our findings advance the understanding of Cav3.2 regulation not only through kinase phosphorylation but also via calcineurin phosphatase dephosphorylation.

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to hu-man error. Recognizing this limitation, the rise of deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain alongside pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using auto-encoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: remarkable 100% accuracy in most of the binary classifications, impressive performance exceeding 95% for three-class and four-class challenges, and a commendable rate exceeding 93.5% for the five-class classification.

Deciphering protein–protein interactions (PPIs) in vivo is crucial to understand protein function. Bimolecular fluorescence complementation (BiFC) is one of extensively used PCAs over the last decade in many different native contexts, including human live cells. It is based on the reconstitution of a monomeric fluorescent protein (FP) such as cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP) from two complementary nonfluorescent sub-fragments (N- and C-terminal sub-fragments) upon spatial proximity. Interaction between a bait protein and a prey protein fused to such complementary sub-fragments is sufficient to lead to the reconstitution of the FP, resulting in an emission signal upon excitation (Figure 1). In this comprehensive review, we commence by tracing the development of fluorescent proteins (FPs) tailored for BiFC, followed by the highlighting of BiFC advantages over other PPI detection methods. Concurrently, the essential factors and nuanced considerations when employing BiFC are thoroughly explored. To vividly showcase the versatility of BiFC, the implementations at different throughputs are also presented, encompassing both single PPI experiments and large-scale studies, along with noteworthy variants of BiFC.

For a comprehensive exploration of the Bimolecular Fluorescence Complementation (BiFC) approach, we critically assess whether protein pairs establish direct or indirect interactions within this context. Emphasizing the multifaceted nature of protein interactions, this review underscores the imperative of delineating macromolecular complex characteristics across five distinct levels: composition, stoichiometry, copy number, topology, and dynamics. A pivotal discussion revolves around the challenges introduced by the overexpression of target proteins and the potential artificial ramifications of fusion proteins. The significance of stoichiometry in protein interactions is illuminated, with the HOXA9/PBX1 interaction serving as a pertinent case study. The research identifies and elaborates on the inherent limitations of prevailing large-scale BiFC screenings, advocating for the incorporation of rigorous negative controls. A novel proposition is introduced in the form of a high-throughput multicolor BiFC strategy, exemplified using the HOXA9 and PBX1 proteins. The paper culminates with an insightful discourse on the implications of tags within the BiFC system, presenting the TriFC (Tripartite Fluorescence Complementation) assay as a promising alternative.

Revenue-sharing (RS) contracts are a common approach to incentivize innovation of upstream suppliers by addressing the uneven profit distribution between upstream and downstream firms. Considering the possible overconfidence characterizing decision makers in the supply chain, we investigate the effect of the RS contract and the tendency of overconfidence of supply chain members on the investment in R&D of key components of products in the context of an upstream supplier that is the leader in the R&D and production of key components. We find that regardless of the bargaining power of either party, a RS contract can increase the R&D investment in key components. Regarding the effects of overconfidence of either downstream manufacturer or up-stream supplier, it can increase the R&D investment in key components. Supplier (manufacturer) overconfidence can harm their own profits but increase the profits of the manufacturer (supplier), and when the level of overconfidence is below a certain threshold, the damage to their own profits is less than the increase in each other's profits, thus benefiting the whole supply chain. In addition, we also find a joint effect of RS contracts and overconfidence: when the bargaining power of the supplier is low, the RS contract has a certain compensatory effect on the loss of their own profits caused by overconfidence.

We discuss a remote sensing system that is used to simultaneously detect range-resolved differential absorption LIDAR (light detection and ranging; DIAL) signals and integrated path DIAL signals (IP-DIAL) from aerosol targets for ranges up to 22 km. The DIAL/IP-DIAL frequency converter consists of an OPO pumped at 1064 nm to produce light at 1.6 μm and operates at 100 Hz pulse repetition frequency. The probe light is free space coupled to a movable platform that contains one transmitter and two receiver telescopes. Hybrid photon counting/current systems increase the dynamic range for detection by two orders of magnitude. Range resolved and column integrated dry-air CO2 and CH4 mixing ratios are obtained from line shape fits of CO2 and CH4 centered at 1602.2 nm and 1645.5 nm, respectively and measured at 10 different frequencies over ≈ 1.5 cm-1 bandwidth. The signal-to-noise ratios (SNR) of the IP-DIAL returns from cloud aerosols approach 1000:1 and the uncertainties in the mixing ratios weighted according to the integrated counts over the cloud segments range from 0.1 % to 1 %. The range averaged DIAL mixing ratios are in good agreement with the IP-DIAL mixing ratios at the 1 % to 2 % level for both CO2 and CH4. These results can serve as a validation method for future active and passive satellite observational systems.

This paper provides a new multi-function Blockchain Technology-enabled Pharmaceutical Supply Chain (BT-enabled PSC) mathematical cost model, including PSC costs, BT costs, and uncertain demand fluctuations. The purpose of this study is to find the most appropriate algorithm(s) with minimum prediction errors to predict the costs of the BT-enabled PSC model. This paper also aims to determine the importance and cost of each component of the multi-function model. To reach these goals, we combined four Supervised Learning algorithms (KNN, DT, SVM, and NB) with two Evolutionary Computation algorithms (HS and PSO) after data generation. Each component of the multi-function model has its own importance, and we applied the Feature Weighting approach to analyse their importance. Next, four performance metrics evaluated the multi-function model, and the Total Ranking Score determined predictive algorithms with high reliability. The results indicate the HS-NB and PSO-NB algorithms perform better than the other six algorithms in predicting the costs of the multi-function model with small errors. The findings also show that the Raw Materials cost has a stronger influence on the model than the other components. This study also introduces the components of the multi-function BT-enabled PSC model.

The study focuses on solving urban challenges such as temperature reduction, urban stormwater management, noise reduction, air quality improvement, and CO2 concentrations reduction, and suggests terrestrial and freshwater habitat types (HTs) found in Europe, as innovative forms of nature-based solutions (NBS) for them. Establishment of native HTs in different urban environments to solve urban challenges would enhance biodiversity at different levels and integrate this aspect in urban planning. This contribution builds on the recognition that surfaces of vegetated terrain are the most versatile NBS in addressing the broadest range of environmental problems in urban areas and that the processes running within these green spaces offer the key to socio-ecological improvements of such areas. Employing narrative literature review, qualitative content analysis, and interdisciplinary expert discussion this paper defines why and how native HTs of unchanged nature can be implemented as NBS in the urban environment, indicate potential HTs for specific urban challenges and presents an approach to the inclusion of HTs as NBS into spatial planning documents at national, regional, and local levels. The proposed planning approach attributes added value to HT, and by linking the concepts of NBS and HT integrates them into urban planning.

This study investigated mortality discrepancies in Korea, 2000-2020, with a focus on age, gender, and preventable causes of death, and their impact on life expectancy at birth. The study utilized data from Korean Standard Classification of Diseases and OECD/Eurostat lists of preventable and treatable causes of death and employed Arriaga's method. Noteworthy findings revealed gender disparities in life expectancy increases, with male gaining an advantage over females from 2000-2020 (8.142 years for males versus 6.806 years for females). The most significant increase in life expectancy was found to be in males aged 70-74 and females aged 75-79. The gender gap in LEB is narrowing and that the main age group contributing to the gender gap are getting older. Reduced gains in life expectancy at birth resulted from avoidable mortality, contributing 57.3% to the increase. Cerebrovascular disease and transport accidents were positive contributors, whereas suicide and certain cancers were negative contributors. The changes in mortality for individuals aged 65 and over had an impact of 45.44% (males) and 71.01% (females) on life expectancy gains. This study offers a novel approach in evaluating avoidable mortality and life expectancy, having the potential to improve international health indicators and inform health policies.

The persistent burden of chronic hepatitis B among ≤5-year-old children in Africa suggests missed opportunities for controlling mother-to-child transmission (MTCT) of the hepatitis B virus (HBV). This scoping review maps the evidence base on the risk of HBV MTCT, the status of HBV MTCT mitigation strategies including hepatitis B birth-dose vaccination, and the role of systems complexity on the suboptimal adoption and performance of hepatitis B birth-dose vaccination programs in Africa. Overall, 88 peer-reviewed and grey literature sources published between 2000–2022 were included in this review. The evidence base consistently argues for a growing risk of HBV MTCT amidst the HIV co-epidemic in the region. Without universal HBV screening programs integrated within broader antenatal care services, current selective hepatitis B birth-dose vaccination is unlikely to effectively interrupt HBV MTCT. We underscore critical health systems-related barriers to universal adoption and optimal performance of hepatitis B birth-dose vaccination programs in the region. To better conceptualize the role of complexity and system-wide effects on the observed performance of the program, we propose an adapted systems-based logic model. Ultimately, exploring contextualized complex systems approaches to scaling-up universal hepatitis B birth-dose vaccination programs should form an integral part of the regional research agenda.

Laccases are versatile and relatively accessible catalysts with practical/industrial applications. Their proteic nature entails limited stability under non-native conditions, especially such as those that would be required for industrial applications. Here, derivatization of a laccase from Sclerotinia sclerotiorum is reported, using an (methyl-PEG12)3-PEG4) N-succinimide ester in order to improve the thermostability of the enzyme. The PEGylated laccase is characterized using gel electrophoresis, size-exclusion chromatography, catalytic parameters and thermal stability. A 50% increase in residual activity after incubation at 50°C for up to two hours was achieved for the PEGylated laccase compared to the native enzyme.

Unlike animals, persistent dynamic environmental perturbations threaten plants, as they are sessile. Plants keep an inbuilt database of all epigenetic episodes during their lifetime. Present knowledge for exploiting information about how the gene network of plants contributes to epigenetic memory needs to be improved, and extensive in-depth studies are needed. Data from epigenetic memories imparts intelligent behaviour to plants, and these memories can be decoded further to investigate how these plants respond when the same abiotic stress recurs. The epigenetic phenomenon refers to gene expression patterns, including vital factors such as nucleic acids (small ribonucleic acids), histones modelling and methylation of DNA. These changes are cell cycle-associated events that may be transmitted to successive generations. Epigenetic memories due to abiotic stress are necessary for maintaining healthy ecophysiological behaviour of plants and impart the tendency to resist and adapt to challenging environmental conditions. These intelligent memories can be stored and interpreted to prepare for future challenges.

Geothermal energy is a weather-independent, permanently available renewable energy source. In addition to producing heat and electricity, geothermal energy can be used to extract critical raw materials dissolved in the heat-carrying fluid. Researchers working on the frontiers of geothermal energy utilisation may be able to transform ultra-deep metallic mineral formations into “orebody-Enhanced Geothermal Systems”. In the planned technology, the metal-containing geological formation would be manipulated in such a way that cogeneration of energy and metals is possible and can be optimised in the future according to market needs. The potential of geothermal energy for sustainable mineral extraction and energy production aligns with Sustainable Development Goal (SDG) 7, which aims to ensure access to affordable, reliable, sustainable, and modern energy for all. This new technology could substantially decrease Europe’s dependency on importing critical metallic minerals (such as lithium and tungsten) and energy; over and above, it has a small environmental footprint, including very low carbon emissions

The problem of blood delivery has been a critical concern in past decades. However, the unsteady behavior of blood donors, along with the uncertainties associated with demands in a crisis imposes several challenges for resource management. In this research work, we analyze the blood delivery behavior in a crisis modeled by coupled queues of patients and blood sources. Using Markovian modeling and results associated with G-network, we derive two critical probabilities, namely lack of unit of blood (unit of donations) or not having any storage capacity left to accept new donors' blood. We propose a closed-form solution to calculate the optimal blood storage size, and also we suggest sufficient conditions that guarantee the feasibility of the model. Finally, we conduct a sensitivity analysis to investigate the impact of model parameters on storage size and the proportion of time that storage is full.

This study investigated the presence and characteristics of Juglans neotropica in three ecosystems in Southern Ecuador: Montane Evergreen Forest, Evergreen Seasonal Lower Montane Forest, and Semideciduous Foot Montane Forest. The main focus was on the species' multipurpose nature as both a fruit and timber source. Six study sites, totaling at least 0.5 hectares each, were established, with four in Loja province and two in Zamora Chinchipe province.The results showed significant differences in dendrometric variables across the sites, with the most favorable growth recorded in The Tundo, where trees exhibited an average diameter at breast height (DBH) of 45.16 cm, basal area (G) of 1.41 m2, total height (TH) of 19.22 m, canopy height (CH) of 13 m, cubic volume (CV) of 3.55 m3, and total volume (TV) of 5.22 m3. The species displayed a clumped distribution pattern, as indicated by a Morisita index greater than 1. Regarding abundance, the highest density of 297 trees per hectare was found in Argelia, while Victoria had the lowest density of 46 trees per hectare. This research highlights the importance of Juglans neotropica as a multipurpose species, providing both valuable fruits and wood resources in the studied ecosystems of Southern Ecuador.

Glaucoma, a group of diseases characterized by progressive retinal ganglion cell loss, cupping of the optic disc and by a typical pattern of visual field defects, is a leading cause of severe visual impairment and blindness worldwide. Elevated intraocular pressure (IOP) is the leading risk factor for glaucoma development. However, glaucoma can also develop at normal pressure levels. An increased susceptibility of retinal ganglion cells to IOP, systemic vascular dysregulation, endothelial dysfunction and autoimmune imbalances have been suggested to play a role in the pathophysiology of normal tension glaucoma. Since inflammation and oxidative stress play a role in all forms of glaucoma, the goal of this review article is to resent an overview on the inflammatory and prooxidant mechanisms in the pathophysiology of glaucoma and to discuss immunomodulatory and antioxidant treatment approaches.

Petrosamine (1) – a colored pyridoacridine alkaloid from the Belizean sponge, Petrosia sp. that is also a potent inhibitor of acetylcholine esterase (AChE) – was investigated by spectroscopic and computational methods. Analysis of the petrosamine free energy landscapes, pKa and tautomerism revealed an accurate electronic depiction of the molecular structure of 1 as the di-keto form, with net charge of q = +1, rather than a dication (q = +2) under ambient conditions of isolation-purification. The pronounced solvatochromism (UV-vis) reported for 1, and related analogs, was investigated in detail and is best explained by charge delocalization and stabilization of the ground state (HOMO) of 1 rather than an equilibrium of competing tautomers. Refinement of the molecular structure of 1 by QM methods complements published computational docking studies to define the contact points in the enzyme active site that may improve design of new AChE inhibitors based on the pyridoacridine alkaloid molecular skeleton.

Background: The important periodontal disease pathogen Porphyromonas gingivalis produces large biofilms that increase its pathogenicity. Finding natural antimicrobial agents is crucial because of the rise in antibiotic resistance. Objective: The purpose of this study was to determine if plant extracts such as Symphytum officinale (S) and Panax ginseng (G) were effective against P. gingivalis separately and in combination with a common antibiotic Metronidazole (F). Materials and Procedures: Six different dilutions were produced the plant extracts and antibiotic separately and in combination with G and F and S and F using two-fold serial dilution technique. To evaluate the effects of these substances biofilm inhibition experiments were conducted. Plaque samples were collected from periodontitis patients to isolate P. gingivalis and standard strain of P. gingivalis (ATCC 33277) were bought. Additionally, Acylated Homoserine Lactones (AHLs) detection was carried out to look for any activity that would interfere with quorum sensing. GraphPad prism was used for statistical analysis with p value &lt; 0.05. Results: The combinations of Symphytum Officinale and Metronidazole (S+F) showed the maximum effectiveness in biofilm inhibition (98.7%), which was better than G+F (98.2%) with substantial variations in biofilm inhibition levels in different treatment regimes. Notably, the isolate from the patient was more active than the standard strain. Additionally, the plant extracts and their combinations at particular dilutions had notable inhibitory effects on the generation of AHL (p &lt;0.05). Conclusion: The study highlights the possibility of Symphytum Officinale and Panax Ginseng as effective treatments for P. gingivalis biofilm and AHLs, both on their own and in combination with Metronidazole. These organic substances may open the door to cutting-edge methods of treating periodontal disorders.

The service sector, whose value has started to increase in recent years, has gained momentum with the acceleration of economic developments especially after the 2000s in Turkey. In a world where competition is increasing and social purchase perception is changing, companies started to differentiate in the service sector to create a competitive advantage. Companies that would like to grow in the industry and aspire to a greater proportion need credit to extend. There are many factors to consider in order to allocate the credit. Banks make financial and non-financial analysis to make credit decisions. This study has been prepared to assess the effect of financial and non-financial features of middle segment companies that need credit in the service sector on the credit decision to be allocated by banks. The group of variables that explain the credit decision at the highest level in these companies are non-financial variables.

Brain-computer interfaces (BCI) from electroencephalography (EEG) provide a practical approach to support human-technology interaction. In particular, motor imagery (MI) is a widely-used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, named Kernel-based Regularized EEGNet (KREEGNet), leveled on Centered Kernel Alignment and Gaussian Functional Connectivity, explicitly designed for EEG-based MI classification. The approach proactively tackles the challenge of intra-subject variability brought on by noisy EEG records and the lack of spatial interpretability within end-to-end frameworks applied for MI classification. KREEGNet is a refinement of the widely accepted EEGNet architecture, featuring an additional kernel-based layer for regularized Gaussian functional connectivity estimation based on CKA. The superiority of KREEGNet is evidenced by our experimental results from binary and multi-class MI classification databases, outperforming the baseline EEGNet and other state-of-the-art methods. Further exploration of our model’s interpretability is conducted at individual and group levels, utilizing classification performance measures and pruned functional connectivities. Our approach is a suitable alternative for interpretable end-to-end EEG-BCI based on deep learning.

The subgenus Ochthomantis is an obligate forest and stream-dwelling group of Mantellid frogs, endemic to Madagascar, with six species currently recognized. However, this group suffers from ongoing taxonomic confusion due to low numbers of examined specimens, and failure to con-sider morphological variation from development and sexual dimorphism. Here we examined the morphology of 637 adult sexed specimens, collected by us in the field, and from other Mu-seum collections. We also sequenced the 16S mtDNA gene for each lineage, to determine con-gruence between morphological and molecular data sets, and to help delimit species. Our results demonstrate that the subgenus Ochthomantis includes 11 valid species: five already recognized; M. catalai and M. poissoni that we resurrect from synonymy, and four new species which we de-scribe here. Because Mantidactylus majori groups with other Mantidactylus, we do not consider it a member of the subgenus Ochthomantis. All species have restricted distributions and elevational ranges in the humid forests of Madagascar. This study demonstrates the utility of studying cryp-tic species using both diagnostic morphological characters and molecular data. It is hoped that discovery of this new cryptic biodiversity will initiate conservation activities for those species with the most restricted distributions.

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.

Mountain summit in Madagascar have generally a specific habitat and that figure home a local endemicity of herpetofauna. Mantidactylus pauliani, a typically aquatic and critically endangered amphibian from Ankaratra Mountain, is among of them. This species inhabits high elevation with restricted distribution range. Our study highlights here a new data of this species and its viability with environmental change. So that, we carried out annual monitoring from 2018 to 2021. Nine fixed transects of 100 m were done along the streams between 1762 and 2378 m a.s.l. We followed the herpetofaunal standard technique for collecting data (systematic ecological niche searching and direct observation), and data analysis was performed using χ² tests and Fac-tor Correspondence Analysis. Here we present the new ecological data of M. pauliani. This spe-cies home between 1900 - 2378 m a.s.l. within the humid forest and savannah habitat. We noticed also a fluctuation in population size and migration to the upper throughout the years across the season, and the stream quality and volume of water variation. This habitat change makes it vul-nerable and the annual bushfire surrounding plays a negative impact. Knowledge and under-standing of its ecological guilds is needed for running and updating conservation activities

This study presents structural analysis and optimization for the lightweight design of a buoyant rotor-type permanent magnet (BRPM) generator, which was first presented in Bang (2010), and compares its structural performances to a conventional generator with a spoke arm-type rotor and stator. The main benefit of a BRPM is that it can be realized as a bearing-less drive system, free from the mechanical failure of rotor bearings, by using a buoyant rotor. Also, the deformation of a generator by blade vibration can be effectively suppressed using joint couplings between the blades and the rotor. For the design optimization, the objective is set as the mass of the rotor and the stator, and the maximum deformation of the airgap clearance between the rotor and the stator by external forces is constrained below 10% of the gap width. The commercial software Optistruct is used for the analysis and optimization. In this investigation, the analysis and optimization are conducted for a 10 MW wind turbine generator. However, the proposed methods can be extended to larger generator designs without requiring considerable modification. The mass of the opti-mized 10MW BRPM generator is 160.7 tons (19.3 tons for the rotor and 141.4 tons for the stator) while that of an optimized conventional spoke arm-type generator is obtained as 325.6 tons.

In an era characterized by rapid technological advancements, economic fluctuations, and global competition, adaptability and resilience have become critical success factors for businesses navigating uncertainty and complexity. This article explores the role of enterprise agility in today’s business landscape at Latam branch of Tata Consultancy Services (TCS), where organizations face complex and diverse operations. We aim to examine how companies can become more agile in the face of emerging challenges and seize opportunities swiftly to drive growth and deliver value. Since 2014, the division has embarked on an agile transformation journey to drive growth, deliver value, foster innovation, and build resilience in an increasingly dynamic environment. We scrutinize an approach to measuring and enhancing enterprise agility, employing statistical analysis and continuous improvement methodologies to tackle real-world challenges while offering valuable insights and recommendations for organizations aiming to implement similar systems. The results of an agile transformation in a certain company’s Latam branch serve as a compelling case study, demonstrating how the implementation of targeted measures and continuous improvement can significantly bolster enterprise agility. Methodologically, our work applies a novel sequence of parametric statistical tests which, to the best of our knowledge, have not been used in the industry to validate the results of business agility metrics. In future work, we aim to create a new workflow considering non-parametric tests to address data with other statistical distributions. We conclude our work by proposing a sequence of steps for organizations to implement business agility metrics.

Children five years or younger in low- and middle-income countries (LMICs) are severely affected by diarrhoea disease, especially in the sub-Saharan region. Hence, the study aimed at determining the prevalence and determinants of diarrhea disease among children under 5 years in Epworth Township, Zimbabwe. A descriptive cross-sectional study was conducted at a local clinic in Epworth Township, Harare. A convenience sampling strategy was used to recruit study participants for participation and 386 children were enrolled in the study. The majority were male children (n=229; 59.3%), while there were more female caregivers (n=370; 95.9%) than male caregivers (n=16; 4.1%). The prevalence of diarrhea disease in the study was 25.1%. The determinants associated with diarrhea were being partially vaccinated (OR 2.38, CI 95% 2.80–8.22), collecting water more than 1 kilometre from a household (OR 4.55; CI 95% 2.10–9.85) and using untreated water (OR 6.22; CI 95% 2.13–18.20). The age of the caregiver and using a clean water container (OR 0.05; p=0.020) were protective factors. Provision of primary health care, especially prevention of disease through immunization and rendering environmental health services could reduce the prevalence of diarrhea in disadvantaged townships.

With the increase of intelligent voice phishing and the increasing reliance on open banking systems, there has been a rise in cases where individuals' personal information has been exposed, resulting in significant financial losses for the victims. Non-face-to-face transactions in the financial sector face challenges such as customer identification, ensuring transaction integrity, and preventing transaction rejection. Blockchain-based distributed ledgers have been proposed as a solution, but their adoption is limited due to the difficulty of managing private keys and the burden of gas fees management. This paper proposes a non-face-to-face P2P real-time token payment system that minimizes the risk of key loss by storing private keys in a keystore file and database through a server-based key management module. The proposed system simplifies token creation and management through a server-based token management module and implements an automatic gas charging function for smooth token transactions. Transaction integrity and non-repudiation are ensured through a transaction confirmation module that uses transaction IDs without exposing personal information. Furthermore, advanced security measures such as blocking foreign IP access and DDoS defense are implemented to securely protect user data. The proposed system aims to provide a convenient, secure, and accessible online payment solution to the public by implementing a self-authentication function using a web application that is not limited to smart phones or application platforms.

Abstract The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in people in the city of Wuhan at the end of December 2019, and it has since spread to all continents. Global interest has been focused on discovering a treatment and developing a successful coronavirus vaccine as a result of the emergence of this new coronavirus. In this research, we sequenced the whole genome of the SARS-COV-2 virus that were isolated from 40 patients in Duhok, Iraq. 95 different mutations were identified in our isolation when such the whole genome sequence of the SARS-COV-2 isolated from the city of Wuhan, China (Accession number: NC 045512.2) was matched to the virus's sequence using Sequencing technology (Illumina , USA ) and Assembly method (iVar 1.3.1). Sequence analysis revealed that 38 mutations were found at spike glycoprotein (S), 30 of which were found in the ORF1b , 11 mutations were found in ORF1b ,7,3,2,1 mutations were found in ( N,M,ORF6 ,ORF9 /E ) genes which had been linked to structural changes at various places. The conclusion that the cases in Iraq were of different origins of infections and had a close relationship with the isolates from different country and state were further confirmed by phylogenetic analysis and transmission. The whole genome sequence of the SARS-CoV-2 , which were identified from the Iraqi Kurdistan region, and reported as a first study in Kurdistan region.

The Burkholderia cepacia complex (Bcc) are a group of increasingly multi-drug resistant opportunistic bacteria that can cause severe pulmonary infections. This resistance is driven through a combination of intrinsic factors and the carriage of a broad range of conjugative plasmids harbouring virulence determinants. Therefore, novel treatments are required to not only treat Bcc infection but also to prevent further spread of these virulence determinants. In the search for phages infective for two clinical Bcc isolates, CSP1 phage, a PRD1-like phage was isolated. CSP1 phage was found to require pilus machinery commonly encoded on conjugative plasmids to facilitate infection of multiple Gram-negative bacteria genera including Escherichia and Pseudomonas. Whole genome sequencing and characterisation of one of the clinical Burkholderia isolates revealed it to be Burkholderia contaminans. B. contaminans 5080 was found to contain a genome of over 8 Mbp encoding multiple intrinsic resistance factors, such as efflux pump systems, but more interestingly, carried three novel plasmids encoding multiple putative virulence factors for increased host fitness, including antimicrobial resistance. Even though PRD1-like phages are broad host range, their use in novel antimicrobial treatments shouldn’t be dismissed, as the dissemination potential of conjugative plasmids is extensive. Continued survey of clinical bacterial strains is

Obstructive Sleep Apnea (OSA) is nowadays one of the respiratory pathologies with a higher in-cidence globally in developed countries. This situation led to an increase in the demand for medical appointments and diagnostic studies related to that condition, especially those based on poly-somnographies and cardiorespiratory polygraphies. These studies are limited in resources, causing long waiting lists with the subsequent impact on the patients’ health. Furthermore, it is necessary to mention that OSA’s symptomatology is not very specific, and it is typically present in the general population (excessive sleepiness, snore, etc.). In this regard, this paper proposes a novel intelligent clinical decision support system for the diagnosis of OSA which could be used to help medical teams, both in primary care settings and in units specialized in respiratory pathologies. The aim of the proposed system is to help discriminate the patients suspected of suffering from the pathology from those who are not. To this end, two types of information sets of heterogeneous nature are consid-ered. The first one encompasses objective data, related to the patient's health profile with infor-mation usually available in electronic health records. The second type comprises subjective data, referred to the symptomatology reported by the patient in a previous interview. To process the first group of information, a Machine Learning classification algorithm is used, Bagged Trees in this case. For processing the second information set, related with the symptomatology of the patient, a col-lection of expert systems based on fuzzy inferential systems arranged in cascade are employed. As a result, the system is able to determine two risk indicators related to the patient's risk of suffering from OSA: the Statistical Risk and the Symbolic Risk respectively. Subsequently, by interpreting both risk indicators mentioned it will be possible to determine the severity of the patients’ health, proposing a preliminary evaluation on their condition. For the initial tests of the system, a software artifact has been built using a dataset with 4,978 selected patients, suspected of suffering from OSA, from the Álvaro Cunqueiro Hospital in Vigo. The results obtained are promising, demonstrating the potential usefulness of this type of tools in medical diagnosis. Once the system has been validated with new data from clinical environments, it is considered as possible to obtain a relevant improvement in the quality of the healthcare services, and a reduction in the associated costs.

Jaundice is a common condition for newborns, and its complications can be severe and cause permanent damage to the patient’s brain if no action is taken at its early stages. Current methods for jaundice detection are invasive, which include collecting blood samples from the patient, which can be painful and stressful and may cause some complications. Alternatively, a non-invasive approach can be used to diagnose jaundice through image-processing and artificial intelligence (AI) techniques, requiring a database of infant images to achieve a high-accuracy diagnosis. This data article provides a collection of newborn images, called NJN, with various birthweight and skin tones, with ages ranging from 2 to 8 days, and an excel sheet file in CSV format for the values of RGB and YCrCb channels and the status for each raw which is freely accessible at (https://sites.google.com/view/neonataljaundice). It also provides Python code for data testing using different AI techniques. Thus, this article offers a unique resource for all AI researchers to train their AI system and develop algorithms to help neonatal intensive care unit (NICU) healthcare specialists monitor neonates and provide fast, real-time, non-invasive, and accurate jaundice diagnosis.