Many assets of clinical medicine, such as clinical chemistry and diagnostic imaging, make clinical diagnosis outstanding compared to postmortem diagnosis. An assessment of functional status certainly has priority over the postmortem, cross-sectional use of diagnostic tests and laboratory equipment. In addition, the cost of these tools is sometimes steep, and their use does not always fit into a reasonable cost–benefit ratio. However, sometimes postmortem observations, such as inflammation, pulmonary edema, or infiltration and cerebral swelling, cannot be explained without implementing immunohistochemical markers for postmortem diagnosis. Introducing blood-based biomarkers in postmortem care could significantly reduce the rates of inconclusive postmortems and discrepancies in autopsy findings and clinical diagnoses. This is particularly relevant in the scope of vascular pathology, considering the great burden of vascular diseases in overall mortality. Expanding traditional autopsy with blood-based (circulating) biomarkers to avoid invasive postmortem examination would have cultural, religious, and potential economic advantages. All of the target molecules are discussed in the context of the process they up-regulate or down-regulate and which turns out to be the final cause of death. Ultimately, it is evident that further studies are needed to provide concrete validation for a combination of markers on a specific case order to reach a postmortem diagnosis with or without clinical records.

Fe rich soft magnetic alloys are candidates for applications as magnetic sensors and actuators. These alloys can also be added to hard magnetic compounds to obtain spring magnets. In this work we produce two nanocrystalline Fe-Zr-B-Cu alloys by the powder metallurgy powder technology of mechanical alloying. The increase of the boron content favors the reduction of the crystalline size. Thermal analysis (by differential scanning calorimetry) shows at temperatures between 450 and 650 K wide exothermic processes, associated with the relaxation of tensions of the alloys produced by milling. At high temperatures, a main crystallization peak is found. The apparent activation of this process was determined by Kissinger and isoconversional methods. The values are compared with those found in the scientific literature. Likewise, adapted thermogravimetry permits to determine the Curie temperature. The functional response has been analyzed by hysteresis loop cycles. According to the composition, the decrease of the Fe/B ratio diminishes the magnetic soft behavior.

The oncologic disease is a big global health issue that causes thousands of deaths annually, and it has a significant impact in the life quality of patients. Prostate cancer (PCa) is the second most diagnosed cancer and the fourth leading cause of cancer-related death in men in the western world. Delineation of pathogenetic pathways and key driver molecular alterations involved in PCa development has provided a roadmap for the evaluation of biomarkers in predicting disease outcome and to identify potential therapeutic targets. Chemotherapeutic agents introduced from the 1990s include the taxanes (paclitaxel, docetaxel and cabazitaxel), which are the most anticancer drugs used for PCa treatment. This review presents the current knowledge about the onset and development of PCa, state-of-art on the use of taxane-based therapy, and their combination with targeting different transmembrane oncoproteins in PCa. The silencing of some transmembrane proteins can improve taxane sensitivity, and therefore, may be a mechanism to improve the ef-fectiveness of these drugs in PCa treatment. This combined therapy needs to be explored as po-tential therapeutic agent for reducing cell proliferation, migration, and invasiveness in PCa.

Objective: To investigate the feasibility and preliminary effects of a 12-week home-based moderate intensity cycling programme in stroke survivors. Design: Single-blinded, parallel group, Randomised Controlled Trial Setting: Home-based Participants: Participants will be survivors of first-ever stroke and randomly assigned to an Active Cycling Group (ACG) or control group. Intervention: The ACG performed a 12-week home-based cycling programme, three sessions per week. Work intervals were executed at 75% of the maximal heart rate (HR_max), recovery intervals at 50% of HR_max. Work interval duration increased from three ten-minute intervals to 30 minutes continuous cycling. The control group completed a 12-week neurocognitive exercise programme. Main outcome measurements: Primary outcome measures are compliance with training, adherence to protocol and safety as measured by adverse events. Secondary outcome measurements included a: simplified modified Rankin Scale questionnaire (stroke severity), Six-Minute Walking Test (walking ability), Ten-meter Walk Test (walking ability), Rivermead Mobility Index (walking ability), graded submaximal cycling test (exercise capacity), Physical Activity for Individuals with Physical Disability questionnaire (physical activity), EQ-5D-5L (quality of life), Stroke Specific Quality of Life questionnaire (quality of life), Montreal Cognitive Assessment (cognition), General Self-Efficacy Scale (self-efficacy), and Exercise Self-Regulation Questionnaire (self-regulation).

Close to half (45.4%) of 2.3 million breast cancers (BC) diagnosed in 2020 were from Asia. While the burden of breast cancer has been examined on the level of broad geographic regions, literature on more in-depth coverage of the individual countries and subregions of the Asian continent is lacking. This review examines the breast cancer burden in 47 Asian countries. Breast cancer screening guidelines and risk-based screening initiatives are discussed.

ASEAN SME has a role as the regional socioeconomic stabilizer. This particular role is inseparable from endogenous multi-sector collaboration. Although, Indonesian SMEs were struggled in adopting Industry 4.0 correspond to digital infrastructure and digital literacy problems. This study evaluates Indonesian SME collaboration dynamics with government and technology startup (TS). The integration of agent-based model and causal loop simulation were employed to assess the TS collaboration impact on SME Industry 4.0 adoption and SME competition with larger competitors. The simulation results imply the SME collaboration with TS can lead to early adoption of Industry 4.0 which balances the business competition environment. The model also shows rising the government aid exponentially can help the SME to late adoption of Industry 4.0 which unable to sustain the SME in business competition. Thus, the developed integrative simulation model is a state-action planning model with each state result bounded to the previous state result that determined by initial input parameters. Conclusively, the model can be used as a resiliency planner for SME Industry 4.0 adoption.

After a long limbo, RNA has gained its credibility as a druggable target, fully earning its de-served role in the next-generation area of pharmaceutical R&D. We have recently probed the Trans-Activation Response element (TAR), a RNA stem–bulge–loop domain of the HIV-1 genome with bis-3-chloropiperidines (B-CePs), and revealed the compounds unique behavior in stabiliz-ing TAR structure, thus impairing in vitro the chaperone activity of the HIV-1 nucleocapsid (NC) protein. Seeking to elucidate the determinants of B-CePs inhibition, we have further characterized here their effects on the target TAR and its NC recognition, while developing quantitative analyti-cal approaches for the study of multicomponent RNA-based interactions.

Cadmium (II) contamination in the environment is an emerging problem due to its acute toxicity and mobility, so it is very urgent to remove this species from industrial wastewater before it is discharged into the environment. Thus, a starch-based activated carbon (AC) with a specific surface area of 1600 m2g-1 is used as an adsorbent for the capturing of toxic Cadmium (II) ions from synthetic solution. The sorbent is characterized by BET, SEM, TEM, XRD, FT-IR, TGA, and zeta potential. The maximum uptake (284 mg g-1) of Cadmium (II) ion is obtained at pH 6. The thermodynamics parameters like ∆G, ∆H, ΔS are found to be -17.42 kJmol-1, 6.49 kJ mol-1, and 55.66 Jmol-1K-1 respectively, revealing that the adsorption mechanism is endothermic, spontaneous, and feasible. The experimental data follows the D-R and Langmuir models well. The mass transfer is controlled by pseudo 2nd order kinetics. Furthermore, the density functional theory simulations demonstrate that the activated carbon strongly interacted with the Cd (II) ion through its various active sites. The adsorption energy noted for all interactive sites is highly negative (-0.45 eV to -10.03 eV), which shows that the adsorption process is spontaneous and stable which is in agreement with the experimental thermodynamics analysis.

Due to the continuing high suicide rates among young men, there is a need to understand help-seeking behaviour and engagement with tailored suicide prevention interventions. The aim of this study was to compare help-seeking among younger and older men who attended a therapeutic centre for men in a suicidal crisis. In this case series study, data were collected from 546 men who were referred into a community-based therapeutic service in North West England. Of the 546 men, 337 (52%) received therapy; 161 (48%) were aged between 18 and 30 years (mean age 24 years, SD=3.4). Analyses included baseline differences, symptom trajectories for the CORE-34 Clinical Outcome Measure (CORE-OM) and engagement with the therapy. For the CORE-OM there was a clinically significant reduction in mean scores between assessment and discharge (p<0.001) for both younger and older men. At initial assessment, younger men were less affected by entrapment (46% v 62%; p=.02), defeat (33% v 52%; p=.01), not engaging in new goals (38% v 47%; p=.02), and positive attitudes towards suicide (14% v 18%; p=.001) than older men. At discharge assessment, older men were significantly more likely to have an absence of positive future thinking (15% v 8%; p=0.03), have less social support (45% v 33%; p=.02) and feelings of entrapment (17% v 14%; p=.02) than younger men. Future research needs to assess the long-term effects of help-seeking using a brief psychological intervention for young men in order to understand whether the effects of the therapy are sustainable over a period of time following discharge from the service.

Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfillment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.

Modern agent-based models (ABM) and other simulation models require evaluation and testing of many different parameters. Managing that testing for large scale parameter sweeps (grid searches) as well as storing simulation data requires multiple, potentially customizable steps that may vary across simulations. Furthermore, parameter testing, processing, and analysis are slowed if simulation and processing jobs cannot be shared across teammates or computational resources. While high-performance computing (HPC) has become increasingly available, models can often be tested faster through the use of multiple computers and HPC resources. To address these issues, we created the Distributed Automated Parameter Testing (DAPT) Python package. By hosting parameters in an online (and often free) "database", multiple individuals can run parameter sets simultaneously in a distributed fashion, enabling ad hoc crowdsourcing of computational power. Combining this with a flexible, scriptable tool set, teams can evaluate models and assess their underlying hypotheses quickly. Here we describe DAPT and provide an example demonstrating its use.

Due to the continuing high suicide rates among young men, there is a need to understand help-seeking behaviour and engagement with tailored suicide prevention interventions. The aim of this study was to explore help-seeking behaviour and engagement for young men aged 18 to 30 years who attended a therapeutic centre for men in a suicidal crisis. In this prospective cohort study, data were collected from 546 men who were referred into a community-based therapeutic service in North West England. Of the 546 men, 337 (52%) received therapy; 161 (48%) were aged between 18 and 30 years (mean age 24 years, SD=3.4). One third (n=54; 34%) of the men were seen within 48 hours of their referral. Analyses included baseline differences, symptom trajectories for the CORE-34 Clinical Outcome Measure (CORE-OM) and engagement with the therapy. For the CORE 34 there was a clinically significant reduction in mean scores between assessment and discharge (p<0.001), with all outcomes demonstrating a large effect size. Future research needs to assess the long-term effects of help-seeking using a brief psychological intervention for young men in order to understand whether the effects of the therapy are sustainable over a period of time following discharge from the service.

It’s always a challenge for a teacher to get their students to be more engaged or active in the classroom. Engagement happens when students are interested in the subject, have fun, and feel welcome in the classroom. But how do we make students more engaged? To make this happen, Educational escape rooms were introduced into studies. In this paper, we will discuss about the creation and evaluation of educational escape rooms within an engineering learning context. As part of our research project, four educational escape rooms were created for students and surveys were conducted among them to evaluate the success of our project. Our findings indicate that the escape room learning model is well accepted by the students. As a result of the activity, high levels of dedication and enthusiasm are recorded and students shows the eagerness to do more escape room activities.

In some parts of Nigeria, many girls do not attend school, and among those sent to school, many still drop out early. This and other socio-cultural factors affect girls psychologically. There is no doubt that girls need consistent love and tutoring to guide them through the turbulent teen years and beyond. They need a mentor who acts as a friend and a role model. The Mobile-based Mentoring Platform seeks to leverage on mobile technology's affordances to focus on the needs of the girl-child, such as improvement in academic achievement, guidance in career choice, development of self-concept, and esteem. The girl-mentees comments revealed that using the platform provided them frequent access to mentors and access to learning opportunities. The challenges they faced include epileptic internet network, intrusions by parents, and others. Therefore, this paper examined the challenges and benefits of mentoring girls via a mentoring platform.

Course-based undergraduate research experiences (CUREs) provide students with opportunities for the same gains that apprenticed research with faculty members offer. As their popularity increases, it is important that critical elements of CUREs are supported by thoughtful design. Student experiences in CUREs can provide important insights into why CUREs are so effective. We present evidence from students who participated in CUREs at the introductory, intermediate, and advanced levels, as well as from graduate teaching assistants for an introductory lab course that included a CURE. Students and teaching assistants describe repetition as a valuable element in CUREs and other laboratory experiences. We used student work and open-ended interviews to identify which of five previously described elements of CUREs students found important. Because repetition was particularly salient, we characterized how students described repetition as they experienced it in courses that contained full-length or “micro”-CUREs. In prompted interviews, students described how repetition in CUREs provided cognitive (learning concepts) and practical (learning technical skills) value. Recent graduates who had participated in CUREs at each level of their Biology education were particularly aware that they placed value in repetition and acknowledged it as motivational in their own learning. Many students described repetition in metacognitive terms, which also suggests that as students advance through laboratory and CURE curricula, their understanding of how repetition supports their learning becomes more sophisticated. Finally, we integrated student descriptions to suggest ways in which repetition can be designed into CUREs or other laboratory courses to support scientific learning and enhance students’ sense of scientific identity.

Starting from the importance of risk perception for taking certain preventive measures to protect people and their property from disasters, the subject of the research is to examine the factors influencing public perception of mythically-based human behavior in disaster conditions. Using the random sampling method, 250 adult respondents were surveyed in the city of Belgrade, using a specially created and adapted survey questionnaire. The results of the research show that there is no statistically significant influence of gender, age, educational and economic factors on the public perception of human behavior in disaster conditions. The results of the research can be used to improve strategies and campaigns based on risk assessment, aimed at improving the safety of people in disasters.

High levels of meat consumption are increasingly being criticised for ethical, environmental, and social reasons. Plant-based meat substitutes have been identified as healthy sources of protein in comparison to meat. This alternative offers several social, environmental and health benefits and may play a role in reducing meat consumption. However, there has been a lack of research on how specific meat substitute attributes can influence consumers to replace or partially replace meat in their diets. Research demonstrates that in many countries consumers are highly attached to meat. They consider it as an essential and integral element of their daily diet. For these consumers which are not interested in vegan or vegetarian alternatives to meat, so-called meathybrids could be a low-threshold option for a more sustainable food consumption behaviour. In meathybrids only a fraction of the meat product (e.g. 20% to 50%) is replaced with plant-based proteins. In this paper, the results of an online survey with 501 Belgium consumers are presented with focus on preferences and attitudes relating to meathyrids. The results show that more than fifty percent of consumers substitute meat at least occasionally. Thus, about half of the respondents reveal an eligible consumption behaviour in respect to sustainability and healthiness to a certain degree. Concerning the determinants of choosing either meathybrid or meat it becomes evident that a strong effect is exerted by the health perception. The healthier meathybrids are perceived, the higher is the choice probability. Thus, this egoistic motive seems to outperform altruistic motives like animal welfare or environmental concerns when it comes to choice for this new product category.

High levels of meat consumption are increasingly being criticised for ethical, environmental, and social reasons. Plant-based meat substitutes have been identified as healthy sources of protein that, in comparison to meat, offer a number of social, environmental and health benefits and may play a role in reducing meat consumption. However, there has been a lack of research on the role they can play in the policy agenda and how specific meat substitute attributes can influence consumers to replace partially replace meat in their diets.

High levels ofmeat consumption are increasingly being criticised for ethical, environmental, 2 and social reasons. Plant-based meat substitutes have been identified as healthy sources of protein in 3 comparison to meat. This alternative offers several social, environmental and health benefits and may 4 play a role in reducing meat consumption. However, there has been a lack of research on how specific 5 meat substitute attributes can influence consumers to replace or partially replace meat in their diets. 6 Research demonstrates that in many countries consumers are highly attached to meat.They consider 7 it as an essential and integral element of their daily diet. For these consumers which are not interested 8 in vegan or vegetarian alternatives to meat, so-called meathybrids could be a low-threshold option 9 for a more sustainable food consumption behaviour. In meathybrids only a fraction of the meat 10 product (e.g. 20% to 50%) is replaced with plant-based proteins. In this paper, the results of an online 11 survey with 500 German consumers are presented with focus on preferences and attitudes relating 12 to meathyrids. The results show that more than fifty percent of consumers substitute meat at least 13 occasionally. Thus, about half of the respondents reveal an eligible consumption behaviour in respect 14 to sustainability and healthiness to a certain degree. Concerning the determinants of choosing either 15 meathybrid or meat it becomes evident that the highest effect is exerted by the health perception. The 16 healthier meathybrids are perceived, the higher is the choice probability. Thus, this egoistic motive 17 seems to outperform altruistic motives like animal welfare or environmental concerns when it comes 18 to choice for this new product category.

Latin binomials, popularised in the eighteenth century by the Swedish naturalist Linnaeus, have stood the test of time in providing a stable, clear and memorable system of nomenclature across biology. However, relentless and ever-deeper exploration and analysis of the microbial world has created an urgent unmet need for huge numbers of new names for Archaea and Bacteria. Manual creation of such names remains difficult and slow and typically relies on expert-driven nomenclatural quality control. Keen to ensure the legacy of Linnaeus lives on in the age of microbial genomics and metagenomics, we propose an automated approach, employing combinatorial concatenation of roots from Latin and Greek to create linguistically correct names for genera and species that can be used off the shelf as needed. As proof of principle, we document over a million new names for Bacteria and Archaea. We are confident that our approach provides a road map for how to create new names for decades to come.

Universitas Sebelas Maret (UNS) through SMART UNS Company has conducted research and development of e-motorcycle conversion using Li-ion battery pack as a substitute for ICE energy source from the conventional motorcycle. Currently, the battery-pack that used for e-motorcycle conversion is in the development phase towards commercialization. The challenge of estimating production costs is the complicated production process and storing hidden expenses that can be a problem. This hidden cost is often a missing or varied factor that costs less or more expensive. This study presents an integrated parametric cost estimation model with activity-based cost assignments to estimate production costs through cost calculations for each activity. Activity-based costs break the production process into a specific cost element for each step. Each activity's cost is put into a parametric cost estimation model to calculate the cost of each activity into the total cost of production. Cost estimation results will be analyzed using a regression method to determine which variables most affect the production cost of Li-ion battery packs for the conversion of e-motorcycles in the SMART UNS company.

The complexity of the design in high-rise residential projects is a challenge for the construction industry in completing projects that fit the needs of users. Performance-Based Building Design (PBBD) appears as a design concept that can describe these needs into performance requirements. In this case designing a building can be considered as an iterative process of exploration, where desired functional properties can be created, the shapes are suggested, and evaluation processes is used, so as to bring together the shapes and functions of the building. This concept is a container for designers to produce high-performance buildings. This study aimed to identify the performance-based building design factors applied by architect designers and engineers in high-rise residential building in Surabaya. As part of this study, primary data was collected based on surveys conducted through observation and questionnaire distributed to designers who had or were involved in the high-rise residential design process in Surabaya. A total of sixty-eight respondents were included in this study. Descriptive analysis through a mean and standard deviation scatter plot was used to rank the application of PBBD. Meanwhile, factor analysis was used in the analysis of PBBD application factors. From the results of the analysis, four factors were obtained for the application of PBBD in high-rise residential buildings in Surabaya, namely; the interests of occupants, the sustainability of building operations, the design collaboration process, and the risk of loss. Future research is the influence relationships and measure the success model of PBBD at a higher level into BIM (Building Information Modeling) interoperability.

Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 1.6 million cases while more than 105,000 have died due to it, with these figures rising on a daily basis across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is therefore crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is an effective paradigm that allows for the utilization of cases’ specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study therefore aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in classification of suspected cases of Covid19. The CBR model leverages on a novel feature selection and semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories at an accuracy of 97.10%. The study found that the proposed model can support physicians to easily diagnose suspected cases of Covid19 base on their medical records without subjecting the specimen to laboratory test. As a result, there will be a global minimization of contagion rate occasioned by slow testing and as well reduce false positive rates of diagnosed cases as observed in some parts of the globe.

Visualization of the seabed terrain is one of the key functions of marine geographic information system. The major challenge here is that the obtained data for seabed terrain usually consists of elevation and sediment only but does not include remote sensing images, which is important for ground terrain visualization, as they can be used as textures to reveal detailed information and achieve realistic visual results. Existing seabed terrain visualizing methods (including annotations, color-mapping, and texture-mapping) have limitations in reality and intuition, as they are inadequate to express the physical characteristics of sediment accurately. This paper presents a novel and advanced 3D visualization method of seabed terrain, which introduces the Physically Based Rendering (PBR) theory into the field of geographic visualization. We analyze the main categories of seabed sediments and their optical features respectively, then develop a procedural method to generate physically based rendering materials for different sediments. Then an enhanced bidirectional reflectance distribution function is employed to achieve accurate and intuitive terrain rendering. We also refine the texture sampling method and propose a procedural seabed objects generation method to construct a more natural and realistic undersea environment. Experimental results reveal our method can make good use of the limited seabed terrain data and get significant improvements in visual effect, which can help users to cognize and analyze the seabed geographic environment more accurately and intuitively.

The microstructure and solidification behavior of nickel based GTD222 superalloy at different cooling rates are studied. The solidification of the GTD222 superalloy proceeds as follows: L→L+γ, L→L+γ+MC, L→L+(γ/γ ′)-Eutectic and L→η phase. The temperature of liquidus of GTD222 superalloy is 1360 °C while the solidus is slightly lower at 1310 °C, which due to the alloying elements redistribution. It was found that the dendrite arm spacing of the alloy decreased with the increase of cooling rate (From 200 μm at 2.5 K/min to 100 μm at 20 K/min).

Previously, we described the initial use of Flipped Team‐Based learning (FTBL) defined as TBL approach combined with flipped classroom learning methodology, in which students previewed online lectures and applied their knowledge in different in-class activities. The purpose of the present study is to review the progress within this approach and to investigate how constant changes in team allocation can affect student’s perception regarding this modified FTBL approach. Although students showed reluctance initially to get out of their ‘comfort zone’, our findings show that learners perceived the adoption of the continued random allocation, and became accustomed to this learning approach, which finally assisted them to enhance their team-work skills and classroom performance, to develop their reflective capabilities as well as improving their rapport building skills, learning and academic performance. Learners also believed that this learning strategy that creates critical incidents can simulate their future work environment as they might be expected to work in unfamiliar situations. Therefore, the present study indicated strong support for the modified FTBL method and was seen to work exceptionally well, despite some minor problems that students can experience working in a team and/or with different teammates in every session.

The effect of microstructure and chemistry of passive films on the kinetics of passive layer growth and passivity breakdown of some Ti-based alloys, namely Ti-6Al-4V, Ti-6Al-7Nb and TC21 alloys was studied. The rate of pitting corrosion was evaluated using cyclic polarization measurements. Chronoamperometry was applied to assess the passive layer growth kinetics and breakdown. Microstructure influence on the uniform corrosion rate of these alloys was also investigated employing Tafel extrapolation and dynamic electrochemical impedance spectroscopy. Corrosion studies were performed in 0.9% NaCl solution at 37 oC, and the obtained results were compared with ultrapure Ti (99.99%). The different phases of the microstructure were characterized by X-ray diffraction and scanning electron microscopy. Chemical composition and chemistry of the corroded surfaces were studied using X-ray photoelectron analysis. For all studied alloys, the microstructure consisted of α matrix, which was strengthened by β phase. The highest and the lowest values of the β phase’s volume fraction were recorded for TC21 and Ti-Al-Nb alloys, respectively. The uniform corrosion rate and pitting corrosion resistance (R_pit) of the studied alloys were enhanced following the sequence: Ti-6Al-7Nb < Ti-6Al-4V << TC21. The corrosion resistance of Ti-Al-Nb alloy approached that of pure Ti. The obvious changes in the microstructure of these alloys, together with XPS findings, were adopted to interpret the pronounced variation in their corrosion rates.

Bangladeshi construction industry suffers a lot of safety and accidental issues than other developing countries in the world. Among many of these, accident of construction project goes far beyond and shape a horrific figure of death for every year. The aims of this study is that analysis and discussion of causes of accident at construction project in Bangladesh. A widespread statistical data collection and data analysis take place to identify the causes and design the questionnaire. The questionnaire-based survey was used to elicit the attitude of four stakeholders as workers, owners, consultants, and contractors towards passive causes of fatal accident at construction site. These study also identify 77 passive causes under 14 major groups and ranked them based on Relative Importance Index (RII). The top 5 major group of causes are (1) Management related, (2) Consultant related, (3) Technology related, (4) Labour related and (5) Contractor related. The top 5 passive causes are: (1) Unaware of safety-related issue, (2) Lack of personal protective equipment, (3) Lack of safety eliminating/ avoiding design, (4) Unfit equipment, (5) Lack of knowledge and training on equipment.

Portfolio weights solely based on risk avoid estimation error from the sample mean, but they are still affected from the misspecification in the sample covariance matrix. To solve this problem, we shrink the covariance matrix towards the Identity, the Variance Identity, the Single-index model, the Common Covariance, the Constant Correlation and the Exponential Weighted Moving Average target matrices. By an extensive Monte Carlo simulation, we offer a comparative study of these target estimators, testing their ability in reproducing the true portfolio weights. We control for the dataset dimensionality and the shrinkage intensity in the Minimum Variance, Inverse Volatility, Equal-risk-contribution and Maximum Diversification portfolios. We find out that the Identity and Variance Identity have very good statistical properties, being well-conditioned also in high-dimensional dataset. In addition, the these two models are the best target towards to shrink: they minimise the misspecification in risk-based portfolio weights, generating estimates very close to the population values. Overall, shrinking the sample covariance matrix helps reducing weights misspecification, especially in the Minimum Variance and the Maximum Diversification portfolios. The Inverse Volatility and the Equal-Risk-Contribution portfolios are less sensitive to covariance misspecification, hence they benefit less from shrinkage.

Some contaminants of emerging concern (CECs) are known to survive conventional wastewater treatment plants, which introduce them back to the environment and can potentially cycle up in drinking water supplies. This is especially concerning because of the inherent ability of some CECs to induce physiological effects in humans at very low doses. Advanced oxidation processes (AOPs) such as TiO2 based photocatalysis are of prominent interest for addressing CECs in aqueous environments. Natural water resources often contain dissolved metal cations concentrations in excess of targeted CEC concentrations. These cations may significantly, adversely impact degradation of CECs by scavenging TiO2 surface generated electrons. Consequently, simple pseudo first order or Langmuir-Hinshelwood kinetics are not sufficient for reactor design and process analysis in some scenarios. Rhodamine B dye and dissolved copper cations were studied as reaction surrogates to demonstrate that TiO₂ catalyzed degradation for very dilute solutions is very nearly completely due to homogeneous reaction with hydroxyl radicals and that in this scenario the hole trapping pathway has negligible impact. Chemical reaction kinetic studies were then carried out to develop a robust model for RB/metal reactions that is exact in the electron pathways for hydroxyl radical production and metal scavenging.

To date, several independent methods and algorithms exist exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives, (ii) the identification of gene target-sets according to logical gene-protein-reaction associations, (iii) minimization of the number of network perturbations, and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

Background: Few studies have investigated the longitudinal association between cervical spondylosis (CS) and migraine by using a nationwide population-based database. Methods: We conducted a retrospective cohort study from 2000 to 2011 identifying 27,930 cases of cervical spondylosis and 111,720 control subjects (those without cervical spondylosis) from a single database. The subjects were frequency-matched on the basis of sex, age, and diagnosis date. The non- cervical spondylosis cohort was four times the size of the cervical spondylosis cohort. To quantify the effects of cervical spondylosis on the risk of migraine, univariate and multivariate Cox proportional hazard regression analyses were used to calculate the hazard ratio (HR) and 95% confidence interval (CI). Results: After a 10-year follow-up controlling for potential confounding factors, overall migraine incidence was higher in the cervical spondylosis cohort than in the non- cervical spondylosis cohort (5.16 and 2.09 per 1,000 people per year, respectively; crude hazard ratio = 2.48, 95% confidence interval = 2.28–2.69) with an adjusted hazard ratio of 2.03 (95% confidence interval = 1.86–2.22) after accounting for sex, age, comorbidities, and medication. Individuals with myelopathy in the cervical spondylosis cohort had a 2.19 times (95% confidence interval = 1.80–2.66) higher incidence of migraine compared than did those in the non- cervical spondylosis cohort. Conclusion: Individuals with cervical spondylosis exhibited a higher risk of migraine than those without cervical spondylosis. The migraine incidence rate was even higher among individuals with cervical spondylotic myelopathy.

This paper outlines evidence-based practice in the context of professionalism, and highlights the contribution evidence-based practice can make to the professional practice of higher education course managers. Implications of the changing HE landscape for the status of academics as professionals are reviewed, and evidence-based practice is proposed as a solution for both enhanced course management and to remedy perceived deprofessionalisation. Finally, questions regarding researching professional practice within one’s own institution are addressed.

A comprehensive kinetic model of the Fischer-Tropsch synthesis (FTS) is developed in a fixed bed reactor under operating conditions (temperature, 230–235°C, pressure, 20–25 bar, gas hourly space velocity, 4000–5000 cm³(STP)/h/g_catalyst ,H₂/CO feed molar ratio, 2.1) over a Co based catalyst. Reaction rate equations based on Eley-Rideal (ER) type model for initiation step and Langmuir-Hinshelwood-Hougen-Watson (LHHW) type model for propagation and termination steps of the FTS reactions have been considered and the readsorption of olefins were taken into account. The model that was reported in the literature was modified in order to explain many significant deviations from the ASF distribution. Optimum parameters have been obtained by Genetic Algorithms (GA). The calculated activation energies to produce n-paraffins and 1-olefins were in the range of 82.24 to 90.68 kJ/mol and 100.66 to 105.24 kJ/mol, respectively. The hydrocarbon distribution in FTS reactions was satisfactorily predicted particularly for paraffins.

Decades of research suggest that Mindfulness-Based Stress Reduction (MBSR) training supports a greater capacity to live with chronic medical conditions and contributes to lowering stress levels. This paper introduces a model for a Mindfulness-Based Recovery from Stroke (MBRfS) for promoting stroke recovery, informed by the lived experience of the author (a stroke survivor and certified MBSR instructor), the research literature regarding MBSR training, and the specific challenges of stroke recovery. Four themes emerged from the autoethnographic analysis that informed the proposed model: Readiness to accept the stroke event and the acquired brain injury; Navigating uncertainties of stroke recovery with awareness and self-responsibility for outcomes; Trusting the inherent wisdom of the body as a stroke recovery “teacher”; and Increased capacity to integrate complex emotions with self-compassion, and a sense of wholeness. A four component MBRfS model is offered, which consists of an integration amongst a modified MBSR framework, emergent attitudinal themes, and insights from the autoethnographic vignettes. The MBRfS model offers a path for providing participants with a supportive experience within stroke recovery. Recommendations and suggestions for future studies are offered to support the development of MBRfS for stroke survivors and their caregivers, as well as contributing to health care providers.

In the context of modern healthcare, the concept of patient-centered care (PCC) has emerged as a crucial aspect, prompting the need for innovative frameworks to empower patients and enhance healthcare outcomes. This paper introduces the Patient-Oriented Medical Ontology Conceptual Framework (POMOCF), a powerful framework aimed at giving patients control over their personal and healthcare information. It prioritizes patient independence by allowing for direct control of electronic health records (EHRs) and facilitates the gathering of information through document upload or scanning. In the POMOCF front-end framework, we propose to maintain scalability, along with robust authorization, authentication, and secure data protocols, all of which are detailed within the framework. This approach enhances data accuracy and security, improving data management efficiency. As part of our ongoing development, we are planning to implement an innovative inference system that will automatically analyze and extract relevant details from the uploaded authorized document, provide real-time updates, and maintain data accuracy. This approach further strengthens data accuracy and prevents unauthorized modifications, thereby making the data management process more efficient. POMOCF leverages medical ontology principles to ensure standardized clinical concepts and improved semantic interoperability. Our integration plans involve integrating genotypic data for personalized medicine initiatives and strengthening the front-end infrastructure. By linking front-end interfaces with advanced technological solutions, we aim to provide effective healthcare delivery, promoting personalized care and enhancing patient outcomes.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

The rescue, digitisation, quality control, preservation and utilization of long and high quality meteorological and climate records, particularly related to historical marine data, are crucial for advancing our understanding of the Earth's climate system. In combination with land and air measurements, the historical marine records serve as foundational pillars in linking present and past weather and climate information, offering essential insights into natural climate variability, extreme events in marine areas, baseline data for assessing current changes, and inputs for en-hancing predictive climate models and reanalyses. This paper provides an overview of marine data rescue activities covering the past centuries, presents and highlights several ongoing projects across the world and how the data are used in an integrative and international framework. Current and future continuous efforts in data rescue, digitisation, quality control and the de-velopment of temporally high resolution meteorological and climatological observations from the Sea, will greatly help to further complete our understanding and knowledge of the Earth system including extremes, as well as improving the quality of reanalysis.

The ability to monitor Sprained Ankle Rehabilitation Exercises (SPAREs) in home environments can help therapists to ascertain if exercises have been performed as prescribed. Whilst wearable devices have been shown to provide advantages such as high accuracy and precision during monitoring activities, disadvantages such as limited battery life, users' inability to remember to charge and wear the devices are often the challenges for their usage. Also, video cameras, which are notable for high frame rates and granularity, are not privacy-friendly. This paper, therefore, proposes the use and fusion of unobtrusive and privacy-friendly sensing solutions for data collection and processing during SPAREs in home environments. Two Infrared Thermopile Array (ITA-32) thermal sensors and two Frequency Modulated Continuous Wave (FMCW) Radar sensors were used to simultaneously monitor 15 healthy participants during SPAREs which involved twisting their ankle in 4-fundamental movement patterns namely (i) extension, (ii) flexion, (iii) eversion and (iv) inversion. Experimental results indicated the ability to identify thermal blobs of participants performing the 4 fundamental movement patterns of the human ankle. Cluster-based analysis of data gleaned from the ITA-32 sensors and the FMCW Radar sensors indicated average classification accuracy of 96.9% with K-Nearest Neighbours, Neural Network, AdaBoost, Decision Tree, Stochastic Gradient Descent and Support Vector Machine, amongst others.

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-censor data will have a specific strength in further classification of nemoral forest landscapes owing to the distinct seasonal patterns of the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and machine learning (random forest) trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90 %, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34-74%. Species groups with coniferous species were the least confused whereas the broadleaf groups, especially Oak, had higher error rates. The results are applied in Danish National accounting of greenhouse gas emissions from forests, resource assessment and assessment of forest biodiversity potentials.

Obesity is a global public health problem and the environment as its major determinant. To identify interventions an evidence base is warranted. To this aim we investigate the relationship between the consumption of foods and eating locations (like home, school/work and others) in British adolescents, using data from the UK National Diet and Nutrition Survey Rolling Program (2008–2012 and 2013-2016). Cross-sectional analysis of 62,523 food diary entries from this nationally representative sample then focused on foods contributing up to 80% total energy to the daily adolescent´s diet. Correspondence Analysis (CA) was first used to generate food-location relationship hypotheses and Logistic Regression (LR) to quantify the evidence in terms of odds ratios and formally test those hypotheses. The less-healthy foods that emerged from CA were chips, soft drinks, chocolate and meat pies. Adjusted Odds Ratios (99% CI) for consuming specific foods at a location “Other” than home (H) or school/work (S) in the 2008-12 survey sample were: for soft drinks 2.8 (2.1 to 3.8) vs. H and 2.0 (1.4 to 2.8) vs. S; for chips 2.8 (2.2 to 3.7) vs. H and 3.4 (2.1 to 5.5) vs. S; for chocolates 2.6 (1.9 to 3.5) vs. H and 1.9 (1.2 to 2.9) vs. S; and for meat pies 2.7 (1.5 to 5.1) vs. H and 1.3 (0.5 to 3.1) vs. S. These trends were confirmed in the 2013-16 survey sample. Interactions between location and BMI were not significant in either sample. In conclusion, our study showed that adolescents are more likely to consume specific less-healthy foods at locations away from home and school/work, irrespective of BMI. Such locations include leisure places, food outlets and “on the go”, hence public health policies to discourage less-healthy food choices in these locations is warranted for all adolescents.

A Natural gas is increasingly being sought after as a vital source of energy, given that its production is very cheap and does not cause the same environmental harms that other resources, such as coal combustion, do. Understanding and characterizing the behavior of natural gas is essential in hydrocarbon reservoir engineering, natural gas transport, and process. Natural gas compressibility factor, as a critical parameter, defines the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial model based on the group method of data handling (GMDH) is presented to determine the compressibility factor of different natural gases at different conditions, using corresponding state principles. The accuracy of the model evaluated through graphical and statistical analyses. The results show that the model is capable of predicting natural gas compressibility with an average absolute error of only 2.88%, a root means square of 0.03, and a regression coefficient of 0.92. The performance of the developed model compared to widely known, previously published equations of state (EOSs) and correlations, and the precision of the results demonstrates its superiority over all other correlations and EOSs.

There is much discussion regarding the Sustainable Development Goals’ (SDGs) capacity to promote inclusive development. While some argue that they represent an opportunity for goal-led alignment of stakeholders and evidence-based decision-making, other voices express concerns as they perceive them as a techno-managerial framework that measures development according to quantitatively defined parameters and does not allow for local variation. We argue that the extent to which the positive or negative aspects of the SDGs prevail depends on the monitoring system’s ability to account for multiple and intersecting inequalities. The need for sub-nationally (urban) representative indicators poses an additional methodological challenge – especially in cities with intra-urban inequalities related to socio-spatial variations across neighbourhoods. This paper investigates the extent to which the SDG indicators’ representativeness could be affected by inequalities. It does so by proposing a conceptual framing for understanding the relation between inequalities and SDG monitoring, which is then applied to analyse the current methodological proposals for the indicator framework of the “urban SDG”, Goal 11. The outcome is a call for 1) a more explicit attention to intra-urban inequalities, 2) the development of a methodological approach to “recalibrate” the city-level indicators to account for the degree of intra-urban inequalities, and 3) an alignment between methodologies and data practices applied for monitoring SDG 11 and the extent of the underlying inequalities within the city. This would enable an informed decision regarding the trade-off in indicator representativeness between conventional data sources, such as censuses and household surveys, and emerging methods, such as participatory geospatial methods and citizen-generated data practices.

Augment reality (AR) is crucial for immersive human-computer interaction (HCI) and vision of artificial intelligence (AI). Labeled data drove object recognition in AR. However, manual annotating data is expensive and labor-intensive, and furthermore, scanty labeled data limits the application of AR. Aiming at solving the problem of insufficient training data in AR object recognition, an automated vision data synthesis method called BAGAN is proposed in this paper based on the 3D modeling and GAN algorithm. Our approach has been validated to have better performance than other methods through image recognition task on natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand the application scope of AR, which is of great significance to immersive interactive systems.

Affective computing in general and human activity and intention analysis in particular, is a rapidly growing field of research. Head pose and emotion changes, present serious challenges when applied to player’s training and ludology experience in serious games or analysis of customer satisfaction regarding broadcast and web services or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need of generating comprehensible visual representations from different sets of data, in this paper we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor).

This paper presents a hybrid modelling approach that combines physics-based electromigration modelling (PEM) and statistical methods to evaluate the electromigration (EM) limits of nano-interconnects in mesh networks. The approach, which is also compatible with standard Place and Route (P&R) tools and practices, takes into account the positive impact of network redundancy on EM current limits. The study shows that conventional methods underestimate the EM current limits of a power delivery network (PDN) unit-cell by 80% due to their lack of consideration for redundancy. Additionally, the time-to-failure (TTF) distributions of a PDN unit-cell adhered to a lognormal distribution, where the lognormal sigma, σlogn, exhibits a 55% reduction compared to that of the single constituent interconnects. The study also found the negative voltage (i.e. ground or Vss) grid to be more susceptible to EM than the positive voltage, i.e. Vdd grid. In the examined grid unit-cell design, both the number of interconnect sites prone to voiding and also the magnitude of the peak tensile stress within the nano-interconnects were found to be 2-folds as high in the Vss case compared to Vdd. lognormal sigma of TFF for the grid unit-cells, σ_(logn-tile), show a marked reduction compared to the lognormal sigma of the constituent single interconnects, σ_logn, with a 50% and 66% decrease compared to single interconnects, for downstream (Vss) and upstream (Vdd), respectively. In addition, σ_(logn-tile) was 3-folds higher for downstream (Vss) compared to upstream (Vdd), whilst, in contrast this difference was only 2-folds at single interconnect level. 4.13-folds higher TTF50% was observed at grid unit-cell level for upstream compared to downstream operation which was also more pronounced than in the single interconnect level where the difference was only 2-folds. This research provides valuable insights into EM aging of nano-interconnects in mesh networks and could pragmatically enhance the accuracy of EM compliance evaluation methods.

Populations with healthcare needs often reside in post-conflict settings where basic services needed to maintain good health may be non-existent or hard to access. Therefore, there is a need for better identification and reallocation of resources as part of the post-conflict health rehabilitation effort. This study applies an asset-based approach to explore the more optimal design of health services and to identify the resource constraints for basic health service delivery to the most vulnerable communities in eastern Congo. We implemented the asset mapping in two phases. Firstly, we combined a qualitative survey with community walks to identify the assets already present in the communities. Secondly, we conducted group discussions to map out assets that are the core of Asset-Based Community Development (ABCD) practice. We finally documented all assets in a Community Asset Spreadsheet. Overall, 209 assets were identified as available and potentially valuable resources for the communities in eastern Congo. Among them, 60 were local associations, 24 were land and physical environment, 43 were local institutions, 46 were individuals, 32 to economy and exchange, and only 6 were related to culture, history, and stories. Drawing upon the findings from the qualitative survey, community walks, and group discussions, we conclude that an important number of resources were in place for basic health service delivery. By activating the existing and potential resources, the most vulnerable populations in eastern Congo might have the required resources for basic health service delivery. Our findings support the use of an asset-mapping research method as appropriate to identify existing and potential resources for basic health services in a post-conflict setting.

This paper proposes two max-pooling engines, named the RTB-MAXP engine and the CMB-MAXP engine, with a scalable window size parameter for FPGA-based convolutional neural network (CNN) implementation. The max-pooling operation for the CNN can be decomposed into two stages, i.e., a horizontal axis max-pooling operation and a vertical axis max-pooling operation. These two one-dimensional max-pooling operations are performed by tracking the rank of the values within the window in the RTB-MAXP engine and cascading the maximum operations of the values in CMB-MAXP engine. Both the RBM-MAXP engine and the CMB-MAXP engine were implemented using VHSIC Hardware Description Language (VHDL) and verified by simulations. They have been employed for and tested in our CNN accelerator targeting at the CNN model YOLOv4-CSP-S-Leaky for object detection.

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines Particle Swarm Optimisation (PSO) with a turbulence intensity based Jensen wake model (TI-JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ($C_P$) or deflecting wakes by applying yaw-offsets for maximising net farm production. First, TI-JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimized strategies are evaluated using simulations based on TI-JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 seconds for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.

Traditional high pressure mechanical compressors account for over half of the car station’s cost, have insufficient reliability and are not feasible for a large-scale fuel cell market. An alternative technology, employing a two-stage, hybrid system based on electrochemical and metal hydride compression technologies, represents an excellent alternative to conventional compressors. The high-pressure stage, operating at 100-875 bar, is based on a metal hydride thermal system. A techno-economic analysis of the metal hydride system is presented and discussed. A model of the metal hydride system was developed, integrating a lumped parameter mass and energy balance model with an economic model. A novel metal hydride heat exchanger configuration is also presented, based on mini-channel heat transfer systems, allowing for effective high-pressure compression. Several metal hydrides were analyzed and screened, demonstrating that one selected material, namely (Ti_0.97Zr_0.03)_1.1Cr_1.6Mn_0.4, is likely the best candidate material to be employed for high-pressure compressors under the specific conditions. System efficiency and costs were assessed based on the properties of currently available materials at industrial levels. Results show that the system can reach pressures on the order of 875 bar with thermal power provided at approximately 150 °C. The system cost is comparable with the current mechanical compressors and can be reduced in several ways as discussed in the paper.

Abstract:The digital landscape is rapidly reshaping under the influence of AI-powered cloud-based e-commerce platforms (PCEPs). These platforms leverage the transformative power of AI to personalize customer experiences, optimize pricing strategies, enhance security, and streamline operations, offering businesses a significant competitive edge. However, challenges surrounding data privacy, algorithmic bias, and potential job displacement raise crucial ethical questions regarding widespread AI adoption in e-commerce.This study delves into the untapped potential and ethical considerations of PCEPs, distinguishing itself by proposing practical strategies for responsible development and ethical implementation.Objectives: Investigate the transformative potential of PCEPs: Personalized product recommendations:Analyze effectiveness and potential bias. Dynamic pricing strategies:Explore impact on consumer fairness and market stability. Enhanced fraud detection:Evaluate accuracy and privacy implications. Optimized supply chain management:Assess efficiency gains and job displacement risks. Critically analyze the ethical considerations of AI in e-commerce: Data privacy:Assess current practices and develop mitigation strategies. Algorithmic bias:Identify potential sources and propose solutions for fairer decision-making. Job displacement:Evaluate impacts and propose reskilling initiatives. Propose comprehensive and practical strategies for responsible PCEP development and implementation: Transparency:Enhance user awareness and control over data usage. Fairness:Design AI systems to mitigate bias and uphold consumer rights. Accountability:Establish clear guidelines and oversight mechanisms for ethical AI development.Conclusion: While PCEPs offer a plethora of transformative benefits, their ethical implementation is paramount. By adhering to principles of transparency, fairness, and accountability, businesses can unlock the full potential of PCEPs while minimizing potential risks and shaping the future of online commerce in a sustainable and equitable manner

This paper examines the impact of stringent regulations on personal data protection on customer perception of data security and online shopping behavior. In the context of the rapidly expanding e-commerce landscape, ensuring the security of personal data is a complex and crucial task. The study of several legal frameworks, including Malaysia's compliance with EU regulations and Indonesia's Personal Data Protection Law, provides valuable insights into consumer data protection. The challenges of balancing data safeguarding and unrestricted movement and tackling misuse by external entities are significant and require careful consideration. This research elucidates the pivotal role of trust in e-commerce environments and the deployment of innovative e-commerce models designed to minimize personal data sharing. By integrating advanced privacy-enhancing technologies and adhering to stringent regulatory standards such as the GDPR, this study demonstrates effective strategies for robust data protection. The paper contributes to the academic discourse by providing a comprehensive framework that synergizes legal, technological, and procedural elements to fortify data security and enhance consumer trust in digital marketplaces. This approach aligns with international data protection standards and offers a pragmatic blueprint for achieving sustainable data security in e-commerce.

In the digital age, social media platforms have become a critical channel for brands to engage with their audience and manage their reputation. This paper explores the application of sentiment analysis in social media to aid brand reputation management. Sentiment analysis, a subset of natural language processing (NLP), involves determining the emotional tone behind online discussions and opinions. By leveraging machine learning algorithms and NLP techniques, brands can analyze vast amounts of social media data to gauge public sentiment, identify emerging trends, and detect potential issues before they escalate. This study reviews current methodologies in sentiment analysis, including supervised and unsupervised learning approaches, and discusses their effectiveness in extracting actionable insights from social media content. Additionally, it examines case studies where sentiment analysis has been successfully implemented to enhance brand reputation, address customer concerns, and tailor marketing strategies. The findings highlight the importance of continuous monitoring and adaptation to dynamic social media landscapes for maintaining a positive brand image. This paper provides a comprehensive overview of sentiment analysis tools and techniques, offering practical recommendations for brands seeking to leverage social media sentiment data for proactive reputation management.

Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially in the local scale as maintaining and deploying networks is costly. With increasing interest in smaller yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosion Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either “explosion,” “ambient,” or “other” with true positive rates (recall) greater than 96% for all three categories.

This study offers an in-depth analysis of the COVID-19 pandemic’s trajectory in several member countries of the European Union (EU) to assess similarities in their crisis experiences. We also examine data from the United States to facilitate a larger comparison across continents. We introduce our new approach, which uses a spatio-temporal algorithm to identify five distinct and recurring phases that each country undergoes at different times during the pandemic. These stages include a Comfort Period characterized by minimal COVID-19 activity and limited impacts. The Preventive Situation demonstrates the implementation of proactive measures, with relatively low numbers of cases, deaths, and Intensive Care Unit (ICU) admissions. The Worrying Situation is defined by high levels of concern and preparation as deaths and cases begin to rise and reach substantial levels. The Panic Situation is marked by a high number of deaths relative to the number of cases and a rise in ICU admissions, denoting a critical and alarming period of the pandemic. Finally, the Epidemic Control Situation distinguishes itself by limiting COVID-19 deaths despite a high number of new cases. By examining these phases, we identify the various waves of the pandemic, indicating periods where the health crisis has a significant impact. This comparative analysis highlights the time lags between countries as they transition through these different critical stages and navigate the waves of COVID-19.

Mortality rates in sub-Saharan Africa remains unacceptably high, and disproportionately affecting economically disadvantaged communities. And healthcare delivery which is considered a major contributor to this is challenged by the limited resources and high service costs, prompting many patients to opt for outpatient department services. This study addresses the need for a data-driven approach to healthcare policy formulation by proposing a model for the implementation of a data warehousing system that integrates outpatients into the national healthcare framework. By leveraging data warehousing, healthcare delivery can be made more efficient through a robust repository that supports decision-making and policy formulation. The research focuses on the outpatient department in resource-scarce settings, proposing a novel multidimensional data model and practical framework implementation. The benefits include improved patient experiences, cost-effective service delivery, and enhanced patient management through electronic health records. Additionally, mobile apps are explored for patient navigation and reminders to enhance treatment adherence. The study’s results indicate that data warehousing can significantly improve healthcare delivery by consolidating critical data and facilitating better planning, resource allocation, and policy formulation. Ultimately, this approach offers valuable insights for optimizing healthcare systems in resource-constrained environments, enabling data-driven policies, and improving health outcomes across sub-Saharan Africa.

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model that is often referenced in both academics and practitioner circles will be discussed and shown to be faulty on several levels, including. a lack of definitional agreement. A new DIKW framework will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move to Intelligent Digital Twins (IDT) with Artificial Intelligence (AI).

OBJECTIVE: Analyzing the effects of different physical exercise programs on the anthropometric, cardiovascular, metabolic, and strength variables of the elderly participating in health care programs. METHODS: Controlled clinical trial, with 60 elderly participants from health care groups, allocated into four groups: Group of resistance exercises in open-air gyms – GT1 (n = 17); Group of aerobic and localized exercises - GT2 (n = 11); Group of resistance exercises at GT3 gym (n = 17); Control group: non-exercise practitioners - CG (n = 15). Anthropometric (BMI, % body fat, and waist circumference), cardiovascular (SBP, DBP, HR, and DP), metabolic (total cholesterol, triglycerides, and glycemia), and strength variables were evaluated before and after 16 weeks of intervention. Descriptive statistics, Shapiro-Wilk and Kolmogorov-Smirnov normality test, equal variance test, T-Student test, non-parametric Mann-Witney test, ANOVA One Way, multivariate data analysis using data mining and machine learning techniques, Pearson and Spearman correlation tests, Classical Clustering (Agglomerative Hierarchical Method); Principal Component Analysis (PCA), Z score, Fruchterman-Reingold algorithm, Euclidian Similarity Index and, Cohen’s equations were applied. RESULTS: To observe effect size, morphofunctional variables for the GT1 group show a small effect for fat percentage, WHR, and ULS and a medium effect for LLS. For the GT2 group, there was a small effect for fat percentage, WHR, and ULS and a large effect for LLS. For the GT3 group, there was a small effect for the percentage of fat and HC, a medium effect for ULS, and a large effect for LLS. A small effect for glycemia in GT1, a medium effect for glycemia and triglycerides for GT2, a small effect for total cholesterol for GT3, and a large effect for glycemia in CG, with this effect being an increase in this analyte. For the cardiovascular variables, there is a small effect for SBP and HR in GT1, a small effect for DBP, HR, and DP, and a large effect for SBP in GT2, a small effect for SBP, DBP, and HR in GT3 and small effects for SBP and HR in CG. The correlation between BMI (P = 0.0007) and Body Fat% (P = 0.007) with ULS. The variables TG, LLS, and ULS were the ones that differed most in each type of stimulus chosen as an intervention in the present study. CONCLUSIONS: The results of this study indicate that GT2 caused more significant percentage reductions in body mass and BMI. However, GT3 caused greater fat percentage reduction. The GT2 caused the greatest decrease in waist circumference, while the GT3 caused the largest decline in hip circumference. The three groups induced an increase in strength (ULS and LLS). However, in ascending order, GT1 caused the smallest increase, GT2 an intermediate increase, and GT3 the greatest increase in strength, with considerable effects in GT2 and GT3. Finally, GT2 caused a greater percentage reduction in systolic blood pressure (large effect) and double product (small effect) than the other groups. The three methods proved to be efficient, but with particularities that may reflect the choice of one over another due to health conditions, objectives to be achieved, or characteristics of the patient at the time of their choice by the prescriber.

Multiple imputation with chained equations (MICE) is a widespread approach to account for missing data in empirical research. Combining MICE with bootstrapping, that is, repeatedly resampling with replacement from the data to estimate variances and confidence intervals of statistics of interest, is not straightforward. The current document provides an overview of how to use bootstrapping with imputed data in Stata. Two main approaches (impute first and then bootstrap or vice-versa) are discussed and shortly compared. Code is provided for Stata.

With the development of electricity spot markets, accurate electricity load forecasting enables power generation companies to supply the right amount of electricity, Significantly avoid power waste. As a result, time series forecasting in the field of power can bring significant benefits. Previously, the Informer model successfully introduced the Transformer into long time series forecasting(LTSF) by proposing the ProbSparse self-attention mechanism, which solved the inherent problem of high memory complexity in self-attention. Recent research has further demonstrated the potential of the self-attention for mining complex dependencies. However, the limited amount of historical data has become one of the main challenges in applying deep learning techniques to power LSTF tasks. Previous reserches often add a large number of time covariates to provide more information. In this paper, to address this issue, (i) we design a simple but effective Season-aware Block to enhance the model’s ability to mine artificial prior information in temporal covariates; (ii) we conduct experiments using the provincial power data of Zhejiang Province, China, from 2019 to 2022, and our model outperforms other models, achieving a 19 percent MSE relative improvement; (iii) we conduct ablation experiments to assess the efficacy of the Season-aware Block in extracting temporal periodic features. Furthermore, we elucidate the underlying reasons for the effectiveness of both the self-attention mechanism and the Season-aware Block through visualization experiments.

1. Background Septic shock is a rare and serious infectious disease. Patients surviving the early phase are at high risk of multiple amputations of limbs. They are potential sources of serious disability. 2. Methods The objective was to describe the functional prognosis and quality of life of the four-limb amputee patients following septic shock. This is a descriptive, retrospective and mono-centric study. The patients included between 2010 and 2019 had septic shock after 18 years, responsible for amputation of the four limbs. We studied the mean age of septic shock, the causative organisms, the infectious door enter, the different levels of amputation, the fitting process, the resumption of walking, the Functional Independence Measure (FIM) and the SF-12 Quality of Life Questionnaire. 3. Results We suceed to include 15 patients.. 53.3% women and 46.7% men with an average age of 46.1 years. 80% presented bilateral trans-tibial amputations, all were able to return walking even over short distances, within an average of 8.4 months. 40% did not require a technical walking aid, 33.3% had no walking perimeter limitation. 47% had a higher mental and social quality of life score than the general population. The younger the patients, the better their physical quality of life. After two years of follow-up, the younger the patients, the better their functional autonomy. Patients with better functional autonomy had a better physical quality of life. 4. Conclusion The number of amputations due to spetic shock is likely to increase in the years to come throughout the world. Our study prove that quadri-amputee patient can regain a fully acceptable functional and quality of life being monitored in a specialized health center with multidisciplinary health professional to help him.

The objective was to estimate the correlation between VIs and grain yield and identify the optimal timing and VIs for precise corn grain yield estimation. Furthermore, the study aims to employ photographic quantification to measure corn ear traits and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in CRB with three rep-lications at three locations. Vegetation indices and green leaf area were estimated throughout the cycle using an unmanned aerial vehicle (UAV) and subsequently corre-lated with grain productivity. In addition, photographs were taken of the corn ear to esti-mate their length, width and total number of kernels and compare these values with manual measurements. The experiments consistently demonstrated significant experi-mental quality across sites, with accuracy ranging from 79.07% to 95.94%. UAV flights carried out at the beginning of the crop cycle revealed a positive correlation between grain productivity and the evaluated indices (NGRDI, VARI, GLI). Regarding the phenotyping of corn ears, the regression coefficients for width, length and TNG were 0.92, 0.88 and 0.62, respectively, indicating an association with manual measurements. However, stage V5 in the localities of Lavras and Ijaci and stage V8 in the locality of Nazareno showed a posi-tive correlation with productivity. The use of images for ear phenotyping is promising as a method for measuring corn components.

Satellite-derived soil moisture observations typically rely on bias-correction (BC) prior to assimilation in land surface models. Current techniques include rescaling or machine learning approaches to map the observations to the modelled soil moisture climatology. However, these approaches do not allow for non-stationary biases and recalibrations require a long training period, which is not always feasible. In this study we evaluate a two-stage filter to dynamically correct soil moisture biases from satellite-derived active ASCAT C-band and passive L-band SMOS surface soil moisture observations in the European Centre for Medium Range Weather Forecasts (ECMWF) land data assimilation system. This adaptive soil moisture BC approach is designed to complement the operational seasonal rescaling of the ASCAT observations and the SMOS neural network retrieval used at ECMWF, while allowing the assimilation to correct sub-seasonal scale errors. Over a 3-year test period, the adaptive BC reduces the seasonal-scale first guess-observation departures by 20-30% for ASCAT and SMOS. The adaptive BC leads to (i) slight improvements in soil moisture performance against in situ data; and (ii) moderate but statistically significant reductions in the 1-5 day relative humidity forecast errors in the boundary layer of the northern hemisphere midlatitudes.

Keywords: AI Readiness Assessment; Business Processes; and Data Infrastructure.

The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph processing system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, graph processing systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and low-memory fast graph mutations. Existing graph structures offer a hard tradeoff between read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists to achieve the best of both worlds. We compare CSR++ to CSR, adjacency lists from the Boost Graph Library, as well as state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular graph processing algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average), while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed that of several state-of-the-art graph structures, while maintaining low memory consumption when the workload includes updates.

The performance of 5 forecasting models was investigated for predicting nursery mortality using the master table built for 3,242 groups of pigs (~ 13 million animals) and 42 variables, which concerned the pre-weaning phase of production and conditions at placement in growing sites. After training and testing each model’s performance through cross-validation, the model with the best overall prediction results was the Support Vector Machine model in terms of Root Mean Squared Error (RMSE=0.406), Mean Absolute Error (MAE=0.284), and Coefficient of Determination (R2=0.731). Subsequently, the forecasting performance of the SVM model was tested on a new dataset containing 72 new groups, simulating ongoing and near real-time forecasting analysis. Despite a decrease in R2 values on the new dataset (R2=0.554), the model demonstrated high accuracy (77.78%) for predicting groups with high (5&gt;%) or low (5&lt;%) nursery mortality. This study demonstrated the capability of forecasting models to predict the nursery mortality of commercial groups of pigs using pre-weaning information and stocking conditions variables collected post-placement in nursery sites.

The prediction of system degradation is very important as it serves as an important basis for the formulation of condition-based maintenance strategies. An effective health indicator (HI) plays a key role in the prediction of system degradation as it enables vital information for critical tasks ranging from fault diagnosis to remaining useful life prediction. To address this issue, a method for monitoring data fusion and health indicator construction based on autoencoder (AE) and long short-term memory (LSTM) network is proposed to improve the predictability and effectiveness of health indicator in this study. Firstly, an unsupervised method and overall framework for HI construction is built based on deep autoencoder and LSTM neural network. The neural network is trained fully based on the normal operating monitoring data and then the construction error of the AE model is adopted as the health indicator of the system. Secondly, we propose related machine learning techniques for monitoring data processing to overcome the issue of data fusion, like mutual information for sensor selection and t-distributed stochastic neighbor embedding (T-SNE) for operating condition identification, etc. Thirdly, in order to verify the performance of the proposed method, experiments are conducted based on CMAPSS dataset and results are compared with algorithms of principal component analysis (PCA) and vanilla autoencoder model. Result shows that LSTM-AE model outperforms the PCA and Vanilla-AE model in metrics of monotonicity, trendability, prognosability and fitness. Fourthly, in order to analyze the impact of the time step of the LSMT-AE model on HI construction, we construct and analyze the system HI curve under different time steps of 5, 10, 15, 20, and 25 cycles. Finally, the results demonstrate that the proposed method for HI construction can effectively characterize the health state of system, which is helpful for further failure prognostics and converting the scheduled maintenance into condition-based maintenance.

Background: Ceftobiprole is a fifth-generation cephalosporin that has been approved in Europe solely for the treatment of community-acquired and nosocomial pneumonia. The objective was to analyze the use of ceftobiprole medocaril (Cefto-M) in Spanish clinical practice in patients with infection in hospital or outpatient parenteral antimicrobial therapy (OPAT). Methods: This retrospective, observational, multicenter study included patients treated from September 1, 2021 to December 31, 2022. Results: 249 individuals were enrolled, aged 66.6±15.4 years, 59.4% male, with Charlson index of 4 (IQR 2-6); 13.7% had COVID-19, and 4.8% were in intensive care unit (ICU). The most frequent type of infection was respiratory (55.8%), followed by skin and soft tissue infection (21.7%). Cefto-M was administered to 67.9% as empirical treatment, being in monotherapy for 7 days (5-10) in 53.8% of cases. The infection-related mortality was 11.2%.The highest mortality rates were for ventilator-associated pneumonia [40%], and infections due to methicillin-resistant S. aureus (20.8%) and Pseudomonas aeruginosa (16.1%). Mortality-related factors were age (OR: 1.1, 95%CI [1.04-1.16]), ICU admission (OR:42.02, 95%CI[4.49-393.4]), and sepsis/septic shock (OR:2.94, 95%CI [1.01-8.54]).Conclusions: In real life, Cefto-M is a safe antibiotic, with only half of prescriptions in respiratory infections, mainly administered as rescue therapy in pluripathological patients with severe infectious diseases.

Climate data are important in building a hydrological risk assessment model. The ClimInonda web application enables interactive and dynamic visualizations of different data collected from different weather stations in the study area on a single platform, allowing users to explore and analyze data in an easy way. This can assist decision-makers and stakeholders in understanding the current state of the environment and in identifying areas of flooding risk. Visualizations can include different types of data, such as satellite imagery, weather data, and terrain data, and can be displayed using various techniques, such as heat maps, contour maps, and 3D models by providing easy-to-understand visualizations. The different stations of the Gafsa and Kasserine governorates in the study area are included and other stations of the Algerian territory (Tebessa governorate) are incorporated. This web application also provides the capability to include each user's stations.

Technological innovations and the expansion of Internet access have produced significant changes in the configurations of organizations and, consequently, in the relationships between employees and employers. This new scenario generates the need for greater monitoring in the workplace in order to control inappropriate behavior or situations that may generate misfortunes. Two important problems faced are the dissemination of hate through networks and data leakage that can have social, psychological, and financial impacts. Thus, monitoring tools can be incorporated to assist in surveillance, and thus ensure the achievement of organizational objectives. This paper presents a workplace computer monitoring solution that integrates Spyware techniques, and text sentiment classification, along with a distributed microservices architecture, which aims to collect a range of information and generate alerts to managers regarding hate speech and vulnerabilities. Preliminary tests have been conducted to evaluate the performance of Spyware integrated with prediction models.

With the development of data science, more and more machine learning technologies have been designed to solve complicated and challenging real-world tasks containing a large volume of data. And many significant real-world datasets contain data in the form of networks or graphs. Graph Neural Networks is one of the powerful machine learning tools, which could provide a perfect solution to processing a large amount of non-Euclidean data. And because most bio information data in bioinformatics is in the non-Euclidean domain, Graph Neural Networks could then directly be applied to solve problems in bioinformatics. Much research has been done in the field of GNN, and there are also some surveys related to GNN and its applications. However, there has been little research focusing on GNN in bioinformatics, and we think in the future we could better utilize GNN in the field of biology, so we would like to write a literature review to help take a comprehensive look at GNN and their applications in the field of bioinformatics. In this paper, we would first introduce SOTA models in Graph Neural Networks, and second, introduce their applications in bio information. And then we would provide future directions of Graph Neural Networks in bioinformatics.

We present a differentially private extension of the block coordinate descent based on objective perturbation. The algorithm iteratively performs linear regression in a federated setting on vertically partitioned data. In addition to a privacy guarantee, the algorithm also offers a utility guarantee; a tolerance parameter indicates how much the differentially private regression may deviate from an analysis without differential privacy. The algorithm’s performance is compared with the standard block coordinate descent algorithm and the trade-off between utility and privacy is studied. The performance is studied using both artificial test data and the forest fires data set. We find that the algorithm is fast and able to generate practical predictions with single-digit privacy budgets, albeit with some accuracy loss.

In the past decades, a significant rise in the adoption of streaming applications has changed the decision-making process for the industry and academia sectors. This movement led to the emergence of a plurality of Big Data technologies such as Apache Storm, Spark, Heron, Samza, Flink, and other systems to provide in-memory processing for real-time Big Data analysis at high throughput. Spark Streaming represents one of the most popular open-source implementations which handles an ever-increasing data ingestion and processing by using the Unified Memory Manager to manage memory occupancy between storage and processing regions dynamically, which is the focus of this study. The problem behind memory management for data-intensive stream processing pipelines is that the incoming data is faster than the downstream operators can consume. Consequently, the backpressure of Spark acts in the opposite direction of downstream operators. In such a case, the incoming data overwhelms the memory manager and provokes memory leak issues. As a result, it affects the performance of applications generating, e.g., high latency, low throughput, or even data loss. In such a case, the initial intuition motivating our work is that memory management became the critical factor in keeping processing at scale and system stability of Spark. This work provides a deep dive into Spark backpressure, evaluates its structure, presents the main characteristics to support data-intensive streaming pipelines, and investigates the current in-memory-based performance issues.

As its capital, Jakarta plays a critical role in boosting Indonesia’s economic growth and setting the precedent for broader change outside of the city. One crucial avenue of inquiry to better understand, and prepare for, the future of a country so heavily impacted by disastrous weather events is understanding the effects of climate change through data. This study investigates meteorological data collected from 1996 to 2021 and compares the application of the SARIMA and the Holt-Winters methods to predict the future influence of climatic parameters on Jakarta’s weather. The performance of the SARIMA method is proven to provide better results than the Holt-Winter models and both methods showed the best performances when forecasting the humidity data. The results of the forecast are able to demonstrate the characteristic of the climate in Jakarta, with dry season ranging from May to October and wet season ranging from November to April.

Background: Objectives were to determine the real-life effectiveness and safety of DT with dolutegravir (50 mg/QD) plus lamivudine (300 mg/QD) in multiple-tablet regimen (MTR) in naïve PLHIV followed up for 48 weeks and to evaluate the compliance and satisfaction of patients. Material and methods: Open, single-arm, multicenter, non-randomized clinical trial from May 2019 through September 2020 with 48-week follow-up. Results: The study included 88 PLHIV (91% male) with mean age of 35.9 years; 76.1% were MSM. Mean baseline CD4 was 516.4 cells/uL, with viral load (VL) of 104,828 cop/mL, and 11.4% were in AIDS stage. DT started within 7 days of first specialist consultation in all patients and the same day in 84.1%; 3.4% had baseline resistance mutations (K103N, V106I+E138A, and V108I); 12.5% were lost to follow-up. At week 48, 86.3% had VL< 50 cop/uL by intention-to-treat analysis and 98.7% by per-protocol (PP) analysis. Virological failure (VF) was recorded in 1.1%, with no resistance mutation. One blip was detected in 5.2%, without VF. Three reported anxiety, dizziness, and cephalgia, respectively, at week 4 and one insomnia at week 24; none reported adverse events at week 48. Mean weight was 4 kg higher at 48 weeks (p=0.0001) and abdominal circumference 3 cm larger at 24 weeks (p=0.022). No forgetfulness occurred in 98.7% of patients. Patient satisfaction was 90/100 at 4, 24, and 48 weeks. Conclusion: Real-world data demonstrate that dolutegravir plus lamivudine in MTR is effective, safe, and satisfactory, moderately increasing weight and abdominal circumference and administrable on a test-and-treat strategy.

By applying a running average (with a window-size= d), we could transform Discrete data to broad-range, Continuous values. When we have more than 2 columns and one of them is containing data about the tags of classification (Class Column), we could compare and sort the features (Non-class Columns) based on the R2 coefficient of the regression for running averages. The parameters tuning could help us to select the best features (the non-class columns which have the best correlation with the Class Column). “Window size” and “Ordering” could be tuned to achieve the goal. this optimization problem is hard and we need an Algorithm (or Heuristics) for simplifying this tuning. We demonstrate a novel heuristics, Called Simulated Distillation (SimulaD), which could help us to gain a somehow good results with this optimization problem.

Radar observation data with high temporal and spatial resolution are used in the data assimilation experiment to improve precipitation forecast of a numerical model. The numerical model considered in this study is Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated radar equivalent reflectivity factor using higher resolution WRF and compared with radar observations in South Korea. To compare the precipitation forecast characteristics of three-dimensional variational (3D-Var) assimilation of radar data, four experiments are performed based on different precipitation types. Comparisons of the 24-h accumulated rainfall with Automatic Weather Station (AWS) data, Contoured Frequency by Altitude Diagram (CFAD), Time Height Cross Sections (THCS), and vertical hydrometeor profiles are used to evaluate and compare the accuracy. The model simulations are performed with and with-out 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The radar data assimilation experiment improved the location of precipitation area and rainfall intensity compared to the control run. Especially, for the two convective cases, simulating mesoscale convective system was greatly improved.

The newly discovered coronavirus (COVID-19) has disrupted traditional methods of conducting research, particularly qualitative research. However, there remains a number of methods by which qualitative data can still be collected. These include the use of digital voice, video, and text-based tools, online surveys, and content analysis. Text-based sources can help to overcome the limitations of time and space, and also can be cost-effective. This chapter draws from data collected from 12 participants across Zimbabwe and demonstrates how these tools can be used to generate data or to sample data that is already available to satisfy research questions and meet research objectives. It recommends researchers to experiment with new ways of collecting qualitative data while also observing safety protocols and ethical considerations.

Ransomware is continually evolving. The most recent tactic is to combine crypto-ransomware with data exfiltration, mandate fraud, and other such schemes to steal from victims and attempt to blackmail them. This paper examines the new trend, its underlying motives, and proposes practical recommendations to help organizations defend against this menace

The rate of fires, and particularly residential-fires, is a serious concern in industrialized countries. However, there is considerable uncertainty regarding the reported numbers of residential-fire incidents as official figures are based on fires reported to fire response agencies only. This population-based study aims to quantify the total number of residential-fire incidents regardless of reporting status. The cohort comprised linked person-level data from Fire and Rescue New South Wales (FRNSW) and health system and death records. It includes all persons residing at a residential address in New South Wales, Australia, that experienced a fire between 1 January 2005 – 31 December 2014. Capture-Recapture method was used to estimate the underreporting number of residential fire-related incidents. Over the study period, 43,707 residential-fire incidents were reported to FRNSW and there were 2,795 residential fire-related health service utilization of which 2,380 were not reported. Using Capture-Recapture method, the total number of residential-fire incidents was estimated at 267,815 to 319,719 which is more than six times the official records. This study found that 15% of residential-fire incidents that were identified in health administrative dataset were reported. The residential-fire incidents that were not reported occurred mainly in socio-economically disadvantaged areas among the males and adults.

Wearable and mobile technology has advanced in leaps and bounds in the last decade with technological advances creating a role from enhancing healthy living to monitoring and treating disease. However, the discussion about the ethical use of such commercial technology in the community, especially in minors, is lacking behind. In this paper, we first summarize the major ethical concerns that arise from the usage of commercially available wearable technology in children, with a focus on smart watches, highlighting issues around the consent process, mitigation of risk and potential confidentiality and privacy issues, as well as the potential for therapeutic misconceptions when used without medical advice. Then through a relevant thought experiment we move on to outline some further ethical concerns that are connected to the use of wearables by minors, to wit the issue of informed consent in the case of minors, forcing them to live in the spotlight, and compromising their right to an open future. We conclude with the view that mitigating potential pitfalls and enhancing the benefits of wearable technology especially for minors requires brave and comprehensive moral debates.

We report world record high data transmission over standard optical fiber from a single optical source. We achieve a line rate of 44.2 Terabits per second (Tb/s) employing only the C-band at 1550nm, resulting in a spectral efficiency of 10.4 bits/s/Hz. We use a new and powerful class of micro-comb called soliton crystals that exhibit robust operation and stable generation as well as a high intrinsic efficiency that, together with an extremely low spacing of 48.9 GHz enables a very high coherent data modulation format of 64 QAM. We achieve error free transmission across 75 km of standard optical fiber in the lab and over a field trial with a metropolitan optical fiber network. This work demonstrates the ability of optical micro-combs to exceed other approaches in performance for the most demanding practical optical communications applications.

Microcombs provide a potential compact and efficient light source for multi-Terabit-per-second optical superchannels. However, as the bandwidth of these multi-wavelength light sources is increased, this can result in low per-line power. Optical amplifiers can be used to overcome power limitations, but the accompanying spontaneous optical noise can degrade performance in optical systems. To overcome this issue, we propose wideband noise reduction for comb lines using a high-Q microring resonator, whose resonances align with comb lines. When applying the proposed distillation to a superchannel system with 18 Gbaud, 64-QAM sub-channels in a > 10 Tb/s optical superchannel, we find that noise-corrupted comb lines can reduce the optical signal-to-noise ratio required for the comb by ~ 9 dB when used as optical carriers at the transmitter side, and by ~ 12 dB when used as a local oscillator at the receiver side.

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria – hypothesis, data types, strategies, study design and study focus – to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw a particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

Li-ion battery packs are the heart of modern electric vehicles. Due to their perishable nature, it is crucial to supervise them closely. In addition to on-board supervision over safety and range, insights into the battery’s degradation are also becoming increasingly important, not only for the vehicle manufacturers but also for vehicle users. The concept of digital twins has already emerged on the field of automotive technology, and can also help to digitalize the vehicle’s battery. In this work, we set up a data pipeline and digital battery twin to track the battery state, including state of charge (SOC), state of health (Capacity) (SOHc ) and state of health (Resistance) (SOHr ). To achieve this goal, we reverse-engineer the diagnostics interface of a 2014 e-Golf to query for Unified Diagnostic Services (UDS) messages containing both battery pack and cell-individual data. An on-board diagnosis (OBD) logger records the data with edge-processing capability. Pushing this data into the cloud twin system using IoT-technology, we can fit battery models to the data and infer cell individual internal resistance from them. We find that the resistances of the cells differ by a magnitude of two. Furthermore, we propose an architecture for the battery twin in which the twin fleet shares resources like models by encapsulating them in Docker containers run on a cloud stack. By using web technology, we present the analyzed results on a web interface.

In the field of machine learning, cluster analysis has always been a very important technology for determining useful or implicit characteristics in the data. However, the current mainstream cluster analysis algorithms require comprehensive analysis of the overall data to obtain the best parameters in the algorithm. As a result, handling large-scale datasets would be difficult. This research proposes a distributed related clustering mechanism for Unsupervised Learning, which assumes that if adjacent data are similar, a group can be formed by relating to more data points. Therefore, when processing data, large-scale datasets can be distributed to multiple computers, and the correlation of any two datasets in each computer can be calculated simultaneously. Later, results are processed through aggregation and filtering before assembled into groups. This method would greatly reduce the pre-processing and execution time of the dataset; in practical application, it only needs to focus on how the relevance of the data is designed. In addition, the experimental results show the accuracy, applicability, and ease of use of this method.

The importance of energy systems and its role in economics and politics is not hidden for anyone. This issue is not only important for the advanced industrialized countries, which are major energy consumers, but is also important for oil-rich countries. In addition to the nature of these fuels which contains polluting substances, the issue of their ending up has aggravated the growing concern. Biofuels can be used in different fields for energy production like electricity production, power production or for transportation. Various scenarios have been written about the estimated biofuels from different sources in the future energy system. The availability of biofuels for the electricity market, heating and liquid fuels is very important. Accordingly, the need for handling, modelling, decision making and future forecasting for biofuels can be one of the main challenges for scientists. Recently, machine learning and deep learning techniques have been popular in modeling, optimizing and handling the biodiesel production, consumption and its environmental impacts. The main aim of this study is to evaluate the ML and DL techniques developed for handling biofuels production, consumption and environmental impacts, both for modeling and optimization purposes. This will help for sustainable biofuel production for the future generations.

Data from sensor bearing satellites requires processing aboard terrestrial data centers that use water for cooling at the expense of high data transfer latency. The reliance of terrestrial data centers on water increases their water footprint and limits the availability of water for other applications. Therefore, data centers with low data transfer latency and reduced reliance on earth’s water resources are required. This paper proposes space habitat data centers (SHDCs) with low latency data transfer and that use asteroid water to address these challenges. The paper investigates the feasibility of accessing asteroid water and the reduction in computing platform access latency. Results show that the mean asteroid water access period is 319.39 days. The use of SHDCs instead of non-space computing platforms reduces access latency and increases accessible computing resources by (11.9% – 33.6%) and (46.7% – 77%) on average respectively.

We present a method of effectively concealing data in two quantization levels representing blocks based on Absolute Moment Block Truncation Coding (AMBTC) using Hamming code. The reason for choosing AMBTC as the cover media is that AMBTC is not only easily compressible by simple arithmetic operations, but is also efficient for exchanging data in small portable terminals, such as smart devices, because compression efficiency and image quality are satisfactory. AMBTC is expressed in the form of a $trio (a, b, BM)$, where $a$ and $b$ are quantization levels and BM is a bitmap. The quantization level is a pixel value used as a representative value when dividing an image into $4 \times 4$ blocks. Bai \& Chang introduced a method of hiding data using Hamming code. However, this method presented a problem with efficiency compared to the existing methods. In this paper, in order to improve the problem of this method, we minimize the error that occurs when concealing the data using the optimized Hamming code lookup table. We verified that the performance of the proposed method is satisfactory in terms of embedding capacity and quality of the image through sufficient experimental results.

This paper presents a new interactive visualization system to represent and compare rate of spread of COVID 19 pandemic across different countries over time. We surveyed existing visualization techniques used in various websites and media outlets and introduced use of a knee detection algorithm that divides the exponential spread in multiple linear components. A set of use cases demonstrates the utility of the system in comparing the spread across different countries for both analysis and prediction.

Introduction: The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of our study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. Materials and methods: The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, and submission-to-publication time were analysed in R. Biblioshiny application was used to create a world collaboration map. Results: Our preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, our results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. Conclusion: In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.

Implications of the novel usage adoption of the internet of things in various sectors of works and life are researched and documented at pace. This is related to the overall high rate at which new technologies are adopted in modern society. Healthcare is a vital aspect of everyday activities and as such overlaps with the increasingly important role played by use of the internet and associated technologies. The purpose of this review article is to draw attention to the potential social, ethical, legal and professional limitations to using IoT in the context of healthcare. The social and ethical aspects in particular, focus on IoT usage in care of the elderly with relevant case studies as reference.

Three Dimensional Variational data assimilation or analysis (3DVAR) is one of most classical methods for providing the initial values for numerical models. In this method, the dimensions of the background error covariance and the observational error covariance matrices are large. Therefore, it is difficult to get the inverse of the covariance matrices and to reduce the orders of these matrices without information loss. With the use of the Sylvester Equation, on the basis of a new linear regression, a new cost function for 3DVAR was given. For the first-guess m×n field, there is an approximate 1−(m²+n²)/(mn×mn) reduction with m>1 & n>1 by using the cost function. The results of the numerical experiments show that the effect of this algorithm is no worse than that of the old cost function for 3DVAR.

The concept of patient confidentiality is nearly as old as the practice of health professions and, has evolved and transformed over the years, from one jurisdiction to the other. Patient confidentiality can be a fundamental human right, an ethical duty or, a legal duty. The Saudi laws have evolved around its Shari’ah-based legal culture, its history and the international human right laws (IHRLs). These elements have moulded the Saudi Arabia’s unique perspective on patient confidentiality. Its confidentiality laws are found scattered in several legislations. Is the Saudi patient confidentiality law able to adequately deal with the contemporary challenges? The study reviewed the relevant Saudi laws in the light the International Humana Rights Laws. Findings suggest that there are issues bordering on the lack of quality comprehensive data protection laws, on clarity and foreseeability of the existing laws, and on the accessibility of the courts. Furthermore, the lack of a system of law reporting and stare decisis potentially gave the judges a wider latitude of discretion in interpreting the laws. Therefore, the study recommends for a comprehensive data protection law with a clear definition of “confidential information”, of data controllers and their role, and of specific safeguards against potential abuses. Others include defining legitimate purposes for using the patient’s data, and his role, and the extent to which he can control the use of his own data. Consistency in legal interpretations, and an improved law reporting system could positively enhance the overall outcome.

Typical modern information systems are required to process copious data. Conventional manual approaches can no longer effectively analyze such massive amounts of data, and thus humans resort to smart techniques and tools to complement human effort. Currently, network security events occur frequently, and generate abundant log and alert files. Processing such vast quantities of data particularly requires smart techniques. This study reviewed several crucial developments of existent data mining algorithms, including those that compile alerts generated by heterogeneous IDSs into scenarios and employ various HMMs to detect complex network attacks. Moreover, sequential pattern mining algorithms were examined to develop multi-step intrusion detection. These studies can focus on applying these algorithms in practical settings to effectively reduce the occurrence of false alerts. This article researched the application of data mining algorithms in network security. The academic community has recently generated numerous studies on this topic.

This work is developed over the question ”How to automatically create a good clustering on spatial dataset with high different local densities?” opened by previus work of Berzi [5]. To answer the main question, this work describe a approach of recursive clustering process based on a technique of finding ”vague-solution”, where the output is an hierarchical clustering of initial dataset. In particularly the the approach is developed and tested on DBSCAN algorithm [11] with large dataset gathered by Google Place in Metropolitan Area of Milan. The core solutions developed in this algorithm are condensed in the capacity of generation a Hierarchical Clustering with a recursive select the best solutions in according tothe our goals, previously dfined by some sets of rules. The algorithm described here, and developed in my Master Thesis, rosolve two problem: - When we use an algorithm of clustering that can create a set of differents clustering, but equally valid and don’t know exctly what we must have as good solution, we areled to ask ourselves: ”What are the better?” This obviously depends by our goals, sonow the question is: ”What are our goals?”; - The second problem is condensed in the sentence ”Not all clusters can be found in one-shot clustering process, more often we must reapply the process to some part of datataset”, so there we have a second question that this paper answering: ”How to create clustering of data with ad-hoc processing for each different part of input dataset?”; These questions are resolved by the approaches named in this work as: Space of Solutions, Vague-Solution, Vague-Solution finding Method and finaly Recursive Clustering. All of these approaches was drafted and tested in mine Master Thesis titled ”Geospatial data analysis for Urban informatics applications: the case of the Google Place of the City of Milan” [25].

Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis’ sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al.). These minimum requirements are presented in the format of a questionnaire checklist in an R markdown template file. The R Markdown reporting template proposed here can be presented as a starting point to encourage the data analysis section of a metabolomics manuscript to have a more logical presentation and to contain enough information to be understandable and reusable. The idea is that these guidelines would be open to user feedback, modification and updating by the metabolomics community via GitHub.

This study focuses on the ability of the global land data assimilation system LDAS-Monde to improve the representation of land surface variables (LSVs) over Burkina Faso through the joint assimilation of satellite derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5, leading to a 0.25° x 0.25° spatial resolution reanalysis of the LSVs. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF. Results of two experiments are compared: open-loop simulation (i.e. no assimilation) and analysis (i.e. joint assimilation of SSM and LAI). After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below). The assimilation is able to improve the simulation of both SSM and LAI. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows the superior performance of proposed method to the previous works on Stanford dog breeds datasets.