Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

The catastrophic failure of two dams in Libya in the night of 10th and 11th September 2023, resulted in a significant amount of water and debris destroying a large part of the city of Derna, which is located downstream of the two dams. The flood caused more than 6000 fatalities and the dis-placement of thousands of people from the city. The dams failed due to heavy rainfall caused by the Medicane Daniel, resulting in intense precipitation and the dams overspilling after reaching full capacity. This paper analyses the consequences of the dam breach by modelling the flow discharge and the flooding of the city of Derna. The analysis compares the computed quantities with the findings of the post-event satellite image analysis from UNOSAT and Copernicus, the field reports, and information gathered from the social media through AI detection. The results allows to better understand the dynamics of the dam breach, its initial parameters and an assessment of the accu-racy of the results.

Spatial planners are nowadays strongly interested in the benefits of greenspace for the health of urban residents. However, concrete and context-specific guidance on effective greening strategies is still lacking. We present a novel, model-based approach to support the development of such strategies. The approach is quantitative and spatially-explicit, and accounts for multiple health benefits as well as burdens. In this study, we applied the approach to the city of Maastricht (The Netherlands), and conducted an integrated, city-scale assessment of the health benefits and burdens of four urban greenspace designs. These included: ‘No greenspace’, ‘Current greenspace’, ‘Green parking lots and squares’, and ‘Optimized greenspace locations’. For each greenspace design, indicator values were calculated for five health determinants: heat stress, air pollution, perceived unsafety, unattractive views, and tick-bite risk. To assess the health contribution of urban greenspace in a given design, these indicator values were compared with the values in the ‘No greenspace’ design. The study produced clear, quantitative conclusions about the health benefits and burdens of the urban greenspace designs for the case of Maastricht, but also generated novel, more general insights relevant to planning of urban greenspace for health. These insights concern the importance of translating health policy objectives into specific target values or thresholds, and the importance of ‘smart’ choices in greenspace type and location that can effectively reduce trade-offs between health benefits and burdens. Priorities for future research, addressing limitations of the presented approach, concern a further expansion of the range of health benefits and burdens covered by the model, and the development of a common metric for the entire range of health benefits and burdens allowing optimization of greenspace design to maximize its overall net health benefit.

Two coupled climate models which participated the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0) and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3), were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air-sea heat flux, radiation penetration and absorption, melt ponds, ice-ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis product from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes of spatial pattern of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have potential for broad application in climate models, especially for those with underestimated SIT.

In the modern power grid, characterized by the increased penetration of power electronics and extensive utilization of renewable energy, inverters-based power plants play a pivotal role as the principal interface of renewable energy sources (RESs) and the grid. Considering the stability characteristics of grid-following (GFL) inverters when the grid is relatively weak, the application of grid-forming (GFM) controls becomes imperative to enhance the stability of the entire power plant. Thus, there is an urgent need for suitable and effective models to study the interaction and stability of the paralleled inverters employing GFL and GFM controls. Thus, the small-signal modeling with full-order state space model and eigenvalues analysis is presented in this paper. First, the small-signal state-space model of the individual GFL and GFM inverters is obtained considering the control loop, interaction, reference frame, transmissions, and time delays. Then, the models of the individual inverter are extended to the hybrid inverters to study the effects of the GFM inverters on the small- signal stability of the entire system. And, the impacts of the inertia and damping are analyzed by the eigenvalues of the state-transition matrix. Case comprising three parallel GFL inverters and two GFL inverters with one GFM inverter, respectively, are studied to examine the effectiveness and accuracy of the model. Finally, the stability margin obtained from the eigenvalue analysis of the entire system is verified by time-domain simulations.

Maritime data not only identify financial resources that can significantly enhance the efficiency of shipping companies but also play a pivotal role in promoting eco-friendly and sustainable shipping practices. However, active corporate strategies are required to include maritime data essential for the commercial operation of autonomous ships in the form of corporate assets by transparent evaluating the data. The aim of this study is to review previous studies on da-ta-valuation models, identify a valuation model suitable for maritime business, consider the characteristics of maritime data. The analytic hierarchy process (AHP) method is used to derive priorities for valuing maritime data assets, with emphasis placed on the characteristics of mari-time data, followed by the character of the future-oriented maritime-data market and the da-ta-valuation model. The results obtained that the most important factors in selecting a maritime data-valuation model by analyzing the AHP method were identified as the characteristics of the maritime data(M1). The next most important factors analyzed were the features of the mari-time-data market(M2) and the features of the maritime data-valuation model(M3) in order. In addition, the characteristics of maritime data were preferentially considered among the main criteria, and the market approach (A2) was selected as the most optimal valuation model. The potential impact of this implementation could contribute to the establishment of an intelligent technology market by estimating the value of data and developing a platform for maritime data trading, allowing for more efficient data sharing and utilization by maritime autonomous sur-face ship(MASS).

We introduce a novel stochastically correlated two-factor (i.e., bivariate) diffusion process under the square-root format, for which we obtained, analytically, the corresponding solutions for the conditional moment generating functions and conditional characteristic functions. Such solutions recover verbatim those of the uncorrelated case. We believe that the The aforementioned model encompasses a range of processes similar to that produced by a bivariate square-root process in which entries are correlated in the standard way, i.e., via a constant correlation coefficient. Note that, for the latter, closed-form solutions for the conditional characteristic functions are not available. We focus on the financial scenario of obtaining closed-form expressions for the exact price of a zero-coupon bond and Asian option prices via a Fourier cosine series method.

Building energy demand impacts a myriad of interconnected economic, societal, and environmental aspects. As a result, Buildings Energy Models (BEM) play an important role in the process of urban design and planning. While previous studies have investigated the effects of building interventions on energy efficiency, their applicability may be limited due to the BEM’s high computational complexity. This limits their ability to systematically study important aspects of energy demand on a large scale. The development of Machine Learning Models (MLM) allows to design the required detailed analysis and solutions, while reducing the computational burden, making MLM attractive for urban designers. The capability of MLM to generalize well for multiple contexts (in our case, multiple buildings) is a crucial contributor to their applicability. However, the validation process in a wider context is often overlooked, therefore its generalization capabilities are not quantified. In this paper, we present a framework to train and validate a surrogate model derived from a physics-based BEM. Our method employs a Multiple Linear Regression model to predict Energy Use Intensity (EUI) for office buildings in Singapore using 36 input parameters (covariates), based on a training dataset of 23,000 samples. Model validation is performed by comparing the results of the Surrogate Model (SM) to a widely used BEM for a sample of 120 buildings. Our results indicate that the SM has an accuracy of NRMSE of 13%, NMBE of −3.56%, and R2 of 0.92, which suggests it can effectively and accurately predict building EUI. We also conduct a sensitivity analysis, which indicates that the parameters associated with internal loads and internal space usage are the most influential. Additionally, we present a reduced order model trained with only the 11 most influential parameters, which exhibits negligible loss in accuracy compared to the full SM while providing reduced complexity. Finally, we demonstrate an application of our SM to evaluate energy efficiency under uncertainty scenarios. The analytically derived results indicate a potential reduction of EUI of offices in Singapore from 227kWh/m2 to 99kWh/m2 by altering the building parameters that were identified as most influential.

In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research is the use of innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. The study introduces new types of adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing the current robustness and security of AI models. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against an array of adversarial challenges. Importantly, the study also delves into the ethical and societal implications associated with employing advanced 'jailbreak' techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance the reliability and ethical soundness of AI technologies, paving the way for safer and more secure AI applications.

A comprehensive theory is developed for the chiral optical response of two configurations of the N-oscillator Born-Kuhn model (NOBK): the helically stacked and the corner stacked models. In the helical NOBK model, there is always a chiral response regardless of the value of N, whereas in the corner NOBK, only configurations with even N demonstrate a chiral response. Generally, the magnitudes of optical rotatory dispersion (ORD) and circular dichroism (CD) increase with N when the parameters of each oscillator are fixed. In cases of weak coupling, the spectral shapes of ORD and CD remain invariant, while strong coupling significantly alters the spectral shapes. For large damping, the spectral amplitude becomes smaller, and the spectral features become broader. In the presence of small damping, strong coupling introduces degeneracy in the coupled oscillator system, leading to multiple spectral features in both ORD and CD across the entire spectral region. This simple model not only can help in the design of tunable chiral metamaterials but also enhance our understanding of chiro-optical responses in structures with different configurations.

This study presents a novel approach to enhance Large Language Models (LLMs) like Alpaca by dynamically integrating real-time information. This method addresses the issue of content hallucination and data relevancy by automatically collecting and integrating current data from credible sources into model prompts. Experiments show a significant improvement in accuracy and a decrease in content hallucination, with a manageable increase in response time. The research underscores the potential of real-time data integration in making LLMs more accurate and contextually relevant, setting a foundation for future advancements in dynamic data processing in AI.

The study’s objective is to analyse the mechanical properties of steel pipe piles as a part of a trestle bridge subjected to five years of natural marine corrosion degradation. Sixteen tensile specimens were extracted from the steel pipe piles in the splash, tidal, and immersion zones. The experimental tensile test results are used to establish regression equations defining elastic modulus, yield strength, strain hardening index, and strength coefficient for the true stress-strain curves of the three regions. A non-linear time-dependent mathematical model is exploited to identify the corrosion degradation using the data from one single corrosion degradation measurement campaign. The analysis indicated that the splash zone experiences the most severe corrosion degradation, and there are progressive losses in the mechanical properties of each zone as the corrosion degradation progresses. The established relationships of the mechanical properties, as a function of the ratio of corroded plate thickness to the as-built one, can be used as a fast-engineering approach to identify the mechanical properties of severely corroded piles. The corrosion degradation allowance is also defined using the First Order Reliability Method, accounting for existing uncertainties covered by the partial safety factors. By examining the impact of marine corrosion on the mechanical properties of marine structures and developing predictive models to assess the corrosion’s effect on material strength and corrosion allowance, the study aims to improve offshore structures’ safety, design, and maintenance.

The chemical reaction of ion-adsorption type rare earth during in-situ leaching process is accompanied by ion migration and charge movement, making the leaching process electrochemical in nature. The chemical reaction rate plays an important role in the leaching rate of rare earth elements. In this work, electrochemical impedance spectroscopy (EIS) was used to reveal characteristics of electrical resistance alteration and leaching rate of rare earth elements during in-situ leaching. The equivalent circuit model of the leaching process was established, and two critical parameters of solution resistance Rs and charge transfer resistance Rt were analyzed to reflect the electrochemical characteristics. According to the characteristics of electrical resistance alteration, the leaching process was divided into four stages: wetting, reaction, equilibrium, and top water stage. The resistance parameters Rs and Rt decreased first and then increased during the leaching process. The maximum value of Rs was 1330Ω∙cm2 at the end of the top water stage, and the minimum value was 135Ω∙cm2 at the beginning of the equilibrium stage. The maximum value of Rt was 8310Ω∙cm2 at the beginning of the leaching stage, and the minimum value was 21Ω∙cm2 at the end of the reaction stage. Rs and Rt were negatively correlated with the pore size and chemical reaction rate during leaching. With increasing of pore size and reaction rate , the resistance parameter decrease. This study provide a new idea for intelligent monitoring of rare earth ore.

The first paper of this two-part series paper discussed the development of a novel lightweight 3D wood dowel-reinforced glass epoxy hybrid composite material (3DdrFRP) and its manufacturing procedures. It also experimentally compared the performance of scaled utility poles made from conventional 2D E-glass epoxy and 3DdrFRP materials. In the second part, we developed robust finite element (FE) models in LS-DYNA, calibrated them based on experimental results, and used them to simulate the performance of equivalent 2D and 3D poles, proving the integrity of the numerical model. Additionally, we developed a simplified analytical calculation method for engi-neers to determine the stiffness of 3D-DrFRP poles accurately and quickly.

Urban Green Space (UGS) has important impacts on human health, but an integrated participatory approach to UGS design for improved residents’ health was lacking to date. The aim of our study was to develop and evaluate such a novel approach to address this gap. The approach was developed following guiding principles from the literature, and tested with groups of children and elderly as participants in two neighborhoods of Maastricht (The Netherlands) with a low score on economic and health indicators. Novel aspects of the approach are the inclusion of both positive and negative health effects, the combination of participant self-assessment and model-based assessment of the health effects of UGS designs, and the use of maps to visualize UGS designs and health effects. The participant-generated UGS designs resulted in a considerable self-assessed increase in use of the UGS for meeting, stress reduction or leisure-based physical activity, as compared to the current situation. The model-assessed positive and negative health effects of the participant-generated UGS designs were limited. The major strength of the developed approach is that it combines active participation of residents in UGS (re)design with assessment of the health effects of these UGS designs. Whereas in other participatory approaches to UGS design it often remains unclear whether the resulting designs represent an improvement in terms of health, our combination of computer model-based assessment and a participatory process produced clear outcomes regarding the health benefits and use of UGS designs.

Background: With continuous advances in next-generation sequencing (NGS), novel hepadnaviruses have been discovered in many species over the last 10 years. Methods: In this study, the cloud search and analysis of NGS data published in Nature by Edgar et al. was used as a basis to re-mine and reanalyze public NGS data for new hepadnaviruses. Results: Ultimately, at least 41 new species of hepadnaviruses were identified, including hepadnaviruses from the model animals hamster and mouse, frog hepadnavirus with pan-species infectivity, and diverse African cichlid hepadnaviruses that circulate within populations. Conclusions: The discovery of the new species of hepadnaviruses not only provides new clues for the study of the origin and evolution of hepadnaviruses, but also can be used to construct new hepadnavirus animal infection models, which will be helpful for the research of eradicative drugs for hepatitis B virus (HBV).

The treatment of critically ill patients remains an evolving and controversial issue. Mechanical circulatory support through a percutaneous approach for the management of cardiogenic shock has taken place in recent years. The combined use of IABP and Impella 2.5 device may have a role to play for this group of patients. A simulation approach may help with a quantitative as-sessment of the achievable degree of assistance. In this paper, we analyse the interaction be-tween Impella 2.5 pump applied as LVAD and IABP using the numerical simulator of the cardi-ovascular system developed in our laboratory. Starting with pathological conditions reproduced using literature data, the simulations were performed setting different rotational speeds for the LVAD and driving the IABP in full mode (1:1) or partial mode (1:2, 1:3 and 1:4). The effects in-duced by drug administration during the assistance were simulated too. The haemodynamic pa-rameters under investigation were: mean aortic pressure, systolic and diastolic aortic pressure, mean pulmonary arterial pressure, mean left and right atrial pressure, cardiac output, cardiac index, left and right ventricular end-systolic volume, left ventricular end-diastolic volume and mean coronary blood flow. The energetic variables considered in the study were: left and right ventricular external work and left and right atrial pressure-volume area. The outcome of our simulations shows that the combined use of IABP and Impella 2.5 achieves adequate support in the acute phase of cardiogenic shock compared to each standalone device. This would allow fur-ther stabilisation and transfer to a transplant centre should escalation of treatment be required.

Understanding surface water evaporation in tropical catchments is pivotal for effective water management, but limited research exists on stable isotopic (δ2H and δ18O) assessment of water evaporation losses. Our study bridges that gap, examining stable isotopes in meteoric water and surface waters across tropical regions, both humid and semi-arid. Using the Craig-Gordon model, we calibrated evaporation-to-inflow (E/I) ratios, identifying distinct E/I variations between seasons and regions. Notably, the semi-arid region consistently exhibited higher E/I ratios due to climatic characteristics. By integrating the Hamiltonian Monte Carlo and the No-U-Turn Sampler, we were able to quantify uncertainties in isotopic and meteorological parameters, resulting in E/I estimates within 68% and 95% confidence intervals. Our Bayesian model insights are critical for freshwater resource optimization and hydrological modeling in tropical regions.

The main aim of this study is to predict whether the type of breast cancer is “benign” or “malignant” by classical generalized linear model (GLM) approach and an extended family of GLM called generalized linear mixed model (GLMM) approach for binomially distributed response variable with binary link functions. In this study, an advanced statistical modeling approach based on the GLMM to the traditional statistical modeling approach based on the GLM for binomially distributed response variable with various binary link functions is proposed to investigate the relationships between the “malignant or benign diagnosis of the BC in patients” and “nine attributes” of 699 BC diagnosed patients. This study also focuses on the statistical significance of the accurate classification of the BC diagnosed patients in cancer studies in medicine in “benign” or “malignant” type based on the WBC dataset. In this study, the superiority of the GLMM approach over the GLM approach for the binary response variable especially belonging to the WBC dataset is emphasized in the field of cancer diagnosis in medicine. Also the importance and the power of the IC and performance metrics as the goodness-of-fit test statistics are strongly emphasized for accurate statistical inferences from the “best” fitted model. In this study, from the main findings, the best fitted model among the GLM and GLMM approaches for the binary response variable is determined as the GLMM under “logit” link function with “id” random effect with the most statistically significant odds of the occurance of the BC being “malignant” as 7.9104, 5.6888, 5.6643, 4.9842, 4.1212, 2.0679, 1.8755, and 1.3970 times more than being “benign” for every one-unit increase in the quantities of “clump thickness”, “bland chromatin”, “mitoses”, “bare nuclei”, “cell shape”, “marginal adhesion”, “epithelial cell size”, and “cell size”, respectively.

In this paper presents analytical findings regarding the influence of gravity disturbance on INS. A data from the high-precision gravity model, EGM2008, we introduce a novel real-time gravity disturbance compensation approach for INS. This method estimates gravity disturbances along paths for platforms on both land and water, utilizing the Multi-Layer Perceptron (MLP) technique. To estimate these gravity disturbances, we developed four distinct MLP models and compared their training results using Half-Mean Square Error (half-MSE) and Root Mean Square Error (RMSE). This comparative analysis allowed us to identify the MLP model that exhibited the best performance. In order to validate the proposed gravity compensation method, field test was conducted using a test vehicle. The results of this field test unequivocally demonstrated the effectiveness of our approach in enhancing positioning accuracy. Over the course of a 2-hour field experiment, it was evident that employing the proposed gravity compensation method improved positioning accuracy by a notable 24%.

The investigation of the spatiotemporal propagation characteristics of the "meteorological-agricultural-hydrological" drought system, under diverse climatic conditions, is crucial for the development of a robust drought warning system and the effective implementation of proactive drought prevention and resilience strategies. To achieve this, the current study utilizes the Soil and Water Assessment Tool (SWAT) model to simulate key components of the watershed water cycle, such as evaporation, surface water, soil water, and groundwater. Specifically, the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Soil Moisture Index (SSMI), and the Nonlinear Joint Hydrological Drought Index (NJHDI) are employed to characterize meteorological drought, agricultural drought, and hydrological drought, respectively. By analyzing the correlation between these types of drought, the propagation characteristics of the "meteorological-agricultural-hydrological" drought system are elucidated using the rigorous strongest correlation coefficient method. The Yellow River Basin (YRB) is chosen as the case study for this research. Results showed that (1) The propagation time from meteorological to agricultural drought exhibited distinct seasonal characteristics, with durations of 5-6 months in spring, 2-3 months in summer, 3-5 months in autumn, and 6-8 months in winter. Compared to 1961-1990, the propagation time increased in spring and summer but decreased in autumn and winter during 1991-2010 for most YRB regions. (2) The agricultural to hydrological drought propagation showed no clear seasonal differences but increased over time. Specifically, zone C (arid/semi-arid with moderate temperatures) had shorter propagation time of 1-5 months, while zones B (transitional plateau to mid-latitude) and E (semi-arid/semi-humid temperate continental climate) experienced longer propagation time of 7-12 months. (3) Despite the extended timescales, agricultural-hydrological drought correlation was weaker than meteorological-agricultural linkage. This is because meteorological deficits directly reduce soil moisture, rapidly inducing agricultural drought. However, groundwater sustaining baseflow during agricultural drought delays streamflow deficits, prolonging the agricultural-hydrological propagation time.

Air pollution includes particle-bound Polycyclic Aromatic hydrocarbons (PAHs) which eventually reach the placenta triggering adverse perinatal outcomes by long-term exposure. Late-ly, air pollution has increased over the Metropolitan Area of Medellin-Colombia (MAMC) but its effects on pregnancy are still unknown. In this research, we made a real-time analysis of airborne total PAHs using a photoelectric sensor for residential places influenced by industrial and traffic sources contrasting southern and northern MAMC during the second peak of the bimodal ten-dency for PM2.5 emissions in this region. Additionally, we analyzed individual PAHs by GC/MS coupled to pressurized hot water extraction methodology. Data was applied in an Inhalation In-take Model to assess pregnancy exposure. The average concentration of PAHs over southern MAMC was three times higher than over northern MAMC where the abortion rate has been 1.4 times higher presented in database. Previous research found that PAHs act as an Endo-crine-Disrupting Chemical (EDC) during pregnancy and even heavy congeners could reside in umbilical cord blood. Finally, the annual series of abortion rates in the MAMC showed a signifi-cant correlation with the annual average levels of PM2.5 which are associate to PAHs. Although this significant correlation does not imply causality, our results suggest an important connection between both variables. This latter finding opens a gap in deeply understanding how regions with high PAHs-convergence influences abortion rates in MAMC.

Recent global trends related to the increasing use of e-commerce are becoming a challenge for courier transport especially in the last-mile process of delivering products to the final retail recipient. One of the delivery methods is the personal collection of the parcel in an automated post box, available 24/7 for the customer. This research aims to define the most important attributes of the courier services quality, including the delivery of parcels through automated sending and receiving parcel lockers, leveraging advanced technologies, data, and connectivity to enhance the quality of life, sustainability, and efficiency for city residents. The research was based on a preliminary selection of the most important features of parcel lockers’ service quality, which were extracted from the analysis of scientific literature and previous research. The analysis was carried out by conducting a survey among city parcel locker users that provided data coded according to the dimensions of the Kano model. This allowed to conclude that the location of parcel stations, ensuring improvements for the disabled, adjusting the size of the parcel to the size of the box, proper placement of the parcel in the box and a properly functioning dedicated application are the most important features in the process of automatic delivery of parcels to recipients in urban areas. This paper enriches the literature on customer service quality of self-service technologies for last-mile delivery with the use of automated parcel lockers.

An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based system. In this paper, we present a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consume substantial computational resources and increase the risk of overfitting, especially when limited medical image samples are available for training. To address these challenges, we propose a novel memory-efficient strategy that exploits graph structures derived from the images. Specifically, we introduce a lightweight superpixel-based Graph Neural Network (GNN) and break new ground by introducing superpixel metric learning to segment nucles and cytoplasm. Remarkably, our proposed segmentation model (SMGNN) achieves state-of-the-art segmentation performance while utilizing at most 10000$\times$ less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes show satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories.

Paratuberculosis (PTB) is a chronic granulomatous enteritis caused by Mycobacterium avium subsp. paratuberculosis (MAP) that affects a wide variety of domestic and wild animals. It is considered as one of the diseases with the highest economic impact in the ruminant industry. Despite many efforts and intensive research, PTB control is still controversially discussed and diagnostic and immunoprophylactic tools lack great limitations. Thus, models play a crucial role in understanding the pathogenesis of infection and disease, and in testing novel vaccine candidates. Here, we review the potential and limitations of different experimental approaches currently used in PTB research, focusing on laboratory animals and cell based models. The aim of this review is to offer a vision of the models that have been used and what has been achieved or discovered with each one so that the reader can choose the best model to answer their scientific questions and prove their hypotheses. Also, we bring forward new approaches that we consider worth exploring in the near future.

People with Intellectual Disability (ID) encounter several problems in their daily living regarding their needs, activities, interrelationships, and communication. On this concept, an interactive platform is proposed, aiming to provide personalized recommendations for information and entertainment, including creative and educational activities, tailored to the special users’ needs of this population. Furthermore, the proposed platform integrates capabilities of automatic recognition of health related emergencies, such as fever, oxygen saturation decline and tachycardia, as well as location tracking and detection of wandering behavior based on smartwatch/smartphone sensors, while providing automated assistance and appropriate alerts and notifications to the caregivers.

Wearable assistant devices play an important role in daily life for people with disabilities. Those who are hearing impaired may face dangers while walking or driving on the road. The major danger is their inability to hear warning sounds from cars or ambulances. Thus, the goal of this study is to develop a wearable assistant device for the hearing impaired to recognize emergency vehicle sirens on the road using edge computing. An EfficientNet-based fuzzy rank-based ensemble model was proposed to classify seven audio sounds, including human vocalizations and emergency vehicle sirens. This model was embedded in an Arduino Nano 33 BLE Sense development board. The audio files were respectively obtained from the CREMA-D dataset and Large Scale Audio dataset of emergency vehicle sirens on the road, with a total number of 8756 files. The seven audio sounds included neutral vocalization, anger vocalization, fear vocalization, happy vocalization, car horn sound, siren sound, and ambulance siren sound. The audio signal was converted into a spectrogram by the short-time Fourier transform as the feature. When one of the car horns, sirens, or ambulance sirens was detected, the wearable assistant device presented alarms through vibration and messages on the OLED panel. The performances of the EfficientNet-based fuzzy rank-based ensemble model in offline computing achieved an accuracy of 97.1%, precision of 97.79%, sensitivity of 96.8%, and specificity of 97.04%. In edge computing, the results were an accuracy of 95.2%, precision of 93.2%, sensitivity of 95.3%, and specificity of 95.1%. Thus, the proposed wearable assistant device has the potential benefit of helping the hearing impaired avoid traffic accidents.

Brazil has a large availability of natural resources and its economy was historically built around their exploitation. Changes in climate trends are already causing several environmental impacts, which affect the economic and social organization of the country. Impacts linked to the hydrological cycle are particularly concerning since water resources are used for electricity production, representing approximately 65% of the Brazilian electricity matrix. This study, therefore, aims to evaluate the hydroclimatic extreme trends of river basins located in the Northeast and South regions of the country. For this purpose, we carried out a flow analysis from 2020 to 2100, considering the precipitation data from the BCC CSM1-1, CCSM4, MIROC5, and NorESM1-M models presented in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change - IPCC. We used the SMAP rainfall-runoff model to obtain future flow projections for the RCP4.5 and RCP8.5 scenarios. As a result, we observed a trend toward water loss and intensification of extreme events, with an increase in variability in both scenarios. We also noted that these climate models have difficulty reproducing the natural variability of southern basins, as the simplification of atmospheric phenomena parameterization prevents them from correctly projecting the precipitation.

This study, which was supported by a quantitative research paradigm with two experiment and two control groups of 4x4 quasi-experimental design, aimed to find out the impact of a sustainable progressive STEAM (SP-STEAM Model) application model on 5th grade primary school students critical thinking dispositions and mathematic achievements in Northern Cyprus. The treatment model was applied to experimental groups for 14 weeks of time. Split-plot multiple group analysis of variance (Split-plot ANOVA) statistical technique was used to calculate between and within group significances regarding exogenous variable. SPSS24 software package was used for the analysis. Pre-test and post-test results derived from experiment and control groups revealed significant effect of SP-STEAM Model on 5th grade primary school students’ critical thinking dispositions as measured with CCTDI and mathematic achievements as measured with a dedicated ex-am. The results were discussed in detail under the light of related literature and suggestions for further studies were proposed.

Conducted within the scope of geochemical exploration in Eastern Thailand, this study aims to detect geochemical anomalies and potential mineral deposits. The objective was to interpret intricate spatial dispersion patterns and concentration levels of deposit pathfinder elements, specifically arsenic (As), copper (Cu), and zinc (Zn), using a comprehensive array of stream sediment geochemistry data. Methodologies involved integrating multifractal properties and traditional statistics, facilitated by the GeoDAS and ArcGIS platforms as instrumental analytical tools. In total, 5,376 stream sediment samples were collected and evaluated, leading to the development of an in-depth geochemical map. The results indicated distinct geological units marked by substantially elevated average values of the aforementioned elements. Identification of geochemical anomalies was achieved through the spatial distribution method and the subsequent application of the Spectrum-Area (S-A) multifractal model. An intriguing link was found between high As concentrations and gold deposits in the area, suggesting As as a viable pathfinder element for gold mineralization. The anomaly maps, generated from the stream sediment data, spotlighted potential zones of interest, offering valuable guidance for future mineral exploration and geological inquiries. Nonetheless, it is vital to recognize that the increased values noted in these maps may be influenced by regional geological factors, emphasizing the necessity for a diverse set of analytical methods for accurate interpretation. The study's significance lies in its pioneering use of the S-A multifractal model in geochemical data analysis. This innovative approach has deepened our comprehension of geochemical dispersion patterns and improved the precision of mineral exploration.

The response of plasmonic metal particles to an electromagnetic wave undergoes significant features at the nanoscale level. Different properties of the internal composition of a metal, such as its ionic background and the free electron gas, begin to manifest more prominently. As the dimensions of the nanostructures decrease, the classical local theory gradually becomes inadequate. Therefore, Maxwellʹs equations need to be supplemented with a relationship determining the dynamics of current density which is the essence of nonlocal plasmonic models. In this field of physics, the standard (linearized) hydrodynamic model (HDM) has been widely adopted with great success, serving as the basis for a variety of simulation methods. However, ongoing efforts are also being made to expand and refine it. Recently, the GNOR (general nonlocal optical response) modification of the HDM has been used, with the intention of incorporating the influence of electron gas diffusion. Clearly, from the classical description of fluid dynamics, a close relationship between viscosive damping and diffusion arises. This offers a relevant motivation for introducing the GNOR modification in an alternative manner. The standard HDM and its existing GNOR modification also do not include the influence of interband electron transitions in the conduction band and other phenomena that are part of many refining modifications of the Drude‐Lorentz and other models of metal permittivity. In this article, we present a modified version of GNOR‐HDM that incorporates the viscosive damping of the electron gas and a generalized Drude‐Lorentz term. In the selected simulations, we also introduce Landau damping which corrects the magnitude of the standard damping constant of the electron gas based on the size of the nanoparticle. We have chosen a spherical particle as a suitable object for testing and comparing HD models and their modifications because it allows finding precise analytical solutions for the interaction and, simultaneously, it is a relatively easily fabricable nanostructure in practice. Our contribution also includes our own analytical method for solving the HDM interaction of a plane wave with a spherical particle. This method forms the core of calculations of the characteristic quantities, such as the extinction cross‐sections and the corresponding components of electric fields and current densities.

A cell filtration platform that affords accurate size separation and minimizes fouling was developed. The platform features an ultra-thin porous membrane (UPM) filter, a pumping head for backflushing (PHB), and cell size measurement (CSM) software. The UPM chip is an ultrathin free-standing membrane with a large window area of 0.68 mm2, a pore diameter of 5 to 9 μm, and a thickness of less than 0.9 μm. The PHB prevents filter fouling. The CSM software analyzes the size distributions of the supernatants and subnatants of isolated cells and presents the data visually. The D99 particle size of cells of the chronic myeloid leukemia (CML) line K562 decreased from 22.2 to 17.5 μm after passage through a 5-μm filter. K562 cells could be separated by careful selection of the pore size; the recovery rate attained 91.3%. The method was compared to conventional blocking models by evaluating the mean square errors (MSEs) between the measured and calculated filtering volumes. The filtering rate was fitted by a linear regression model with a significance that exceeded 0.99 based on the R2 value. The platform can be used to separate various soft biomaterials and affords excellent stability during filtration.

Ultrasonic-guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects experienced in metallic pipelines. Signal processing of the guided waves is often challenged due to the complexity of operational conditions and environments in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for signal process and data classification of complex systems, thus great potential for damage detection of critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model is utilized for decoding ultrasonic guided waves for damage detection of metallic pipelines, and twenty-nine features are extracted as input to classify different types of defects from metallic pipes. The prediction capacity of the CNN-LSTM model is assessed by comparing it to the CNN and LSTM. The results demonstrate that the CNN-LSTM hybrid model exhibits much higher accuracy, with 94.8%, as compared to those of CNN and LSTM. Interestingly, the results also reveal that predetermined features, including time-, frequency-, and time-frequency domains, could significantly improve the robustness of the deep learning approaches, even though the deep learning approaches are often believed they include automated feature extraction, without the hand-crafted steps as the shallow learning do. Furthermore, the CNN-LSTM model displays higher performance when the noise level is relatively low (e.g., SNR=9 or higher), as compared to the other two models, but its prediction drops gradually with the increase of the noise.

In the future, the pursuit of high-quality economic development and a focus on ecological environmental protection in China will inevitably result in significant conflicts between land use and ecological land use. The challenge lies in achieving sustainable high-quality development while simultaneously protecting the ecological environment, optimizing the land use structure, and promoting a harmonious relationship between humans and the land. These challenges are faced by all regions. Land use conflicts primarily occur in peri-urban areas characterized by prominent economic development and urban agglomeration. Previous studies have mainly focused on analyzing the effects of land use on habitat quality during intense urbanization. However, it is important to recognize that land pressure encompasses economic, ecological, and social aspects. To gain a comprehensive understanding of the spatial conflict of land use and the impact on habitat quality in Ankang, a city that has been advocating ecological protection for the past two decades, this study aims to objectively analyze the spatial trends in land use changes in such cities. Additionally, it aims to provide insights for the harmonious development of land use in eco-region-oriented cities. Using the SSP-RCP scenarios provided by CMIP6, this paper employs a system analysis method, PLUS model, InVEST model, and land use conflict measurement model to dynamically simulate the future habitat quality and spatial conflict patterns of land use in Ankang City. The study explores the spatial coupling effect of both factors under different scenarios. The results indicate the following:(1) Under different future shared socio-economic path scenarios, land use intensity and land conflict levels follow the order of SSP585 (high forcing scenario), SSP370 (medium to high forcing scenario), SSP245 (medium forcing scenario), and SSP126 (low forcing scenario), with intensity and conflict decreasing accordingly. (2) The overall development trend in Ankang City reveals an intensification of land use conflicts and a decrease in habitat quality. The expansion rate of construction land is increasing and exhibiting aggregation, while agricultural land area is expanding and forest land area is continuously decreasing. (3) Land use intensity exhibits a significant positive correlation with land conflict levels, while land conflict levels demonstrate a significant negative correlation with habitat quality. These findings suggest that land use has had some impact on the ecological environment, with indications of habitat degradation. Even in Ankang, where ecological development is highly valued, the city will gradually face conflicts between ecological protection and economic development in future scenarios. The study highlights that Ankang's future development space will be constrained within the context of environmental protection, leading to greater land use conflicts in urban and surrounding areas. Consequently, the quality of habitats will inevitably decline. Therefore, it is crucial to allocate sufficient space for economic development while simultaneously prioritizing ecological protection. This approach will ensure a healthy economic development trajectory and foster a harmonious relationship between humans and the land.

This paper delves into the dynamics of asset pricing within Bachelier’s market model (BMM), elucidating the representation of risky asset price dynamics and the definition of riskless assets. It highlights the fundamental differences between BMM and the Black-Scholes-Merton market model (BSMMM), including the extension of BMM to handle assets yielding a simple dividend. Our investigation further explores Bachelier’s term structure of interest rates (BTSIR), introducing a novel version of Bachelier’s Heath-Jarrow-Morton model and adapting the Hull-White interest rate model to fit BMM. The study concludes by examining the applicability of BMM in real-world scenarios, such as those involving environmental, social, and governance (ESG)-adjusted stock prices and commodity spreads.

This study explores the intriguing relationship between personality traits, self-rated fitness (SRF), and physical activity (PA) variables among German university students (N = 4,244), and sheds light on the impact of personality on adherence to PA guidelines. Employing an online cross-sectional study, the short-form of the Big Five Inventory-2 assessed five domains of personality traits (Extraversion, Negative Emotionality, Agreeableness, Conscientiousness, and Open-Mindedness). PA, including sitting time, was assessed using the International Physical Activity Questionnaire (short-form). SRF and muscle-strengthening activities (MSA) were assessed with one item each. Multiple linear and logistic regression analyses examined associations of individual personality trait domains and all domains combined with SFR, PA variables, and adherence to PA guidelines, controlling for sociodemographic, behavioral, and (mental) health covariates. Most reliably, Extraversion and Conscientiousness revealed positive associations with PA variables, while Negative Emotionality yielded inverse relationships with PA variables. For instance, each unit increase in Extraversion corresponded to additional 17 minutes weekly MSA. Contrary, daily sitting time was unrelated to personality. Of note, high Open-Mindedness was associated with lower odds for adhering to current PA guidelines. The findings have implications for developing targeted interventions that promote a physically active lifestyle and support students' well-being and academic success.

The implementation of grey and green infrastructure is an effective mean to address urban flooding and nonpoint source pollution, but due to the complexity of the process and the diver-sity of benefits, there is a lack of measurement for their comprehensive benefits. Adopting a typ-ical university in Beijing as an example, this paper simulated the multidimensional benefits of the water quantity, water quality, and ecology of grey and green facility renovation by coupling the storm water management model (SWMM) and InfoWorks Integrated Catchment Manage-ment (ICM). Monetization methods and economical means were employed to characterize the comprehensive benefits. The results showed that grey and green infrastructure retrofitting re-duces the number of severe overflow nodes in the study area by 54.35%, the total overflow vol-ume by 22.17%, and the nonpoint source pollution level by approximately 80% under the heavy rain scenario and 60% under the rainstorm scenario. The annual benefits of grey and green infra-structure renovation reached ¥765,600/year, of this amount, ¥83,100/year is from hydrological regulation, ¥454,100/year is from nonpoint source pollution reduction, and ¥228,300/year is from ecological improvement. The benefits of green facilities were higher than those of grey facilities, and the combined benefits were negatively correlated with the rainfall level, with a total bene-fit–cost ratio of 1.19. The results provide methodological and data support for grey and green in-frastructure retrofitting within the context of sponge cities.

In recent years, the need to make the built environment more resilient and adaptable to climate change has become increasingly evident. In Europe, this aspect concerns the vast majority of existing buildings, which present several deficiencies from the energy-efficiency point of view, considering they were designed before the introduction of modern energy codes. Nowadays, it is possible to retrofit existing buildings using advanced and high-efficient technologies such as Double Skin Façades (DSFs). The research aims to evaluate the use of properly designed DSFs for the energy restoration of existing buildings. In detail, various DSF configurations are applied to a residential building located in central Italy and investigated under present and future climate conditions, estimated through regional climate models. The results underline that all the analysed Double Skin Façades confirm to be a useful option, and, in particular, the Multi-Storey typology allows drastic energy needs decrement. Moreover, the general increase in temperatures and solar radiations could affect the building energy performance, and the insertion of DSF can mitigate the climate change effects, reducing the predicted energy consumption and ensuring better behaviours than the building in its original state.

Natural image matting is an essential technique for image processing that enables various applications, such as image synthesis, video editing, and target tracking. However, the existing image matting methods may fail to produce satisfactory results when computing resources are limited. Sampling-based methods can reduce the dimensionality of the decision space and therefore reduce computational resources by employing different sampling strategies. While these approaches reduce computational consumption, they may miss an optimal pixel pair when the number of available high-quality pixel pairs is limited. To address this shortcoming, we propose a novel multi-criterion sampling strategy that avoids missing high-quality pixel pairs by incorporating multi-range pixel pair sampling and high-quality samples selection method. This strategy is employed to develop a multi-criterion matting algorithm via Gaussian process, which searches for the optimal pixel pair by using the Gaussian process fitting model instead of solving the original pixel pair objective function. Experimental results demonstrate that our proposed algorithm outperforms other methods even with 1% computing resources, and achieves alpha matte results comparable to those yielded by the state-of-the-art optimization algorithms.

Crisis environments, which are becoming systemic, pose significant challenges to smart government strategies. The paper aims to contribute to academic debate by proposing an analytical framework for examining the institutional capacities of smart government systems in addressing local crises. The paper focuses on the recent approach of robust governance and highlights a set of variables that promote effective smart government: contingency planning capacity, analytical capacity, organizational management capacity, and collaborative capacity. The study presents an analytical model for evaluating the robustness and effectiveness of local smart government systems in crises. One of the significant findings of this study has been the identification of critical indicators that inform institutional capacities of smart government systems. By analyzing these indicators, the proposed analytical framework provides a comprehensive approach to assess the preparedness of smart government systems in dealing with crises. Moreover, it can be used to benchmark the performance of local smart government systems in similar contexts and identify best practices for improving crisis management.

This paper investigates the classical but yet intriguing question of modeling orthotropic RC floor systems endowed with lightening elements, by means of a uniform orthotropic slab, in order to describe accurately the building response under seismic loads. The simplified method, commonly adopted by engineers, based on the equivalence between the transverse stiffness of the RC elements of the real floor and those of the orthotropic slab is presented. A case study in which this simplified method is used is also provided. Then, an advanced finite elements (FE) based method to determine the elastic properties of the equivalent homogenized orthotropic slab is proposed. Based on the results obtained from the application of this method to a case study, a discussion on the adequacy of the simplified method is also provided.

Background: The infant mortality rate (IMR) is an important reflection of the well-being of infants and the overall health of the population. This study aims to examine the macroeconomic (ME), sociodemographic (SD), and health status and resources (HSR) effects on IMR, as well as how they may interact with each other. Methods: A retrospective time-series study using yearly data for Oman from 1980 to 2022. Partial Least Squares-Structural Equation Modelling (PLS-SEM) was utilized to develop the exploratory model of the determinants of IMR. Results: The model indicates that HSR determinants directly but negatively affect IMR (= -0.617, p<0.001). SD directly and positively affects IMR (= 0.447, p<0.001). ME only indirectly affects IMR (=-0.854, p<0.001). ME determinants also exert some direct influences on both HSR (= 0.722, p<0.001) and SD (= -0.916, p<0.001) determinants. Conclusions: These findings indicate that an integrated policy that addresses socioeconomic and health-related factors and the overall ME environment is necessary for the health and well-being of the children and the population overall in Oman.

This paper has a twofold objective. The first aim is to present a comprehensive bibliographical analysis of journal articles and book chapters on fuzzy-random option pricing (FROP) over the WoS and SCOPUS databases. It follows PRISMA criteria and takes special care of developments in continuous time. Thus, we described the principal findings about the research streams, outlets and authors of this topic and lets us suggest further research. The second contribution is motivated by the fact that the bibliographical revision has identified a lack of developments on equilibrium models on the yield curve. This question motivates extending Vasicek’s yield curve equilibrium model to introduce fuzziness in the parameters that govern interest rate movements (speed of reversion, equilibrium short-term interest rate, and volatility). Likewise, this paper develops an empirical application on the term structure of fixed income public bonds with the highest credit rating in the Euro area.

The exceptional Jordan algebra is the algebra of $3 \times 3$ Hermitian matrices with octonionic entries. It is the only one from Jordan’s algebraic formulation of quantum mechanics which is not equivalent to the conventional formulation of quantum theory. It has often been suggested that this exceptional algebra could explain physical phenomena not currently explained by the conventional approach, such as values of the fundamental constants of the standard model of particle physics, and their relation to gravitation. We show that this is indeed the case; and this also unravels the connection between general relativity and the weak force. The exceptional Jordan algebra also predicts a new U(1) gravitational interaction which modifies general relativity, and which provides a theoretical basis for understanding the Modified Newtonian Dynamics (MOND).

Building fires are preventable incidents that have proven to be both deadly and costly. Addressing their root causes will lead to safer neighbourhoods for families and businesses to live and operate in. Multiple studies have established the effect of residents’ socioeconomic compositions on an area’s building fire rates; however, the existing model based on the classical stepwise regression procedure has several limitations. This paper aims to construct a more accurate predictive model of building fire rates based on a set of explanatory socioeconomic variables. In building the socioeconomic model, a backward elimination by Robust Final Predictor Error (RFPE) criterion is proposed to enhance the forecasting capability of the model. The proposed method has been implemented on the census data and the fire incident data of the South East Queensland region in Australia. A cross-validation was then conducted to assess the model’s accuracy. In addition, comparative analyses of other elimination criteria, such as p-value, adjusted R-squared, Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC) and predicted residual error sum of squared (PRESS), were conducted. The cross-validation analyses demonstrate that the proposed criterion is a more accurate predictive model based on a couple of goodness-of-fit measures. All in all, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modelling of building fires.

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.

For more than half a century, Manfred Deistler has been contributing to the construction of the rigorous theoretical foundations of the statistical analysis of time series and more general stochastic processes. Half a century of unremitting activity is not easily summarized in a few pages. In this short note, we chose to concentrate on a relatively little-known aspect of Manfred's contribution which nevertheless had quite an impact on the development of one of the most powerful tools of contemporary time series and econometrics: dynamic factor models.

The aim of this study was to investigate the spatio-temporal association between socio-economic deprivation and the incidence of COVID-19 and how this association changes through the seasons and due to the existence of restrictive public health measures. A retrospective observational study was conducted among COVID-19 cases that occurred in the Apulia region from 29 February 2020 to 31 December 2021, dividing the period into four phases with different levels of restrictions. A generalized estimating equations model was applied to test the independent effect of deprivation on the incidence rate of COVID-19, taking into account age, sex, and regional incidence rate as possible confounding effects and covariates such as season and levels of restrictions as possible modifying effects. The highest incidence rate was in areas with a Very High deprivation Index (DI) in winter (107.2 for 100,000 ab. ± 7.5), while in autumn, the highest Rate Ratio (RR) was estimated between Very High vs. Low DI (3.83, p<.001). During total lockdown, no RR between areas with different levels of DI was significant, while during soft lockdown, areas with Very High DI were more at risk than all other areas. The effects of social inequalities on incidence rate of COVID-19 change in as-sociation with the seasons and restrictions on public health. Disadvantaged areas showed a higher incidence rate of COVID-19 in the cold seasons and in the phases of soft lockdown.

This paper results from our investigation into novel means of electromagnetic propulsion. It requires the basis of our claims to be put on a sound theoretical footing regarding the purported momentum exchange with the electromagnetic field. One of these concerns is the huge discrepancy between the energy density of the Zeropoint and its purported manifestation as the Cosmological Constant. Here we state that it is manifestly wrong to introduce the zeropoint at zero order into the stress-energy tensor, because it is something which describes zero particle count. As a fluctuation, it belongs in a higher order Taylor expansion in frequency of the stress-energy tensor. Furthermore in the 3^rd order in the Einstein constant our procedure is some 9 orders of magnitude too small. We make up this difference by suggesting that vacuum energy is much higher still and that more degrees of freedom exist in physics beyond the Standard Model or that there is interaction energy between the modes.

We study the μ-μ45-T phase diagram of the 2+1-dimensional Gross-Neveu model, where μ denotes the ordinary chemical potential, μ45 the chiral chemical potential and T the temperature. We use the mean-field approximation and two different lattice regularizations with naive chiral fermions. An inhomogeneous phase at finite lattice spacing is found for one of the two regularizations. Our results suggest that there is no inhomogeneous phase in the continuum limit. We show that a chiral chemical potential is equivalent to an isospin chemical potential. Thus, all results presented in this work can also be interpreted in the context of isospin imbalance.

The SARS-CoV-2 polybasic furin cleavage site is still a missing link. Remarkably, the two arginine residues of this protease recognition site are encoded by the CGG codon, which is rare in Betacoronavirus. However, the arginine pair is common at viral furin cleavage sites, but are not CGG-CGG encoded. The question is: Is this genetic footprint unique to the SARS-CoV-2? To address the issue, using Perl scripts, here I dissect in detail the NCBI Virus database in order to report the arginine dimers of the Betacoronavirus proteins. The main result reveals that a group of Middle East respiratory syndrome-related coronavirus (MERS-CoV) (isolates: camel/Nigeria/NVx/2016, host: Camelus dromedarius) also have the CGG-CGG arginine pair in the spike protein polybasic furin cleavage region. In addition, CGG-CGG encoded arginine pairs were found in the orf1ab polyprotein from HKU9 and HKU14 Betacoronavirus, as well as, in the nucleocapsid phosphoprotein from few SARS-CoV-2 isolates. To quantify the probability of finding the arginine CGG-CGG codon pair in Betacoronavirus, the likelihood ratio (LR) and a Markov model were defined. In conclusion, it is highly unlikely to find this genetic marker in betacoronaviruses wildlife, but they are there. Collectively, results shed light on recombination as origin of the virus CGG-CGG arginine pair in the S1/S2 cleavage site.

In systems biology, biological phenomena are often modeled by ODE and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulations, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the SBSCL, and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code available from https://github.com/TakahiroYamada/SBMLWebApp. The program runs online at http://simulate-biology.org.

^{We consider the collective pricing orders in a minimum supply chain that is composed of a monopolistic manufacturer and many retailers that belong to the same chain store firm. The retailers have the freedom to raise or lower the local price. The chain store firm sets up the commercial rules for local retail stores to maximize its total payoff. The monopolistic manufacturer firm controls the total quantity supplied for the market to achieve maximum benefits. We applied the two dimensional Ising model in statistical physics to map the collective distribution of microscopic strategy of local retailers into the macroscopic total payoff of the chain store firm. The local stores choose to raise the price or lower the price based their own mind when the supply in market surpasses the demand. When the supply in market is far less than the demand, the stores synchronously raise prices, even though a local store only have the incomplete information of their nearest neighboring supermarket. We find the critical equation for the balance point between the action of supplier and the action of chain store management based on game theory and statistical physics. The critical equation can identify the Nash equilibrium point of the non-cooperative game between the manufacturer and the chain-store seller, and reveal different levels of collective operations. This statistical physics method also holds for more complicate supply chains and economic systems.}

The need for Transit Oriented Development (TOD) around railway stations has been well accepted and continues to be needed in cities looking to regenerate both transit and urban development. Large parts of suburban areas remain without quality transit down Main Roads which are usually filled with traffic resulting in reduced urban value. The need to regenerate both the mobility and land development along such roads will likely be the next big agenda in transport policy. This paper learns from century-old experiences in public-private approaches to railway systems from around the world, along with new insights from entrepreneurship theory and urban planning to create the notion of a ‘Transit Activated Corridor’ (TAC). TAC’s prioritise fast transit and a string of station precincts along urban Main Roads. TOD’s were primarily a government role, whereas TAC’s will be primarily a private sector, entrepreneurship role. The core policy processes for a TAC are outlined with some early case studies. Five design principles for delivering a TAC are presented in this paper, three principles from entrepreneurship theory and two from urban planning. The potential for Trackless Trams to enable TAC’s is used to illustrate how these design processes can be an effective approach for designing, financing and delivering a ‘Transit Activated Corridor’.

The objective of this paper is to present a mathematical formalism that states a bridge between Physics and Psychology, concretely between analytical dynamics and personality theory in order to open new insights in this theory. In this formalism energy plays a central role. First, the short-term personality dynamics can be measured by the General Factor of Personality (GFP) response to an arbitrary stimulus. This GFP dynamical response is modelled by a stimulus-response model: an integro-differential equation. The bridge between Physics and Psychology is provided when the stimulus-response model can be formulated as a linear second order differential equation and, subsequently, reformulated as a Newtonian equation. This bridge is strengthened when the Newtonian equation is derived from a minimum action principle, obtaining the current Lagrangian and Hamiltonian functions. However, the Hamiltonian is a non-conserved energy. Then, some changes provide a conserved Hamiltonian function: the Ermakov-Lewis energy. This energy is presented, as well as the GFP dynamical response that can be derived from it. An application case is presented: an experimental design in which 28 individuals consumed 26.51 g of alcohol. This experiment provides an ordinal scale for the Ermakov-Lewis energies that predicts the effect of a single dose of alcohol.

A mechanical ventilation system is a big support for breathing complications, in which an external solution is quite necessary to keep oxygen compensation in the patients. Its knowledge is well widespread and different equipment has been developed. However, they are very expensive and their quantity in medical centers is not sufficient, especially in Peru. Hence, it has been required to develop new methods to provide oxygen by a low cost equipment; Protofy, a research group from Spain, designed one of the first low cost mechanical ventilation systems which was medically validated by its government. In this sense, a redesign of the mechanical ventilation system was carried out according to the local requirements and available technology, a different airbag resuscitator with different properties and geometry, but maintaining its working concept based on a cam compression mechanism. Sensors and a display were added to improve the performance with a control algorithm for the rotation frequency and to show the ventilation curves over time to the medical staff. It was necessary to develop a mathematical model to relate the behavior between ventilation curves for a patient and physical variables of the design, especially in the epidemic COVID 19, that many countries are dealing with at the time research is being conducted. The mechanical ventilation system was redesigned, fabricated, and tested measuring its ventilation curves over time. Results indicate that this redesign provides a sturdy equipment able to work during a longer lifetime than the original. The replicability of the ventilation curves behavior is assured, while the mechanism dimensions are adapted for a particular airbag resuscitator. The mathematical model of the whole system can predict satisfactorily the ventilation curves over time and was used to provide the air pressure, volume, and flow as a function of the rotation angle measured by sensors.

In Differentiated Services (DiffServ) architecture, Priority Service System (PSS) plays a vital responsibility to provide Quality-of-Service (QoS) for the applications based on networks. Priority queuing systems has always been a subject of interest for analytical modelling and evaluating the performance. However, previous works have mainly focused on performing priority queuing under range dependent traffics, namely Short Range Dependent (SRD) or Long Range Dependent (LRD). Recent studies revealed that realistic traffic demonstrates a heterogeneous nature for the modern networks that provide multiple services. In this paper, the results of analytical and simulation models of dynamic priority service systems is reviewed to study the heterogeneous traffic impact on designing and performance of the systems based on networks. In this paper, the results of analytical and simulation models of dynamic priority service systems is reviewed to investigate the impact of heterogeneous traffic on the design and performance of network-based systems. Here, the time of presence of requests in limited and unlimited buffer system is restricted. If the restriction is violated, the requests will be lost. The results of experiments conducted on both models are compared and it is identified that they differ in the allowable limit (2-9) %.

Tourism is the main economic sector in the Balearic Islands (Spain) and hotel housekeepers (HHs) are a large occupational group, in which stress is becoming a major issue. This study aims at exploring in-depth factors perceived as stressors by HHs and key-informants, and their effects on work-life balance (WLB). A qualitative design with phenomenological approach was used, conducting six focus groups with 34 HHs and 10 individual interviews with key-informants. Results were analyzed adopting the job demands-resources model and a gender perspective. High demands –e.g work overload, time pressure, physical burden...-, lack of enough resources and little control –derived from role conflict, unexpected events...- were the most important factors explaining HHs’ stress. Additionally, this imbalance was perceived as leading to health problems –mainly musculoskeletal disorders-. Working schedule was mentioned as a facilitator to WLB, whereas an imbalance between job demands and resources led to work-home conflict –preventing them from enjoying leisure time-. Multiple roles at work and at home increased their stress. HHs experienced their job as invisible and unrecognised. Regarding practical implications, our recommendations for hotel organization include reducing workload and increasing resources, which would improve the job demands-resource balance, diminish negative mental and physical outcomes and improve WLB.

The Standard Model (SM) fails to account for either the triplication of fermion families or chiral symmetry breaking in the electroweak sector. Here we show that both phenomena arise from the approach to chaos of quantum theory near the Fermi scale.

Deformation behavior of MXene based polymer composites with bioinspired brick and mortar structures is analyzed. MXene/Polymer nanocomposites are modeled at microscale using bioinspired configurations of nacre-mimetic brick-and-mortar assembly structure. MXenes (brick) with polymer matrix (mortar) are modeled using classical analytical methods and numerical methods based on Finite Elements (FE). The analytical methods provide less accurate estimation of elastic properties compared to numerical one. MXene nanocomposite models analyzed with FE method provide estimates of elastic constants in the same order of magnitude as literature reported experimental results with good consistency. Bioinspired design of MXene nanocomposites results in the effective Young’s modulus of the nanocomposite increase by 25.1 % and the strength (maximum stress capacity within elastic limits) increase by 42.3 %. The brick and mortar structure of the nanocomposites leads to interlocking mechanism between MXene fillers in polymer matrix, resulting in effective load transfer, good strength, and damage resistance. This is demonstrated in this paper by numerical analysis of MXene nanocomposites subjected to quasi-static loads.

We use a system biology approach to translate the interaction of Bacillus Calmette-Gurin (BCG) + interleukin 2 (IL-2) for the treatment of bladder cancer into a mathematical model. The model is presented as a system of differential equations with the following variables: number of tumor cells, bacterial cells, immune cells, and cytokines involved in the tumor-immune response. This work investigates the delay effect induced by the proliferation of tumor antigen-specific effector cells after the immune system destroys BCG-infected urothelium cells following BCG and IL-2 immunotherapy in the treatment of bladder cancer. For the proposed model, three equilibrium states are found analytically. The stability of all equilibria is analyzed using the method of Lyapunov functionals construction and the method of linear matrix inequalities (LMIs).

Stormwater management is a key urban issue in the world, in line with the global issues of urban sprawl and climate change. It is urgent to investigate the effectiveness in managing stormwater with different strategies for maintain urban resilience. A method based on a storm water management model (SWMM) was developed for assessing the control of stormwater runoff volume and the percentage removal of suspended solids by implementing a Sponge City strategy. An interdisciplinary approach was adopted incorporating Low Impact Development (LID) with urban Green Infrastructure and Gray Infrastructure paradigms in a typical old residential community in Suzhou, China. Four types of sponge facilities for reducing stormwater runoff were bio-retention cells, permeable pavements, grassed pitches, and stormwater gardens. The simulation results indicate that the stormwater pipe system can meet the management standard for storms with a five-year recurrence interval. The volume capture ratio of annual runoff is 91% and the reduction rate of suspended solids is 56%. This study demonstrates that Sponge City strategy is an effective approach for managing stormwater, particularly in old and densely populated urban areas. Implementing spongy facilities with a LID strategy for stormwater management can significantly enhance urban water resilience and increase ecosystem services.

In the Standard Model of elementary particles, the rest mass, magnetic moment and electric charge of the electron are considered to be natural constants. In this article it is proposed that these properties can be calculated in a simple way from the corresponding Planck units using the well-known period doubling phenomenon in nonlinear dynamical systems. Periods corresponding to the values of the electron and positron properties belong to a subset of stable periods. The periodic structures of the rest energy and magnetic moment consist of three internal degrees of freedom, whereas the Coulomb energy of the electric charge consists of four. The number of period doublings for the elementary charge determines the value of the fine structure constant alpha.

Production of electricity using hydro-kinetic tidal turbines has many challenges that must to be overcome to ensure reliable, economic and practical solutions. Energy from flowing water is converted by a system comprising: the turbine rotor blades, a gearbox, an electrical generator, control systems, power electronics, export cables, and a connection to the electricity grid. To date these have often been modelled using simulations of independent systems, lacking bi-directional, real-time, coupling. This approach leads to critical effects being missed. Turbulence in the flow, results in large velocity fluctuations around the blades, causing rapid variation in the shaft torque and generator speed, and consequently in the voltage seen by the power electronics and consequently the export power quality. The resulting poor quality power may be unacceptable to the grid. Conversely, grid frequency and voltage changes can also cause the generator speed to change, resulting in changes to the shaft speed and torque and consequently changes to the lift and drag forces acting on the blades. Clearly, fully integrated, bi-directional, models are needed. Here we present two fully coupled models which use different approaches to model the hydrodynamics of rotor blades. The first model uses Blade Element Momentum Theory (BEMT), resulting in an efficient tool for turbine designers. The second model also uses BEMT combines this with an actuator line model of the blades coupled to an unsteady computational fluid dynamics (CFD) simulation. This simulation, implemented in OpenFOAM, uses an energy balance to compute the shaft speed. Both the BEMT and CFD models are coupled to an Open Modellica model of the electro-mechanical system. Both models have been used to simulate the performance of a 1.2m diameter, scale model, of a three bladed horizontal axis tidal turbine tested in the University of Edinburgh FloWave Ocean Energy Research Facility. The turbulent flow around the blades and the mechanical-electrical variables during the stable period of operation are analysed. Time series and tabulated average values of mechanical thrust, power, torque, and rotational speed as well as electrical variables of generator power, electromagnetic torque, voltage and current are presented for the coupled system simulation. The relationship between the mechanical and electrical variables and the results from both tidal turbine approaches are discussed. Our comparison shows that while the BEMT model provides an effective design tool (leading to slightly more conservative designs), the CFD/BEMT simulations provide more accurate predictions of the blade loads which can be especially important in assessing fatigue on the blades (though at an increased computational cost).

The boundary layer height (BLH) distinguishes the interface between the lower and free atmosphere, which is a key variable in numerical simulation, aerosol, and environmental pollution studies. Thus, the goal of this work is to propose a novel method conjuncting with numerical regularization to analyze climate characteristics of the marine boundary layer height (MBLH) using 2007-2011 GPS-RO data from the COSMIC mission. While traditionally, the difference method has been used to achieve this aim, herein, we propose an innovative method, in which the bending angle profile gradient was calculated using the numerical regulation method where the regulation parameters are determined by the double-parameter model function method. Then, the MBLH was determined by employing the maximum gradient method to ascertain the height corresponding to the smallest gradient. The results show a correlation between currents and the MBLH—a relationship that has not been previously demonstrated. A low MBLH is associated with seasons and regions where cold ocean currents are prevailing; whereas a high MBLH is observed in the seasons and places where warm currents are prevailing. This correlation was validated by comparing the obtained results with different occultation data—i.e., atmprf and echprf—which also established that atmprf is more sensitive to convective cloud top capture. In seas with active convection, the armprf calculated bending angle is higher than that from echprf. Subsequently, the standard deviation was used to express the MBLH confidence level. The results show that the MBLH standard deviation is highest in low latitudes and lowest in the middle and high latitudes. Furthermore, we analyzed the interannual MBLH variation trend, which displayed a seasonal variation and spatial distribution corresponding with the current and subsolar point. Finally, we conducted a case study in the South China Sea, and identified a distinctive seasonal change and downward trend.

As traffic congestion gets worse year by year in metropolitan areas, cities search for solutions to improve their traffic performance and reduce their environmental impacts. This paper focuses on parking pricing and congestion pricing and their short-term effects not only on traffic congestion but also on the potential revenue for a city. We develop an easy to implement multimodal macroscopic traffic and parking search model for a central area based on aggregated data at the network level. Our methodology allows us to analyze how introducing parking pricing inside a network, or a congestion toll combined with a park and ride (P+R) scheme can affect the drivers’ decision between entering the network by car (private vehicle) or using P+R instead. This decision directly influences the number of drivers using P+R, and this impacts, in turn, the traffic performance. Based on such analysis, a city can get valuable insights to evaluate whether congestion pricing is a necessity or if the traffic improvements resulting from implementing parking pricing strategies are sufficient when combined with P+R facilities. A search algorithm is used to find the best trade-off between the parking fees and the congestion toll. Any additional revenue collected through these schemes can then be used to improve public transport or the P+R facilities themselves. With minor data collection efforts and little computational costs compared to most existing parking and congestion pricing models, we illustrate our proposed framework in a case study of an area with a high parking demand for public parking spaces within the city of Zurich, Switzerland. Results show that parking pricing combined with P+R is indeed a viable option compared to congestion pricing for improving traffic performance, even if parking pricing schemes do not target all the drivers.

Local binary pattern is one of the visual descriptors and can be used as a powerful feature extractor for texture classification. In this paper, a novel representation for face recognition is proposed, called it Bilateral Line Local Binary Patterns (BL-LBP). This scheme is an extension of Line Local Binary Patterns descriptors in the statistical learning subspace. The present bilateral descriptors are fused with an ensemble learning of calibrated SVM models. The performance of this scheme is evaluated using 5 standard face databases. It is found that it is robust against illumination variation, diverse facial expressions and head pose variations and its recognition accuracy reaches 98 percent, running on a mobile device with a processing speed of 63 ms per face. Results suggest that our proposed method can be very useful for the vision systems that have limited resources where the computational cost is critical.

The COVID-19 pandemic situation, disease intensity, weak healthcare facilities, unawareness, and misinformation led people to fear and anxiety in Bangladesh. This study intended to get peoples’ perception on psychosocial, socio-economic and environmental crisis amidst the pandemi. An online questionnaire was surveyed nationwide (respondents no.1066). Datasets were analyzed through the Principal Component Analysis (PCA), hierarchical Cluster Analysis (CA), Pearson’s correlation matrix (PCM), Linear regression analysis (LRA), and psychometric characteristics were included in the Classical Test Theory (CTT) analysis. There were good associations among the psychosocial, socio-economic and environmental parameters. A significant association between fear of COVID-19 with struggling healthcare system (p<0.05) was found. Also, negative association between fragile health system and government’s ability to deal with the pandemic (p<0.05) revealing poor governance. Again, a positive association of shutdown and social distancing with fear of losing life, and due to lack of health treatment (p<0.05) reveals that shut down hampers normal activities which may lead to mental and economic stress. However, a positive association of socio-economic impact of the shutdown with poor people’s suffering, the price hike of basic need, hamper of formal education (p<0.05) may lead to severe socio-economic and health crisis. There is a possibility of climate-induced disaster during/after the pandemic, which will create severe food insecurity (p<0.01). Daily wage earners and poors will suffer most by food and nutritional deficiency, and the country may face huge economic burden. Proper risk assessment and communications is needed to alleviate fear and anxiety. Thus, financial support and mental boosting is required.

This paper analyzes the stability and usefulness of a caseload management method for COVID-19 or similar epidemics and pandemics. It reduces the total cases by controlling overshoot as groups cross the herd immunity threshold, balances medical resource utilization, and subject to those two constraints reduces economic shutdown duration across significant scenario variation. A quantitative analysis of overshoot is provided. An SIR-type model was used with clear parameters suitable for public information with tracking and predictive capabilities is used. It contains a simulation of a decision-maker for select-day partial unlock so that many scenarios can be quickly and impartially analyzed. Using certain days of the week, already practiced by some countries, is not a necessary part of the method, but was used in the simulation to give a highly quantified unlock scheme. While the model shows total cumulative cases, and therefore deaths, declining initially with flattening, when flattening begins to produce large rebounds the death rate goes back up. Partial unlock to manage critical resources had the consequential effects of reducing economic downtime and bringing the cumulative cases down about 8-12% between now and the second half of 2021, thereby saving lives with some degree of certainty. The optimization of overshoot does leave some risk of creating a residual small infection existing on birth rate and migration, and we provide some guidelines for minimizing the risk.

This study proposes a 3D particle-base (discrete) multiphysics approach for modelling calcification in the aortic valve. Different stages of calcification (from mild to severe) were simulated, and their effect on the cardiac output assessed. The cardiac flow rate decreases with the level of calcification. In particular, there is a critical level of calcification below which the flow rate decreases dramatically. Mechanical stress on the membrane is also calculated. The results show that, as calcification progresses, spots of high mechanical stress appear. Firstly, they concentrate in the regions connecting two leaflets; when severe calcification is reached, then they extend to the area at the basis of the valve.

In cosmology, the cosmic curvature $K$ and the cosmological constant $\Lambda$ are two most important parameters, whose values have strong influence on the behavior of the universe. By analyzing the energy-momentum tensor and equations of state of ideal gas, scalar, spinor and vector potential in detail, we find that the total mass density of all matter is always positive, and the initial total pressure is negative. Under these conditions, by qualitatively analyzing the global behavior of the dynamical equation of cosmological model, we get the following results: (i) $K= 1$, namely, the global spatial structure of the universe should be a 3-dimensional sphere $S^3$. (ii) $0\le\Lambda < 10 ^ {-24} {\rm ly} ^ {-2}$, the cosmological constant should be zero or an infinitesimal. (iii) $a(t)>0$, the initial singularity of the universe is unreachable, and the evolution of universe should be cyclic in time. This means that the initial Big Bang is impossible at all. Since the matter components considered are quite complete and the proof is very elementary and strict, these logical conclusions should be quite reliable. Obviously, these conclusions will be much helpful to correct some popular misconceptions and bring great convenience to further research other problems in cosmology such as property of dark matter and dark energy.

Natural killer (NK) cell therapy is one of the most promising treatments for Glioblastoma Multiforme (GBM). However, this emerging technology is limited by the availability of sufficient numbers of fully functional cells. Here, we investigated the efficacy of NK cells that were expanded and treated by interleukin-2 (IL-2) and heat shock protein70 (HSP70), both in vitro and in vivo. Proliferation and cytotoxicity assays were used to assess the functionality of NK cells in vitro, after which treated and naïve NK cells were administrated intra-cranially and systemically to compare the potential antitumor activities in our in vivo rat GBM models. In vitro assays provided strong evidence of NK cell efficacy against C6 tumor cells. In vivo tracking of NK cells showed efficient homing around and within the tumor site. Furthermore, significant amelioration of the tumor in rats treated with HSP70/Il-2 treated NK cells as compared to those subjected to non-treated NK cells, as confirmed by MRI, proved the efficacy of adoptive NK cell therapy. Moreover, results obtained with systemic injection confirmed migration of activated NK cells over the blood brain barrier and subsequent targeting of GBM tumor cells. Our data suggest that administration of HSP70/Il-2 treated NK cells may be a promising therapeutic approach to be considered in the treatment of GBM.

In this paper, initially a mathematical model is formulated for transient frequency of power system considering time delays which occur while transmitting the control signals in open communication infrastructure. Time delay negligence in a power system leads to improper measurement of frequency variation in power system. The study of impact of time delays on the stability of power system is performed by estimating the decay rate of frequency wave form using Kalman Filter (KF). In power system, there is a possibility of multiple time delays. This paper also focusses on developing Interacting Multiple Model (IMM) Algorithm with multiple model space using Kalman Filter (KF) as state estimator tool. The multiple time delays in power system is considered as multiple model space. The result shows that KF provides better estimate of correct model for a particular input-set. The qualitative properties of Riccati difference equation (RDE) in terms of state error covariance of IMMKF are also analyzed and presented.

A multi-server parallel system dispatches the incoming job, which contains kn tasks into n servers. A job is considered to be computed if all the tasks associated with the job are processed. One job’s tasks can be encoded into at least kn “replicas” such that the job is considered to be served if any kn replicas finishing computation. In this paper, we analyze the random scheduling policy of a multi-server computing system under discrete time model in terms of Quality of Exponent (QoE), which is defined as the probability exponent that a typical job can be computed within a given number of time slots. We let kn/n be a constant. Assuming that any task of any job can be randomly dispatched by a “scheduler” to any server, and computing each task takes exactly one time slot. We divide the calculation of probability exponent into two parts, exponent of numerator and exponent of denominator. For the denominator, we give the almost exact exponent using Lagrange multiplier method, while for the numerator, an upper bound of the numerator’s exponent is provided. In addition, we also express the exponent in terms of information theoretical quantities and reconsider both of exponents in the context of large deviation theory.

Although work events can be regarded as pivotal elements of organizational life, only a few studies have examined how positive and negative events relate to and combine to affect work engagement over time. Theory suggests that to better understand how current events affect work engagement (WE), we have to account for recent events that have preceded these current events. We present competing theoretical views on how recent and current work events may affect employees (e.g., getting used to a high frequency of negative events or becoming more sensitive to negative events). Although the occurrence of events implies discrete changes in the experience of work, prior research has not considered whether work events actually accumulate to sustained mid-term changes in WE. To address these gaps in the literature, we conducted a week-level longitudinal study across a period of 15 consecutive weeks among 135 employees, which yielded 849 weekly observations. While positive events were associated with higher levels of WE within the same week, negative events were not. Our results support neither satiation nor sensitization processes. However, high frequencies of negative events in the preceding week amplified the beneficial effects of positive events on WE in the current week. Growth curve analyses show that the benefits of positive events accumulate to sustain high levels of WE. WE dissipates in the absence of continuous experience of positive events. Our study adds a temporal component and informs research that has taken a feature-oriented perspective on the dynamic interplay of job demands and resources.

Objective: The private healthcare providers (PHCP) are believed to improve access to healthcare services in Public-Private Mix (PPM) projects, as they are considered first point of contact for healthcare. The purpose of this study was to know the satisfaction level of TB patients. Design: A questionnaire-based, cross-sectional study was carried out during November and December 2017 for 572 under-treatment patients registered with PHCPs in the PPM project. Lot quality assurance sampling technique was used to randomly select 19 districts from sample frame of 75 districts. In each selected district, data collector retrieved TB register of 8 months (Jan – Aug 2017) and systematically selected patients by fixed periodic interval. SPSS (version 24.0) was used to analyze the data. Results: Study included 53% (n=301) of males and 47% (n=271) of females, with mean age of 38 years (SD, ±18). Almost half of the participants were illiterate (51%, n=289) and 64% (n=365) were non-earning members of the family. In practice, most of the participants visit private providers (71%, n=407), including private hospital/clinic (44%) and traditional practitioners (27%; n=153). 55% of participants visited the current doctor because of clinic’s proximity to residence. 82% of the participants (n=469) were satisfied with the TB care services and 85% (n=488) said that they would recommend this clinic to others. Conclusion: PHCPs are preferred providers for individuals, which is consistent with findings of other studies. Though they are satisfied with TB care and services, interventions should be introduced to reduce the financial burden on the patient. Partnering PHCP is a way forward to ensure universal health coverage, health system strengthening and better health outcomes of the population.

The fractional telegraph partial differential equation with fractional Atangana-Baleanu-Caputo (ABC) derivative is studied. Laplace method is used to find the exact solution of this equation. Stability inequalities are proved for the exact solution. Difference schemes for the implicit finite method are constructed. The implicit finite method is used to deal with modelling the fractional telegraph differential equation defined by Caputo fractional of Atangana-Baleanu (AB) derivative for different interval. Stability of difference schemes for this problem is proved by the matrix method. Numerical results with respect to the exact solution confirm the accuracy and effectiveness of the proposed method.

Immersion in salt-lake solution was adopted to periodically test the concrete chloride-ion diffusion coefficient. The regression analysis was also completed. Also investigated was the time-dependent law of concrete chloride-ion diffusion coefficient with time. The influence of chloride-ion concentration in solution, water-to-cement ratio, and corrosion time on the largeness and accumulation rate of the concrete chloride-ion diffusion coefficient was also analyzed. Test results show that the concrete chloride-ion diffusion coefficient gradually decreased with increasing time and increased with increasing chloride-ion concentration in a salt lake . Taking into account the influence of factors such as water–binder ratio, chloride-ion concentration, and time-varying characteristics, a multi-factor calculation model for the concrete chloride-ion diffusion coefficient was established. Combining the prediction results and the measured data reported in this paper, the effectiveness and applicability of the established concrete chloride-ion diffusion coefficient calculation model were compared and verified, and the durability design and service life of a concrete structure under cool chlorine were compared. The results of analysis provide important boundary conditions.

In both managed and unmanaged forests, termites are functionally important members of the dead-wood-associated (saproxylic) insect community. However, little is known about regional-scale environmental drivers of geographic distributions of termite species, and how these environmental factors impact co-occurrence among congeneric species. Here we focus on the southern Appalachian Mountains—a well-known center of endemism for forest biota—and use Ecological Niche Modeling (ENM) to examine the distributions of three species of Reticulitermes termites (i.e., R. flavipes, R. virginicus, and R. malletei). To overcome deficiencies in public databases, ENMs were underpinned by field-collected high-resolution occurrence records coupled with molecular taxonomic species identification. Spatial overlap among areas of predicted occurrence of each species was mapped, and aspects of niche similarity were quantified. We also identified environmental factors that most strongly contribute to among-species differences in occupancy. Overall, we found that R. flavipes and R. virginicus showed significant niche divergence, which was primarily driven by dry-season precipitation. Also, all three species were most likely to co-occur in the mid-latitudes of the study area (i.e., northern Alabama and Georgia, eastern Tennessee and western North Carolina), which is an area of considerable topographic complexity. This work provides important baseline information for follow-up studies of local-scale drivers of these species’ distributions. It also identifies specific geographic areas where future assessments of the frequency of true syntopy vs. micro-allopatry, and associated interspecific competitive interactions, should be focused.

We examine how external triggers, including the digital imperative and the need for more sustainable resource and stakeholder employment, spark the development of transformative sustainable business models. Drawing on the resource-based view and the shared value approach we conceptualize a multifaceted framework that helps to identify key determinants and coherent layers of transformative sustainable businesses models. Our theoretical arguments integrate recent research findings on external dynamics, such as digital technological advances and rising global competitive dynamics, with internal capabilities on both the organizational and the individual level, allowing for a more complete understanding of transformative potentials on the firm level. We propose that key determinants of sustainable transformative business models adhere to both, innovative value-creating reconstructionist and sustainable shared-value logic, and include elements such as co-creation with customers, usage-based pricing, agile and adaptive behavior, closed-loop resource employment, asset-sharing, and collaborative business ecosystems. At the same time, organizational, economic, and environmental layers encompassing sustainable business models need to be both horizontally and vertically coherent to unfold their full potential.

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this mini-review, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions and, on other hand, highly simplified structure-based Elastic Network Models of protein flexibility. In contrast to classical Molecular Dynamics the modeling strategies discussed here allow quite accurate modeling of much larger systems and longer time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

This paper addresses the modeling of the iron ore direct reduction process, a process likely to reduce CO₂ emissions from the steel industry. The shaft furnace is divided into three sections (reduction, transition, and cooling), and the model is two-dimensional (cylindrical geometry for the upper sections and conical geometry for the lower one), to correctly describe the lateral gas feed and cooling gas outlet. This model relies on a detailed description of the main physical–chemical and thermal phenomena, using a multi-scale approach. The moving bed is assumed to be comprised of pellets of grains and crystallites. We also take into account eight heterogeneous and two homogeneous chemical reactions. The local mass, energy, and momentum balances are numerically solved, using the finite volume method. This model was successfully validated by simulating the shaft furnaces of two direct reduction plants of different capacities. The calculated results reveal the detailed interior behavior of the shaft furnace operation. Eight different zones can be distinguished, according to their predominant thermal and reaction characteristics. An important finding is the presence of a central zone of lesser temperature and conversion.

There is an important role non-human primates (NHP) play in biomedical research. Phylogenetic proximity of any of the NHP species to Homo sapiens assures that much better translatability of research outcomes from model studies involving human diseases can be achieved than from those generated with other pre-clinical systems. Our group and others used during past two decades NHPs in research directed towards viral and autoimmune disorders of the gastrointestinal tract. This review summarizes progress made in the area of enteric viral infections and its applicability to human disease.

Bioprocesses for the production of renewable energies and materials lack efficient separation processes for the utilized microorganisms such as algae and yeasts. Dissolved air flotation (DAF) and microflotation are promising approaches to overcome this problem. The efficiency of these processes depends on the ability of microorganisms to aggregate with microbubbles in the flotation tank. In this study, different new or adapted aggregation models for microbubbles and microorganisms are compared and investigated for their range of suitability to predict the separation efficiency of microorganisms from fermentation broths. The complexity of the heteroaggregation models range from an algebraic model to a 2D population balance model (PBM) including the formation of clusters containing several bubbles and microorganisms. The effect of bubble and cell size distributions on the flotation efficiency is considered by applying PBMs, as well. To determine the impact of the model assumptions, the modeling approaches are compared and classified for their range of applicability. Evaluating computational fluid dynamics (CFD) of a DAF system shows the heterogeneity of the fluid dynamics in the flotation tank. Since analysis of the streamlines of the tank show negligible backmixing, the proposed aggregation models are coupled to the CFD data by applying a Lagrangian approach.

Types of DNA recombination include homologous recombination and nonhomologous recombination. Homologous DNA recombination is a general term that includes exchange of information between chromatids: (reciprocal) crossing-over, gene conversion, and post-meiotic segregation. Gene conversion is now thought to be a type of non-Mendelian segregation of heterozygous markers near the recombination initiation site. Thus, it includes both gene conversion and post-meiotic segregation previously described. DNA non-HR including transpositional recombination and site-specific recombination. Our understanding of the molecular mechanism by which DNA recombination occurs has significantly increased in the past decades. Currently The synthesis-dependent strand annealing model is now thought to give rise to most or all noncrossovers, with the double-strand-break repair model forming mainly crossovers. The Shapiro model proposed by Dr. J. Shapiro explains the molecular mechanism of transpositional recombination. Site-specific recombination results from another distinct model. We previously proposed a novel theory which can provide a more reasonable and simpler explanation accounting for DNA HR including the 3 classes of recombinogenic events described above. In the new supplementedly molecular model, DNA meiotic recombination can be initiated by a copy choice mechanism, that is, copying part of 1 single-stranded DNA template, followed by DNA polymerase switching to another single-stranded DNA template, and then resuming the following DNA synthesis along the new template. The current review suggests that transpositional recombination and site-specific recombination should be initiated by copy choice during DNA synthesis rather than break/join mechanism. The work indicates that review of DNA nonhomologous recombination are very necessary. The novel theory would challenge earlier models accounting for transpositional recombination and site-specific recombination and would be critical to the understanding of the mechanisms. We hope copy choice initiating DNA nonhomologous recombination will be one of the concepts that are explored. Proper and specific experiments are required to reconstruct the detailed mechanism described here.

As the EU is moving towards a low carbon economy and seeks to further develop its renewable energy policy, this paper quantitatively investigates the impact of plausible energy market reforms from the perspective of bio-renewables. Employing a state-of-the-art biobased variant of a computable general equilibrium model, this study assesses the perceived medium-term benefits, risks and trade-offs which arise from an advanced biofuels plan, two exploratory scenarios of a more 'sustainable' conventional biofuels plan and a 'no-mandate' scenario. Consistent with more recent studies, none of the scenarios considered present significant challenges to EU food-security or agricultural land usage. An illustrative advanced biofuels plan simulation requires non-trivial public support to implement whilst a degree of competition for biomass with (high-value) advanced biomass material industries is observed. On the other hand, it significantly alleviates land use pressures, whilst lignocellulose biomass prices are not expected to increase to unsustainable levels. Clearly, these observations are subject to assumptions on technological change, sustainable biomass limits, expected trends in fossil fuel prices and EU access to third-country trade. With these same caveats in mind, the switch to increased bioethanol production does not result in significant market tensions in biomass markets.

Using the culture-work-health model, this study investigates the factors influencing the quality of life of maritime workers. This study conducted a survey of 320 maritime workers who have experience living and working on a ship for more than six months. This self-administered questionnaire included questions on organizational culture and support, self-efficacy, perceived fatigue, as well as the quality of work life. Organizational culture and self-efficacy were identified as factors affecting the quality of work life, while organizational support was found to have an indirect effect after passing through self-efficacy and perceived fatigue. The final model accounts for 63.1% of the variance in maritime workers’ quality of life. As such, this study shows that self-efficacy is important for the quality of life of maritime workers, having both direct and indirect effects. Moreover, organizational support may prove the primary intervention point for relieving perceived fatigue and enhancing self-efficacy, thus improving the quality of work life.

Balanced and coordinated economic development across regions is a critical goal of regional economic development and new-type urbanization in China. However, few studies have examined economic growth convergence clubs at the county level. To extend the research on convergence clubs, this research applies a log t convergence test and a dynamic spatial ordered Probit model (DSOP) to endogenously identify economic growth convergence clubs in counties and to examine the influence of initial states and structures on club convergence probability. The study sample covers 2286 counties of China from 1992 to 2010. The results show significant convergence club patterns at the county levels, resulting in the gradual formation of six convergence clubs. The DSOP estimation results show that per capita fixed assets, population density and industrialization have promoted convergence club formation to varying degrees.

The aim of this work is to introduce new ways to model the I-V characteristic of a PV cell or PV module using straight lines and Bézier curves. This is a complete novel approach, Bézier curves being previously used mainly for computer graphics. The I-V characteristic is divided in three sections, modeled with lines and a quadratic Bézier curve in the first case and with three cubic Bézier curves in the second case. The result proves to be accurate and relies on the fundamental points usually present in the PV cell datasheets: Voc (the open circuit voltage), Isc (the short circuit current), Vmp (the maximum power corresponding voltage) and Imp (the maximum power corresponding current) and the parasitic resistances Rsh0 (shunt resistance at Isc) and Rs0 (series resistance at Voc). The proposed algorithm completely defines all the implied control points and the error is analyzed. The temperature and irradiance influence is also analyzed. The model is also compared using the least squares fitting method. The final validation shows how to use Bézier cubic curves to accurately represent the I-V curves of an extensive range of PV cells and arrays.

The importance of predicting the exposure to environmental hazards is highlighted by issues like global climate change, public health problems caused by environment stresses, and property damages and depreciations. Several approaches have been used to assess potential exposure and achieve optimal results under various conditions, for example, for different scales, groups of people, or certain points in time. Micro-simulation tools are becoming increasingly important in human exposure assessment, where each person is simulated individually and continuously. This paper describes an agent-based model (ABM) framework that can dynamically simulate human exposure levels, along with their daily activities, in urban areas that are characterized by environmental stresses such as air pollution and heat stress. Within the framework, decision making processes can be included for each individual based on rule-based behavior to achieve goals under changing environmental conditions. The ideas described in this paper are implemented in a free and open source NetLogo platform. A simplified modeling scenario of the ABM framework in Hamburg, Germany, further demonstrates its utility in various urban environments and individual activity patterns, and portability to other models, programs and frameworks. The prototype model can potentially be extended to support environmental incidence management by exploring the daily routines of different groups of citizens and compare the effectiveness of different strategies. Further research is needed to fully develop an operational version of the model.

While paresthesia-based spinal cord stimulation (SCS) has been proven effective to treat chronic neuropathic pain, initial benefits may lead to the development of “Failed SCS Syndrome’ (FSCSS) defined as decrease over time related to Loss of Efficacy (LoE) with or without Loss of Coverage (LoC). Development of technologies associating new paresthesia-free stimulation waveforms and implanted pulse generator adapters provide opportunities to manage patients with LoE. The main goal of our study was to investigate salvage procedures, through neu-rostimulation adapters, in patients already implanted with SCS and experienced LoE. We retro-spectively analyzed a cohort of patients who were offered new SCS programs/waveforms through an implanted adapter between 2018 and 2021. Patients were evaluated before, and at 1, 3, 6 and 12-month follow-up. Outcomes included pain intensity rating with Visual Analog Scale (VAS), pain/coverage mappings and stimulation preferences. Last follow-up evaluations (N=27) showed significant improvement in VAS (p = 0.0001), ODI (p = 0.021) and Quality of Life (p=0.023). In the 11/27 patients with LoC, SCS efficacy on pain intensity (36.89%) was accompa-nied by paresthesia coverage recovery (55.57%) and pain surface decrease (47.01%). At 12-month follow-up, 81.3% preferred to keep tonic stimulation in their waveform portfolio. SCS conver-sion using adapters appears promising as a salvage solution, with an emphasis on paresthesia recapturing enabled by spatial retargeting. In light of these results, adapters could be integrated to SCS rescue algorithms or should be considered in SCS rescue.

Exploiting the van der Waals model of liquids, it is possible to derive analytical formulas for the thermodynamic functions governing solvation, the transfer of a solute molecule from a fixed position in the ideal gas phase to a fixed position in the liquid phase. The solvation Gibbs free energy change consists of two contributions: (a) the high number density of all liquids and the repulsive interactions due to the basic fact that each molecule has its own body lead to the need of spending free energy to create a cavity suitable to host the solute molecule; (b) the ubiquitous intermolecular attractive interactions lead to a gain in free energy for turning on attractions between the solute molecule and surrounding liquid molecules. Also the solvation entropy change consists of two contributions: (a) there is an entropy loss in all liquids because the cavity presence limits the space accessible to liquid molecules during their continuous translations; (b) there is an entropy gain in all liquids, at room temperature, due to the liquid structural reorganization as a response to the perturbation represented by solute insertion. The latter entropy contribution proves to be balanced by a corresponding enthalpy term. This scenario emerged from the van der Waals model is in qualitative agreement with experimental results.

In this study the distribution of heavy metal in the agricultural soils in the vicinity of 3 large mine-landfills around Kizhnica mine in Kosovo was investigated. The mining sector is one of the most important sectors of the economic development of Kosovo. The Kizhnica mine is one of the main ore producers in Kosovo. In addition to the positive aspects of production development it has its own side effects such as: industrial waste and the possibility of contamination of sur-rounding areas including the agricultural lands in the surrounding region. Ten sampling sites close to the Kizhnica mining complex were selected. These sampling sites have been characterized and evaluated as the most critical points, due to the anthropogenic influences of the discharge of industrial water from mining flotation and open tailing dumps in the Kizhnica. Concentration of Pb, Zn, Cu, As, Cd, Ni, Mn and Sb in selected samples was carried out by inductively coupled plasma–optical emission spectrometry (ICP–OES). Obtained data were used in order to generate geochemical maps and for calculatation the contamination factor (CF), pollution load index (PLI) and geo-accumulation index (Igeo). The statistical clustering analysis (CA), Pearson correlation coefficient (r) and air spatial distribution patterns using AERMOD View-Gaussian air dispersion model are used to perform evaluation within the area. The results demonstrated higher contents of the heavy metals in the vicinity of the mining area. It may be concluded that the heavy metal contents are impacted by anthropogenic nature of the contamination that confirms a current eco-logical threat of the mining activities in the region. Finally, a paper proposes the potential reas-signment of the deposed waste material into different combinations in construction in order to enhance the waste management and reduce the continued risk to: residents, lands and all living beings in the region.

In this paper, we introduce a control method for the linear quadratic tracking (LQT) problem with zero steady-state error. It is achieved by augmenting the original system with an additional state representing the integrated error between the reference and actual outputs. In its essence, it is a linear quadratic integral (LQI) control embedded in a general LQT control framework, with the reference trajectory generated by a linear exogenous system. During the simulative implementation for the specific real-world system Car-in-the-Loop (CiL) test bench, we assume that the 'real' system is completely known. Therefore, for the model-based control, we can have a perfect model identical to the 'real' system. It becomes clear that stable solutions can scarcely be achieved with controller designed with the perfect model of the 'real' system. Contrary, we show that a model learnt via Bayesian Optimization (BO) can facilitate a much bigger set of stable controllers. It exhibits an improved control performance. To the best of the authors' knowledge, this discovery is the first in the LQT related literature.

Mucus plugging of the respiratory tract occurs in airway diseases, including asthma, chronic obstructive pulmonary disease and cystic fibrosis. It can cause blockage of airways, leading to breathlessness and lung failure. Here, we demonstrate the effect of BromAc® in dissolving mucus plugs in an novel ex-vivo ovine obstructive lung model using a ventilatory setup. Mucus simulant was filled into the trachea of freshly slaughtered ovine lungs and ventilated via an endotracheal tube (ETT) using Continuous Mandatory Ventilation. Predetermined single or repeated doses of Bromelain, Acetylcysteine (Ac), BromAc® and saline control were administered via an Aerogen® vibrating nebuliser and ventilated for 30 or 60 minutes. Ventilatory recording of resistance, compliance, tidal volume was conducted and rheology pre and post treatment were measured. A significant decline in airway resistance (p&lt;0.0001) compared to saline control was observed when treated with Bromelain, Ac and BromAc®, with the latter showing a stronger mucolytic effect than single agents. The decline in resistance was also effective in shorter timepoint (p&lt;0.05) at lower doses of the drugs. Changes in compliance, peak pressure and tidal volume was not observed post-administration of the drugs. Rheology measurements revealed that BromAc®™ significantly reduced the viscosity of the mucin at the end of 30-minute and 60-minute time points (p&lt;0.001) compared to the saline control. BromAc® showed complete dissolution of the respiratory mucus simulant and improved ventilatory airflow parameters in the ex-vivo ovine model.

Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. Employing the Newmark method, we systematically assess the susceptibility to coseismic landslides through the application of six distinct displacement regression models. The efficacy of these models is validated against the actual landslide inventory using the area under the curve. A hazard map of coseismic landslides is generated based on the displacement regression model with the highest degree of fit. The results show that Moxi Town, Detuo Town, the flanks of the Daduhe River, Wandonghe River, Hailuogou River, and Yanzigou River are high susceptibility areas for coseismic landslides. The study explores factors influencing model fit, revealing that the inclusion of both epicentral distance and the distance to seismogenic fault and in displacement prediction enhances model performance. Nevertheless, in close proximity to fault zones, the distance to seismogenic fault exerts a more significant influence on the spatial distribution density of coseismic landslides compared to the epicentral distance. Conversely, in regions situated further from fault zones, the epicentral distance has a greater impact on the spatial distribution density of coseismic landslides compared to the distance to seismogenic fault. These findings contribute to a nuanced understanding of coseismic landslide susceptibility and offer valuable insights for future Newmark method-based coseismic landslide displacement calculations.

The morphology, internal architecture, and emplacement mechanisms of the Central Atlantic Magmatic Province (CAMP) lava flows of the Hartford and Deerfield basins (USA) are presented. The Talcott, Holyoke, and Hampden formations within the Hartford basin constitute distinct basaltic units, each exhibiting chemical, mineralogical, and structural differences corresponding to flow fields. Each flow field was the result of several sustained eruptions that produced both inflated pahoehoe flows and subaquatic extrusions: 1–5 eruptions in the Talcott formation and 1-2 in Holyoke and Hampden basalts, where simple flows are dominant. The Deerfield basin displays the Deerfield basalt unit, characterized by pillow lavas and sheet lobes, aligning chemically and mineralogically with the Holyoke basalt unit. Overall, the studied flow fields are composed of thick, simple pahoehoe flows that display the entire range of pahoehoe morphology, including inflated lobes. The three-partite structure of sheet lobes, vertical distribution of vesicles, and segregation structures are typical. The characteristics of the volcanic pile suggest slow emplacement during sustained eruptive episodes and are compatible with a continental basaltic succession facies model. The studied CAMP basalts of the eastern United States are correlated with the well-exposed examples on both sides of the Atlantic Ocean (Canada, Portugal, and Morocco).

We investigate emergent conductive phenomena triggered by collinear antiferromagnetic orderings. We show that an up-down-zero spin configuration in a triangle cluster leads to linear and nonlinear spin conductivities even without the relativistic spin–orbit coupling; the linear spin conductivity is characterized by the Drude-type one, while the nonlinear spin conductivity is characterized by the Hall-type one. We demonstrate the emergence of both spin conductivities in the breathing kagome system consisting of a triangle cluster. The nonlinear spin conductivity becomes larger than the linear one when the Fermi level lies near the region where a small partial band gap opens. Our results indicate that collinear antiferromagnets with the triangle geometry give rise to rich spin conductive phenomena.

Product attribute extraction is an growing field in e- commerce business, with several applications including product ranking, product recommendation, future assortment planning and improving online shopping customer experiences. Under- standing the customer needs is critical part of online business, specifically fashion products. Retailers uses assortment planning to determine the mix of products to offer in each store and channel, stay responsive to market dynamics and to manage inventory and catalogs. The goal is to offer the right styles, in the right sizes and colors, through the right channels. When shoppers find products that meet their needs and desires, they are more likely to return for future purchases, fostering customer loyalty. Product attributes are a key factor in assortment planning. In this paper we present PAE, a product attribute extraction algorithm for future trend reports consisting text and images in PDF format. Most existing methods focus on attribute extraction from titles or product descriptions or utilize visual information from existing product images. Compared to the prior works, our work focuses on attribute extraction from PDF files where upcoming fashion trends are explained. This work proposes a more comprehensive framework that fully utilizes the different modalities for attribute extraction and help retailers to plan the assortment in advance. Our contributions are three-fold: (a) We develop PAE, an efficient framework to extract attributes from unstructured data (text and images); (b) We provide catalog matching methodology based on BERT representations to discover the existing attributes using upcoming attribute values; (c) We conduct extensive experiments with several baselines and show that PAE is an effective, flexible and on par or superior (avg 92.5% F1-Score) framework to existing state-of-the-art for attribute value extraction task.

Background and Objectives: To assess femoral shaft bowing (FSB) in the coronal and sagittal planes and introduce the clinical implications of total knee arthroplasty (TKA) by analyzing a three-dimensional (3D) model with virtual implantation of the femoral component. Materials and Methods: Sixty-eight patients (average age: 69.1 years) underwent 3D model reconstruction, incorporating medullary canals, by importing computed tomography (CT) data into Mimics® software. A mechanical axis (MA) line was drawn from the midportion of the femoral head to the center of the intercondylar notch; proximal/distal straight centerlines (length, 60 mm; diameter, 1 mm) were placed in the center of the medulla canal. Acute angles between the two centerlines were measured as lateral and anterior bowing. The acute angle by distal centerline and MA line was measured as distal coronal and sagittal alignment in both anteroposterior (AP) and lateral views. The diameter of curve (DOC) of the medulla along the posterior border was measured. Results: The mean lateral bowing on the AP view was 3.71°, and the mean anterior bowing on the lateral view was 11.82°. The average DOC of the medullary canal was 1501.68 mm. The distal coronal alignment of all femurs was an average of 6.40°, and the distal sagittal alignment was 2.66°. Overall, 22 femurs had coronal bowing, 42 had sagittal bowing, and 15 had both. Conclusion: In Asians, FSB is present in coronal, sagittal, or both directions. Increased anterolateral FSB may lead to cortical abutment in the sagittal plane despite limited space in the coronal plane. During TKA, distal coronal alignment can guide distal femoral valgus cut angle, whereas distal sagittal alignment helps anticipate femoral component positioning, avoiding anterior notching. Osteotomy along the anterior cortical bone to prevent notching may result in outliers due to differences between distal sagittal alignment and distal anterior cortical axis.