The COVID-19 pandemic has expanded globally and caused serious consequence in both health system and the economy. MPV is one of two oral antivirals for COVID-19 with mild-to-moderate symptoms that have received FDA approval so far. This study aims to evaluate the cost-utility of using MPV in COVID-19 treatment. To compare the utility costs of MPV and non-MPV treatments for COVID-19 patients, we used a decision tree model. Data on clinical efficacy, cost, and quality of life were gathered from previous studies. The incremental effective cost ratio (ICER) calculated based on costs incurred and quality-adjusted life years (QALY), was considered as primary out-come. The total cost of MPV treatment and not using MPV was 15,362,516 VND (618 USD) and 15,771,262 VND (634 USD), respectively. The estimated QALY with using-MPV was 28.33, while without MPV was 19.72. The ICER was dominant, resulting in savings of 47,453 VND/QALY (1.9 USD). MPV is a cost-effective medication used to treat COVID-19. In mild-to-moderate COVID-19 patients, MPV considerably lowers costs and improves quality of life. However, several clinical factors of the drug still need to be taken into account.

Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based architecture to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data is extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimizes the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimizing computational performance compared to other models.

Natural killer (NK) cells are part of the innate immune system, protects against pathogens and tumor cells and are the main cell effectors of monoclonal antibodies (mAbs) generating antibody-dependent cell cytotoxicity (ADCC). Hence it is relevant to understand NK physiology and status to investigate the biological effect of mAbs in the clinic. The presence of viral-sculpted NK cell populations has already been described, but the presence of cancer-sculpted NK remains unknown. Cancer induces a broad NK cell dysfunction. We investigated the NK cell population by Uniform Manifold Approximation and Projection (UMAP) embed maps in Hodgkin lymphoma (HL) and acute myeloid leukemia (AML) patients at diagnosis and at least 30 days after treatment, which correlates with tumor cell clearance. The NK lineage largely responded to the tumor by generating antitumor NK cells and renewing the population with a subset of immature NK cells. However, we failed to identify specific “memory-like” subsets. Moreover, in patients in relapse we found essentially the same NK populations that at diagnostic, suggesting that NK cells equally respond to the first or second tumor rise. Finally, previous CMV infection largely affects tumor-associated changes in NK population, but the CMV-associated CD57+NKG2C+ NKs do not appear to play any role in tumor immunity.

INTRODUCTION: The purpose of the study was to determine (a) the overall preclinical character; (b) the cumulative cutoff values and the risk ratio, and (c) the factors associated with severity by a unidimensional and multidimensional analysis on 2173 Sars-Cov2 patients. METHODS: The machine learning study population consisted of 2173 patients (1587 mild and non symptoms patients, 377 moderate patients, 209 severe patients). The status of the patients was recorded from September 2021 to March 2022. RESULTS: The Covid19 Severity directly links with a significant correlation to Age, Score index of the chest X-ray, percentage and quantity of neutrophils, Albumin, C reactive protein, and ratio of Lymphocytes. Their important cut off values (from regression analysis) respectively are: 77.56 years old (the mild-moderate group), 5.53 (the mild-moderate group) and 10.51 (the moderate-severe group), 84.80% (the mild-moderate group) and 87.74%(the moderate-severe group), 11.77G/L (the moderate-severe group), 29.73g/L (the moderate-severe group), 7.46mg/dL (the mild-moderate group), 6.32% (the moderate-severe group). Their significant (p<0.0001) R score correlation with the severity of Covid19, are: 0.44, 0.52 and 0.52, 0.33 and 0.44, 0.42, -0.43, 0.40, -0.41. Their significant risk ratio (p<0.00001) from the meta-analysis, respectively are: 4.19 [3.58-4.95], 3.29 [2.76-3.92] and 3.03 [2.4023;3.8314], 3.18 [2.73-3.70] and 3.32 [2.6480;4.1529], 3.15 [2.6153;3.8025], 3.4[2.91-3.97], 0.46 [0.3650;0.5752] (p<0.00001), 0.34 [0.2743;0.4210]. The pair ALT – Leucocytes and Transferrin – Anion Chloride get the most important correlation shift. ALT – Leucocytes show the important negative link (R=-1, p<0.00001) in the mild group to the significant positive correlation in the moderate group (R=1, p<0.00001). Transferrin–anion Chloride has an important positive association (R=1, p<0.00001) in the mild group with a significant negative correlation in the moderate group (R=-0.59, p<0.00001). The network map and HCA show that in the mild-moderate group, the closest neighbors with the Covid19 severity are ferritins, Age. Then there is C-reactive protein, SI of X-ray, Albumin, and Lactate dehydrogenase, which are the next close neighbors of these three factors. In the moderate-severe group, the closest neighbors with the Covid19 severity are Ferritin, Fibrinogen, Albumin, the quantity of Lymphocytes, SI of X-ray, white blood cells count, Lactate dehydrogenase, and quantity of neutrophils. CONCLUSIONS: Complete multidimensional study in 2173 Covid19 patients in Vietnam shows the whole picture of all the preclinical factors, which may become the clinical reference marker for surveillance and diagnostic management

Reliable access to electricity has become a necessity worldwide, and without it, no country can operate health systems and achieve sustainable development goals (SDGs). However, while developed countries have been successfully electrified for many decades, most developing countries have fallen short, contributing to significant impacts, particularly the health sector. The present study explores the relationship between electrification and health vulnerability in developing countries between 1990 and 2020 using the Feasible Generalized Least Square (FGLS) approach. The varied electrification across countries motivates the use of non-linear associations between electrification and health vulnerability. We demonstrate that electrification and health vulnerability have an inverted U-shaped association. Further analysis indicates that most developing countries have achieved a mature level of an inverted U-shaped curve, showing a reduction in health vulnerability with increasing electrification. There are few developing countries, mainly African countries, struggling to reach maturity. The results of this study indicate the need for increased access to electrification, in combination with a reduction in the use of biomass and fossil fuel feedstocks, to mitigate health vulnerability in developing countries.

Despite advances in the reliability of sensory devices used by drones, the integrity of information from some devices is still considered an obstacle to ensuring successful flight plans. It is widely known that GNSS can suffer attacks or lose the signal from satellites, which can cause the drone to fail to complete its flight plan. In this context, we propose SiaN-VO, a Siamese network for visual odometry prediction. In our initial studies, this approach proved satisfactory for flights in static conditions (speed and height). Although interesting, these conditions do not reflect real flight conditions. In this sense, we have advanced our studies to propose the SiaN-VO, which fuses data from different sensors to enable displacement predictions to be made in dynamic flight conditions.

Knee osteoarthritis is the most popular type of osteoarthritis that causes extreme pain in elderly. Currently there is no cure for osteoarthritis. To lessen clinical symptoms, glucosamine was suggested. The primary goal of our study is to evaluate the efficacy and safety of glucosamine from recent studies. Electronic databases such as PubMed, Scopus, Cochrane were used to assess RCT. From the beginning through March 2023, the papers were checked, and if they fulfilled the inclusion criteria, they were then examined. WOMAC and VAS scales were considered as main outcome measure. A total of 15 studies were selected. Global pain was significantly decreased in comparison to placebo, as measured by the VAS index, with an overall effect size SMD of -7.41 ([95% CI] 14.31, 0.51). The WOMAC scale confirmed that pain, stiffness, and physical function had improved, however the effects were insufficient. A statistical update also revealed that there were no reports of serious medication interactions or significant adverse events. To summarize, glucosamine is more effective than a placebo at reducing pain in knee osteoarthritis patients. In long-term treatment, oral glucosamine sulfate 1500 mg/day is believed to be well tolerated.

It is unavoidable that the characteristics of the robot system alter inexactly or cannot be correctly determined during movement and are impacted by external disturbances. There are many adaptive control methods, such as sliding control or neural control, to improve the quality of trajectory tracking for robot motion systems. Still, those methods require a large amount of computation related to solving the problem of constrained nonlinear optimization problem. The article presents some techniques for determining an intelligent controller's online learning function parameters, including two circuits: the inner circuit is a state feedback linearized controller, and the outer course is an Interactive learning controller and does not use the robot's mathematical mode with function parameters online optimal learning and intelligent online learning closely following the joint model to stabilize the robot system. Finally, the 2-DOF planar robot simulation is performed on Matlab software R2022b. The findings indicate that outstanding tracking performance is achievable.

Dengue fever is a persistent public health issue in tropical regions, including Vietnam, where climate variability plays a crucial role in disease transmission dynamics. This study focuses on developing climate-based machine learning models to forecast dengue outbreaks in Ba Ria Vung Tau (BRVT) province, Vietnam, using meteorological data from 2003 to 2022. We utilized four predictive models—Negative Binomial Regression (NBR), Seasonal AutoRegressive Integrated Moving Average with Exogenous Regressors (SARIMAX), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM)—to predict weekly dengue incidence. Key climate variables, including temperature, humidity, precipitation, and wind speed, were integrated into these models, with lagged variables included to capture delayed climatic effects on dengue transmission. The NBR model demonstrated the best performance in terms of predictive accuracy, achieving the lowest Mean Absolute Error (MAE) compared to other models. The inclusion of lagged climate variables significantly enhanced the model’s ability to predict dengue cases. SARIMAX and LSTM models, although effective in capturing seasonal trends, struggled with overfitting and failed to accurately predict short-term outbreaks. XGBoost exhibited moderate predictive power but was sensitive to overfitting, particularly without fine-tuning. Our findings confirm that climate-based machine learning models, particularly the NBR model, offer valuable tools for forecasting dengue outbreaks in BRVT. However, improving the models’ ability to predict short-term peaks remains a challenge. The integration of meteorological data into early warning systems is crucial for public health authorities to plan timely and effective interventions. This research contributes to the growing body of literature on climate-based disease forecasting and underscores the need for further model refinement to address the complexities of dengue transmission in highly endemic regions.

Background: Research suggests that cognitive and behavioural factors, including lifestyle behaviours, contribute to the mitigation of perceived stress and stress-related health outcomes in later life. Given that stress management and lifestyle behaviour interventions for older adults are an important target for healthcare efforts, there is a need to comprehensively measure stress and coping resources in later adulthood. Additionally, researchers need a relatively short, standardized assessment tool that can robustly measure stress and coping for longitudinal and intervention-based studies to reduce burden on participants and for cross-comparison across research. Methods: The Stress Assessment Inventory (SAI), a valid and reliable 123-item measure designed to assess occupational stress and coping resources in younger adults was examined in 294 independent older adults. Results: A shortened and revised SAI is proposed for older adults, with good internal consistency and strong criterion validity. The revised SAI for older adults was found to have a 4-factor model that captures Adaptive Cognitive Resources, Maladaptive Behavioural and Cognitive Habits, Social Support and Adaptive Health Habits. Conclusion: The current study supports the use of the inventory in community-dwelling older adult populations as a comprehensive tool to assess stress and coping for use by researchers and healthcare professionals.

This paper will present the torque control design of an AFPMSM, one stator, and one rotor, using an FLC and ANFIS in-wheels fed by a three-level T-type inverter. The Surgeon ambiguous inference file of the FLC controller is built by two input vectors, the stator current error and the derivative of the stator error. These input variables include five membership functions, Negative big (NB), Negative small (NS), Equal zero (ZE), Positive small (PS), and Positive big (PB). The FLC controller is implemented with a 5x5 matrix so that the output stator voltage of the controller is required. The ANFIS controller for the neural network-based feature set and the fuzzy system. The neural network develops the dataset on the stator current error (e) and error integral (∆e). Then, the generated dataset is fed to the fuzzy logic method, and the control rules are developed. This ANFIS controller is caused by the training and testing phases. Finally, the FLC and ANFIS torque controllers are compared with the PI controller. The correctness of the proposed control structured solution is demonstrated by the simulation results of MATLAB/SIMULINK.

The osteoarthritis (OA), the main cause of disability, is a chronic condition that most frequently affects older adults. As the population ages, the cost of treatment is placing pressure on the healthcare budget. As a result, it is imperative to evaluate the medicines' cost-effectiveness and the influence they have on health resource allocation. Hence, our study aims to summarize the cost and outcome of utilizing glucosamine in OA treatment. Authentic databases like Medline, Cochrane, and Scopus were adopted for the identification process up until July 2023. Our primary inclusion criteria centered on the economic evaluation of Glucosamine in OA treatments with provided the incremental cost-effectiveness ratio (ICER) at least. The Quality of Health Economic Studies (QHES) instrument was applied to grade the quality of the studies. A total of 7 qualified studies were selected and discussed the cost-effectiveness of glucosamine with or without other formulations. All of them demonstrated that glucosamine was cost-effective. There was an in-creasement of QALY when incorporating Glucosamine into conventional care. Moreover, Crystalline Glucosamine Sulfate (pCGS) was more cost-effective than the Other Formulations of Glucosamine (OFG). In overall, utilizing pCGS was more beneficial than OFG in terms both of cost and quality of life.

In this article, the research team has systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These are control algorithms developed based on Dynamic Surface Control (DSC). Adaptive trajectory tracking DSC algorithm using fuzzy logic system (AFDSC) and radial neural network (RBFNN) with fuzzy logic system to overcome the disadvantages of DSC and expand the application domain for wheeled Mobile Robots (WMR). However, this Adaptive Fuzzy Neural Network Dynamic Surface Control (AFNNDSC) adaptive controller ensures the closed system is stable and follows the preset trajectory in the presence of wheel slippage model uncertainty and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on MATLAB/Simulink software and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the correctness of the theoretical analysis, the effectiveness of the proposed controller, and the possibility of practical application.

Anxiety-based cognitive distortions pertaining to somatic perception (ABCD-SP) - primarily catastrophizing, fear avoidance and kinesiophobia- have been repeatedly linked to worsening chronic, non-cancer pain (CNCP) outcomes of increased disability, amplified pain, ineffective opioid use, and opioid misuse. Several studies have suggested that treating ABCD-SP can improve pain outcomes, yet identification and targeting of ABCD-SP is not part of the standard medical pain assessment and treatment plan. Utilizing a narrative review of proposed mechanisms, published patient perspectives, and study correlates connecting these cognitive distortions with CNCP outcomes, a better practice approach to the delivery of standard medical CNCP care can be deduced and formulated into a Belief and Behavior Action Plan (BBAP) for medical clinicians treating CNCP to implement into initial and maintenance care planning. These recommendations require relatively few resources to implement and have the potential to disseminate more effective CNCP treatment on a large scale now, and into the new frontier of cognitive computing within medicine.

Dengue fever remains one of the most significant public health challenges globally, particularly in tropical and subtropical regions. Traditional early warning systems (EWS) for dengue outbreaks, based on retrospective epidemiological data, suffer from delays and limited predictive accuracy. Modern systems, leveraging real-time data, artificial intelligence (AI), and machine learning (ML), improve accuracy but can be costly and difficult to implement in resource-constrained settings. This paper proposes a novel hybrid model that integrates the strengths of both traditional and modern EWS, with real-world examples from Brazil, and Malaysia. The hybrid model balances accuracy, scalability, and cost-effectiveness, making it a feasible option for low- and middle-income countries.

This paper expressed three regression models that predict the lithium-ion battery life for electric cars based on a supervised machine-learning regression algorithm. The Linear Regression, Bagging Regressor, and Random Forest Regressor models will be compared for capacity prediction of lithium-ion batteries based on voltage-dependent per-cell modeling. When sufficient test data is available, three linear regression learning algorithms will train this model to give a promising battery capacity prediction result. The effectiveness of the three linear regression models will be demonstrated experimentally. The experiment table system is built with an NVIDIA Jetson Nano 4GB Developer Kit B01, a battery, an Arduino, and a voltage sensor. The Random Forest Regressor model has evaluated the model's accuracy based on the average of the square of the difference between the initial value and the predicted value in the data set (MSE (Mean Square Error)), and RMSE (Root Mean Squared Error) is smaller than the Linear Regression model, Bagging Regressor model (MSE is 516.332762; RMSE is= 22.722957). The Linear Regression model with MSE and RMSE is the biggest (MSE is 22060.500669; RMSE is= 148.527777). This result allows the Random Forest Regressor model to remain a helpful life prediction of lithium-ion batteries. Moreover, this result allows rapid identification of battery manufacturing processes and will enable users to decide to replace defective batteries when deterioration in battery performance and lifespan are identified.

Dark pigmentation can be observed in various parts of the plant, ranging from foliage to petals to berries. The molecular basis of black pigmentation appears to vary among species, with anthocyanins playing a significant role, although specific anthocyanin types and their mechanisms differ. These findings suggest that the development of phenotypes is species-specific or varies between larger taxonomic groups; this is further supported by the polyphyletic nature of dark pigmentation. Additionally, several different regulatory mechanisms have been described for the occurrence of dark pigmentation. Firstly, the repression or knockout of the competing flavone biosynthesis has been shown to lead to darker pigmentation while another mechanism is based on the activation and upregulation of the anthocyanin biosynthesis genes in the presence of MYB transcription factors. Potential ecological functions of dark pigmentation were identified as the protection of the photosynthesis apparatus, camouflage against herbivores, and the attraction of pollinators. Promising industrial applications include microbial factories for the production of natural food colourants, induction of novel phenotypes for the ornamental plant industry and lastly the increase of anthocyanins within agriculturally relevant crops.

German noise action plans aim to reduce negative health outcomes from noise exposure and identify quiet areas free of noise pollution. Quiet area identification in German noise action plans is based primarily on noise mapping and spatial analysis and not empirical or qualitative data about acoustic environments, thus leaving a gap in the understanding of the quality of formally recognized quiet areas in noise action plans. This work presents a comparative empirical case study in Dortmund, Germany, with the aim to describe the diurnal dB(A) and biophonic properties of quiet areas versus noise 'hot spots'. Sound observations were collected (n=282,764) in five different natural or recreational land use patch types larger than four acres within 33 proposed quiet areas in Dortmund (n=70) and 23 noise hot spots between April 27^th, 2022 and March 2^nd, 2023. We find that quiet areas are on average more than 20 dB(A) quieter than noise hot spots almost every hour of the day. Forests, managed tree stands, cemeteries, and agriculture diel patterns are dominated by dawn dusk chorus in spring and summer, whereas sport and recreation as well as noise hot spots are dominated by traffic and human noise. A novel composite biophony mapping procedure is presented that finds distinct temporal distribution of biophony in forested and agriculture peri-urban locations positively associated with patch size, distance away from LDEN > 55, proximity to water, and the number of vegetation layers in the plant community. Anthrophony distribution dominates urban land uses in all hours of the day, but expands during day and evening and contracts at night and in dusk hours. The procedures presented here illustrate how qualitative information regarding quiet areas can be integrated into German noise action planning.

This review centers on the pivotal role of the oncoprotein FOSL1, within the dimeric AP-1 transcription factor consisting of FOS-related elements. FOSL1 emerges as a key regulator governing invasion and metastatic dissemination, therefore making it a promising target for therapeutic intervention in cancer patients. The review provides a comprehensive survey of the regulatory mechanisms that exert influence over FOSL1. These encompass a spectrum of transcriptional and post-translational modifications. Notably, we unveiled the intricate involvement of diverse non-coding RNAs, specifically microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), in the modulation of FOSL1 expression through targeted interactions with its mRNA transcripts. Furthermore, an exploration into the functional dimensions of FOSL1 unfolds, encompassing apoptosis, proliferation, and migration. This investigation is accompanied by a thorough examination of the elaborate signaling pathways that underpin these multifaced roles. Additionally, emphasis is placed on the role of FOSL1 in orchestrating the stimulation of various cytokines, including TGF-beta, and the initiation of IL-6 and VEGF production in tumor-associated macrophages (TAMs) that transverse into the tumor microenvironment. A distinct focus is dedicated to appraising the prognostic implications associated with FOSL1. Finally, insights emerge into the evolving roles of FOSL1 in the context of mechanisms governing resistance and dependency on targeted therapeutic modalities.

Hybrid materials are a recent addition in the field of Restorative Dentistry for computer-assisted design/ computer-assisted manufacturing (CAD/CAM) indirect restorations. The long–term clinical success of modern dental restorative materials follows a multifactorial pattern. Among the characteristics, affecting the longevity of a restoration, mechanical properties and physicο – chemical interactions are of utmost importance. While numerous researchers constantly evaluate the mechanical properties, the biological background of resin–based CAD/CAM biomaterials is scarcely investigated and, therefore, less described in the literature. This review aims to analyze the biofilm formation on the surfaces of novel hybrid, resin–based CAD/CAM materials and evaluate the methodological protocols followed to assess microbial growth. It is demonstrated that the surface structure, the composition and the finishing and polishing procedures on the surface of a dental restorative material influence the initial bacterial adhesion; however most studies focus on in vitro protocols, whereas in vivo and/or in situ research of microbiomics in CAD/CAM restorative materials is lacking, obstructing in that manner the accurate understanding of the bioadhesion phenomenon in the oral cavity.

This paper presents experimental results of static and dynamic obstacle avoidance methods for a two-wheel mobile robot with independent control using a deep Q-learning (DQL) reinforcement learning algorithm. This is an alternative method that combines the Q-learning (QL) algorithm with a neural network. Neural networks in the DQL algorithm act as Q matrix table approximators for each pair (State - Action). The effectiveness of the proposed solution was verified through simulations, programming, and experimentation. This DQL algorithm is compared to the QL algorithm. First, the mobile robot communicated with the control script using the robot operating system (ROS). The mobile robot is code programmed using a Python in the ROS operating system combined with the DQL controller in Gazebo software. The mobile robot then performed experiments in a workshop with many different experimental scenarios. This DQL controller is improved in term of the computation time, convergence time, planning trajectory accuracy, and obstacles avoidance. Therefore, the DQL controller solved the path optimization problems for mobile robots better than the Q-learning controller.

CD74 is a type II cell surface receptor found to be highly expressed in several hematological and solid cancers, due to its ability to activate pathways associated with tumor cell survival and proliferation. Over the past 16 years, CD74 is emerging as a commonly detected fusion partner in multiple oncogenic fusion proteins. Studies have found CD74 fusion proteins in a range of cancers including lung adenocarcinoma, inflammatory breast cancer, and pediatric acute lymphoblastic leukemia. To date, there are five known CD74 fusion proteins, CD74-ROS1, CD74-NTRK1, CD74-NRG1, CD74-NRG2a, and CD74-PDGFRB, with a total of 16 different variants, each with unique genetic signatures. Importantly, the occurrence of CD74 in the formation of fusion proteins has not been well explored despite the fact that ROS1 and NRG1 families utilize CD74 as the primary partner for the formation of oncogenic fusions. Fusion proteins known to be oncogenic drivers, including those of CD74, are typically detected, and targeted after standard chemotherapeutic plans fail and the disease relapses. The analysis reported herein provides insights into early intervention of CD74 fusions and highlight the need for improved routine assessment methods so that targeted therapies can be applied while they are most effective.

Candida albicans, a prominent constituent of the human microbiota, poses a significant health threat due to its opportunistic pathogenicity and adept biofilm-forming capabilities. This research delves into the antifungal attributes and biofilm inhibition potential of Tea Tree Oil (Melaleuca alternifolia) cultivated in Vietnam. Renowned for its bioactive compounds, including terpinen-4-ol, 1,8-cineole, and terpinolene, the essential oil of Melaleuca alternifolia, commonly referred to as tea tree oil (TTO), is scrutinized for its impact on Candida albicans. The study meticulously determines the Minimum Inhibitory Concentration (MIC) and Minimum Fungicidal Concentration (MFC) of TTO against Candida albicans in both planktonic and biofilm states. Additionally, it assesses the biofilm-forming capacity of Candida albicans and establishes the Minimum Biofilm Inhibitory Concentration (MBIC) and Minimum Biofilm Eradication Concentration (MBEC) of TTO. Employing a liquid culture dilution method within a defined concentration range of 0.1 μL/mL to 10 μL/mL, the experimental approach adheres to established protocols. The outcomes reveal a noteworthy MIC and MIC₈₀ of 0.1 μL/mL and 2 μL/mL, respectively, and an MFC of 10 μL/mL for TTO against Candida albicans. Furthermore, TTO exhibits an MBIC of 2 μL/mL, showcasing its efficacy in inhibiting biofilm formation. However, the MBEC of TTO remains uncertain, possibly due to insufficient concentration tested, which may not effectively disrupt over 50% of the pre-formed biofilm mass within 24 hours. These results underscore the promising potential of TTO as a potent antifungal agent and biofilm inhibitor against Candida albicans, offering valuable insights for the innovation of novel therapeutic strategies in the realm of fungal infections.

A pandemic happens when a novel influenza A virus is able to infect and transmit efficiently to a new, distinct host species. Although the exact timing of pandemics is uncertain, it is known that both viral and host factors play a role in their emergence. Species-specific interactions between the virus and the host cell determine the virus tropism. These include binding and entering cells, replicating the viral RNA genome within the host cell nucleus, assembling, maturing, and releasing the virus to neighbouring cells, tissues, or organs before transmitting it between individuals. Influenza has a vast and antigenically varied reservoir. In wild aquatic birds, the infection is typically asymptomatic. Avian influenza virus (AIV) can cross into new species, and occasionally, it can acquire the ability to transmit from human to human. A pandemic might occur if a new influenza virus acquires enough adaptive mutations to maintain transmission between people. This review highlights the key determinants AIV must achieve to initiate a human pandemic and describes how AIV mutates to establish tropism and stable human adaptation. Understanding the tropism of AIV may be crucial in preventing virus transmission in humans and may help design vaccines, antivirals and therapeutic agents against the virus.

A better understanding of high temperature processes in slags contributes to facilitate knowledge based design of the solidified product. Here, a slag analogue with a nominal composition of 17 wt% LiMnO2 and 83 wt% Ca2SiO4 was synthesized encountering fairly high cooling rates. The Mn species from 1223 K to 1773 K was simulated using a thermodynamic model assuming a homogeneous melt. The micro-composition including the Mn species of the solidified slag was determined experimentally and was used as basis for molecular dynamics (MD) simulation. The MD simulation provides information on structure and viscosity at high temperatures, otherwise difficult to access. These parameters significantly influence oxidation state of redox-active elements and the solidified product. The micro-composition analyzed by electron probe micro analysis (EPMA) and synchrotron based micro-X-ray fluorescence (micro-XRF) showed that Mn-rich and Ca-Si-rich phases are separated. While the Mn-O phases did not contain noticeable Ca, the Ca2SiO4 phase had incorporated 0.6 wt% of Mn. The slag solidified into round shaped and droplet shaped grains of a Li-Mn-oxide, some Mn3O4 and Ca2SiO4. The powder X-ray diffraction (PXRD) confirmed the formation of larnite, the identity of the Li-Mn-oxide however remained inconclusive. The Mn oxidation state (OS) was identified using synchrotron based micro-X-ray absorption near edge spectroscopy (micro-XANES). The Mn-O grains, matched well with Li-Mn-oxides and a Mn OS: +3 e.g. LiMn3+O2. Small areas matching Hausmannite (Mn2+Mn23+O4) were also identified. The OS of Mn in the silicate phase could not be identified. For comparison a slowly cooled slag analogue with similar composition however higher Si content, was also subjected to micro-XANES. The slowly cooled slag formed long Mn-rich needles in a matrix of large calcium silicate crystals. The Mn-rich crystals matched well with the XANES spectrum of a Mn3+ Li-oxide like LiMn3+O2. At the rim of the needles the Mn-spectra matched well the Hausmannite (Mn2+Mn23+O4) reference. In the silicate phases Mn had a OS: +2, unambiguously. The melt structure at different temperatures of two compositions i.e. LiMn3+O2 and Ca2SiO4 was simulated using molecular dynamics (MD). They serve as model compositions assuming a heterogeneous melt. The results show significant different degrees of polymerization and viscosity. Information from MD simulations can support the identification of potentially different oxygen permeability and with that prediction of oxidation states. The bulk composition was identified by inductively coupled plasma optical emission spectrometry (ICP-OES), bulk structure by PXRD and bulk species by lab-XANES. The synchrotron micro analysis including micro-XRD were performed at the microfocus beamline I18 at the Diamond Light Source. Pure reference compounds were prepared and characterized with the same multi-modal approach.

PARP inhibitors are used in tumor therapy exploiting their inhibitory role in DNA repair. Radiation therapy leads to DNA damage in the affected region comprising both tumor tissue and adjacent normal tissue. In addition, radiotherapy has long-standing effects on DNA integrity. The mechanisms for this are not well understood. We studied normal tissue damage of the lung after application of ionizing radiation (IR) in two well established rat models comprising both external (eIR) and internal (iIR) ionizing radiation. We found a nearly complete (~350-fold) loss of PARP-1 in the irradiated tissue in both models but no change in PARylated protein. This may be explained by the strong decrease in both ARH3 and PARG, both deparylating enzymes. We demonstrate, that of all these proteins are expressed in type II pneumocytes, but also in type I pneumocytes and the bronchiolar epithelium. The data suggest a severe disturbance of PARylation in these cells which may affect their major functions such as maintenance of the integrity of the alveolar wall. It is likely that the application of PARP inhibitors could have cumulative effects in radiation-damaged normal tissue.

Mass insect rearing can have a range of applications, for example in biological control of insects. Since the performance of released biological control agents determines efficacy, the competitive fitness of insects post release is a key variable. Here, we tested whether inoculation with a gut symbiont, Enterobacter cloacae, and gnotobiotic rearing of larvae could improve insect growth and male competitive fitness of a transgenic diamondback moth, which has shown variation in fitness when reared in different insectaries. All larvae were readily infected with the focal symbiont. Under gnotobiotic rearing pupal weights were reduced and there was a marginal reduction in larval survival. However, gnotobiotic rearing substantially improved the fitness of transgenic males. In addition, in gnotobiotic conditions, inoculation with the gut symbiont increased pupal weights and male fitness, increasing the proportion of transgenic progeny from 20 to 30% relative to symbiont-free insects. Gnotobiotic conditions may improve the fitness of transgenic males by excluding microbial contaminants, while symbiont inoculation could further improve fitness by providing additional protection against infections, or by normalizing insect physiology. The simple innovation of incorporating antibiotic into diet, and inoculating insects with symbiotic bacteria that are resistant to that antibiotic, could provide a readily transferable tool for other insect rearing systems.

Extracts of Cordyceps militaris which possess the bioactive capacity such as antimicrobial activity and DPPH-free radical scavenging capacity, are applied to the serum product. This finished serum product has been evaluated the in vitro bioactivity and the stability with expect to find out a good extract with a suitable percentage used for serum product as natural materials on preservatives and antioxidation.

Anthocyanins are well known as colorants of flowers, but many other invisible functions might have been more important during the evolution of complex biosynthesis networks in plants. The number of anthocyanin decorating enzymes, the subtle control of structural genes by numerous transcription factors, and routes of intracellular anthocyanin transport are reviewed here. Various ecological functions of anthocyanins hold the key to understanding evolutionary trajectories that lead to the success of these pigments. Proposed functions include carbon sinks that prevent excessive sugar levels, sun blockers protecting the photosynthesis apparatus, antioxidants scavenging reactive oxygen species, providing camouflage, and attraction of pollinators and seed dispersers. Anthocyanins clearly fulfill different functions in different plant organs. It is currently believed that protective functions in leaves gave rise to the biosynthesis network and that flower and fruit coloration evolved later.

This study aimed to develop a laboratory-scale direct air capture unit for evaluating and comparing amine-based adsorbents under temperature vacuum swing adsorption conditions. The experimental campaign conducted with the direct air capture unit allowed for the determination of equilibrium loading, CO2 uptake capacity, and other main performance parameters of the investigated adsorbent Lewatit VP OC 1065®. The investigations also helped to understand the co-adsorption of both CO2 and H2O on the tested material, which is crucial for improving temperature vacuum swing adsorption processes. This was achieved by obtaining pure component isotherms for CO2 and H2O as well as three different co-adsorption isotherm models from literature. It was found that the weighted average dual-site Toth model emerged as the most accurate and reliable model for simulating this co-adsorption behavior. Its predictions closely align with experimental data, particularly in capturing the adsorption equilibrium at various temperatures. It was also observed that this lab-scale unit offers advantages over thermogravimetric analysis when conducting adsorption experiments on the chosen amine. The final aim of this study is to provide a pathway to develop devices for testing and developing efficient and cost-effective adsorbents for direct air capture.

The coastline in the Ca Mau and the Kien Giang provinces of the Vietnamese Mekong Delta has been severely eroded in recent decades. Pile-Rock Breakwaters (PRBW) are one of the most widely adopted structures for controlling shoreline erosion in this region. These structures are effective for wave energy dissipation, stimulating sediment accumulation, and facilitating the restoration of mangrove forests. These breakwaters are generally considered to be best-engineering practice however there is currently insufficient scientific evidence with regard to specific structural design aspects. This can lead to PRBW structures being compromised when deployed in the field. This study uses a physical model of a PRBW in a laboratory to investigate several design parameters, including crest width and working states (i.e. submerged, transition, and emerged), and investigates their relationship with the wave transmission coefficient, wave reflection coefficient, and wave energy dissipation. To investigate these relationships further, empirical formulas were derived for PRBWs under different sea states and crest widths to aid the design process. The results showed that PRBW width had a significant influence on the wave energy coefficients. The findings revealed that the crest width of the breakwater is inversely proportional to the wave transmission coefficient (K_t) under the emerged state. The crest width is also proportional to the wave reduction efficiency and wave energy dissipation in both working states (i.e., submerged and emerged states). The front wave disturbance coefficient (K_f) was found to be proportional to the wave reflection coefficient, and the wave height in front of the structure was found to increase by up to 1.4 times in the emerged state. The wave reflection coefficient requires special consideration to reduce the toe erosion in the structure. Lastly, empirical equations including linear and non-linear formulas were compared with previous studies for different classes of breakwaters. These empirical equations will be useful for understanding the wave transmission efficiency of PRBWs. The findings of this study provide important guidance for PRBW design in the coastal area of the Mekong Delta.

The European Union Water Framework Directive (2000/60/EC; WFD) aims to achieve a good ecological and chemical status of all bodies of surface water by 2027. The development of an integrated guidance on surface water chemical monitoring (e.g. WFD Guidance Document No. 7/19) has been transferred into national German law (Ordinance for the Protection of Surface Waters, OGewV). For the majority of compounds, this act requires a monthly sampling frequency to assess the chemical water quality status of a surface water body. To evaluate the representativeness of the sampling strategy under the OGewV, high-frequency online monitoring data is investigated under different sampling scenarios and compared with current, monthly grab sampling strategy. About 23 million data points were analyzed for this study, three chemical parameters (dissolved oxygen, nitrate-nitrogen, chloride concentration) and discharge data were selected from four catchment of different sizes ranging from 51391 km² to 84 km² (Elbe, Vereinigte Mulde, Neiße and two stations at Lockwitzbach). In this paper we proposed short-term online-monitoring (STOM) as a sampling alternative. STOM considers the placement of online sensors over a limited duration and return interval. In general we: (I) compare the results of conventional grab sampling with STOM, (II) investigate the different performance of STOM and grab sampling using discharge data as proxy for analyzing event-mobilized pollutants and (III) investigate the related uncertainties and costs of both sampling methods. Results show, that STOM outperforms grab sampling for parameters where minimum/maximum concentrations are required by law as the probability to catch a single extreme value is higher with STOM. Furthermore, parameters showing a pronounced diurnal pattern, like dissolved oxygen, are also captured considerably better. The performance of STOM showed no substantial improvements for parameters with small concentration variability, as nitrogen-nitrate The analysis of discharge events as a proxy parameter for event-mobilized pollutants proved that the probability of capturing samples during events is significantly increased by STOM.

This article presents a study conducted within a wind tunnel to enhance understanding of the excitation mechanism of stay cable vibration under arid conditions. Numerous wind tunnel tests were meticulously analyzed. Initially, the vibration of the stay cable was measured under steady flow conditions at a flow angle of 45 degrees and an inclination of 25 degrees, while varying wind velocities were applied. Additionally, an investigation into the flow field surrounding the stay cable was conducted in both vertical and horizontal directions. By utilizing two hot wire anemometers in the cable wake, an extensive database of flow field measurements was obtained. The experimental results revealed that the vibration characteristics of the stay cable under the arid conditions considered in this study aligned with findings reported in existing literature. Notably, a deeper comprehension of the excitation mechanism of a stay cable in a dry state was attained. This mechanism is closely associated with the inhibition of Karman vortices and the development of low-frequency vortices. At low wind speeds, Karman vortices predominated, resulting in small-amplitude vibrations. However, as the wind speed increased, the influence of Karman vortices diminished progressively, while the low-frequency vortices grew stronger. These low-frequency vortices exhibited high energy and a significant correlation with shedding along the stay cable, thereby inducing cable vibration in a dry environment.

AI integration is becoming more important in today’s world and could potentially be very useful in the life sciences and biotechnology. This study evaluates the effectiveness of AI systems in teaching molecular cloning techniques to newcomers in molecular biology, focusing on a comparison between Gemini Advanced (powered by Gemini Ultra 1.0) and ChatGPT (GPT-4o). Molecular or DNA cloning is a lab technique for creating multiple copies of a specific DNA segment. It involves isolating the DNA fragment, inserting it into a vector, and introducing it into a host cell, typically bacteria, which then generates numerous copies of the DNA. This essential technique underpins genetic engineering, allowing for gene study, protein expression, and the creation of genetically modified organisms. The primary objective of this study was to assess whether individuals with limited laboratory experience could effectively acquire a specific molecular biology skill set solely through an AI system, rather than relying on the more traditional ‘learning by shadowing’ approach. Participants with limited lab experience completed these tasks using both Gemini and GPT-4o, and evaluated on quality of instructions, conciseness, scientific accuracy, and user satisfaction. Results indicated that both AI systems demonstrated strengths in delivering clear and comprehensive instructions, but in different ways. Gemini, although more interactive with images and diagrams lacked directness and had a harder time keeping past steps in its “memory”. On the other hand, GPT-4o was much more direct and concise in the steps listed and had more accurate results akin to what is performed in the lab. This study underscores the potential of AI-driven tools to simplify complex biotechnological procedures, making them accessible for learners at all levels.

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSD) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support to the prevention of back pain or the development of MSD. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, Skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal, valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures and cognitive load detection from physiological signals.

Low-frequency electromagnetic induction (EMI) is a non-invasive geophysical method that is based on the induction of electromagnetic (EM) waves into the subsurface to quantify changes in electrical conductivity. In this study we present an open (design details and software are accessible) and modular system for the collection of EMI data. The instrument proposed allows to adjust the separations between the transmitter and up to four receiving antennas as well as the acquisition frequency (in the range between 3 and 50 kHz) to permit measurements with variable depth of investigation. The sensor provides access to raw data and the software described in this study allows control of the signal processing chain. The design specifications permit apparent conductivity measurements in the range of between 1 mS/m and 1000 mS/m, with a resolution of 1.0 mS/m and with a sampling rate of up to 10 samples per second. The sensor allows for a synchronous acquisition of a time stamp and a location stamp for each data sample. The sensor has a mass of less than 5 kg, is portable and suitable for 1-person operation, provides 4 hours of operation time on one battery charge, and provides sufficient rigidity for practical field operations.

The growing threat of antibiotic pollution in water systems demands innovative and sustainable remediation strategies. Nanocellulose-based magnetic metal-organic frameworks (MOFs) represent a cutting-edge solution for the adsorption and removal of antibiotics, leveraging their high porosity, magnetic properties, and environmentally friendly nature. This paper provides an in-depth review of recent advancements in nanocellulose-based MOFs, focusing on their synthesis, adsorption mechanisms, and applications in environmental remediation between 2020 and 2024. By summarizing key studies, we highlight their performance in targeting widely used antibiotics such as tetracyclines and fluoroquinolones. The integration of nanocellulose with MOFs not only enhances adsorption capacity but also offers easy recovery, recyclability, and environmental compatibility. This review explores their adsorption mechanisms, practical applications, and future research directions, contributing to the advancement of scalable and sustainable water treatment technologies.

Curcuma longa is only a rich source of curcumin (1) and its major analogues demethoxycurcumin (2) and bisdemethoxycurcumin (3) among the Curcuma species. The content ratio of these three curcumins in turmeric is depended on the varieties and growing environment. Recently, curcumin (1) has been reported as potential inhibitor of hepatic and enzymatic protein tyrosine phosphatase 1B (PTP1B) and its related disorders such as hypertriglyceridemia, hyperlipidemia and liver steatosis. Thus, we further purified curcumins (1–3) from C. longa and investigated their inhibitory effects against PTP1B and α-glucosidase enzymes. As the result, curcumins (1–3) exhibited potential PTP1B inhibition with IC50 values of 37.8 ± 1.4, 45.3 ± 0.7, and 72.6 ± 1.1 μM, respectively, and α-glucosidase inhibition with similar manner (IC50 values of 78.2 ± 0.2, 82.4 ± 0.6, and 90.6 ± 1.0 μM, respectively). These results reveal a key role of methoxylation in the variation of PTP1B and α-glucosidase inhibitory activity for these curcumins. In addition, density functional theory (DFT) was used accompanying with molecular docking (MD) to analyze the ligand stability and the interaction of curcumins (1–3) with PTP1B and glucoside hydrolase proteins. Assay-based results and the MD data obtained are highly correlation suggesting that the deterioration of the enzyme activity caused by the distortion of structural conformation of PTP1B and glucoside hydrolase may be related to the arrangement of amino acids in protein structure. This reported for the first time that inhibitory effects of curcumins (1–3) against PTP1B and glucoside hydrolase have been examined in vitro and in silico as well.

To protect skin from harmful ultraviolet (UV), zinc oxide (ZnO) with tannin from gallnuts was treated on cotton and polyester fabrics in this study. Before the treatments, the fabrics were dyed with shikonin and xanthophyll from Gromwell and Cape Jasmine. First, this study found that xanthophyll was more effective at preventing light fade or discoloration than shikonin. Despite the highest K/S, the untreated cotton in violet faded the most intensely when exposed to UV. The color variation of untreated polyester was narrow, with little change in L, a*, and K/S. Due to the non-photochemical xanthophyll, the yellow-dyed samples treated with ZnO/polyphenol did not vary yellowness (b*: 28.838) significantly, while the violet fabrics displayed a significant decrease in K/S and an increase in b*. The combination of ZnO and polyphenol treatment improved UV absorption at 350 to 250 nm. Cu-mordanting after ZnO/polyphenols treatment caused the violet cotton to turn reddish from blueish (negative to positive b*), with a hue change of 316° to 59° and the highest ΔE (25.90±4.34) after UV exposure. In this study, the combination of ZnO/polyphenol with Cu-mordants contributed the xanthophyll-dyed polyester to achieve a minimum ΔE and keep chroma and hue after UV exposure.

Despite all their advantages, load-bearing concrete shell structures with double curvatures are not frequently in use. The main reason is the complexity of their construction. In such a context, this article starts with a brief, critical review of existing technologies while their pros and cons are highlighted. Against that background the authors then propose a new approach for the highly automated fabrication of gridshell structures from variable modules. To demonstrate the feasibility of such a new technology, a demonstrator called ConDIT 1.0, a sphere-like shell structure composed of several frames was designed and built. The frame modules were fabricated automatically using extrusion-based 3D printing and a printable, strain-hardening cement-based composite (SHCC). This article presents the design of ConDIT 1.0, the mechanical material characterization of printed SHCC, the technology of module production, the results of geometry verification for print modules using 3D scanning, and the procedure for the demonstrator’s assembly.

In this paper, a new type of self-excited thermomechanical oscillator containing an oscillating shape memory alloy (SMA) filament with two symmetrically arranged spheres is investigated. The self-excitation of the oscillations is due to a heater of constant temperature, which causes periodic contractions of the filament when it approaches it. The contracted filament moves away from the heater a distance sufficient to cool it. Under the action of the weight of the spheres, the cooled filament re-approaches the heater, causing the above processes to repeat periodically. On the basis of experimental studies, approximating functions of the heater's heat field distribution are derived. A dynamic model of the oscillator has been created, in which the minor and major hysteresis in the SMA alloy and the distribution of the heat field around the heater have been taken into account. By numerical solutions of the differential equations, the laws of motion of the spheres are obtained. The displacements of the spheres in two perpendicular directions were measured using an experimental system. The obtained experimental results validate the proposed dynamic model and its assumptions with a high degree of confidence. Conclusions are drawn about the stochastic nature of the oscillations due to the hysteresis properties of the SMA and the temperature variation of the natural frequency of the oscillating system.

3D-printing with foam concrete, which is known for its distinct physical and mechanical properties, has not yet been purposefully investigated. The article at hand presents a methodological approach for the mixture design of 3D-printable foam concretes and a systematic investigation of the potential application of this type of material in digital construction. Three different foam concrete compositions with water-to-binder ratios between 0.33 and 0.36 and having densities of 1100 to 1580 kg/m³ in the fresh state were produced with a pre-foaming technique using a protein-based foaming agent. Based on the fresh-state tests, including 3D-printing as such, an optimum composition was identified and its compressive and flexural strengths were characterised. The printable foam concrete showed compressive strength above 10 MPa and low thermal conductivity, which make it suitable for 3D-printing applications, while fulfilling both load-carrying and insulating functions.

Numerical simulations and experiments on evaporative condensers in air conditioning systems using R744 were performed. Two configurations were given to study the feasibility of operating the subcritical R744 cycle. It indicated the capable of evaporative cooling condensing R744 to liquify by analyzing the outlet temperature at the end of the condenser reached from 28.7oC to 30.3oC with the operating pressure from 72.6 bar to 68.5 bar. The mass flow rate in the study increases from 14.34 kg/h to 46.08 kg/h, the pressure drop also increases from 0.23 bar to 0.47 bar for the simulation and 0.4 bar to 0.5 bar for the experiment, respectively. This study indicates that a 5-layers of copper tubes configuration causes a higher pressure drop and lower COP when compared to an 8-layers of copper tubes that split into 2 sets for smaller pressure drop. In this study, the evaporative condensers using mini tubes that are flooded in the cooling water tank are suitable for the subcritical R744 air conditioning system. The results obtained from the experimental data are in good agreement with those obtained from the numerical results, with the deviation less than 5%.

Deploying Cellular Internet of Things (CIoT) devices in urban and remote areas faces significant energy efficiency challenges. This is especially true for Narrowband IoT (NB-IoT) devices, which are expected to function on a single charge for up to 10 years while transmitting small amounts of data daily. The 3rd Generation Partnership Project (3GPP) has introduced energy-saving mechanisms in Releases 13 to 16, including Early Data Transmission (EDT) and Preconfigured Uplink Resources (PUR). These mechanisms extend battery life and reduce latency by enabling data transmission without an active Radio Resource Control (RRC) connection or Random Access Procedure (RAP). This paper examines these mechanisms using the LENA-NB simulator in the ns-3 environment, which is a comprehensive framework for studying various aspects of NB-IoT. The LENA-NB has been extended with PUR, and our analysis shows that PUR significantly enhances battery life and latency efficiency, particularly in high-density environments. Compared to the default RAP method, PUR reduces energy consumption by more than 2.5 times and increases battery life by 1.6 times. Additionally, PUR achieves latency reductions of 2.5-3.5 times. The improvements with PUR are most notable for packets up to 125 bytes. Our findings highlight PUR’s potential to enable more efficient and effective CIoT deployments across various scenarios. This study represents a detailed analysis of latency and energy consumption in a simulated environment, advancing the understanding of PUR’s benefits.

(1) Background: To further investigate the link between inadvertent tooth movements (ITMs) and mechanical properties of different retainers, the response upon vertical loading was compared in three different retainers (two stainless steel and one nickel-titanium). The influence of different reference teeth was also considered. (2) Methods: Three retainers (R1, R2, R3) were tested in a newly developed biomechanical analysis system (FRANS). They were bonded to 3D-printed models of the lower anterior jaw and vertically displaced up to 0.3mm. Developing forces and moments were recorded at the center of force. (3) Results: The vertical displacement caused vertical forces (Fz) and labiolingual moments (My) to arise. These were highest in the lateral incisors (up to 2.35±0.59N and 9.27±5.86Nmm for R1; 1.69±1.06N and 7.42±2.65Nmm for R2; 3.28±1.73N and 15.91±9.71Nmm for R3) for all analyzed retainers, and with the R3 retainer for all analyzed reference teeth, while the lowest Fz and My values were recorded with the R1 retainer. (4) Conclusions: Displacements of 0.2mm and larger provided forces and moments which could be sufficient to cause unwanted torque movements, such as ITMs in all analyzed retainers. Clinicians must be mindful of these risks and perform posttreatment checkups on patients with retainers of all materials.

In the last two decades, variably doped strontium barium niobate (SBN) has attracted a lot of scientific interest mainly due to its specific non-linear optical response. Comparably, the parental compound, i.e., undoped SBN, appears to be less studied so far. Here, two different cuts of single-crystalline nominally pure strontium barium niobate in the composition Sr0.61Ba0.39Nb2O6 (SBN61) are comprehensively studied and analyzed with regard to their photoconductive responses. We present conductivity measurements under systematically varied illumination conditions along either the polar z-axis or perpendicular to it (x-cut). Apart from a pronounced photoconductivity (PC) already under daylight and a large effect upon super-bandgap illumination in general, we observe (i) distinct spectral features when sweeping the excitation wavelength over the sub-bandgap region as then discussed in the context of deep and shallow trap states, (ii) extremely slow long-term relaxation for both light-on and light-off transients in the range of hours and days, (iii) a critical dependence of the photoresponse on the pre-illumination history of the sample, and (iv) a current-voltage hysteresis depending on both the illumination and the electrical-measurement conditions in a complex manner.

Energy conversion and distribution (heat and electricity) is characterized by long planning horizons, investment periods and depreciation times, and it is thus difficult to plan and tell the technology that optimally fits for decades. Uncertainties include future energy prices, applicable subsidies, regulation, and even the evolution of market designs. To achieve higher adaptability to arbitrary transition paths, a technical concept based on integrated energy systems is envisioned and described. The problem of intermediate steps of evolution is tackled by introducing a novel paradigm in urban infrastructure design.It builds on standardization, modularization and economies of scale for underlying conversion units. Building on conceptual arguments for such a platform, it is then argued how actors like (among others) municipalities and district heating system operators can use this as a practical starting point for a manageable and smooth transition towards more environmental friendly supply technologies, and to commit to their own pace of transition (bearable investment/risk). environmental friendly supply technologies. Merits are not only supported by technical arguments but also by strategical and societal prospects like technology neutrality and availability of real options.

In the current business environment, firms are eager to adopt new technology as they witness and perceive more and more successful business applications of new technologies, e.g., Big Data, Artificial Intelligence (A.I.), Cloud Computing, etc. As one of the disruptive technologies, Blockchain technology (BCT) is now drawing public attention owing to the cryptocurrency phenomenon (e.g., Bitcoin), for which Blockchain serves as the backbone technology. Given certain innovative features of BCT, especially its transparency, traceability, security, efficiency, confidentiality, and immutability, BCT holds out the promise of impacting supply chain operational and financial efficiencies. Recently, the burgeoning of Metaverse and Non-Fungible Token (NFT) has boosted the BCT as state-of-the-art technology to another notch. Motivated by the proliferating adoption of BCT, we conduct a holistic literature review with a focus on the status of research on BCT in the context of Supply Chain Management (SCM). In particular, this Blockchain-centered research reviews the research up to date on the Blockchain application in SCM. It provides holistic review in terms of (1) the functionality of BCT and its salient features; (2) the prevailing and potential applications of BCT; (3) and the business benefits and impact of BCT in SCM. Finally, we substantiate and highlight a variety of research directions.

The ductile behavior of strain hardening cement-based composites (SHCC) under direct tensile load makes them promising solutions for applications where high energy dissipation is needed, such as earthquake, impact by a projectile, or blast. However, the superior tensile ductility of SHCC due to multiple cracking does not necessarily entail compressive and shear ductility. As an effort to characterize the behavior of SHCC under impact compressive and shear loading, relevant to the mentioned high-speed loading scenarios, the paper at hand studies the performance of a SHCC and its constituent cement-based matrices using the split-Hopkinson bar method. For compression experiments, cylindrical specimens with a length-to-diameter ratio (l/d) of 1.6 were used. The selected length of the sample led to similar failure modes under the quasi-static and impact loading conditions, which was necessary for a reliable comparison of the obtained compressive strengths. The impact experiments were performed in a split-Hopkinson pressure bar (SHPB) at a strain rate that reached 110 s-1 at the moment of failure. For shear experiments, a special adapter was developed for a split-Hopkinson tension bar (SHTB). The adapter enabled performing impact shear experiments on planar specimens using the tensile wave generated in the SHTB. Results showed a dynamic increase factor (DIF) of 2.3 and 2.0 for compressive and shear strength of SHCC, respectively. As compared to the non-reinforced constituent matrix, the absolute value of the compressive strength was lower for the SHCC. Contrarily, under shear loading, the SHCC yielded the higher shear strength than the non-reinforced matrix.

Fission-track modelling rests on etching, counting and measuring the lattice damage trails from uranium fission. The tools for interpreting fission-track data are advanced but the results are never better than the data. Confined-track samples must be an adequate size for statistical analysis, representative of the track population, consistent with the model assumptions and with the calibration data. Geometrical and measurement biases are understood and can be dealt with up to a point. The interrelated issues of etching protocol and track selection are however more difficult to untangle. Our investigation favours a two-step protocol. The duration of the first step is inversely proportional to the apatite etch rate, so that different apatites etch to the same Dpar. A long immersion reveals many more confined tracks, terminated by basal and prism faces. This allows consistent length measurements and permits to orient each track relative to the c-axis. Long immersion times combined with deep ion irradiation reveal confined tracks deep inside the grains. Provided it is long enough, the precise immersion time is not important if the effective etch times of the selected tracks are calculated from their measured widths. Then, whether the sample is mono- or multi-compositional, we can, post hoc, select tracks with the desired properties. The second part of the protocol has to do with the fact that fossil tracks in geological samples appear to be under-etched compared to induced tracks etched under the same conditions. This should be assumed if the semi-axes (l_C, l_A) of a fitted ellipse plot above the induced-track line. In that case an additional etch can increase the track lengths to a point where they are consistent with the model based on lab-annealing of induced tracks, a condition for valid thermal histories. Here too, it is possible to select a subset with effective etch times consistent with the model if the widths of confined tracks are measured along with their lengths and orientations.

The prolonged static sitting at the workplace is considered as one of the main risks for development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar hyperlordosis and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. This publication proposes the low back pain assessment (LBPA) dataset collected through experiment with 100 participants and consisting of photogrammetric images with intentionally placed body markers, calculated postural angles and tags correct and incorrect posture assessed by habilitated rehabilitator, as well as questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed for recognizing incorrect body postures. Two scenarios have been elaborated for modeling purposes: scenario 1, based on natural body posture tagged as correct and incorrect, and scenario 2, based on incorrect body postures, corrected additionally by the rehabilitator. The achieved accuracies of respectively 75.3% and 85% for both scenarios reveal the potential for future research in enhancing awareness and actively managing posture-related issues that elevate the likelihood of developing lower back pain symptoms.

The Hertzian contact theory, as well as most of the other classical theories of normal and tangential contact, provides displacements and the distribution of normal and tangential stress components directly in the contact surface. However, other components of the full stress tensor in the material may essentially influence the material behaviour in contact. Of particular interest are principal stresses and the equivalent von Mises stress, as well as the gradient of the hydrostatic pressure. For many engineering and biomechanical problems, it would be important to find these stress characteristics at least in the contact plane. In the present paper, we show that the complete stress state in the contact plane can be easily found for axially symmetric contacts under very general assumptions. We provide simple explicit equations for all stress components and the normal component of the gradient of hydrostatic pressure in the form of one-dimensional integrals.

This paper investigates experiences of how local cultural norms and social capital influenced the disaster relief process following the 2011 GEJET disaster. To underline social capital’s multiple‐influence aspects, this paper draws from field research that focuses on collective and individual social capital in disaster relief to systematise the findings according to the social capital framework. The paper uncovers new effects of individual social capital and collective capital in a mega-disaster context, thus contributing to pushing the research agenda toward a more critical appraisal of individual social capital and collective capital. We contribute to the nascent but growing literature that clarifies the relationship between social capital and disaster resilience. A qualitative study was conducted to capture the essence of their shared experiences. Findings revealed three main themes capturing the essence of the research participants’ disaster experiences. First, disaster response relied mostly on locally driven relief due to a clear understanding of the local culture. Second, there was an urgency to establish a sense of normalcy by providing quality supplies that would help survivors. Third, Japanese gender roles and expectations were reflected in the disaster relief process. The themes establish a wide array of lived experiences that are important to reflect on the role social capital plays in the policy-making and needs assessment processes in post-disaster relief and response efforts. The findings provide insights for integrating social dimensions into a humanitarian aid culture as citizens work towards a sense of normalcy.

Shell-like, double curved and thus above-average performance structures, are usually produced monolithically on site. For industrial advancement, however, they must be divided into transportable modules which can be assembled on the construction site (design for assembly). Models are lattice shells made of steel and glass, in which predominantly flat sub-surfaces (modules) are used. Therefore, the main question is: Which modularizations are suitable for flow production with mineral building materials? In this paper designed free-form surface is going to be discretized as PQ circular mesh system, suitable modules for 3D concrete printing. Moreover, the multi-criteria optimization is done with Response Surface Methodology (RSM) in order to get optimal final shape. The goal is to start from the arbitrary shape, that can be generated from two curves, with possible two-way division into modules and compare it with the resulted discretized PQ circular mesh system, realized with new algorithm. The comparison can be defined through two main criteria: geometrical and structural.

Power quality is a major concern in electrical power systems. The power quality disturbances such as sags, swells, harmonic distortion and other interruptions have impact on the electrical devices and machines and in severe cases can cause serious damages. Therefore it is required to recognize and compensate all types of disturbances at an earliest to ensure normal and efficient operation of the power system. To solve these problems, many types of power devices are used. At the present time, one of those devices, Dynamic Voltage Restorer (DVR) is the most efficient and effective device used in power distribution system. In this paper, design and modeling of a new structure of multifunctional DVR for voltage correction is presented. The performance of the device under different conditions such as voltage swell, voltage sag due to symmetrical and unsymmetrical short circuit, starting of motors, and voltage distortion are described. Simulation result shows the superior capability of proposed DVR to improve power quality under different operating conditions. The proposed new DVR controller is able to detect the voltage disturbances and control the converter to inject appropriate voltages independently for each phase and compensate to load voltage through three single- phase transformers.

Quorum sensing (QS) is a sophisticated bacterial cell-to-cell communication mechanism that allows bacteria to sense population density through the secretion and detection of diffusible small molecular signals. This process leads to the coordinated expression of specific genes at the transcriptional level. Over time, continuous research has elucidated the genetic elements and regulatory principles of QS. Recently, synthetic biology has leveraged these insights to construct genetic circuits incorporating QS components, enabling both intra-species and inter-species artificial communication. These QS-based genetic circuits hold significant potential for applications in biotechnology and biomedicine. This paper reviews several well-characterized microbial QS systems and their functional roles, while also introducing the application of QS-based genetic circuits in cellular communication across species. The paper further discusses the role of QS in the development of biological computing tools, population density regulation, and metabolic flux control, offering a forward-looking perspective on future advancements. For intra-species communication, the focus is on the use of QS systems in constructing biological computing tools, including toggle switches, biosensors, and logic gates in synthetic biology. These tools, designed on the QS mechanism, can more precisely coordinate cellular behavior by integrating biological control circuits to achieve spatial, temporal, and population-level regulation. In the context of inter-species communication, the introduction of QS systems plays a pivotal role in population density control and metabolic flow regulation. By recombining metabolic networks, QS enables the redistribution of metabolic flux in desired pathways, facilitating the regulation of population density and the co-culture of mixed strains. Moreover, combining QS with oscillator models has shown great potential in synchronizing microbial communities. In summary, in-depth research into QS mechanisms and their applications not only lays a solid foundation for understanding microbial ecological competition and dynamic balance, but also offers promising avenues for regulating pathogenic bacteria and developing innovative disease control strategies.

Phosphodiesterases (PDEs) are the principal superfamily of enzymes responsible for degrading the secondary messengers 3’,5’-cyclic nucleotides cAMP and cGMP. Their refined subcellular localization and substrate specificity contribute to finely regulate cAMP/cGMP gradients in various cellular microdomains. Redistribution of multiple signal compartmentalization components is often perceived under pathological conditions. Thereby PDEs have long been pursued as therapeutic targets in diverse disease conditions including neurological, metabolic, cancer and autoimmune disorders in addition to numerous cardiovascular diseases. PDE2 is a unique member of the broad family of PDEs. In addition to its capability to hydrolyze both cAMP and cGMP, PDE2 is the sole isoform that may be allosterically activated by cGMP increasing its cAMP hydrolyzing activity. Within the cardiovascular system, PDE2 serves as an integral regulator for the crosstalk between cAMP/cGMP pathways and thereby may couple chronically adverse augmented cAMP signalling with cardioprotective cGMP signalling. This review provides a comprehensive overview of PDE2 regulatory functions in multiple cellular components within the cardiovascular system and also within various subcellular microdomains. Implications for PDE2 mediated crosstalk mechanisms in diverse cardiovascular pathologies are discussed highlighting the prospective use of PDE2 as a potential therapeutic target in cardiovascular disorders.

The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or Volunteered Geographic Information (VGI). Key challenges with such ‘opportunistic’ data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area, or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results already provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time.

In the case of solid slabs made of reinforced concrete that are usually subjected to bending, large areas of the structure are stressed well below their load-bearing capacity. Contrary to this are shell structures, which can bridge large spans with little material if designed according to the force flow. To improve the efficiency of ceiling slabs, we want to utilize the shell load-bearing behavior on a smaller scale by resolving the solid interior accordingly. In order to be able to study a wide range of such constructions virtually, a parametric multiobjective simulation environment is being developed in an ongoing research project. The basic analysis approaches which were implemented are presented in this paper. The basic workflow, the used programs and material models and their calibration on the tests on textile-reinforced concrete (TRC) samples are described.

Dengue has long been a serious health burden to the global community, especially for those living in the tropics. In spite of the availability of vaccines, effective treatment for the infection is still needed and currently remained absent. In the present study, antiviral properties of the KSF 103 ME which consisted of a number of potential antiviral compounds were investigated against DENV-2. The effects of this extract against DENV-2 replication were determined using the qRT-PCR. Findings from the study suggested that the KSF 103 ME showed maximum inhibitory properties toward the virus during the virus entry stage at concentrations of more than 12.5 µg/mL. Minimal antiviral activities were observed at other virus replication stages; adsorption (42% reduction at 50 µg/mL), post-adsorption (67.6% reduction at 50 µg/mL), prophylactic treatment (68.4% and 87.7% reductions at 50 µg/mL and 25 µg/mL, respectively) and direct virucidal assay (48% and 56.8% reductions at 50 µg/mL and 25 µg/mL, respectively. The KSF 103 ME inhibited dengue virus repication with an IC50 value of 20.3 µg/mL and SI value of 38.9. The KSF 103 ME showed potent antiviral properties against DENV during the entry stage. Further studies will be needed to deduce the antiviral mechanisms of the KSF 103 ME against DENV.

Human induced climate change led to the recognition of Community-Based Adaptation (CBA) as one of the remedies in building adaptive capacity and resilience of communities. It recognizes the resources and the skills as well as the agency of communities in shaping the optimal strategy. CBA interventions are vital in addressing community needs and contextual specificities, enable participatory and inclusive engagement and thereby facilitate more profound adaptation impacts. Agent-based modelling (ABM) harbors the potential to be the ideal tool to support developing of tailored adaptation strategies while considering capacities, resources, skills, priorities, needs and the cultural context of local communities. ABM allows to capture the complex relationships among various components within community decision making on CBA actions, the CBA actions themselves and the environment. There exist plenty of tools, however they provide for varying degrees of freedom in agent definition, sensitivity analysis, scalability, experiment design and output analysis to name a few. A set of required key criteria for CBA modelling is developed and all available ABM modelling and simulation tools are assessed for their suitability for CBA. NetLogo emerges as the most fitting tool for incorporating and handling the special features of CBA. It is closely followed by GAMA and Envision. The article provides insights and guidance to researchers and practitioners in selecting ABM tools aligned with the specific requirements of their CBA simulations and projects.

Biofilm formation in unicellular eukaryotes, such as Candida albicans and Saccharomyces cerevisiae, represents a significant challenge in various sectors, including healthcare, agriculture, and industry. These biofilms are resistant to conventional antimicrobial treatments due to their protective extracellular matrix and complex regulatory mechanisms, such as quorum sensing (QS) and cyclic AMP (cAMP) signaling. Recent advances in the understanding of biofilm formation pathways have led to the identification of novel compounds that target these regulatory mechanisms. This review highlights breakthroughs from 2024 research, focusing on the inhibition of quorum sensing, disruption of extracellular matrix synthesis, and transcriptional/post-transcriptional regulation of biofilm-related genes. The application of these discoveries in Vietnam is particularly promising, given the country's challenges with biofilm-related infections in healthcare and productivity losses in agriculture and aquaculture. Natural product-based approaches, especially those derived from Vietnam's marine biodiversity, offer environmentally sustainable solutions for biofilm control. However, translating these laboratory findings into scalable, cost-effective applications faces challenges such as resistance development, environmental sustainability, and regulatory constraints. This review provides a comprehensive overview of compounds targeting biofilm regulatory mechanisms, evaluates their potential applications in Vietnam, and discusses future research directions, emphasizing the need for interdisciplinary collaboration to address the complexities of biofilm control.

Model-based design has received considerable attention in biological and chemical industries over the last two decades. However, the parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies were introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computation expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints. We also suggest employing the information from global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies where one is to design a heating/cooling profile for the essential part of a continuous production process and the other is to optimize the feeding profile for a fed-batch reactor of the penicillin fermentation process. The results reveal that the proposed approach can be used successfully for complex (bio)chemical problems in model-based design.

Machine Learning (ML) has proved its capabilities in different scientific and industrial fields, but it needs to be further investigated in the construction industry for practical utilization. One of the use cases of ML is delegating the structural calculation process. In this study, to discuss ML’s capabilities in performing the work of a structural designer, calculations of concrete sections based on ACI (American Concrete Institute) as a case study were selected. At first, all manual design steps and standard considerations for a concrete beam section were coded parametrically in MATLAB. After comparing with structural design references to prove the accuracy of codes in calculating shear and bending capacities, the parametric results were used as initial data (Look−up table) for training the ML operators. Regarding different types of ML techniques and as a comparison between them, in the next steps, all essential codes for Fuzzy Logic (FL), Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were coded in the same platform. The performance of the three coded ML operators to replace (delegate) standard calculations compared to direct calculations was individually investigated and displayed through parametric examples. After initial examples, the influences of the number of parameters and size of the Look−up table on the accuracy of each operator were discussed. The study concluded that although all three operators can delegate the standard calculation, the precision of the results differs considerably. In case of the desirable size of the Look−up table, ANFIS operators can represent the standard calculations with a different number of parameters and entirely high precision.

In the present work, three different atmospheric plasma sprayed (APS) alumina coatings were fabricated using three fused and crushed alumina powders of different particle size fine, medium and coarse. The influence of the particle size on thermal properties and micro-structural features of the produced coating were investigated by thermal insulation test and detailed image analysis technique, respectively. The analyzed micro-structural features include the total porosity, pore size (fine, medium, and large) and cracks. All types of cracks were considered in calculations as voids and were evaluated according to their sizes as pores. All spray parameters except the particle size were fixed throughout the spraying process. The results revealed that the fine starting powder has produced the densest coating with the lowest total porosity and that the total porosity increases with an increasing particle size. This was expected as powders of smaller particle size will reach a higher in-flight temperature and velocity than powders of bigger particle sizes as long as the same spray parameters are applied. However, a detailed image analysis investigation on the three produced coatings showed that the fraction of fine pores and cracks versus the total porosity is substantially higher in coatings produced by using fine starting powders than those produced using medium and coarse powders. In this work, a connection between the thermal insulation and the porosity fraction, which includes fine pores and cracks, was revealed.

Cardiovascular diseases (CVDs) and their risk factors are a major cause of illness and death worldwide, especially in low- and middle-income countries like Cameroon. Physiotherapist-led health promotion (PLHP) has proven effective in improving health and reducing CVD risks. Understanding patient perspectives is crucial for designing effective, context-specific PLHP interventions. This study explored patients' views, experiences, perceived usefulness, acceptability, and preferred methods of PLHP through a sequential explanatory mixed-methods approach. The quantitative data highlights a significant burden of CVD conditions and risk factors among patients seen in physiotherapy services. Qualitatively, three themes were identified and included: 1) perspectives and experiences of people at risk or with CVDs (pwCVDs) on PLHP; 2) Perceived usefulness and acceptability of PLHP; 3) Preferred delivery methods of PLHP. Participants reported positive feedback on PLHP and physiotherapy services. Barriers to effective PLHP included high workloads for physiotherapists, limited service access in rural areas, and prohibitive costs. Despite these challenges, participants expressed strong confidence in physiotherapists' competence, though they also called for improved regulation and ongoing professional development. PLHP components, especially physical treatment and dietary advice, were deemed highly useful and acceptable. Patients suggested various delivery methods, including peer support groups, home visits, and mass media interventions. This study highlights the need to improve the scope of practice, competence of physiotherapists and accessibility of physiotherapy services in Cameroon for pwCVDs. It is necessary to adopt multidisciplinary approaches to achieve better outcomes for risk factors like diabetes and hypertension in context.

With LiDAR (Light Detection and Ranging) time series being used for various applications, optimal realisation of a common geodetic datum over many epochs is a highly important prerequisite with direct impact on accuracy and reliability of derived measures. In our work, we develop and define several approaches to the adjustment of multi-temporal LiDAR data in a given software framework. These approaches ranging from pragmatic to more rigorous solutions are applied to an 8 year time series with 21 individual epochs. The analysis of the respective results suggests that a sequence of bi-temporal adjustments brings the best results while being more flexible and computationally viable than the most extensive approach of using all epochs in one single multi-temporal adjustment. With a combination of sparse control patches measured in the field and one selected reference block, we obtain relative datum discrepancies in the range of 1-2 cm for the complete time series. Based on our findings, we formulate design criteria for setting up and adjusting future time series with the proposed method.

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must carefully be evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis, and energy system modelling to optimize decision-making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends signifi-cantly on three factors: 1. the residential electricity consumption patterns that are influenced by the socio-economic composition of the community, 2. the degree of flexibility of i) PUE assets and ii) operational preferences of the PUE user, and 3. the capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy system can reduce the financial risk of operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project´s financial sustainability, and is achieving the overall objectives of decentralized energy system planning.

Addressing the pressing challenge of global warming, reducing greenhouse gas emissions in the transportation sector is a critical imperative. Battery and fuel-cell electric vehicles have emerged as promising solutions for curbing emissions in this sector. In this study, we conducted a comprehensive life cycle assessment (LCA) for typical passenger vehicles, heavy duty trucks and city buses using either proton-exchange membrane fuel cells or Li-ion batteries with different cell chemistries. To ensure accuracy, we supplemented existing studies with data from the literature, particularly for the recycling phase, as database limitations were encountered. Our results highlight that fuel cell and battery systems exhibit large emissions in the production phase. Recycling can significantly offset some of these emissions, but a comparison of the technologies examined revealed considerable differences. Overall, battery electric vehicles consistently outperform fuel cell electric vehicles regarding absolute greenhouse gas emissions. Hence, we recommend prioritizing battery electric over fuel cell vehicles. However, deploying fuel cell electric vehicles could become attractive in a hydrogen economy scenario where other factors, e. g. the conversion and storage of surplus renewable electricity via electrolysis, become important.

This paper presents a new high-efficiency-high-step-up based converter integrating two stype DC-DC Boost and Flyback coupled magnetic converter with recovery stage dedicated to smart HVDC distributed architecture in renewable energy production systems. Appropriate duty cycle ratio assumes that the recovery stage work with parallel charge and discharge to achieve high step-up voltage gain. Besides, the voltage stress on the main switch is reduced with a passive clamp circuit and thus, low on-state resistance Rdson of the main switch can be adopted to reduce conduction losses. The circuit is simple to control. As a final point of this research, the simulation and the prototype investigational results are presented to demonstrate the effectiveness of this proposed converter.

Domain generalization has led to remarkable achievements in Hyperspectral Image (HSI) classification. Inspired by contrastive language-image pre-training (CLIP), the language-aware domain generalization method has been explored to learn cross-domain-invariant representation. However, existing methods face some challenges: 1) The weak capacity to extract long-range contextual information and inter-class correlation. 2) Due to the inadequacies of the large-scale pre-training for HSI data, the spatial-spectral features of HSI and linguistic features can not be straightforwardly alignment. To address the above problems, a novel network has been proposed with a CLIP framework, which consists of an image encoder, based on an encoder-only transformer to obtain the global contextual information and inter-class correlation, a frozen text encoder, and a cross-attention mechanism, named Linguistic-Interact-with-Visual Engager (LIVE), enhances the interaction between two modalities. Extensive experiments demonstrating superior performance over state-of-the-art methods in HSI Domain Generalization with a CLIP framework.

BACKGROUND&AIMS: Hepatitis B virus (HBV) infection remains a major public health problem. The interaction between HBV and the host inflammatory response is an important factor contributing to liver damage and disease development. We compared the correlation between the subclinical index and PBMCs concentration in two groups of pregnant women (HBsAg positive), which are different in HBV DNA concentration in Vietnam. METHODS: The Hierarchical cluster analysis (HCA) was run with 20 different clustering methods on data collected from 80 Vietnamese pregnant women and their babies (60/80 cord blood). RESULTS: In the high viral load group (HBV DNA ≥ 5x10^7 copies/ml), a strong correlation between CBMCs with serum maternal Haemoglobin concentration and maternal platelet and maternal ALT. Their R values are: -0.88, 0.82, and 0.84 with p=8.97E-03, 2.41E-02 and 1.75E-01, respectively. CONCLUSIONS: We found a significant correlation shift of subclinical index between the two groups, which may be important in diagnosing pregnant women with chronic hepatitis B virus infection.

This article presents a buck converter in which the inductor has been replaced by a transmission line. This approach would be practically conceivable if the power supply and load have a greater spatial distance. In the time domain, this new converter can be described by a system of coupled partial and ordinary differential equations. In the frequency domain, a transcendental transfer function is obtained. For comparison with an equivalently parameterized conventional converter, Padé approximants are derived. A linear controller is designed for the converter topology under consideration.

With increasing interest in the use of additive manufacturing techniques in the construction industry, static rheological properties of fresh concrete have necessarily come into focus. In particular, the knowledge and control of static yield stress (SYS) and its development over time are crucial for mastering formwork-free construction, e.g. by means of layered extrusion. Furthermore, solid understanding of the influences of various concrete constituents on the initial SYS of the mixture and the structural build-up rate is required for purposeful material design. This contribution is concentrated on the effect of aggregates on these rheological parameters. The volume fraction of aggregates was varied in the range of 35 to 55 % by volume under condition of constant total surface area of the particles. The total surface area per unit volume of cement paste was equal to 5.00, 7.25 and 10.00 m²/l, conditioned on the constant volume fraction of aggregates. Both variations were enabled by changing the particle size distributions of the aggregates while holding the cement paste composition constant for all concrete mixtures. To characterise the SYS and the structural build-up, constant shear rate tests with a vane-geometry rotational rheometer were performed. It was found that in the ranges under investigation the variation in volume fraction had a more pronounced effect on the static rheological properties of concrete than did the variation in surface area. An accurate mathematical description of the relationship between the initial SYS of concrete and the relative volume fraction of aggregate based on the Chateau-Ovarlez-Trung model was proposed. Challenges in deriving a similar relationship for the structural build-up rate of concrete were highlighted.

Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

In the context of the increasing migration into Germany that has taken place in recent years and German efforts to establish an inclusive school system, which enables learners from different religious, ethnic, language and social backgrounds with and without disabilities to participate, religious education has become a key topic for interdisciplinary discourse between theology, philosophy, and pedagogy in German schools. The following questions are of special interest: How can we manage diversity in inclusive classroom settings in general, and specifically: how can we do so with regard to worldview diversity? Does worldview diversity in schools exist, and if so, how can we recognize it in its plurality and complexity? How can we acknowledge different worldviews in the context of a changing inclusive school system? In this article, we would like to present the theoretical foundation, the research setting and the first findings of our ongoing pilot studies of worldview education at an inclusive German school. The experiments are part of a larger project context that is also described. The case study presented in this article, in which innovative language and machine learning technology was used for data analysis, illustrates the potential of inclusive methods and didactic concepts such as Universal Design for Learning [1], Learning in the Presence of the Other [2,3] and Reflexive Inclusion [4] for inclusive worldview education in the context of a religiously pluralized and secularized society.

Flavonoids are a biochemically diverse group of specialized metabolites in plants that are derived from phenylalanine. While the biosynthesis of the flavonoid aglycone is highly conserved across species and well characterized, numerous species-specific decoration steps and their relevance remained largely unexplored. The flavonoid biosynthesis takes place at the cytosolic site of the endoplasmatic reticulum (ER), but accumulation of various flavonoids was observed in the central vacuole. A universal explanation for the subcellular transport of flavonoids has eluded researchers for decades. Current knowledge suggests that a glutathione S-transferase-like protein (ligandin) protects anthocyanins and potentially proanthocyanidin precursors during the transport to the central vacuole. ABCC transporters and to a lower extend MATE transporters sequester anthocyanins into the vacuole. Glycosides of specific proanthocyanidin precursors are sequestered through MATE transporters. A P-ATPase in the tonoplast and potentially other proteins generate the proton gradient that is required for the MATE-mediated antiport. Vesicle-mediated transport of flavonoids from the ER to the vacuole is considered as an alternative or additional route.

The increasing popularity of carbon-reinforced concrete (CRC) is attributed to its exceptional tensile properties, low density, no corrosion phenomenon, and remarkable flexibility, allowing it to be easily shaped into various forms. This research investigates the feasibility of using a special 2D Netzgitterträger (NetzGT) reinforcement system, featuring a net-shaped fabricated textile made of multiple diagonally offset rovings with overlapping edge strands, as a viable alternative to traditional steel reinforcement in concrete beams. This reinforcement is manufactured from carbon rovings with three different diagonal angles of 50⁰, 60⁰, and 70⁰ respectively. Laboratory experiments were conducted to assess the mechanical behavior of beams reinforced with the 2D NetzGT reinforcement. Bending and shear tests were performed on beams with varying numbers of overlapped edge roving and roving angles to evaluate the tensile capacity and failure characteristics of beams. The increase in the number of overlapped edge rovings led to a noticeable increase in the maximum tensile force. Tensile tests on strands were also performed with the increasing number of overlapped rovings to analyze their tensile strength. Additionally, single yarn pull-out tests were also conducted to examine the influence of the roving angle on the bond strength between the carbon textile roving and the concrete matrix

Recently, there has been remarkable progress in the development of smart textiles, especially knitted strain sensors, to achieve reliable sensor signals. Stable and reliable electro-mechanical properties of sensors are essential for using textile-based sensors in medical applications. How-ever, challenges associated with significant hysteresis and low gauge factor (GF) values remain for using strain sensors for motion capture. To evaluate these issues, a comprehensive investiga-tion of the cyclic electro-mechanical properties of weft-knitted strain sensors was conducted in the present study to develop a drift-free elastic strain sensor with a robust sensor signal for mo-tion capture for medical devices. Several variables were considered in the study, including the variation of the basic knit pattern, the incorporation of the electrically conductive yarn, and the size of the strain sensor. The effectiveness and feasibility of the developed knitted strain sensors are demonstrated through experimental evaluation, by determining the gauge factor, its non-linearity, hysteresis and drift. The developed knitted piezoresistive strain sensors have a GF of 2.4, a calculated drift of 50 %, 12,5 % hysteresis, and 0.3 % nonlinearity in parts.

The majority of E-commerce transactions reveal private information such as customers' identities, order contents, and payment information during the transaction. Other personal information such as health conditions, religion, and even ethnicity may be also deduced. Even when deploying electronic cryptocurrencies such as Bitcoin, anonymity cannot be fully guaranteed. Also, many anonymous payment schemes suffer from possible double spending circumstances. E-commerce privacy is basically a difficult problem as it involves parties with concurring interests. Three major e-commerce requirements are highly difficult to resolve: anonymous purchase, anonymous delivery, and anonymous payment. This work presents a possible e-commerce system addressing all three anonymity requirements for electronic-items business on open networks. The system offers anonymous entities authentication mechanisms up to completing a fair anonymous e-commerce transaction. The system is based on deploying a physically clone-resistant hardware token for each relevant involved party. The tokens are made clone-resistant by accommodating a Secret Unknown Cipher (SUC) in each hardware-token as a digital PUF-like identity. A set of novel generic system-setups for units, protocols and e-commerce schemes is introduced. The proposed anonymization is basically attained by virtually-replacing relevant e-commerce entities by low-cost, unique and clone-resistant tokens/units using SUCs. The units act as trustable anonymous, authenticated and non-replaceable entities monitored by their acting users.

Objective: The study aimed to provide a picture total of the forest and erosion status of the beach and dykes of Kien Giang and Ca Mau. Theoretical framework: Kien Giang and Ca Mau are located in the West Sea, and in recent years, along the coast of this region, erosion of the coast and sea dykes has been profound. Method: There are 18 sample plots for testing. The map also includes 68 survey plots that are predefined and used as a basis for positioning in the field. The measurement indicators include tree species identification. Results and conclusion: The level of erosion in Ha Tien’s forests varies based on tree density and environmental factors. The forest with no erosion has a thickness of 9,600 trees/ha and a stem diameter of 19 cm, while the extremely erosion forest has 2,000 trees/ha and a stem diameter of 6 cm. Topographically, Ha Tien has little erosion, while U Minh is a low-lying area with high erosion. Erosion levels are 1-2 in January-April, 4-5 in May-October, and 3 in November-December. Currently, the section is divided into 28 sub-sections, with Kien Giang having 23. One sub-section is level 1, 11 sub-sections are level 2, 1 is level 3, 3 are level 4, and 3 are level 5. In Ca Mau, two sub-sections are level 4, and 3 are level 5. Implications of the research: With climate change and rising sea levels, erosion levels tend to increase one level in sub-section areas in the coming years.

Recent advances in microscopy imaging and image analysis motivate more and more institutes world-wide to establish dedicated core-facilities for bio-image analysis. To maximize the benefits research groups at these institutes gain from their core-facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core-facilities can offer. We also discuss potential conflicts of interests between the targeted missions and implementations of services to guide decision makers and core-facility founders to circumvent common pitfalls.

With the continuous improvement and observable benefits of electric vehicles (EVs), major logistic companies are introducing more EVs into their conventional fleets. This gives rise to a new type of vehicle routing problem with mixed vehicles, where heterogeneous internal combustion vehicles (ICVs) and electric vehicles are considered in route planning. In addition, certain deliveries that are not efficient on any type of vehicles, are outsourced to third-party common carriers. In this paper, we define this problem as a mixed vehicle routing problem with common carriers (MVRPC). The objective of such problems is to minimize the transportation costs by considering routes with ICVs and EVs, the possibility of visiting recharging stations, outsourcing options, and drivers’ layover regulations. This variant of the vehicle routing problems has many practical applications, particularly in the design of long-haul transportation and last-mile delivery services. Effective MVRPC solutions play a key role in promoting the going Green image and optimally allocating resources. The problem has received limited attention in the literature likely because addressing all the needed aspects is especially challenging. To solve the large-scale problem, we develop a branch-and-cut pricing framework that relies on strong cuts and customized labeling algorithms. Numerical experiments highlight the effectiveness of our algorithm. This success can be attributed to tailored critical resources, dynamically bounded bidirectional labeling procedures, strong dominance criteria, and implementation strategies.

We are witnessing the revival of the CAM model, which was already used in the past by some authors studying angiogenesis and anti-cancer drugs, that now offers a refined model to fill, in the translational meaning, the gap between in vitro-in vivo studies. It can be used for a wide range of purposes, from testing cytotoxicity, pharmacokinetics, tumorigenesis, invasion, to action mechanisms of molecules, and validation of new materials from tissue engineering research. CAM model is easy to use with a fast outcome and makes experimental research more sustainable since allowing to replace, reduce and refine pre-clinical experimentation ("3Rs" rules [1]). This re-view aims to highlight some unique potentials that CAM-assay presents; in particular, the authors intend to use in the next future the CAM model to verify, in a microenvironment comparable to in vivo conditions, albeit simplified, the angiogenic ability of functionalized 3D constructs to be used in regenerative medicine strategies in the recovery of skeletal injuries of critical size (CSD) that do not repair spontaneously. For this purpose, organotypic cultures will be planned on sever-al CAMs set up in temporal sequence: a sort of organ model for assessing CSD conducted in the CAM bioreactor, rather than in vivo.

Digital dentistry, including CAD/CAM dentistry, is perhaps the most disruptive innovation in dentistry to date. The rapid development of digital dentistry technologies over the past several decades has enabled clinicians to improve patient care by significantly reducing the time necessary for the restorative phase of treatment. Advancements in intra-oral scanning and computer-aided design and manufacturing (CAD/CAM), in conjunction with new dental CAD/CAM ceramic materials, have simplified patient care and made same-day dentistry a reality. This review aims to present the most recent advancements described in current literature as well as to document the successful implementation of digital dentistry into a predoctoral program. The overall process of CAD/CAM same-day dentistry and the accompanying advancements in software and materials were presented and discussed. Implementation of technology and personnel requirements were reviewed. CAD/CAM dentistry has been influential in shaping and improving dental practice and education, and this influence will only continue with time.

Electric moped scooter sharing services have recently experienced strong growth rates, particularly in Europe. Due to their compactness, environmental-friendliness and convenience, shared e-mopeds are suitable modes of transport in urban mobility to help reduce the environmental impact. However, its traffic-related, economic and environmental effects are merely represented in academic research. We used passenger car traffic data in Berlin generated by the multi-agent transport simulation framework MATSim to develop a python-based simulation, resembling an e-moped sharing system. Based on the results, a total cost of ownership and a life cycle assessment for fleet sizes of 2,500, 10,000 and 50,000 vehicles were conducted. The results indicate that a substantial part of all passenger car trips in Berlin can be substituted. The larger the fleet, the more and longer trips are replaced. Simultaneously, the efficiency in terms of fleet utilization decreases. The scenario with 10,000 e-mopeds offers the lowest total distance-based costs for sharing operators, whereas a fleet consisting of 2,500 vehicles exhibits the lowest environmental emissions per kilometer driven over the expected lifespan of a shared e-moped. Based on the renewable energy potential for 2050 forecasted by the German Federal Environment Agency, a significant overall decline in environmental impacts can be achieved.

Nickel-base superalloys like VDM 780 may possess a high content of Cr and Co. This influences solution energies of phase-forming elements like Al and Ta (γ′-phase), Nb (γ′′- and δ-phase), and Ti (η-phase). We perform density functional theory studies of a nickel matrix at 0 K with high concentrations of either Co and Cr and calculate the influence of these elements on solution energies. In the case of Co, the solution energy can be predicted well by the nearest-neighbor interaction in the Co-rich matrix. For Cr, the effect is more complicated because Cr has a larger ionic radius and changes the magnetic state of the material. The effect of a Cr-rich matrix on the energy of Co is dominated by magnetic effects, interactions with the other elements by elastic deformation of the lattice.

Carbon concrete PAN/lignin-based CF composites are a new promising material class for the building industry. The replacement of the traditional heavy and corroding steel reinforcement by carbon fiber (CF) based reinforcements offers many significant advantages: a higher protection of environmental resources because of lower CO2 consumption during cement production, a longer lifecycle and thus muss less damage in structural components and a higher degree of design freedom because lightweight solutions can be realized. However, due to cost pressure in civil engineering, completely new process chains are required to manufacture CF based reinforcement structures for concrete. The article describes the necessary process steps in order to develop CF reinforcement: (1) the production of cost-effective CF using novel carbon fiber lines, (2) the fabrication of CF rebars with different geometry profiles. It was found that PAN/lignin-based CF is currently the most promising material in order to meet the future market demands. However, significant research needs to be undertaken in order to improve the properties of lignin-based and PAN/lignin-based CF, respectively. The CF can be manufactured to CF-based rebars using different manufacturing technologies which have been developed on prototype level in this study.

Automated charging technologies are becoming increasingly important in the electrification of heavy road freight transport, especially in combination with autonomous driving. This study provides a comprehensive analysis of automated charging technologies for electric heavy-duty vehicles (HDVs). It encompasses the entire spectrum of feasible technologies, including static and dynamic approaches, with each charging technology evaluated for its advantages, potentials, challenges, and technology readiness level (TRL). Static conductive charging methods such as charging robots, underbody-couplers or pantographs show good potential, with pantographs being the most mature option. These technologies are progressing towards higher TRLs, with a focus on standardization and adaptability. While static wireless charging is operational for some prototype solutions, it encounters challenges related to implementation and efficiency. Dynamic conductive charging through an overhead contact line or contact rails holds promise for high-traffic HDV routes with the overhead contact line being the most developed option. Dynamic wireless charging, although facing efficiency challenges, offers potential for seamless integration into roads and minimal wear and tear. Battery swapping is emerging as a practical solution to reduce downtime for charging, with varying levels of readiness across different implementations. To facilitate large-scale deployment further standardization efforts are required. The study emphasizes the necessity for continued research and development to enhance efficiency, decrease costs, and ensure seamless integration into existing infrastructures. Technologies that achieve this best will have the highest potential to significantly contribute to the creation of an efficiently automated and environmentally friendly transport sector.

This paper develops the mathematical modeling and deflection control of a textile-reinforced composite integrated with shape memory actuators. The model of the system is derived using identification method and unstructured uncertainty approach. Based on this model and robust stability analysis a robust proportional-integral controller is designed for controlling the deflection of the composite. The performance of the proposed controller is compared with a classical one through experimental analysis.

The determination of crack propagation velocities can provide valuable information for a better understanding of damage processes of concrete. The spatio-temporal analysis of crack patterns developing at a speed of several hundred meters per second is a rather challenging task. In the paper, a photogrammetric procedure for the determination of crack propagation velocities in concrete specimens using high-speed camera image sequences is presented. A cascaded image sequence processing which starts with the computation of displacement vector fields for a dense pattern of points on the specimen’s surface between consecutive time steps of the image sequence chain has been developed. These surface points are triangulated into a mesh, and as representations of cracks, discontinuities in the displacement vector fields are found by a deformation analysis applied to all triangles of the mesh. Connected components of the deformed triangles are computed using region-growing techniques. Then, the crack tips are determined using principal component analysis. The tips are tracked in the image sequence and the velocities between the time stamps of the images are derived. A major advantage of this method as compared to established techniques is in the fact of its allowing for spatio-temporally resolved, full-field measurements rather than point-wise measurements and that information on crack width can be obtained simultaneously. To validate the experimentation, the authors processed image sequences of tests on four compact-tension specimens performed on a split-Hopkinson tension bar. The images were taken by a high-speed camera at a frame rate of 160,000 images per second. By applying to these datasets the image sequence processing procedure as developed, crack propagation velocities of about 800 m/s were determined with a precision in the order of 50 m/s.

This review is dedicated to sustainable practices in liquid chromatography. HPLC and UHPLC methods contribute significantly to routine analytical techniques. Therefore, the transfer of classical liquid chromatographic methods into sustainable ones is of utmost importance in moving toward sustainable development goals. Among other principles to render a liquid chromatographic method green, substitution of the organic solvent component in the mobile phase with a greener one received great attention. This review concentrates on choosing the best alternative green organic solvent to replace the classical solvent in the mobile phase for easy rapid transfer to a more sustainable normal phase or reversed-phase liquid chromatography. The main focus of this review will be on describing the transfer of non-green to green and white chromatographic methods in an effort to elevate the sustainability best practice in analytical chemistry. The greenness properties and greenness ranking in addition to the chromatographic suitability of seventeen organic solvents for liquid chromatography are mentioned to have a clear insight into the issue of rapidly choosing the appropriate solvent to transfer a classical HPLC or UHPLC method into a more sustainable one. A simple guide is proposed for making the liquid chromatographic method more sustainable.

This review paper deals with recently proposed algorithms for distributed blind macro-calibration of sensor networks based on consensus (synchronization), not requiring any fusion center. The basic algorithm, performing the estimation of the local calibration parameters, is derived commencing from appropriate local criteria, and developing the corresponding gradient descent scheme. It is shown that the estimated parameters of the calibration functions asymptotically converge, in the mean-square sense and with probability one (w.p.1), to such values that ensure consensus on calibrated sensor gains and calibrated sensor offsets. For the more realistic case in which additive measurement noise, communication dropouts and additive communication noise are present, two algorithm modifications are introduced: one using a simple compensation term, and a more robust one based on an instrumental variable. By utilizing stochastic approximation arguments it is shown that the modified algorithms also achieve asymptotic agreement for calibrated sensor gains and offsets, in the mean-square sense and w.p.1. Convergence rate is analyzed in terms of an upper bound of the mean-square error. It is also shown that the communications between nodes can be completely asynchronous, which is of substantial importance for real-world applications. Suggestions for design of \textit{a priori} adjustable weights are given. Finally, it is shown that, if there is a subset of (precalibrated) reference sensors with fixed calibration parameters, the calibrated sensor gains and offsets of the rest of the sensors do not achieve consensus - they converge to different points dictated by the reference sensors and the network characteristics. Wide applicability and efficacy of these algorithms are illustrated on several simulation examples.

The study proposes a theoretical method for evaluating the 'safety against derailment' indicator of a specialized train composition for the transportation of very long rails. A composition of nine wagons, suitable for the transportation of rails with a length of 120 m, is considered. For the remaining recommended rail lengths, the number of wagons is reduced or increased, the method being modified depending on the required configuration. In accordance with the requirements of EN 14363:2019, the composition is in a curve with a radius of R=150 m. The rails bend, some of them contact the vertical stanchions of the wagon and cause additional transverse forces, which are balanced in the rail track. This is a prerequisite for derailment of the vehicle. The goal of the study is to determine the additional transverse forces that arise because of the bent rails. The task is statically indeterminate, and considering the dimensions of the rails, its solution becomes seriously difficult. For the purposes of the study, the finite element method was used. Based on the displacements of the support points of the rails (caused by the geometry of the curve) the bending line of the elastic load is determined and the forces in the supports are calculated. A group of five rails is considered, with results multiplied proportionally for cases other than five. The resulting forces are considered when determining the derailment safety criterion defined by EN 14363:2019.

The scarcely investigated myxobacterium Corallococcus coralloides holds a large genome containing many uncharacterized biosynthetic gene clusters (BGCs) that potentially encode the synthesis of entirely new natural products. Despite its promising genomic potential, suitable cultivation conditions have not yet been found to activate the synthesis of new secondary metabolites (SMs). Finding the right cultivation conditions to activate BGCs in the genome remained a major bottleneck and its full biosynthetic potential was so far not retrieved. Here, we therefore applied a bivariate OSMAC approach, using a combination of two elicitor changes at once for activation of BGCs and concomitant SM production by C. coralloides. The bivariate OSMAC screening was carried out in 24-well System Duetz-plates, applying univariate and bivariate OSMAC conditions. We combined biotic additives and organic solvents with minimal media and complex growth medium. The success of the method was evaluated by the number of new mass features detected in the respective extracts. We found synergistic effects in bivariate OSMAC designs. The number of new mass features detected in bivariate OSMAC exceeded the sum of new mass features found in the respective univariate OSMAC with only one elicitor. Overall, the bivariate OSMAC screening led to 26 new mass features, which were not detected in the univariate OSMAC design. Hence, the presence of multiple elicitors in the bivariate OSMAC designs successfully activated the biosynthetic potential in C. coralloides. We propose the bivariate OSMAC designs with a complex combination of elicitors as a straightforward strategy to robustly expand the SM space of microorganisms with large genomes.

3D plotting is an additive manufacturing technology enabling biofabrication, thus the integration of cells or biologically sensitive proteins or growth factors into the manufacturing process. However, most (bio-)inks developed for 3D plotting were not shown to be processed into clinical relevant geometries comprising critical overhangs and cavities, which would collapse without a sufficient support material. Herein, we have developed a support hydrogel ink based on methylcellulose (mc), which is able to act as support as long as the co-plotted main structure is not stable. Therefore, 6 w/v %, 8 w/v % and 10 w/v % mc were allowed to swell in water, resulting in viscous inks, which were characterized for their rheological and extrusion properties. The successful usage of 10 w/v % mc as support ink was proven by multichannel plotting of the support together with a plottable calcium phosphate cement (CPC) acting as main structure. CPC scaffolds displaying critical overhangs or a large central cavity could be plotted accurately with the newly developed mc support ink. The dissolution properties of mc allowed complete removal of the gel without residuals, once CPC setting was finished. Finally, we fabricated a scaphoid bone model by computed tomography data acquisition and co-extrusion of CPC and the mc support hydrogel.

Aggregation of the protein α-Synuclein (αSyn) is a hallmark of Parkinson's disease (PD), dementia with Lewy bodies (DLB) and multiple systems atrophy, and alleviating the extent of αSyn pathology is an attractive strategy against neurodegeneration. The engineered binding protein β-wrapin AS69 binds monomeric αSyn. AS69 reduces primary and secondary nucleation as well as fibril elongation in vitro. It also mitigates aSyn pathology in a mouse model based on intrastriatal injection of aSyn pre-formed fibrils (PFF). Since the PFF-based model does not represent all aspects of PD, we tested here whether AS69 can reduce neurodegeneration resulting from αSyn overexpression. Human A53T-αSyn was overexpressed in the mouse Substantia nigra (SN) by using recombinant adeno-associated viral vector (rAAV). AS69 was also expressed by rAAV transduction. Behavioral tests and immunofluorescence stainings were used as outcomes.Transduction with rAAV-αSyn resulted in αSyn pathology as reported by phospho-αSyn staining and caused degeneration of dopaminergic neurons in the SN. The co-expression of rAAV-AS69 did not reduce αSyn pathology or degeneration of dopaminergic neurons. We conclude that αSyn monomer binding by rAAV-AS69 was insufficient to protect from aSyn pathology resulting from αSyn overexpression.

Magnetoelectric sensors based on microelectromechanical cantilevers consisting of TiN / AlN / Ni are investigated using finite element simulations in regard of the anisotropy of the E effect and its impact on the sensor sensitivity. The E effect is derived from the anisotropic magnetostriction and magnetization of single crystalline Nickel. The magnetic hardening of Nickel in saturation is demonstrated for the (110) as well as the (111) orientation. It is shown further, that magnetostrictive bending of the cantilever has a negligible impact on the eigenfrequency and thus sensitivity. The intrinsic E effect of Nickel decreases in magnitude depending on the crystal orientation when integrated into the magnetoelectric sensor design. The transitions of the individual magnetic domain states are found to be the dominant influencing factor on the sensitivity for all crystal orientations. The peak sensitivity was determined to 41.3 T-1 for (110) in-plane orientated Nickel at a magnetic bias flux of 1.78 mT. It is found, that the transition from domain wall shift to domain rotation along the hard axes yields much higher sensitivity than the transition from domain rotation to magnetization reversal. The results achieved in this work show that Nickel as hard magnetic material is able to reach almost identical sensitivities as soft magnetic materials, such as FeCoSiB.

Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies, however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to re-schedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO, however, under some circumstances, the technologies are on par. This emphasises the need for detailed analysis of the local bus network in order to make an informed procurement decision.

Bone fractures are very common in the geriatric population and poses a great economic burden worldwide. While traditional methods for repairing bone defects have primarily been autografts, there are several drawbacks limiting its use. Bone graft substitutes have been used as alternative strategies to improve bone healing. However, there remains several impediments to achieving the desired healing outcomes. Injectable hydrogels have become attractive scaffold materials for bone regeneration given their high performance in filling irregularly sized bone defects, ability to encapsulate cells and bioactive molecules, and mimic the native ECM of bone. We investigated the use of an injectable chitosan-based hydrogel scaffold to promote differentiation of preosteoblasts in vitro. The hydrogels were characterized by evaluating cell homogeneity, cell viability, rheological and mechanical properties, and differentiation ability of preosteoblasts in hydrogel scaffolds. Cell-laden hydrogel scaffolds exhibited shear thinning behavior and the ability to maintain shape fidelity after injection. The CNC-CS hydrogels exhibited higher mechanical strength and significantly upregulated the osteogenic activity and differentiation of preosteoblasts as shown by ALP activity assays and histological analysis of hydrogel scaffolds. These results suggest that this injectable hydrogel is suitable for cell survival, can promote osteogenic differentiation of preosteoblasts, and structurally support new bone growth.